logistics for energy crops’ biomassbourgogne, france (case study 1: bourgogne pellets) and one in...

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LogistEC

Logistics for Energy Crops’ Biomass Grant agreement number: FP7-311858 Collaborative project (small or medium-scale focused research project targeted to SMEs) SEVENTH FRAMEWORK PROGRAMME Priority: Food, Agriculture and Fisheries, and Biotechnology

Deliverable D1.9 Proposal of several cropping systems

including biomass crops, and their multicriteria assessment

Due date: M36 Actual submission date: M36 Project start date: September 1st, 2012 Duration: 42 months Workpackage concerned: WP1 Concerned workpackage leader: INRA Dissemination level: PU

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Glossary and Definitions CC: Catch Crop GHG: GreenHouse Gas TFI: Treatment Frequency Index TFIh: Treatment Frequency Index Herbicide SNM: Semi Net Margin

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Objectives We aimed at:

1) Designing scenarios of cropping systems including energy crops, 2) Performing a multicriteria assessment of the innovative cropping systems previously

designed. Rationale

To achieve the first objective, two design workshops were organized, one in Bourgogne, France (case study 1: Bourgogne Pellets) and one in Extremadura, Spain (case study 2: Miajadas) to elaborate innovative cropping systems aiming at decreasing GHG emissions and promoting carbon storage in the soil (case study 1) and saving water and energy (case study 2).

For the second objective, a set of indicators dealing with environment (nitrate losses, Treatment Frequency Index (TFI)), energy (energy consumption and energy efficiency) and economy (profitability or semi-net margin (SNM)) were calculated to perform a multicriteria assessment of the cropping systems previously designed.

Teams involved: INRA (UMR Agronomie), Bourgogne Pellets, CENER, Acciona Geographical areas covered: Plain of Dijon (Bourgogne, France) Miajadas (Extremadura, Spain)

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Summary Glossary and Definitions .................................................................................................................. 2

Introduction ........................................................................................................................................ 5 1. The design workshop: management and composition of the group of experts ............ 5

2. Case study 1: Bourgogne Pellets (Bourgogne, France) ...................................................... 8 2.1. Description of the design workshop .............................................................................................. 8

2.1.1. The goal ......................................................................................................................................................... 8 2.1.2. Individual ideas expressed by the experts .......................................................................................... 9 2.1.3. Debriefing by the facilitators ............................................................................................................... 10

2.2. Description of the cropping systems designed ............................................................................ 12 2.2.1. Cropping system “MiscAlf-TC” ............................................................................................................. 12 2.2.2. Cropping system “MiscAlf-TRW” ........................................................................................................ 14 2.2.3. Reference cropping systems ..................................................................................................................... 16

2.3. Multicriteria assessment of the cropping systems ...................................................................... 16 2.3.1. Crop yield estimation ................................................................................................................................. 16 2.3.2. Indicators and calculation methods ....................................................................................................... 17 2.3.3. Results ............................................................................................................................................................. 20

3. Case study 2: Miajadas (Extramadura, Spain) ................................................................ 33 3.1. Description of the design workshop ........................................................................................... 33

3.1.1. The goal ...................................................................................................................................................... 33 3.1.2. Individual ideas expressed by the experts ....................................................................................... 34 3.1.3. Debriefing by the facilitators ............................................................................................................... 35

3.2. Description of the cropping systems designed ......................................................................... 36 3.2.1. Cropping system for irrigated land ........................................................................................................ 36 3.2.2. Reference cropping systems ..................................................................................................................... 37

3.3. Multicriteria assessment of the cropping systems .................................................................. 38 3.3.1. Indicators and calculation methods ................................................................................................... 39 3.3.2. Results ......................................................................................................................................................... 40

Conclusion ....................................................................................................................................... 42

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Introduction The design workshop aims at designing prototypes of innovative cropping systems. It originated from the methodology of prototyping elaborated by Vereijken (1997) and was for instance developed to design banana-based cropping systems in Costa Rica (Stoorvogel et al., 2004), integrated crop management systems for cotton production in West Africa (Lançon et al., 2007; Rapidel et al., 2009), integrated crop protection in arable farming in the Netherlands (Wijnands, 1997), and innovative cropping systems in France (Reau et al., 2012). This approach appeared to be suited to contexts where scientific knowledge is lacking and/or where expert knowledge is poorly capitalized in scientific literature and/or summarized in crop models. As a result, it made it possible to use both scientific and expert knowledge.

This activity is part of the WorkPackage 1, task 1.5 of the LogistEC project. To perform this task we organized two design workshops to develop prototypes of innovative cropping systems.

First, the organization of the design workshop will be presented. Then the case studies will be developed.

1. The design workshop: management and composition of the group of experts

Each participant involved in the design workshop had a specific role (Table 1):

- Domain experts, i.e. scientists specialized in the field of agronomy of energy crops, gathered and presented the available knowledge at the beginning of the workshop (step 1), proposed individual ideas (step 2) and participated to the collective discussion (step 3),

- Local experts working as advisors in extension services proposed individual ideas

(step 2) and participated to the collective discussion (step 3). For the Miajadas case study, one local expert was asked to assess indicators related to energy and water consumption of the first prototypes of cropping systems during the collective discussion,

- Two facilitators

reformulated the personal ideas to ensure a good understanding among participants (step 2), and synthetized the proposals of the participants during the step 3 by progressively describing the cropping system on a whiteboard,

- Observer(s)

took notes, and wrote a report about the links observed between objectives, personal ideas and collective discussion, that was send to the participants of the workshop.

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The design workshop was organized in four steps:

Step 1-Providing scientific knowledge

Scientific experts provided local experts with general knowledge about the goal of the design workshop. They focused their presentation on GHG emissions and C storage in the soil (for the Bourgogne Pellets case study) or on water and energy consumption (for the Miajadas case study).

Step 2-Expressing individual ideas

The facilitators asked the scientific experts and the local experts to express individual ideas on post-it notes (one per idea). These ideas deal with technical options aiming at fulfilling one or several of the objectives defined above. This step aims at ensuring a time for personal thoughts and expression, and at providing a basis for the collective discussion that will follow. Lastly, the observers will use this information to trace the pathway from individual ideas to those ideas that emerged from the collective discussion.

Step 3-Elaborating cropping systems through a collective discussion

The facilitators synthetized the proposals which resulted from the interaction among experts by writing on the paper board the elements of the cropping systems. They ensure that the cropping systems are described with enough details (regarding the crop sequence, the management of each crop and the management of the intercrop period) to allow their assessment with cropping system models such as Persyst (Ballot and Guichard, 2013).

Step 4-Debriefing

The observers reported back at the end of the workshop about the way the participants worked during step 2 and 3.

