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  Influence of ascorbate-recycling, light and temperature during tomato fruit ripening on ascorbate pool and ascorbate-degradation Vincent Truffault, Hélène Gautier, Rebecca Stevens To cite this version: Vincent Truffault, Hélène Gautier, Rebecca Stevens. Influence of ascorbate-recycling, light and temperature during tomato fruit ripening on ascorbate pool and ascorbate-degradation. 11. International POG Conference Reactive Oxygen and Nitrogen Species in Plants, Sep 2013, Varsovie, Poland. hal-

Conference Paper: Influence of ascorbate-recycling, light and temperature during tomato fruit ripening on ascorbate pool and ascorbate-degradation.

V Truffault

Abstract

Ascorbate is a powerful antioxidant in plants. Ascorbate concentration depends on its biosynthesis, recycling and degradation; these are under genetic control and closely related to environmental conditions. Ascorbate recycling, controlled by the enzymes monodehydroascorbate reductase (MDHAR) and dehydroascorbate reductase (DHAR), ensures a high-rate turnover of the ascorbate pool. Transgenic lines silenced for an MDHAR gene in a cherry tomato cultivar have been generated. The objective of this study was to determine whether limiting turnover of the ascorbate pool may affect levels of its reduced form and its degradation products. Degradation occurs via DHA to assayed candidate compounds: oxalate, tartrate and threonate. In order to investigate the impact of the reduction in MDHAR activity under different conditions of light and temperature on ascorbate concentration and ascorbate-degradation products, we used off-vine tomato fruits. Mature green fruits were harvested and placed in environmental chambers at 12EC, 23EC or 32EC, in darkness or light. Results are discussed and shed light on the stability of the ascobate pool in fruits under different environmental conditions.


domain: math info

text:
J.-M Paillat

F Guerrin

Combining individual and collective management of animal manure to reduce environmental impacts on a territory scale

Keywords: Simulation modelling, Animal waste management, Individual-collective interaction, Agricultural production systems, Environmental impact assessment

Combining life cycle analysis (LCA) and simulation modelling (SM) has been used to assess and improve a collective manure management plan set up in Brittany (north-western France) by a group of farmers to comply with current Nitrogen reduction regulations in agriculture. The plan studied aimed at organizing the spreading of slurry surpluses produced by 11 pig farms (representing 57.6 tons N/year) on crop land loaned by 22 crop farms located approximately 44 km away.

LCA was used according to its normalized methodology to statically assess the potential environmental impacts based on 4 criteria: eutrophication, climate change, acidification and use of non-renewable energy.

COMET, the dynamic systems model used in this study, has been implemented with the Vensim® software by coupling:

• logistics models, to simulate the transportation and spreading of manure, both at the individual (within farms) and collective (transfer plan) levels;

• biophysical models, mainly empirical, to simulate ammonia (NH 3 ) and methane (CH 4 ) emissions as the main criteria of the environmental evaluation with COMET.

The approach encompassed four steps that alternated between the LCA and SM methodologies:

(i) LCA was initially performed to assess the environmental impacts of two disposal scenarios, i.e. slurry biological treatment or transfer for application to remote crop farms. The analysis concluded the transfer scenario had the least environmental impact as, if properly implemented, it may save on the use of chemical fertilizers.

(ii) SM using the COMET model simulated the logistics and agricultural feasibility of the transfer scenario to verify to what extent the collective management plan can be fully completed in due time on appropriate cropping systems.

(iii) A second iteration of LCA was made to assess the environmental impacts of simulated variants of the transfer scenario, this time using simulation outputs instead of reference database information. These analyses showed important differences of impacts among the management options simulated.

(iv) Finally, simulations with COMET were performed to examine the interaction between the individual level of management (manure spreading within animal farms) and the collective level (manure transfer plan to land loaners' crop farms). The variability of impacts between individual situations was very high, suggesting that farmer equipment should be adapted and collective rules made flexible to secure farm stock management.

