# Design and development of Integrated farming system module for various agro ecosystems of Hyderabad- Karnataka region

Bheemsainrao Desai *±1*, Satyanarayan Rao, S. Biradar, Prahlad Ubhale & Sangeeta NP

## 1 Introduction

More than 70% of the Indian farming community own less than one hectare of land. They belong to the small and marginal category. These farmers feel that, except going for a single cropping system, there is hardly any possibility of trying new practices or methods for farm improvement. To address the felt needs of such small and marginal farmers, a farming system model has been developed. This low cost model integrates local farming practices. Farmers have to adapt to continuously changing conditions to produce food. ‘Integrated farming systems design’ is an approach that aims at modifying designs of farming systems to sustainably increase the overall productivity and profitability of the systems and hopefully, the welfare of individual farming families- while considering interactions in the system. The main objective this model is to develop, demonstrate and popularize integrated farming system model with farmers’ participatory approach for irrigated and dry land ecosystems, to educate farmers’ regarding integrated farming systems technology through training programs, field visits through exposure trips and field days and to create employment generation under different enterprises of integrated farming system.

## 2 Material and methods

### Design of the model for irrigated eco system (1 ha), Main Agricultural Research Station, UAS, Raichur

A unique IFS design was developed and tested in 10 Agricultural research stations of UAS, Raichur, where in 1 ha module was divided into 5 segments of 2000m² each consisting of

**I. Cropping activities**

<table><tr><td>Paddy</td><td>- 0.25 ha</td></tr><tr><td>Cotton</td><td>- 0.25 ha</td></tr><tr><td>Brinjal</td><td>-0.1 ha</td></tr><tr><td>Bhendi</td><td>- 0.1 ha</td></tr><tr><td>Onion</td><td>- 0.1 ha</td></tr></table>

Curry leaf - on bunds of fish pond Drumstick - on bunds of fish pond Banana - on bunds of fish pond

Fodder - 0.1 ha

Green manure - in the inter space of wide row crops

**II. Dairy component**

Dairy unit of 5 cross bred cows / buffaloes.

**III. Fishery component** - 0.06 ha

**IV. Vermi compost unit** - 0.04 ha

**V. Tree farming** - on farm boundaries

<figure><img src="image_216.png" /></figure>

<table><thead><tr><th>I-Segment</th><th>II-Segment</th><th>III-Segment</th><th>IV-Segment</th><th>V-Segment</th></tr></thead><tbody><tr><td>Jasmine<br>Marigold</td><td>Bt.Cotton<br>+<br>Onion</td><td>Maize -<br>Chickpea</td><td>Mango and Fig, Guava<br>- Vegetables Chilli<br>Onion Carrot<br>Clusterbean+Raddish<br>Brinjal+Tomato<br>Menthi+Corriander<br>Raajgi<br>Sabbasige Pundi<br>Hunsikki Garlic</td><td>Fish Pond+ Poultry<br>and Rabbit<br>Cage+Cucumber<br>Farm house Kitchen<br>Garden Nutrition<br>garden Farm pond<br>Azolla unit Biogas<br>plant Shed for<br>livestock Biodigester<br>Compost or vermi pit</td></tr></tbody></table>

<footnote n="1">¹ Department of Agronomy College of Agriculture, Raichur, Karnataka-India University of Agricultural Sciences, Raichur, Karnataka (UASR)- www.uasr.edu.in</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author:</footnote>
## 3 Discussion

Results of four years of Integrated farming systems are discussed here the productivity of the farming system was based on the quantity of marketable produce obtained during all four years. IFS method records higher net returns and benefit cost ratio in all the four years because this method comprising the components like cropping, Vegetables, Vermi compost, Goat rearing, poultry and cattle (bullocks, cow, calves) rearing. At the end of fourth successive year Irrigated IFS contributed a net return was Rs.1, 70,563 and from cropping system alone Rs.55, 025. The average income Rs.1, 55,538 of additional profit can be earned. Similar results were also reported by Ugwumba et al. (2010) and Ortega et al. (2009) a). Higher net income generated during fourth year compared to first, second and Third year due to proper recycling of farm resources each other through use of vermin compost, FYM and also from yielding of horticulture components like drumstick, curry leaf, adoption of floriculture and good planning of vegetables according to good seasonal demand might be contributed to good returns. These results are in accordance with channabasavanna et al. (2009) were he stated IFS approach recorded 26.3 and 32.3 per cent higher productivity and profitability respectively over conventional rice- rice system. The results indicated IFS become more profitable during perennial years compared to single year.