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Table 1: Exhaustive list of the participants involved in the workshops. Name Organization Role

Cas

e st

udy

1: B

ourg

ogne

Pel

lets

(Bou

rgog

ne, F

ranc

e) Eric Bizot Chambre d’Agriculture

de l’Yonne Local expert

Michael Geloen Chambre d’Agriculture de la Nièvre

Local expert

Laurence Guichard UMR Agronomie INRA-AgroParisTech

Facilitator

Anabelle Laurent UMR Agronomie INRA-AgroParisTech

Observer

Claire Lesur-Dumoulin INRA SAD, Alénya Scientific expert Chantal Loyce UMR Agronomie

INRA-AgroParisTech Scientific expert

Josuah Morlet Chambre d’Agriculture de la Nièvre

Student

Marie Sophie Petit Chambre Régionale d’Agriculture de Bourgogne

Local expert

Raymond Reau UMR Agronomie INRA-AgroParisTech

Observer

Marion Soulié UMR Agronomie INRA-AgroParisTech

Facilitator

Antoine Villard Local expert Céline Zanella Local expert

Cas

e st

udy

2: M

iaja

das (

Ext

ram

adur

a, S

pain

)

Assumptio Antón IRTA Scientific expert Maximino Caballero Izquierdo

ACCIONA Local expert

Ines Echeverria Goñi CENER Scientific expert Jerónimo Gonzales CICYTEX Local expert Antonio Guerra Farmer Local expert Anabelle Laurent UMR Agronomie

INRA-AgroParisTech Facilitator

Alfredo López Mendiburu

ACCIONA Local expert

Chantal Loyce UMR Agronomie INRA-AgroParisTech

Scientific expert and observer

Roberto Montero Castaño

Agro-Riegos Montero S.L

Local expert

Fabiana Morandi DTU Eduardo Otazu Vidarte CENER Local expert Raymond Reau UMR Agronomie

INRA-AgroParisTech Facilitator

Jemma Rodriga Agro-Riegos Montero S.L

Local expert

Emilio Torres Association of agricultural cooperatives

Local expert

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2. Case study 1: Bourgogne Pellets (Bourgogne, France) In this study area, a Miscanthus x giganteus chain has been established by the cooperative Bourgogne Pellets since 2009. A first design workshop was held during a former research project (projet FUTUROL) with a similar objective. Hence, most of the local experts have been already identified.before the LogistEC project.

2.1. Description of the design workshop This workshop was held in March 2014. It focused on the design of energy-oriented cropping systems on a deep clay-loamy soil in the Plain of Dijon and aiming at decreasing agricultural GHG emissions and promoting carbon storage in the soil.

2.1.1. The goal The goal is defined by a set of objectives and constraints. It should be ambitious to promote creativity.

Goal Objectives

-‐ Main objective: decrease agricultural GHG net emissions (i.e. N2O et CO2 emissions minus carbon storage) by 75% compared to the reference cropping system (crop succession Brassica napus-Triticum aestivum-Hordeum vulgare). This objective originates from the requirement presented in the European Directive on Renewable Energy (2009): a minimum saving of 35% of GHG emissions compared with the substitute fossil fuels

-‐ Secondary objective: decrease the TFI by 50% compared to the reference cropping system. This objective indirectly derives from the European Directive on Renewable Energy (2009) which constraints biofuel production from agricultural feedstocks to minimize environmental impacts. In France, Agricultural production of energy crops also needs to fulfil the goals of the Ecophyto plan1 (French action plan aiming at reducing pesticide use, as required by the directive 2009/128/EC to achieve a sustainable use of pesticides)

1 http://agriculture.gouv.fr/Ecophyto-‐in-‐English

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Constraints

-‐ To have at least 1/3 of energy crops in the crop sequence -‐ To have three kinds of crops in the crop sequence (i.e. annual,

pluri-annual and perennial crops) -‐ To decrease the yield value of 33% at most -‐ To limit risks of nitrate leaching -‐ To consider the management before and after Miscanthus x

giganteus in the crop sequence

2.1.2. Individual ideas expressed by the experts Table 2: Exhaustive list of the ideas proposed by the participants Idea Reduce the use of nitrogen fertilizer: crops with low nitrogen requirement, favourable previous crop, Catch Crop (CC) with a high nitrogen content, lower yield target Crop succession Medicago sativa-Miscanthus x giganteus to reduce nitrate losses Minimize plowing; strip-till on maize (Zea mays) CC: produce a high amount of dry matter, balance C/N, early sowing in the crop; mixture based on legumes Cereal with a high amount of straw: winter wheat (Triticum aestivum), rye (Secale cereale) CC buried or harvested before being mature? Reduce nitrogen fertilization to reduce GHG emissions (N2O, CO2): Introduce legume (Medicago sativa) or CC and producing straw cereal such as triticale (x Triticosecale) Grow together cereal (to store C in the soil) and legume (to reduce GHG emissions) (in the same space (intercropping) or in the same crop succession) A mixed cropping system (including food and energy crops) and complementary for Bourgogne Pellets (alfalfa (Medicago sativa) - Miscanthus x giganteus) Introduce Miscanthus x giganteus: to reduce nitrate leaching, to increase carbon storage, to reduce TFI Increase carbon storage in the soil: catch crop and cereal straws (Triticale instead of winter wheat) Maximize percentage of legumes and use stems for energy Cereal-legume intercropping: maize-soybean (Glycine max) Sow under a crop already established: winter wheat under alfalfa or alfalfa under maize Introduce one or several CC and manage it/them to store carbon in the soil Localize the application of nitrogen when it is possible Introduce legumes Reduce nitrogen dose and modify the timing of applications Use cereal straws as a source of energy Introduce spring crops to reduce TFI herbicide Use CC on each inter-cultural season Agroforestry with wood used for heating Adjust the N fertilizer dose to the previous crop Introduce grain legumes (and take them into account to manage the following crop) Promote soil aeration (soil tillage, low pressure tyres)

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CC based on legumes and crucifers Direct seeding under alfalfa Grow Brassica napus intercropped with legumes Grow Miscanthus x giganteus in strip (as in agroforestry) Grow ley

Figure 1: Reading of the personal ideas by the facilitators

2.1.3. Debriefing by the facilitators The figure below (Fig. 2) was elaborated from the personal ideas expressed by the experts (Table 2, Fig.1). The goal of the workshop is located on the top of the figure, with objectives derived from the goal. Figure 2 represents the functions (answering the question “what for?”) and solutions (answering the question “what?”) expressed during the workshop. Hence, this figure recorded of the exchange about personal ideas. It could be used within the context of a second design workshop on the same topic.

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Figure 2: Tree of functions and solutions based on personal ideas; a function (“what for?”) is represented by a rectangle. A solution (“what?”) is represented by an oval. You can read this figure top-down asking you the question “how?” or bottom-up asking you the question “why?”

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2.2. Description of the cropping systems designed

2.2.1. Cropping system “MiscAlf-TC” The main principles to design this cropping system (Figure 4) were:

-‐ To include lignocellulosic crop(s) within a food crop sequence, -‐ To decrease GHG net emissions, -‐ To achieve a limited decrease in yields,

Maize and straw cereal were chosen because they are considered as the economic pillars of the cropping system.

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Figure 3: Description of the cropping system “MiscAlf-TC” NPK rates for each crop of this cropping system are described in the table below (Table 3): Table 3: NPK fertilizer for each crop of the crop succession M.giganteus Alfalfa Winter wheat

or Triticale Catch crop Maize

N (kg/ha) 60 every 3 years

0 130-140 0 120

P (kg/ha) 30 every 6 years

50-70 at the establishment period

0 0 40

K (kg/ha) 60 every 6 years

150 at the establishment period + 80 during the third year of production

0 0 40

After 10-15 years of Miscanthus x giganteus, alfalfa is grown for 4 years to enhance

the soil nitrogen content (to cope with the decrease in nitrogen in the soil after the production of Miscanthus x giganteus). Alfalfa will be harvested three times per year. These cuts could help to prevent the Miscanthus x giganteus from re-establishing.

A straw cereal (such as triticale or winter wheat) is grown after alfalfa to benefit from

the preceding effect of alfalfa and thus reduce the nitrogen dose to be applied to the straw cereal. Alfalfa allows decreasing the amount of weeds, and thus decreases the TFI of the herbicide for the following crop. As straw cereal is considered as an economic pillar, the yield potential target amounts 7.5 t/ha.