Beyond the results from the first three steps, this paper emphasises the more realistic outcomes achieved in the fourth and final step. This step allowed the variability of individual pig farm logistics and gas emissions to be analyzed, as well as simple techniques, that could be implemented by individual farmers to improve the whole system agricultural and environmental performance, to be checked. This paper concludes that benefits can be drawn from combining LCA and SM, as this makes possible to consider the diversity of actual farming situations and practices and, so, to design management rules balancing individual and collective needs.

INTRODUCTION

Several simulation models to assess management strategies of livestock wastes on a farm or territory scale have been devised [START_REF] Guerrin | Modelling biomass fluxes and fertility transfers: animal wastes management in the Reunion Island[END_REF]. These models are based on an 'Action-Flow-Stock' ontology, in which elementary production units are represented by material stocks, with inflows and outflows being controlled by farmers' actions [START_REF] Guerrin | Dynamic simulation of action at operations level[END_REF]. Agricultural system functioning is thus seen as the interaction between natural biophysical processes and man-controlled workable processes. While developing the side of representing human activities in agricultural systems [START_REF] Guerrin | Dynamic simulation of action at operations level[END_REF], assessing the environmental impacts of simulated management strategies is also performed. Although some output variables of these models can account for some of the impacts, provided sub-models exist, this approach appeared limited to account completely for both local and global environmental impacts.

Other research is investigating the application of Life Cycle Analysis (LCA) to multi-criteria assessment of agricultural systems [START_REF] Basset-Mens | Scenario-based environmental assessment of farming systems: the case of pig production in France[END_REF][START_REF] Aubin | Pisciculture et environnement, apports de l'Analyse de Cycle de Vie[END_REF]. This "cradle to grave" approach is relevant so far as it takes into account both the direct and indirect potential impacts of a product. However, it has the drawback of considering 'standard' production systems, with 'average' flows and impacts, by using parameters taken from international reference databases. Although some research has been done to remediate these defects [START_REF] Payraudeau | Environmental impact assessment for a farming region: a review of methods[END_REF], it is a static, accounting approach that disregards the dynamics and spatial aspects linked to actual farming practices and processes. Being relatively removed from the reality of varied and adaptive farming practices makes LCA probably less relevant than simulation modelling (SM) if the aim is to advise farmers on their management.

The LCA approach, aggregating emission flows into impact categories, and the SM approach, simulating some of these flows dynamically, may however be regarded as complementary. Hence, combining both approaches has been conducted in two cases: a pig farm, using the MELODIE model [START_REF] Rigolot | Mathematical modelling of manure production by pigs. Part II: NH 3 , N 2 O and CH 4 emissions and nutrient and matter flows in animal house and during manure storage and treatment[END_REF], and a collective slurry management plan, using the model called COMET (COllective Management of Effluents on a Territory scale). The second case is presented here. Both cases were located in Ille-et-Vilaine, Brittany (north-western France), where more than half of the cantons are classified as "Structural Surplus Areas", where more than 170 kg nitrogen of animal origin per spreadable hectare are produced. The regulation, EU Nitrate directive 91/676/CEE, which aims at reducing excess nitrogen, imposes the requirement on farmers that, either, excess nitrogen is treated, or otherwise exported to areas with nitrogen burdens less than 140 kg N of animal origin per ha. To comply with this regulation, a group of livestock farmers set up a collective slurry spreading plan. In this study, a group of 11 pig farms is considered that produce 'Mix' slurry (from sows and piglets) and 'FP' slurry (from fatteners). Around 2/3 of the total FP slurry (57.6 tons N/year) is to be spread on 1 024 ha of land, loaned by 22 crop farmers located 33 to 55 km away from the pig farms.

Based on this, the main research question addressed is to find how the use of LCA and SM can help better manage manure by balancing the individual farm and collective territory managements.

This paper is organized as follows. Section 2 describes the methodology which encompasses four successive steps alternating between LCA and SM, and the main results obtained for the first three steps. Section 3 provides details on the fourth step, in which the variability of pig farm logistics and gas emissions were analysed and simple techniques that could be implemented by individual farmers to improve the whole system performances were checked. Section 4 discusses the benefits drawn from combining LCA and SM and concludes with the necessity to consider the diversity of actual farming situations and practices.