<figure><img src="image_217.png" /><figcaption>Fig. 1. Integrated farming system (Irrigated condition)</figcaption></figure>

## 4 Conclusions

It is clear from the above results that IFS method for irrigated situations enhances productivity, profitability and nutritional security of the farmer and sustains soil productivity through recycling of organic sources of nutrients from the enterprises involved. The total average net income obtained from Irrigated IFS was Rs.1, 70,563 and from cropping system alone Rs.55, 025. The average income Rs.1, 55,538 of additional income can be earned. Cropping system+vegetable+Dairy+Vermi compost+ fodder on bunds will be good practice under irrigated condition in Hyderabad Karnataka region. This innovative 1 ha IFS Module designed by University of Agricultural Sciences Raichur, Karnataka, is being adopted by 465 farmers who are repeating the benefits in terms of income and employment generation.

## References

* Ugwumba, C.O.A., Okoh, R.N., like, P.C., Nnabuife, E.L.C. and Orji E.C 2010. Integrated Farming system and its effect on farm cash income in Awka South Agricultural Zone of Anambra State, Nigeria, American-Eurasian journal of Agriculture and Environmental Science **8** (1): 01-06.

* Ortega. and Maximiliano 2009a. Integrated Farming System – A training report. Presented at Workshop on Integrated Farming System held on 27 February 2009 at Central Farm, Cayo District, Belize.

* Channabasavanna, A.S., Biradar, D.P., Prabhudev, K.N. and Mahableshwar Hegde 2009. Development of profitable integrated farming system model for small and medium farmers of Tungabhadra project area of Karnataka. *Karnataka Journal of Agriculture Science* 22(1): 25-27.
# Decisional-model for analyzing and scaling out innovative cropping systems

Raymond Reau *±<fnref n="1" />*, Vincent Cellier <fnref n="2" />, Violaine Deytieux <fnref n="2" />, Marie-Sophie Petit <fnref n="3" />, Anne Schaub <fnref n="4" />, Patrice Cotinet <fnref n="5" /> & Jean-Luc Giteau <fnref n="6" />

## 1 Introduction

Since the 1950s, the evolution of agriculture in France has led to quite homogeneous and simplified cropping systems (CS), with numerous negative environmental impacts. The transition towards higher sustainability needs a deep change, which can be enhanced by the identification of high performant systems, followed by the scaling out of part of them.
However, systemic change is difficult. It appears necessary to understand and describe the way these CS are decided, managed and implemented, through a bio-decisional model (Le Gal et al., 2009). In this aim, we proposed a four-step functional approach to analyze and to represent the decisional model of a CS. It is based on a conceptual framework where the main agroecosystem services are explained, and includes:(1) the identification of main services of the CS, (2) for each service, the precise targets defined and formulated by the crops' manager (functions), (3) the combination of techniques and decision rules that allow reaching these targets (solutions), and (4) the way the crops' manager monitors the results achieved and prepares his strategic choices.

## 2 Materials and Methods

Choosen out of more than fifty studies realized with the Joint technology network for 'innovative cropping systems' (ICS-JTN), this paper describes the decisional model of a CS experimented at the station of Kerguéhennec (Brittany region, France) managed by the Chambre d'Agriculture. One of its main functions is to have low nitrogen (N) losses, in order to contribute to water quality with low nitrate pollution (Step 1).
In order to precisely describe the decisional process, some members of the JTN realized a 2-hour interview of the crops' manager of the experimented CS. First questions were about the targets for the control of N losses, which were considered as a routine: what are the criteria you observe or measure on the field to assess that your CS is successful for this goal? What is your target value? (Step 2).