A catch crop (grass-legume mixture) follows the straw cereal to reduce the risk of

nitrate leaching. It is incorporated in the soil as late as possible to increase the amount of dry matter produced and thus to enhance carbon storage in the soil. Nitrogen will be released for the following crops thanks to the legume crop in the mixture.

Maize benefits from the nitrogen released by the catch crop, which allows reducing N

fertilizer dose. The stubbles of maize remaining in the field after harvest will contribute to increase carbon storage in the soil.

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During the duration of the cropping system, soil compaction should be limited (by limiting the number of passes of machines, and the use of suitable tires) to favour soil oxygenation and thus reduce N2O emissions (since anoxic conditions could involve denitrification).

Nitrogen fertilizer inputs will be split to increase the efficiency of nitrogen use.

2.2.2. Cropping system “MiscAlf-TRW” The main principles used to design this cropping system (Figure 4) were similar to those presented in part 2.2.1, except the fact that maize is replaced by rapeseed.

Miscanthus* Alfalfa* wheat*Tri23cale*

Year%

Rapeseed*

15%years%

19%years%

22%years%

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Figure 4: Description of the cropping system “MiscAlf-TRW” NPK rates for each crop of this cropping system are described in the table below (Table 4): Table 4: NPK fertilizer for each crop of the succession M.giganteus Alfalfa Triticale Rapeseed Winter

wheat N (kg/ha) 60 every 3

years 0 130-140 110-120 150-160

P (kg/ha) 30 every 6 years

50-70 at the establishment period

0 40 0

K (kg/ha) 60 every 6 years

150 at the establishment period + 80 in the third year of growth

0 40 0

Description of the cropping system: For the first three crops of the crop sequence (i.e. Miscanthus x giganteus, alfalfa and triticale), the description is similar to the cropping system “MiscAlf-TC” (part 2.2.1). Rapeseed benefits from the long-term effect of alfalfa, which allows reducing the nitrogen fertilizer dose (and thus the N2O and CO2 emissions related to nitrogen fertilizer). Brassica napus is one of the economic pillars of this cropping system. As winter wheat is also considered as an economic pillar of the cropping system, a yield potential of 75 dt/ha is targeted. As for the cropping system “MiscAlf-TC”, soil compaction should be limited and nitrogen fertilizer dose should be split. N.B: During the workshop, we ran out of time to describe a third prototype of cropping system proposed by the experts: a cropping system without Miscanthus x giganteus in the crop sequence (using alfalfa as energy crop for example).

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2.2.3. Reference cropping systems Cropping systems described above will be compared to two reference cropping systems:

-‐ A cropping system widely practiced in the study area: « rapeseed - winter wheat - winter barley », hereafter called « REF ». The crop management techniques of this cropping system are also the most widely practiced in Burgundy

-‐ A prospective integrated cropping system based on food crops: « rapeseed – winter

wheat – winter barley – sunflower – spring protein pea », hereafter called « INT ». The crop management techniques of this cropping system are integrated ones.

2.3. Multicriteria assessment of the cropping systems

2.3.1. Crop yield estimation Yields of a crop within a crop sequence were estimated using the cropping system model Persyst, which was parameterized in Bourgogne by local experts (Ballot and Guichard, 2013) for food and feed crops. To assess yields of Miscanthus x giganteus until the age of 15 years, we combined yield data collected from nine farmers’ fields of young Miscanthus x giganteus in Bourgogne (Fig. 5.a) with a logistic model (Miguez et al., 2008) (Fig. 5.b). The logistic model was fitted to the yield data gathered from 2011 to 2014 using a Bayesian approach, which allows us to assess the uncertainty associated with the median prediction. a) b) Figure 5: a) Yield of Miscanthus x giganteus for the nine farmers’ fields established in 2009. Points linked by a line belongs to the same field; b) The logistic model by Miguez et al. (2008) describes the yield trend of Miscanthus x giganteus across years.

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The results of the predictions of yield (expressed in tons of dry matter per ha) are presented below (Table 5). As Miscanthus x giganteus was crushed and left on the soil the first year of production, yield is equal to zero for year 1. Table 5: Estimations of Miscanthus x giganteus for 15 years of cultivation. Year Percentile 10% Median Percentile 90% 1 (year of establishment) 0 0 0 2 5.52 13.58 16.34 3 6.41 14.82 16.66 4 6.65 15.2 16.84 5 6.8 15.3 16.91 6 6.85 15.34 16.97 7 6.87 15.36 16.98 8 6.89 15.37 16.99 9 6.9 15.38 17 10 6.9 15.38 17 11 6.9 15.38 17.01 12 6.9 15.39 17.01 13 6.9 15.39 17.01 14 6.9 15.39 17.01 15 6.9 15.39 17.01

2.3.2. Indicators and calculation methods Our assessment included all the crop management techniques of each crop from seeding to harvesting. a) Net GHG emissions (kg eq CO2/ha) Net GHG emissions = direct emissions (CO2, N2O) + indirect emissions (CO2, N2O) – carbon storage in the soil Direct emissions include;

-‐ CO2 emissions due to technical operations (kg CO2/ha) (source: GES’tim) -‐ N2O emissions due to fertilization (kg CO2/ha) = amount of nitrogen * 0.01 * 1.5714 *

298 (source: IPCC, 2006) -‐ N2O emissions due to crop residues (kg eq CO2/ha) = FCR *EF*1.5714*GWPN2O avec

FCR = BIOMASSRES*NAG + (BIOMASSRES + YIELD)*RBG/AG*NBG (source: IPCC, 2006) with:

FCR kg N year-1

Annual amount of nitrogen contained in aboveground and underground crop residues

NAG % Amount of nitrogen contained in aboveground crop residues

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NBG % Amount of nitrogen contained in underground crop residues RBG/AG % Ratio between the amount of underground crop residues and yield at

harvest time EF Emission factor 1.5714 Conversion factor from N-N2O to N2O GWPN2O kg eq

CO2 Global warming of N2O

Indirect emissions include: -‐ CO2 emissions due to technical operations (kg CO2/ha) (source: GES’tim) -‐ Indirect emissions due to volatilization (kg eq CO2/ha) = amount of NH3 losses

(source: Persyst) * 0.01 * 1.5714 * 298 (source: IPCC, 2006) -‐ Indirect emissions due to nitrate leaching (kg eq CO2/ha) = amount of NO3

- losses *

0.075 * 1.5714 * 298 (source: IPCC, 2006) NB: Persyst estimated NH3 losses except for Miscanthus x giganteus. NO3

-losses were estimated by Persyst except for Miscanthus x giganteus. For this crop, we used the data provided by Lesur et al. (2014). NB: GHG emissions due to the production of pesticides are not included in our assessment because Persyst estimated the TFI without mentioning the active substances used. Carbon storage in the soil was estimated by the SIMEOS-AMG tool (Duparque et al., 2007) except for Miscanthus x giganteus. For Miscanthus x giganteus, we used the result of the meta-analysis implemented by Poeplau and Don (2014) to assess carbon storage under Miscanthus x giganteus. This meta-analysis showed that the carbon stock under Miscanthus x giganteus increased by 0.4 C t.ha-1.year-1 (+/- 0.2 t.ha-1.year-1). Initial carbon stock was calculated thanks to the soil analyses of the farmers’ fields which have been done before the establishment of Miscanthus x giganteus (as part of the Futurol project). b) TFI TFI was calculated by Persyst and defined by the following formula: TFIplot = ΣT [DAT/DRT * PPT] with: T = treatment DA = applied dose per hectare (kg/ha or l/ha) DH = recommended dose per hectare (kg/ha or l/ha) PP = proportion of the treated plot (ha) c) Nitrate losses Nitrate losses due to nitrate leaching were estimated by Persyst, except for Miscanthus x giganteus, thanks to the formula used in Indigo (Christian Bockstaller and Girardin, 2008):