METHODOLOGY AND PRELIMINARY RESULTS

Life Cycle Analysis (LCA): comparison of two management scenarios

Firstly, the environmental performance of two manure disposal scenarios envisaged by local farmers was compared: 1) biological treatment, very widespread in Brittany, and 2) transfer of raw slurry to remote agricultural land loaners (Lopez-Ridaura et al., 2008a). LCA was used to assess the potential environmental impacts of these two scenarios based on four criteria: eutrophication (kg PO 4 -eq.), climate change (kg CO 2eq.), acidification (kg SO 2 -eq.) and use of non-renewable energy (MJ Lower Heating Value-eq.). For climate change, both scenarios have the same impact. However, when the substitution of slurry for chemical fertilizers is considered the "Transfer" scenario has a much lower impact in terms of eutrophication, acidification and energy use (Lopez-Ridaura et al., 2008a). These results assume the transfer plan is fully completed and allows farmers to save on the use of chemical fertilizers. On-farm slurry storage crucially influences gaseous emissions: ammonia (NH 3 ) and methane (CH 4 ). Ammonia volatilization following field slurry application is low, based on the assumption farmers practise deep injection of slurry into the soil.

Paillat and Guerrin, Combining individual and collective management of animal manure on a territory scale

Simulation Modelling (SM): feasibility of an intended slurry management plan

As LCA emphasized, the actual feasibility of the transfer scenario has implications for its performance. The COMET model was used to check this according to logistics (number and availability of transport and spreading vehicles, speeds, capacities of storage, etc.), crops (cropping plan, fertilization needs), soils (hydromorphy) and climate (pluviometry) [START_REF] Lopez-Ridaura | Agronomic and environmental evaluation of collective manure management for a group of farms[END_REF]. COMET, implemented with the Vensim ® software, is based on the coupling of several models:

• Logistics models simulate the transportation and spreading of manure, both at the individual (within farms) and collective (transfer plan) levels. The MAGMA [START_REF] Guerrin | Magma: A model to help manage animal wastes at the farm level[END_REF] and APPROZUT [START_REF] Guerrin | Simulation of stock control policies in a two-stage production system. Application to pig slurry management involving multiple farms[END_REF]) models, respectively, were used for this, coupled together with an original module accounting for slurry spreading with an automotive spreader (Terragator ® ) used at the land loaners'.

• Semi-empirical biophysical models simulate NH 3 and CH 4 emissions as the main criteria of environmental evaluation. The STAL model [START_REF] Morvan | Vers une prévision opérationnelle des flux de N résultant de l'épandage de lisier : paramétrage d'un modèle dynamique de simulation des transformations de l'azote des lisiers (STAL)[END_REF] simulates NH 3 emissions after spreading and an original module accounts for NH 3 and CH 4 emissions during storage, based on equations borrowed from [START_REF] Loyon | Gaseous emissions (NH 3 , N 2 O, CH 4 and CO 2 ) from the aerobic treatment of piggery slurry-Comparison with conventional storage systems[END_REF] and [START_REF] Pelletier | Ammonia emissions from swine manure storage tank[END_REF].

As used in this case study, COMET simulates the transfer of slurry from several pig farms that also produce crops to several land loaners' crop farms that also produce livestock (Fig. 1). Mix slurry tanks (fed by sows and piglets) can also possibly receive stock overflows of FP slurry (from fatteners). Only FP slurry is delivered to the land loaners of the collective spreading plan to be applied on their crops (winter cereals, maize, rape and grassland), in addition to their own livestock manure. FP slurry is shipped by 25m 3 -trucks and unloaded into a 200-m 3 mobile tank located close to the land loaners' crops where it is applied by the Terragator ® . This latter is assumed to have a nominal spreading outflow of 60 m 3 h -1 and work 10 hrs per day, 5 days a week, all year round. Independent of collective management, Mix slurry is applied by pig farmers to their own crops (same as the land loaners') with their own equipment (tractors with slurry tankers).