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NO3- losses (kg N.ha-1) = N balance during the intercrop season * coefficient of nitrate

leaching during the intercrop season For Miscanthus x giganteus , we used the data provided by Lesur et al. (2014). d) Semi net margin (SNM) SNM = gross product – input cost – mechanization cost (without labour force and without fuel cost) (source: Entraid’EST, 2012) Gross product= yield (t/ha) * crop price (€/t) Expenses = fuel cost + fertilizers cost + seeds cost + pesticides cost During the design workshop, eight economic scenarios combining minimum (min) and maximum (max) inputs prices (I), crops prices (C) and yields (Y) were defined by the experts: IminCminYmin IminCminYmax ImaxCminYmin ImaxCminYmax IminCmaxYmin IminCmaxYmax ImaxCmaxYmin ImaxCmaxYmax The range of price is described above:

-‐ Crop prices (Cmin-Cmax)

Winter wheat: 150-200 €/t Rapeseed: 300-400 €/t Maize: 120-180 €/t Triticale: 150-160 €/t Miscanthus x giganteus: 73 €/t Alfalfa: 220-230 €/t

-‐ Input prices (fertilizers and fuel) (Imin-Imax)

N: 0.8-1.2 €/kg P: 0.6-1 €/kg K: 0.8-1.2 €/kg Fuel: 0.7-0.9 €/l

Persyt estimated “minimum” and “maximum” yields of each crop in the context of the cropping system designed, except for Miscanthus x giganteus. For Miscanthus x giganteus, “minimum” and “maximum” yields represent the percentile 10% and percentile 90% of the logistic model, respectively (Table 5).

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e) Energy consumption (Ec) The energy consumption (related to seed, fertilizer and technical operations) was calculated with GES’tim. Ec = energy consumption related to seed + energy consumption related to fertilizer + energy consumption related to technical operations f) Energy efficiency (Ee) Energy efficiency at the cropping system scale is defined by: Ee = total energy product / total energy consumption With energy product = Σi [Yield i * net calorific value i] i = crop species included in the cropping system yield is expressed in t DM/ha net calorific value is expressed in GJ/t of DM g) Food capacity (Ca) Food capacity represents the potential of the cropping system to produce food crops: Ca = food efficiency * % of food crops in the crop sequence Food efficiency was computed as the ratio between the simulated yield in Persyst and the potential yield parameterized in Persyst.

2.3.3. Results

a) Direct and indirect emissions of GHG Cropping systems including Miscanthus x giganteus and alfalfa have the lowest quantity of GHG emissions (Fig. 6). GHG emissions are reduced by 20% between REF and INT, by 65% between REF and MiscAlf-TC and by 61% between REF and MiscAlf-TRW. For REF and INT, the main GHG emissions are indirect emissions of CO2 for the production of fertilizers and direct emissions of N2O due to nitrogen fertilization. The main difference between REF and INT on one hand and MiscAlf-TC and MiscAlf-TRW on the other hand lies in the number and quantity of fertilized crops (with low N fertilizer for Miscanthus x giganteus and no N fertilizer for alfalfa).

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REF INT MiscAlf-TC MiscAlf-TRW

GH

G e

mis

sion

s (k

g eq

CO

2/ha

/yea

r)

0500

1000

1500

2000

2500

CO2 dirCO2 fertilizersCO2 seedCO2 cultivation practicesCO2 miscanthus destructionN2O fertilizersN2O residuesvolatilization lossesleaching losses

Figure 6: GHG emissions (kg eq CO2/ha/year) at the cropping system scale.

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0 5 10 15 20

4446

4850

5254

year

C s

tock

(t/h

a) w

ithin

0 to

30c

m

MiscAlf-TCMiscAlf-TC straws exportedREFINT

0 5 10 15 20

4446

4850

5254

year

C s

tock

(t/h

a) w

ithin

0 to

30c

m

MiscAlf-TRWMiscAlf-TRW straws exportedREFINT

b) Carbon storage in the soil (0-30 cm) Fig. 7 represents the temporal evolution of carbon stock in the soil (soil horizon: 0-30cm) for each cropping system. Initial carbon stock (t=0) was calculated thanks to the soil analyses of farmers’ fields done before the establishment of Miscanthus x giganteus. The red line shows the evolution of carbon stock during the cropping system MiscAlf-TC (Fig. 7a). The dotted lines show the uncertainty in the calculation during 15 years of Miscanthus x giganteus cultivation based on the confidence interval reported above. From the 16th year of growth, carbon stock gets slightly lower. At the end of the crop sequence, the gain is equal to 5.65 t C/ha. The pink line shows the evolution of carbon stock if the straws of triticale and catch crop were exported. Note that carbon storage is higher when straws are buried (with a difference of 0.76 t C/ha). Orange and blue lines represent REF and INT cropping systems respectively, during the same period of time as the cropping systems including Miscanthus x giganteus. Thus, several iterations of the crop sequence of REF and INT were done. There is a loss of 1.8 and 1.52 t C/ha for REF and INT, respectively, over the 21-year cycle For the cropping system MiscAlf-TRW (Fig. 7b), explanations are the same (with red line instead of green line and pink line instead of light green). The gain is equal to 5.72 t C/ha (and to 5.02 t C/ha if straws are exported) for MiscAlf-TRW. a) b) Figure 7: Temporal evolution of carbon storage in the soil within 0-30cm. a) for the cropping system MiscAlf-TC, b) for the cropping system MiscAlf-TRW. Dotted lines represent the uncertainty in the calculation under Miscanthus x giganteus cultivation.

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GH

G n

et e

mis

sion

s (k

g eq

CO

2/ha

/yea

r)

0500

1000

1500

2000

2500

3000

direct and indirect GHG emissionsCarbon storageGHG net emissions

+2682

+2236

-128 -118

c) Net GHG emissions Figure 8 represents net GHG emissions. REF and INT cropping systems emit more than 2000 kg eq CO2 /ha/year whereas the two cropping systems including Miscanthus x giganteus stock more than they emit (net GHG emissions about -100 kg eq CO2 /ha). Net GHG emissions are reduced by 17% between REF and INT and by 104% between REF and MiscAlf-TC and MiscAlf-TRW too. The main objective is thus largely achieved. Figure 8: GHG net emissions (kg eq CO2/ha) at the cropping system scale

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insecticidefungicideherbicide

TFI

02

46

8

d) TFI The value of TFI is reduced by 24% between REF and INT, by 93% between REF and MiscAlf-TC and by 88% between REF and MiscAlf-TRW. This reduction is explained by the presence of Miscanthus x giganteus and alfalfa in the crop sucession: Miscanthus x giganteus was only treated from the year of establishment and the second year of growth and alfalfa was grown without pesticides. The second objective is thus also achieved. Figure 9: Value of TFI at the cropping system scale.