The main environmental indicators simulated by COMET are NH 3 emissions at storage and spreading, CH 4 emissions at storage, stock overflow (assumed to denote water pollution risk by nitrates), and nitrogen that is added as chemical fertilizers to meet crop requirements when slurry spreading is incomplete (Fig. 1).

Based on actual farm characteristics, simulations have demonstrated the sensitivity of the collective spreading plan to logistics (e.g. number of trucks, speed of spreading) and climatic conditions. Consequently, the plan is not always completed annually, e.g. on winter cereals or maize in early spring, in years with wet winters, on poorly drained soils. Reduced availability of the Terragator ® (e.g. 3 instead of 5 days per week) also greatly affects the successful application of the plan as it may reduce manure applications on winter cereals to less than half the required amount when the soil and climate conditions are unfavourable. Introducing technical or organizational changes in simulations, namely modifying crop rotation (e.g. substituting 10% of winter cereals acreage for rape onto which manure applications are done in summertime), dramatically improves the successful application of the spreading plan [START_REF] Paillat | Simulation de la faisabilité d'un plan d'épandage de lisier de porc et conséquences sur les émissions gazeuses au stockage et à l'épandage[END_REF].

LCA: Environmental assessment of the 'Transfer' scenario based on simulation outputs

The third step used LCA again to assess the 'Transfer' scenario but, this time, along with the volumes of slurry spread and the gas emissions simulated by COMET, instead of average values as in step 1. The simulation outputs corresponded to the above mentioned management alternatives: varying the length of delay after rainfall to access fields according to the soil quality (hydromorphy) and availability of spreading equipment. These analyses showed large gaps between LCA in step 1, based on 'average' coefficients (cf. § 2.1; Lopez-Ridaura et al., 2008a), and those based on COMET outputs. The eutrophication and acidification impacts are higher from LCA used in this step than those from step 1. These results are due to underestimating NH 3 Paillat and Guerrin, Combining individual and collective management of animal manure on a territory scale emissions as slurry was assumed to be injected into the soil for all crops whereas, more realistically, this only occurs for maize and rape in the COMET simulations. Discrepancies as large as 60% are noted among these management variant analyses, particularly for the climate change criterion. They are due mainly to the amounts of CH 4 emitted, which depends on the slurry storage duration, which is linked to the actual realization of slurry field applications [START_REF] Lopez-Ridaura | Environmental systems analysis of agricultural systems: Coupling dynamic simulation models with life-cycle assessment[END_REF].

SM: analysing the variability of environmental performances among individual farms

Although in the previous analyses ( § 2.1 to 2.3) aggregated results were used to assess global system performances, COMET simulates each farm's parameters (slurry stocks, amounts applied to crops, N concentrations, gaseous emissions, etc.) meaning inter-farms variability can be analysed to understand the interaction between individual and collective management. Using simulation, simple techniques to be implemented by individual farmers that are likely to improve the whole system performance were tested. This study involved the following four scenarios, the results of which will be discussed in section 3:

• S 0 : reference scenario that considers slurry management based on current farm characteristics;

• S 1 : increasing storage and spreading capacities of pig farms to better comply with slurry on farm;

• S 2 : covering slurry tanks to decrease water evaporation, rain addition, and ammonia volatilization;

• S 3 : saving water (e.g. washing the piggery, watering animals) to produce more concentrated slurry.

SIMULATION RESULTS

Inter-farms variability for several environmental indicators

The environmental indicators used to compare the pig farms committed to the collective spreading plan were:

• Ammonia emissions (N-NH 3 ) at storage in pig farms and after spreading in both pig and land loaners' farms, which impact on the eutrophication and acidification criteria. NH 3 emitted at storage is proportional to the area of the slurry tank, temperature and N-NH 4 + concentration of slurry.

• Methane emissions (C-CH 4 ) at storage in pig farms. They vary with slurry volume and temperature.

• Water pollution risk by nitrates (N-NO 3 ), which impacts eutrophication, is assessed by slurry stocks overflowing considered, implicitly, as spreading in excess or outside authorised periods.