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nitra

te lo

ss (k

g N

O3-

N/h

a/ye

ar)

010

2030

4050


nitra

te lo

ss (m

g N

O3-

/l/ye

ar)

010

2030

4050

e) Nitrate losses due to nitrate leaching Nitrate leaching of REF and INT are higher than for MiscAlf-TC and MiscAlf-TRW (Fig. 10). Unfertilized crops, as spring pea or alfalfa for MiscAlf-TC and MiscAlf-TRW, explain the small losses due to nitrate leaching. Cropping systems including Miscanthus x giganteus have a concentration of nitrate in drainage water below 50 mg/l. Nitrate leaching under Miscanthus x giganteus are very low from the 3rd to the 15th year of growth (Fig. 11b). a) b) Figure 10: a) Nitrate losses at cropping system scale (kg N/ha/year); b) estimations of nitrate losses at cropping system scale (mg/l/year). The bars represent the mean value and the square represent the 1st and 5th quintile estimated by Persyst.

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rapeseed winter wheat barley

nitra

te lo

ss (m

g N

O3-

/l)

010

2030

4050

6070

mis

c 1

mis

c 2

mis

c 3

mis

c 4

mis

c 5

mis

c 6

mis

c 7

mis

c 8

mis

c 9

mis

c 10

mis

c 11

mis

c 12

mis

c 13

mis

c 14

mis

c 15

alfa

lfa 1

alfa

lfa 2

alfa

lfa 3

alfa

lfa 4

triticale

rapeseed

wheat

nitra

te lo

ss (m

g N

O3-

/l)

0

10

20

30

40

50

60

70

a) b) Figure 11: Estimations of nitrate losses at crop scale (mg/l) a) for each crop of REF; b) for each crop season of MiscAlf-TRW.

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REF_Imax

REF_Imin

INT_Imax

INT_Imin

MiscAlf-TC_Imax

MiscAlf-TC_Imin

MiscAlf-TRW_Imax

MiscAlf-TRW_Imin

fuelseedsfertilizerspesticides

expe

nses

(eur

os/h

a/ye

ar)

0

100

200

300

400

500

600

700

f) Input costs

Figure 12 represents the cost for fuel, fertilizers (N,P,K), seeds and pesticides. Costs were calculated here for two price scenarios: minimum and maximum input costs (fertilizers and fuel), called Imin and Imax respectively. Pesticide costs are lower for the cropping systems including Miscanthus x giganteus than for REF and INT. This can be explained by the four years of alfafa cultivation because no pesticides and fertilizers are applied for this crop. Moreover, Miscanthus x giganteus received pesticides only during the first two years of cultivation. Lastly, as Miscanthus x giganteus is not systematically fertilized (60/0/0 units NPK in 4th and 10th year of cultivation; 60/30/60 units NPK in 7th et 13th year of cultivation), fertilizer costs are lower for the cropping system including this crop. Figure 12: Input costs (euros/ha/year) at the cropping system scale (scenarios: Imin and Imax).

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REF

INT

MiscAlf-TC

MiscAlf-TRW

SN

M (e

uros

/ha)

0

200

400

600

800

1000

1200

1400YminYmax

Cmin Imin

REF

INT

MiscAlf-TC

MiscAlf-TRW

SN

M (e

uros

/ha)

0

200

400

600

800

1000

1200

1400YminYmax

Cmax Imin

REF

INT

MiscAlf-TC

MiscAlf-TRW

SN

M (e

uros

/ha)

0

200

400

600

800

1000

1200

1400YminYmax

Cmin Imax

REF

INT

MiscAlf-TC

MiscAlf-TRW

SN

M (e

uros

/ha)

0

200

400

600

800

1000

1200

1400YminYmax

Cmax Imax

g) Semi-net margin (SNM) SNM depends on input prices, crop prices and yield (Fig. 13). When the yield assumption changes (Ymin or Ymax), the ranking of the cropping systems differs between the scenario “a” and the scenario “c” (with a context Cmin). SNM is thus sensitive to the yield assumption for these scenarios. Lastly, for a context of maximum crop price (scenarios b and d), REF and INT have higher SNM than MiscAlf-TC and MiscAlf-TRW whatever the yield assumptions. a) b) c) d) Figure 13: SNM at the cropping system scale. a) context Cmin Imin, b) context Cmax Imin, c) context Cmin Imax, d) context Cmax Imax. For each context, both assumptions of yield are presented (Ymin and Ymax).

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5 10 15 20

-3000

-2000

-1000

01000

2000

3000

year

SN

M (e

uros

/ha)

CmaxIminYmaxCminImaxYmin

5 10 15 20

-3000

-2000

-1000

01000

2000

3000

year

SN

M (e

uros

/ha)

CmaxIminYmaxCminImaxYmin

The figure below represents the evolution of the SNM for REF (Fig. 14a) and MiscAlf-TC upon time (Fig. 14b). For MiscAlf-TC, the first year, SNM is negative due to the very high rhizomes expenses (2700 €/ha) associated with no gross product (because Miscanthus x giganteus is not harvested the year of establishment). The following year of cultivation, SNM is stable but sensitive to the yield of Miscanthus x giganteus. For REF, SNM vary less whatever the economical context. a) b) Figure 14: Evolution of the SNM upon time a) for REF; b) for MiscAlf-TC

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ener

gy e

ffici

ency

020

4060

80

YminYmax

h) Energy efficiency Energy efficiency is sensitive to yield assumption (Fig. 15). REF and INT produce 5 to 6 times and 7 to 9 times, respectively, more energy than they consume (depending on the yield assumption). MiscAlf-TC and MiscAlf-TRW produce 35 to 77 times and 32 to 73 times, respectively, more energy than they consume (depending on the yield assumption). In a context of maximum yield, Miscanthus x giganteus has an energy efficiency equal to 130 when it is not fertilized, and equal to 40 when it is fertilized. Figure 15: Energy efficiency at the cropping system scale

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aver

age

ener

getic

cos

t GJ/

ha/y

ear

02

46

810

1214

i) Energy consumption REF and INT consume 2 to 3 times more energy than the cropping systems including Miscanthus x giganteus (Fig. 16). Figure 16: Energy consumption at the cropping system scale (GJ/ha/year)

j) Food capacity (Ca) Food capacity is presented below (Table 6): Table 6: Food capacity Cropping systems Food capacity REF 0.974 INT 0.88 MiscAlf-TC 0.276 MiscAlf-TRW 0.323 Ca is higher for REF and INT than for MiscAlf-TC and MiscAlf-TRW, mainly because these cropping systems included Miscanthus x giganteus.

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0"0,2"0,4"0,6"0,8"1"GHG"

TFI"

Nitrate"losses"

SNM"Ee"

Ec"

Ca"REF"

INT"

MiscAlf?TC"

MiscAlf?TRW"

0"0,2"0,4"0,6"0,8"1"GHG"

TFI"

Nitrate"losses"

SNM"Ee"

Ec"

Ca"REF"

INT"

MiscAlf?TC"

MiscAlf?TRW"

h) Overview Figure 17 provides an overview of the multicriteria assessment of the cropping systems. Figure 17.a and 17.b represent the results achieved with the scenario IminCmaxYmax and ImaxCminYmax, respectively. With most of the economic scenarios (6 out 8), the economic results are similar as the one obtained from the scenarios IminCmaxYmax, i.e. in favour of REF and INT regarding SNM. The cropping systems including energy crops are more profitable concerning environmental and energetic indicators. REF is more profitable in terms of semi net margin for most of the economic scenarios. a) b)

Figure 17: Cropping system multicriteria assessment. GHG = Greenhouse gas net emissions; TFI = treatment frequency index; SNM = semi net margin; Ee = energy efficiency; Ec = energy consumption; CA = food capacity. Economic scenario a) IminCmaxYmax; b) ImaxCminYmax

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3. Case study 2: Miajadas (Extramadura, Spain) In Miajadas, Acciona (a Spanish company) has a energy plant.which uses forestry and agricultural biomass to produce electricity.