• Amount of nitrogen that should be added as chemical fertilizers (N-Cf) to compensate for the possible lack of slurry application and N losses by volatilization. Using synthetic fertilizers has impacts on both climate change and nonrenewable energy consumption.

• Spreading and transport times, as directly related to the consumption of non-renewable energy and greenhouse gases emissions by vehicles.

Simulation of the S 0 reference scenario showed large inter-farm variability for most indicators when considering the extremes (Fig. 2). These values were linked to the slurry storage facilities, spreading equipment and cropping plans of individual farms. NH3 emissions at spreading varied according to the slurry quality and the amount applied, as well as the type of crop which determines the use of slurry injection (e.g. deep injection is never performed on wheat and grassland). The variability was lower in the case of spreading on land loaners' crops as the quality of slurry issued from fattening pigs was less variable than that of Mix slurry. The Terragator ® , which allows deep soil injection, was always used for spreading at land loaners' whereas it was not Paillat and Guerrin, Combining individual and collective management of animal manure on a territory scale used on pig farms. The quantity of added chemical fertilizers was lower for land loaners' crops than for pig farm crops, because of the priority given to the collective plan.

Considering two contrasting climatic years (wet late winter in 2001, dry late winter in 2002) inter-farm variability was not equivalent across years for all indicators. Added chemical fertilizer was on average higher in 2002 than in 2001 (Fig. 2), because of higher NH 3 emissions at spreading, due to the slurry being more concentrated in nitrogen, and of higher temperature.

Comparing the performance of two pig farms

As an example, farms F8 and F11, which contain similar livestock (about 1000 sows each) and crops (wheat, rape, maize and grassland), exhibited very different environmental performances (Fig. 3). F8, with 69 ha, often had very low slurry stocks (Fig. 3a, lower curves) which resulted in little overspreading (lower curve in Fig. 3b) and few NH 3 and CH 4 releases at storage (Fig. 3c and 3d lower curves). Whereas F11, with 100 ha, often slurry stocks (Fig. 3a upper curves), with a mean FP filling rate of 0.81 vs. 0.35 for F8, which resulted in large overspreading (upper curve in Fig. 3b) and higher gas emissions (Fig. 3c and 3d upper curves). There are some large management differences between these farms. Firstly, they have different cropping plans: F8 has relatively few winter cereals (10 ha), a significant acreage of grassland (24 ha) and good soils, while F11 has half its acreage planted to wheat (50 ha), most of which (44 ha) is grown on poorly drained soil, and very little grassland (4 ha). Therefore, F11 spread little slurry on its own crops (4 600 m 3 vs. 6 500 m 3 for F8) but this was compensated by more export to the collective plan (1 400 m 3 vs. 875 m 3 ). Overall, F11 processed similar amounts of slurry to F8 (17% less). Secondly, F11 has double the storage capacity with an open surface area of 4.5 (FP slurry) to 7 (Mix slurry) times the area of F8. Therefore, F11's mean slurry stock retention time was double F8's for Mix slurry (60 vs. 119 days) and quadruple for FP slurry (55 vs. 239 days). Combined these resulted in much higher emissions from F11 than F8 at storage: 3-6 times for NH 3 ; 4-8 times for CH 4 (FP and Mix slurry, respectively).