3.1. Description of the design workshop The workshop was organized in cooperation with Acciona (http://www.acciona.com/) and the National Center of Renewable Energy CENER (http://www.cener.com/es/index.asp). These two companies helped us to identify scientific and local experts and to organize the design workshop.

3.1.1. The goal The goal of the design workshop was to save 50% of water and energy consumption compared to the reference cropping system in rainfed land in Extremadura (clay loamy soil) and in irrigated land in Vega Alta (sandy soil). The constraints were to decrease the yield value of 33% at most, to maintain the quality of the products and to not decrease the profitability for the farmers. In order to know whether the goal is reached or not with the new cropping system during the workshop, one participant was requested to calculate two indicators: water consumption (m3/ha) and energy consumption (MJ/ha). These indicators were calculated for the reference cropping system in irrigated land with the references values given below for the main inputs involved: Electricity: 1kWh = 10 MJ1 Electricity for irrigation: 1m3 = 0,2 kWh = 2 MJ (source: participant, pers. comm.) Fuel: 1 l = 50 MJ1 Nitrogen: 1 kg = 50 MJ1

1 Gaec, A., Deltour, L., Cariolle, M., Dollé, J.-‐B., Espagnol, S., Flénet, F., Guingand, N., Lagadec, S., Le Gall, A., Lellahi, A., Malaval, C., Ponchant, P., Tailleur, A., 2010. GES’TIM Guide méthodologique pour l’estimation des impacts des activités agricoles sur l’effet de serre. Version 1.2. Juin 2010. These values do not correspond to primary energy.

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3.1.2. Individual ideas expressed by the experts Table 7: Exhaustive list of the ideas proposed by the experts Topic Irrigated (I)

rainfed (R) Idea

Low water consumption

I

Irrigation: Design sectorized Filter, low pressure drip irrigation system (slow flow)

Substitution of fossil energy

I & R

Substitution with renewable energy Use renewable energy: electric (solar, etc., small installations) & transport (bio combustibles, electric motor)

R 1/3 years with energy crop I

Use renewable energy Renewable energy: crop WUE & NUE

Precision agriculture

R

Technology: GPS for the tractor Drones detection of the soil moisture

I Dynamic fertilization I & R

Daily or multi-annual planning on soil structure and crop stage with teledetection or prediction model to determine technical operations Drones or flight systems (for example satellites)

Reduction of technical operations

I & R Reduction of technical operations with sowing and fertilization at the same time (microgranule) -> Reduce fuel consumption

R

Conservation tillage Conservation tillage, improve organic matter to reduce fertilizer input (soil fertility, low fuel consumption)

Reduction of fertilization

R

Introduce legume crop Intercropping: cereal + legume Legume – short cycle (feed): low nitrogen consumption To use crop residues as manure

I Livestock farming + agriculture: Diversification auto consumption maize for feed -> natural fertilizer

I & R

Close the nutrient loop (fertilizer): Substitution chemical fertilizer for organic fertilizer Reduction of fertilizer input Substitution chemical fertilization for organic fertilization Develop harvest machine to grow crops at the same time in order to decrease fertilization rate.

Adaptation to climate change

I & R Adapt crop to climate change Crop: “radical” change

Genetic improvement

I & R Research for seed more drought resistant without loss of production -> energetic cost for irrigation

Machine: efficiency

I & R

Improve efficiency: tractor with enhanced power Cooperatives: to invest in more efficient machine

Water recycling I & R Recycling waste waters (artificial wetland)

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Foss

il en

ergy

con

sum

ptio

n <1

5 G

J/ha

/yea

r an

d irr

igat

ion

wat

er c

onsu

mpt

ion

< 30

00 m

3/ha

To g

et a

low

tota

l en

ergy

con

sum

ptio

nTo

get

a lo

w ra

teof

foss

il en

ergy

con

sum

ptio

n

To u

se o

r to

prod

uce

rene

wab

le e

nerg

y

To

sub

stitu

te fo

sil

ener

gy (

fuel

) To

get

a lo

w e

nerg

yco

nsum

ptio

n fo

r irr

igat

ion

To g

et a

low

ene

rgy

cons

umpt

ion

for c

hem

ical

fert

ilize

r

To g

et a

low

wat

er c

onsu

mpt

ion

Low

pre

ssur

e dr

ip ir

rigat

ion

To u

se

sola

r ene

rgy

Sola

r pho

tovo

ltaic

w

ater

pum

ping

sys

tem

To u

se

biom

ass

To u

se

biof

uel

Tran

spor

t with

bi

ofue

l

To g

et a

low

fuel

con

sum

ptio

n fo

r agr

icul

tura

l ope

ratio

ns

Few

agr

icul

tura

l op

erat

ions

Sow

ing

and

fert

iliza

tion

at th

e sa

me

time

Con

serv

atio

n ti

llage

Prec

isio

n ag

ricul

ture

Dro

nes

GPS

for

the

trac

tor

No

plou

ghin

g

To in

trod

uce

adap

ted

crop

s

To in

trod

uce

ef

ficie

nt c

rop Gen

etic

impr

ovem

ent

for c

rop

varie

ties

Seed

mor

e dr

ough

t res

ista

ntD

esig

n se

ctor

ized

an

d po

wer

To c

lose

nu

trie

nts

loop

To in

trod

uce

effic

ient

cro

ps

Inte

rcro

ppin

g

Cer

eal +

legu

me

Intr

oduc

e le

gum

e cr

op

Alfa

lfa

To u

se a

n ef

ficie

ntdr

ip ir

rigat

ion

Rec

yclin

g w

ater Art

icifi

al

wet

land

Subs

titut

ion

by o

rgan

ic fe

rtili

zatio

n

Live

stoc

k fa

rmin

g

Aut

ocon

sum

ptio

n:

corn

for f

eed

To d

iver

sify

re

ssou

rces

Sym

biot

ic

nitr

ogen

fixa

tion

Org

anic

fe

rtili

zatio

n

To g

et a

n ef

ficie

nt fe

rtili

zatio

n

Tele

dete

ctio

nPr

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tion

mod

elC

rop

resi

dues

us

ed a

s m

anur

e

1/3

of b

iom

ass

crop

in

the

succ

esio

n cr

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m

otor

To g

et m

achi

nes

with

a h

igh

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ienc

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Trac

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ithen

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Coo

pera

tives

: in

vest

in m

ore

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mac

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Har

vest

mac

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ps

at th

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Dro

ne

Filte

r

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e ap

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atio

ns

Sate

lites

To g

et a

hig

h or

gani

c m

atte

r rat

e

3.1.3. Debriefing by the facilitators The figure below (Fig. 18) was elaborated from the personal ideas expressed by the experts.. The goal of the workshop is located on the top of the figure, with the objectives derived from the goal. This figure represents the functions (answering the question “what for?”) and solutions (answering the question “what?”) expressed during the workshop. Figure 18: Tree of functions and solutions based on personal ideas; a function (“what for?”) is represented by a rectangle. A solution (“what?”) is represented by an oval. You can read this figure top-down asking you the question “how?” or bottom-up asking you the question “why?”