Assessment of individual mitigation techniques

Several alternatives were simulated for all farms and compared with the current situation (S 0 ). Increasing storage and spreading capacities of some deficient farms (S 1 ) mainly allowed one farm to reduce the N-NO 3 risk. The N lost:N produced ratio was reduced from 15% to 5% on average (Fig. 4 comparing S 1 and S 0 ). Storage and spreading emissions change very little and added chemical fertilizers decreased slightly. Covering slurry tanks (S 2 ) reduced ammonia emissions at storage, from 10% to 4% of N produced (comparing S 2 and S 0 in Fig. 4), and pig farm use of chemical fertilizers, because the N concentration of Mix slurry was higher. However, CH 4 emissions were higher, increasing from 10 to 14% of C produced on average, because the mean slurry storage duration and stored volume were increased (Fig. 4). Ammonia emissions at spreading were relatively unmodified although the slurry was more concentrated. Because of this higher concentration, slurry deliveries and, consequently, total delivery times were decreased by as much as 30% (Fig. 5). This contributed to reduced non-renewable energy consumption and greenhouse gases emissions by trucks. Spreading time was only slightly reduced as more acreage was fertilised. Reducing slurry dilution (S 3 ) reduced the addition of chemical fertilizers, on both pig and crop farms' (S 3 vs. S 0 , Fig. 4), as well as reducing spreading and delivery times (Fig. 5). Other influential factors on farm emissions were also checked using COMET. For example, logistics of the collective management plan greatly influenced the capacity of spreading on pig farms. When the number of 25 m 3 -trucks used to collect slurry was reduced (e.g. from 6 to 4), the NO 3 risk doubled and the alternative of chemical fertilizers was increased by 80% at land loaners', while being reduced by 15% on pig farms. Establishing flexible rules, like allowing pig farmers with too little storage capacity or constrained cropping plans to also use FP slurry instead of reserving it all to the collective plan, reduced the NO 3 risk and decreased the use of additional chemical fertilizer. Adapting collective management as much as possible to individual characteristics and constraints proved to be crucial for improving the whole system's overall functioning and environmental performance.

DISCUSSION AND CONCLUSIONS

This simulation study conducted using COMET and another using MELODIE shared the same general conclusion even they had different aims: simulating collective slurry management on a territory scale (this paper) vs. all pollutant flows within a farm [START_REF] Chardon | MELODIE: A whole-farm model to study the dynamics of nutrients in integrated dairy and pig farms[END_REF]. The variability linked to farming structures and practices is very important and greatly influences the agronomic and environmental performances of farming systems. Therefore, using LCA based on average standard data or one-year data surveys on small sets of farms [START_REF] Kanyarushoki | EDEN : un outil opérationnel pour l'évaluation environnementale des systèmes de production laitiers[END_REF]) may be tenuous for comparing farming systems while disregarding their diversity and variability across-time. Although encompassing very useful aspects (multi-criteria, cradle to grave, etc.), it seems that LCA should be limited to studying relative improvements according to some key criteria, not assessing absolute impacts. In effect, using 'average' coefficients may lead to erroneous conclusions. For example, although the preliminary LCA study concluded the superiority of the Transfer scenario compared with on-farm treatment (cf. § 2.1), using COMET simulation outputs in a new LCA (see § 2.3) put this result into perspective. Actually, this demonstrated that local idiosyncrasies (geographic, climatic, edaphic, structural, organisational, etc.) can greatly influence farm emissions. Accounting for these contingencies, simulation modelling can explain the part of uncertainty, commonly seen in LCA, which is linked to the diversity of farming systems and the concrete realization of farming activities [START_REF] Payraudeau | Analysis of the uncertainty associated with the estimation of nitrogen losses from farming systems[END_REF]. The remaining uncertainty is linked to emission factors, which depend upon the availability of knowledge of the processes concerned. The use of COMET has shown that, as soon as accurate, adapted, models of biophysical processes are available, they can be easily combined into this operations management simulation framework. This research emphasized that LCA and SM can be fruitfully combined to evaluate environmental impacts of farming systems. It demonstrated the necessity to address the collective territory level along with the dynam-

Figure 1 .

 1 Figure 1. Agricultural system as represented by the COMET model.

Figure 2 .

 2 Figure 2. Inter-farm variability of environmental indicators for pig and crop farms based on their current characteristics (S 0 ).

Figure 3 .

 3 Figure 3. Two-year evolution of slurry stocks, accumulated overspreading and gaseous emissions at storage in pig farms F8 and F11.

Figure 4 .Figure 5 .

 45 Figure 4. NH 3 and CH 4 emissions from slurry at storage, NO 3 pollution risk, NH 3 emission from slurry at spreading and added chemical fertilizers among pig or crop farm in scenarios S 0 -S 3 in 2002.