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Maize Soya Wheat Sorghum Tomato Zucchini Broccoli AlmondPloughing 1 * 1 * * 0 1 1Chisel * 1 1 * 1 1 1 1

Cover1crop 3 1 2 3 2 3 1 1Disc1harrow * 3 * * * * * *

Sowing1machine 1 1 1 1 1 1 1 manual

Nitrogen 280 * 75 * 220 200 901+1120/140 70

Potassium1sulfate * * * * 150 * * *Sulphur * * * * 100 * * *

N*P*K1Compound1(4*6*20) * 380 * * * * * *

N*P*K1Compound1(13*11*21) * * * 206 * * * *

N*32 * * * 375 * * * *MgO * * * * * 50 * *K2O * * * * * 350 84 35P2O5 * * * * * 150 34 20

Herbicide 2 * 1 2 3 * 2 *Insecticide 1 * * * * 5 1 2Fungicide * * * * * * 1 *

Irrigation9(m3/ha) Irrigation 61500 * * 5000 5500 2700 2000 3500Harvest1machine 1 1 1 1 1 * * 1Manual1harvest * * * * * 1 1 *

Yield9(kg/ha) minimum*average*maximum

10000*14000*18000

2700*3500*6000

1000*2600*3000 71800 60000*80000*

100000 261000 221000 121000

Agricultural9machine9(passing/ha)

Harvest9(passing/ha)

Fertilization9(kg/ha)

Pesticides9(passing/ha)

3.2. Description of the cropping systems designed

3.2.1. Cropping system for irrigated land The cropping system includes 7 different crops over a five-year period (Table 8). The crop succession starts with grain maize. Soya is grown after maize for human consumption as soya milk (market mentioned by the local experts). During the third year, a winter cereal is grown (such as wheat) and followed by sorghum for energy production. Tomato crop is grown after sorghum. During the fifth year, two vegetables crops are grown: a summer vegetable crop, such as lettuce or zucchini, and then a winter vegetable crop, such as broccoli or cauliflower. This crop succession would concern 80% of the agricultural area of the farm whereas the remaining 20% would be grown with woody crops (such as almond tree). The main idea of the cropping system was to diversify the cropping system by including crops with low water consumption compared to the irrigated reference cropping system. Moreover, as maize price is decreasing, almond tree was included because of a favourable future regarding its price. There were no specific ideas about the order of the crop in the crop succession: the idea of growing tomato crop after soya to decrease the amount of nitrogen expressed during the collective discussion was finally abandoned. NB: The participants used two individual ideas represented in Fig. 18: “use biomass crop” and “introduce WUE crop” (soya and wheat). Table 8: Description of the cropping system designed in irrigated land. Yield is either described with three values (minimum, average and maximum yield) or only with the average yield. Data given by CENER.

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Year%1%Corn%

Year%2%Soya%

Year%3%Wheat%%%%%%%%Sorghum%

Year%4%Tomato%

Year%5%Zucchini%%%%%%%%%%%Broccoli%

Drip%irriga(on%

!

No%irriga(on%

Figure 19: Representation of the cropping system in the irrigated area N.B: During the workshop, we ran out of time to develop a rainfed cropping system.

3.2.2. Reference cropping systems Two reference cropping system were described for irrigated and rainfed areas respectively. They represented the most widely practiced cropping systems in the Province of Caceres in irrigated and rainfed areas. The rainfed reference cropping system includes two crops over a period of three-years. It starts with two years of cereal crops such as wheat, triticale or oat and then a legume crop, such as pea or soya (Table 9). Minimum yield for the legume crop could be zero kg/ha: when the yield is inferior to 500 kg/ha, legume crop is actually not harvested. In irrigated land, the reference cropping system includes one year of maize and one year of tomato (Table 10). Nitrogen fertilization is applied using ferti-irrigation.

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Table 9: Description of the reference cropping system in rainfed land. Yield is described with three values: minimum, average and maximum yield (data provided by CENER)

Cereal& Cereal& LegumePloughing 1 1 *Chisel 1 1 1

Cover0crop 2 2 1Disc0harrow * * 3

Sowing0machine 1 1 1Nitrogen 75 75 *

N*P*K0Compound(4*6*20) * * 380

Pesticides&(passing/ha) Herbicide 1 1 *Combine0harvester 1 1 *Harvest0machine0for0

legume0crop * * 1

Yield&(kg/ha) minimum*average*maximum

1000*2600*3000

1000*2600*3000 0*800*1000

Harvest&(passing/ha)

Agricultural&machine&(passing/ha)

Fertilization&(kg/ha)

Table 10: Description of the reference cropping system in irrigated land. Yield is described with three values: minimum, average and maximum yield (data provided by CENER)

Corn TomatoPloughing 1 *Chisel * 1

Cover0crop 3 2Sowing0machine 1 1

Nitrogen 280 220Potassium0sulfate * 150

Sulphur * 100Herbicide 2 3Insecticide 1 *

Irrigation,(m3/ha) Irrigation 6500 5500Combine0harvester 1 *

Tomate0combine0harvester * 1Yield,(kg/ha) minimum*average*

maximum 1000*14000*18000 60000*80000*100000

Agricultural,machine,(passing/ha)

Fertilization,(kg/ha)

Pesticides,(passing/ha)

Harvest,(passing/ha)

`

3.3. Multicriteria assessment of the cropping systems N.B: soya was replaced by pea

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3.3.1. Indicators and calculation methods Our assessment included all the steps from seeding to harvesting.

a) Water consumption Water consumption for irrigation is expressed en m3/ha and was provided by the local experts during the design workshop.

b) Energy consumption Energy consumption (MJ) = energy consumption for irrigation + energy consumption for fuel + energy consumption for fertilization

Energy consumption for irrigation = irrigation water (m3/ha) * irrigation coefficient (MJ/m3) Energy consumption for fuel = quantity of fuel (l/ha) * energetic coefficient (MJ/l) Energy consumption for fertilization =

i∑ quantity of fertilizer i (kg/ha) * energetic

coefficient (MJ/kg) We used the reference values given below: - Energy consumption for irrigation: 0,2 kWh/m3 of wáter irrigation = 0,72 MJ/m3

1 kWh = 3,6 MJ (or is equal to11,6 MJ if we considered primary energy) - Energy consumption for fuel: 1 litre of fuel = 40 MJ (source: GES’tim) - Energy consumption for fertilization (source: GES’tim) 1 kg of nitrogen = 50,7 MJ 1 kg of potassium sulphate = 9,48 MJ 1 kg of solution 36-S = 21,2 MJ 1 kg of P2O5 = 9,8 MJ 1 kg of K2O = 7,4 MJ 1 kg of NPK 4-6-20 = 4,42 MJ

c) Semi net margin (SNM) SNM = gross product – input costs Gross product= yield (t/ha) * crop price (€/t)

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Input costs = fuel costs + fertilizer costs + seed costs + pesticides costs + water irrigation costs We used the reference values given below: Crop price (€/ha) Maize = 191 Sorghum = 237 Winter wheat = 199 Tomato = 510 Pea = 284 Zucchini = 302 Broccoli = 300 Almond = 3540

Seed price (€/ha) Maize = 300 Sorghum = 120 Winter wheat = 50 Tomato = 1600 Pea = 90 Zucchini: 1600 Broccoli = missing data Almond = 866

Fuel price = 1 €/l Water irrigation price = 0,5 €/m3

The prices of the different fertilizers, pesticides, crop prices and seed prices were given by CENER and ACCIONA. Some data are missing (described below).

3.3.2. Results The ex ante assessment of the cropping systems in irrigated area was not completely implemented due to missing data (Tables 11 and 12):

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-‐ Fertilizer costs: MgO for zucchini, NPK compound for broccoli and sulphur for tomato

-‐ Pesticide costs for tomato -‐ Seed costs for broccoli

Thus, we decided not to estimate the semi-net margin and we performed the ex ante assessment by using only the criteria “water consumption” and “energy consumption”. Due to a lack of references, we assumed that sowing machine for tomato, zucchini and broccoli consumed the same amount of fuel than a potato planter (Source: Barême d’entraide 2012-2013). Lastly, we did not include the transport from the field to the farm after the harvest.