ACKNOWLEDGMENT

This study was conducted in the research project Systèmes de production animales et développement durable supported by the French "Agence Nationale de la Recherche" (projet ANR-06-PADD-017 SPA/DD).


SEED ABORTION AND YIELD STABILITY: A KEY-ROLE FOR GROWTH PROCESSES OF REPRODUCTIVE ORGANS

Olivier Turc

Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), UMR 759 INRA -Montpellier SupAgro, 2 place Viala, 34060 Montpellier Cedex 2, France.

Grain yield is generally viewed as the result of carbon acquisition and allocation which drives grain set and grain filling. Nevertheless, early stages of reproductive development including initiation and growth of floral organs may be crucial for grain yield of crop plants, not necessary through carbon availability. This is illustrated here with two examples: (i) the sunflower capitulum, which actively expands during the period of floret initiation, and (ii) the maize silks, equivalent to stigma and styles of female flowers, which greatly elongate during a short time to be accessible to pollen from male inflorescences. Growth of both organs is very sensitive to drought and impacts the final yield through a reduction in the potential number of grains.

Reproductive development and expansive growth are tightly linked during floret initiation at the meristem of the sunflower capitulum. Floret primordia are centripetally initiated at the generative front which divides the inner zone of the capitulum surface, or meristem proper, from the outer zone supporting the previously initiated florets. The rate of tissue expansion in the central meristematic zone of the capitulum plays an essential role by renewing meristem area, and thus prolonging the period of primordium initiation. The development of the sunflower capitulum is divided into three successive phases between its initiation and the end of floret initiation, each of them corresponding to a specific combination of both processes of tissue expansion and primordium initiation in the meristem. Floret production depends directly from the rate and duration of tissue expansion during the three phases.

The analysis of primordium initiation, tissue expansion and assimilate supply, under various environmental conditions during the period when the number of florets is determined, led to establish a set of equations, which explicitly describe and quantify the relationships between net carbon input, capitulum growth, soluble sugar content in the capitulum, and initiation of floret primordia. Results support the following main assumptions: (i) floret initiation and meristem tissue expansion remain strongly co-ordinated, irrespective of environmental conditions, and both depend on local carbon availability when water supply is unlimited, (ii) the three macroscopic processes, namely C availability (photosynthesis and C allocation), expansive growth (rate of tissue expansion) and reproductive development (duration of phases) are affected in parallel and differentially by water deficits, and (iii) C allocation to organs (and the resulting sugar content) is the consequence, and not the cause, of the differential effect of water deficit on processes (photosynthesis vs tissue expansion) and organs (leaf growth vs capitulum growth).

The growth and emergence of maize silks has a considerable importance in the determination of yield under drought. When soil water deficit occurs before flowering, silk emergence out of the husks is delayed while anthesis is largely unaffected, resulting in an increased anthesis-silking interval (ASI). ASI is a good predictor of grain yield under stress and selection for reduced ASI has been used successfully to increase the drought tolerance of maize. This is generally explained by considering ASI as a negative symptom of assimilate partitioning to the developing spikelets. However analyses of metabolite content in silks and ovaries of plants subjected to soil water deficit indicate carbon accumulation rather than carbon starvation in these organs. On the other hand the temporal and spatial patterns of silk expansion and cell division as affected by soil water deficit demonstrate that ASI is directly related to the processes of cell expansion in the silks, and corresponds to the duration between the end of cell division in the silk and the cessation of elongation in silk apex. The rates of tissue expansion and cell division are reduced according to the intensity of water deficit, resulting in a longer duration of this developmental phase and hence a delayed silk emergence. Genotypes widely differ in the sensitivity of silk elongation to soil water deficit.

For example the soil water potential for which silk growth ceases ranges from -0.7 to -1.3 MPa in a panel of 8 inbred lines. Similar range and ranking are observed for the soil water potential for which leaf elongation rate reaches zero: silk and leaf sensitivities are correlated for the studied set of genotypes. Common genetic determinisms also exist between leaf elongation rate and ASI in response to soil and air water deficit (co-location of six QTLs) in a population of inbred lines from tropical parental lines known for segregating for ASI. This suggests common mechanisms for the response of silks and leaf to drought and supports the hypothesis that drought tolerance in maize could partly rely on the maintenance of processes of tissue expansion in both vegetative and reproductive organs. Methods for phenotyping the maintenance of silk growth are currently developed.


B Freytag-Leyer

D Yusuf

PO3073 STREET FOOD AND ITS EFFECT ON SOCIETY AND CUSTOMER SATISFACTION IN MALANG CITY, EAST JAVA, INDONESIA

Background and objectives:

In developing countries, street food vending is a very important sector to generate income. Street food vendors have an important function in society. Especially people with low income have the chance to buy adequate food. To evaluate street food vendors, socio-economic profiles of the street food vendors were compiled and the customer satisfaction of street food was gathered.

Methods: After pretests a questionnaire with 17 closed questions was used for the street food vendors and a questionnaire with 22 closed questions for their customers in Malang City. 35 street food vendors and 245 customers responded between August to October 2010.

Results: The results showed that there was an increase in the level of welfare at the vendors´street food over time. This trend was characterized by an increased average income per month and an increase of buying some consumer goods, such as motorcycle, home, furniture, and television. The customers were satisfied with the location and price of street food as being tasty. There was a stromg correlation between customer satisfaction and location, price, tasty and food safety (34 %). The level of customer satisfaction was about 66 %.

Conclusions: The survey showed that the local government should consider to support street food vendors beyond providing capital or funding for their venture. Rather training programs, cheap sources of materials, and good selling location should be provided. Supporting street food vending therefore helps many small businesses which are important in a globalized economy.

Key words: street food, vendors, customers.

PO3074 TO WHAT EXTENT ARE DIETARY SHIFTS A POTEN-TIAL LEVER TO REDUCE GREENHOUSE GAS EMIS-SIONS? A SIMULATION STUDY

G. Masset 1 , F. Vieux 1 , M. Maillot 1 , N. Darmon 1 1 UMR NORT (Nutrition, Obésité et Risque Throm- botique) INRA1260, INSERM 1062, Aix-Marseille Université , France
Background and objectives: Food-related greenhouse gas emissions (GHGE) contributing around 30% of EU emissions, dietary changes may represent a major lever to achieve the 80% GHGE reduction target set by the EU 2050 roadmap. However, few studies assessed the GHGE reduction potential of dietary changes. Our aim was to simulate the maximum GHGE reduction achievable through dietary changes only, and the effect of fulfilling nutrient recommendations on the reduction potential.

Methods: A mean observed diet was derived from 1,142 women participating in the French INCA2 survey. Diet-related GHGE were estimated based on GHGE values collected in the literature for 73 widely consumed foods. Linear programming was then used to simulate three dietary changes scenarios which aimed to minimize diet-related GHGE while (i) keeping the energy intake constant; (ii) keeping weight intake constant; (iii) reaching all French nutrient recommendations. Scenarios' social acceptability was ensured by imposing maximum food quantities based on reported intakes. Nutritional adequacy of reported intakes and scenarios was assessed using the mean adequacy ratio (MAR).

Results:

The observed diet's GHGE were 3,478g CO2eq/d; the MAR was 76%. GHGE were reduced by 82% in scenarios (i) and (ii). This was achieved by: (i) reducing diet weight shifting towards more energy dense foods; (ii) shifting towards low GHGE foods and lowering energy intake. These shifts led to poor nutritional adequacy, with MAR of 50% (i) and 29% (ii). Introducing the nutrient recommendations in scenario (iii), i.e. reaching a MAR of 100%, led to a much weaker GHGE reduction of 39%, mainly achieved through reduced content of meats and increased content of plant-based foods, dairy products and fish.

Conclusions: Dietary changes can help reducing GHGE but the potential is limited by nutrient needs. Further reduction of diet-related GHGE must be achieved through improvements in the food supply chain.

Key words: greenhouse gas emissions; food choices; sustainable diet.