Table 11: Ex ante assessment of the crops of the cropping system in irrigated area (red boxes indicate missing data)

Indicadors corn sorghum wheat tomato pea zucchini broccolicrop%price%(€/ha) 2674 1849 517 40800 284 7852 6600

pesticides%cost%(€/ha) 1100 53 17 0fertilizers%cost%(€/ha) 63 122 180 133seed%cost%(€/ha) 300 120 50 1600 90 1600fuel%cost%(€/ha) 58 163 55 46 105 104 59water%cost%(€/ha) 3250 102 0 2750 0 1350 1000

water%consumption%(m3/ha) 6500 5000 0 5500 0 2700 2000fuel%consumption%(l/ha) 58 163 55 46 105 104 59

SNM%(€/ha) E2097 1289 216 E44energy%consumption%for%water%(MJ/ha) 4680 3600 0 3960 0 1944 1440energy%consumption%for%fuel%(MJ/ha) 2324 6506 2180 1838 4194 4173 2369

energy%consumption%for%fertilization%(MJ/ha) 14196 6084 3803 14696 1680 14200 12109total%energy%consumption 21200 16190 5983 20494 5874 20317 15918

Table 12: Ex ante assessment of the crops of the reference cropping system in irrigated area (red boxes indicate missing data)

Indicadors corn tomatocrop%price%(€/ha) 2674 40800

pesticides%cost%(€/ha) 1100fertilizers%cost%(€/ha) 63seed%cost%(€/ha) 300 1600fuel%cost%(€/ha) 58 46water%cost%(€/ha) 3250 2750

water%consumption%(m3/ha) 6500 5500fuel%consumption%(l/ha) 58 46

SNM%(€/ha) D2097energy%consumption%for%water%(MJ/ha) 4680 3960energy%consumption%for%fuel%(MJ/ha) 2324 1838

energy%consumption%for%fertilization%(MJ/ha) 14196 14696total%energy%consumption 21200 20494

Figure 20 shows the value of water consumption and total energy consumption at the cropping system scale for the reference cropping system in irrigated area and the cropping system described by the experts in irrigated area.

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0 2000 4000 6000 8000

10000

15000

20000

25000

water consumption (m3/ha/year)

tota

l ene

rgy

cons

umtio

n (M

J/ha

/yea

r)

reference CSCS described by the experts

Figure 20: Water consumption and energy consumption at cropping system scale for the two cropping systems in irrigated area. Horizontal and vertical black lines indicate the value needed to achieve the goal. At the cropping system scale, water consumption and total energy consumption for the cropping system designed during the workshop is decreased by 48,3% and by 27,4%, respectively, compared to the reference cropping system in irrigated area. In conclusion, the goal (to save 50% of water and energy consumption compared to the reference cropping system) is achieved for water consumption thank to the choice of crops with low water consumption compared to the irrigated reference cropping system. However, total energy consumption is important for maize, tomato, zucchini and broccoli mainly because the energy consumption for fertilization is important.

Conclusion Four cropping systems including Miscanthus x giganteus as an energy crop were designed for the case study 1 and one cropping system was designed for the case study 2. For the case

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study 1, these cropping systems achieved good results regarding environmental and energy indicators mainly because Miscanthus x giganteus uses low quantities of N fertilizer and pesticides, and stores C in the soil. However a tradeoff needs to be found among (i) environmental and energy impacts and (ii) profitability and food capacity. For the case study 2, including crops with low water consumption allowed to achieve the goal previously defined about water saving. Lastly, beyond the results obtained by the cropping systems on each case study, this design activity represents a fruitful way to raise discussions among local experts and scientists on the way to deal with ambitious goals at the cropping system scale, and to identify research priorities by pointing out knowledge gaps on the agrecosystem. References

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Ballot, R., Guichard, L., 2013. PERSYST: Un outil d’évaluation des PERformances agronomiques, pour l’évaluation de la durabilité des SYStèmes de culture. Guide utilisateur & paramétrage en Bourgogne- Version du 09/02/2013 69. Christian Bockstaller, Girardin, A., 2008. Mode de calcul des indicateurs agri-environmentaux de la méthode Indigo. Duparque, A., Boizard, H., Damay, N., Julien, J.-L., Leclercq, C., Mary, B., 2007. Evolution de l’état organique du sol à l’échelle de la parcelle: de nouveaux outils pour une démarche de conseil fondée sur le bilan humique AMG. COMIFER-GEMAS Blois 16p. Entraid’EST, 2012. Barème d’entraide 2012-2013. Gaec, A., Deltour, L., Cariolle, M., Dollé, J.-B., Espagnol, S., Flénet, F., Guingand, N., Lagadec, S., Le Gall, A., Lellahi, A., Malaval, C., Ponchant, P., Tailleur, A., 2010. GES’TIM Guide méthodologique pour l’estimation des impacts des activités agricoles sur l’effet de serre. Version 1.2. IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories Volume 4 Agriculture, Forestry and Other Land Use. Lançon, J., Wery, J., Rapidel, B., Angokaye, M., Gérardeaux, E., Gaborel, C., Ballo, D., Fadegnon, B., 2007. An improved methodology for integrated crop management systems. Agron. Sustain. Dev. 27, 101–110. doi:10.1051/agro:2006037 Lesur, C., Bazot, M., Bio-Beri, F., Mary, B., Jeuffroy, M.-H., Loyce, C., 2014. Assessing nitrate leaching during the three-first years of Miscanthus × giganteus from on-farm measurements and modeling. GCB Bioenergy 6, 439–449. doi:10.1111/gcbb.12066 Miguez, F.E., Villamil, M.B., Long, S.P., Bollero, G.A., 2008. Meta-analysis of the effects of management factors on Miscanthus×giganteus growth and biomass production. Agric. For. Meteorol. 148, 1280–1292. doi:10.1016/j.agrformet.2008.03.010 Poeplau, C., Don, A., 2014. Soil carbon changes under Miscanthus driven by C 4 accumulation and C 3 decompostion - toward a default sequestration function. GCB Bioenergy 6, 327–338. doi:10.1111/gcbb.12043 Rapidel, B., Traoré, B.S., Sissoko, F., Lançon, J., Wery, J., 2009. Experiment-based prototyping to design and assess cotton management systems in West Africa. Agron. Sustain. Dev. 29, 545–556. doi:10.1051/agro/2009016 Reau, R., Monnot, L., Schaub, A., Munier-Jolain, N., Pambou, I., Bockstaller, C., Cariolle, M., Chabert, A., Dumans, P., 2012. Les ateliers de conception de systèmes de culture pou construire, évaluer et identifier des prototypes prometteurs. Innov. Agron. 20, 5–33. Stoorvogel, J.., Bouma, J., Orlich, R.., 2004. Participatory research for system analysis: prototyping for a Costa Rica banana plantation. Agron. J. 96, 323–336. Vereijken, P., 1997. A methodical way of prototyping integrated and ecological arable

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farming systems (I/EAFS) in interaction with pilto farms 7, 235–250. Wijnands, F.., 1997. Integrated crop protection and environment exposure to pesticides: methods to reduce use and impact of pesticides in arable farming 7, 251–260.

logistics for energy crops’ biomassbourgogne, france (case study 1: bourgogne pellets) and one in...

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