Then, the questions were about the solutions implemented. In order to express how the solutions are combined into the system (Step 3), the information were collected on a white board by a facilitator, and organized through two axes: the horizontal axis representing the time of the crop sequence, the vertical axis representing the different categories of solutions bringing decisive contribution to the target : crop sequence, detailed aspects of chemical N fertilization and organic fertilization management, tillage effect on mineralization efficiency, and N mineral uptake.
Lastly, the questions were about the results obtained last years in the experiment, and especially what were the deviations between obtained results and target values (Step 4). In this case, the conclusion was a collective reflexive activity, about how should be improved the CS in order to be more frequently successful for the analyzed function.

<figure><img src="image_218.png" /><figcaption>Fig. 1. Description of the decisional model of control of N losses into the experimental cropping system</figcaption></figure>

<footnote n="1">¹ INRA, UMR 211 Agronomie, F-78850, Thiverval-Grignon, France</footnote>

<footnote n="2">² INRA, UE 115 Domaine d'Epoisses, Réseau PIC F-21110, Bretenière, France</footnote>

<footnote n="3">³ Chambre Régionale d'agriculture de Bourgogne, 3 rue du golf, F-21800 Quétigny, France</footnote>

<footnote n="4">⁴ Association pour la Relance Agronomique en Alsace, BP 30022 Schiltigheim, F-67013 Strasbourg, France</footnote>

<footnote n="5">⁵ Chambre d'Agriculture du Morbihan, Station expérimentale de Kerguehennec, F-56500 Bignan, France</footnote>

<footnote n="6">⁶ Chambre d'Agriculture de Bretagne, 4, avenue du Chalutier Sans Pitié BP 10540, F-22195 Plérin Cedex, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: raymond.reau@grignon.inra.fr</footnote>
## 3 Results – Discussion

The practical criteria used by the crops' manager of the experimented CS to assess year after year the risk of N losses, was the potential leachable nitrogen contained in the soil, which is measured in each plot during autumn. He was expecting an average value less than 50 kg N.ha⁻¹. He described a six-year crop sequence: 6 main crops (maize, winter wheat, spring field bean, winter wheat, winter oil seed rape, and triticale) plus 4 catch crops between the main crops (Fig. 1). While maize and oilseed rape were mainly fertilized with poultry manure compost, the field bean was not N fertilized, triticale and wheat received 110 to 140 kg N.ha⁻¹ of mineral fertilizers. In order to catch nitrogen during summer and autumn, catch crops were generalized except after maize and before winter oilseed rape.

<figure><img src="image_219.png" /><figcaption>**Fig. 2.** Ex post assessment and success' analysis with the crops' manager of the experimented cropping system</figcaption></figure>

The measurements of potential leachable nitrogen in the soil observed during the last years were low and below the target value during the first part of the CS sequence, thanks to pre-crop effect of maize and to the high efficiency of catch crop between a cereal and a spring crop (Fig. 2). But average potential leachable nitrogen was over the threshold (56 kg N.ha⁻¹), due to high mineral soil nitrogen after oilseed rape and field beans even with catch crop, and under oilseed rape after a summer organic fertilization.

<table><caption>**Table. 1.** Ideas and proposals to improve the cropping system in order to be successful with the N losses function</caption><thead><tr><th></th><th>After field bean</th><th>After oilseed rape</th><th>Between wheat &amp; oilseed rape</th><th>Other</th></tr></thead><tbody><tr><th>Technical categories</th><td></td><td></td><td>Reducing N amount</td><td>Exportation of catch crops for fodder feed</td></tr><tr><th>Organic fertilization and management</th><td></td><td></td><td></td><td></td></tr><tr><th>Crop sequence</th><td>Changing the order of the crops : oilseed rape or maize after field bean</td><td></td><td></td><td></td></tr><tr><th>Chemical fertilization</th><td></td><td>Reducing N amount</td><td></td><td></td></tr><tr><th>N mineral catchment</th><td></td><td>Replacing volunteers with mustard catch crop</td><td>Improving emergence date of catch crops and winter crops for high N uptake</td><td></td></tr></tbody></table>

## 4 Conclusions

The decisional model description explains the function and the solutions proposed for each service targeted from the CS. In the studies cases, it successfully allowed to better understand the combinations of techniques, defining the CS, implemented to reach the service, and gave useful information to agronomists and farmers for out scaling the successful parts of the CS (sequence triticale-maize-wheat in this case). The decisional model was also a useful boundary object to analyze the failure of the second part of the CS (sequence field bean-wheat-oilseed rape), to propose ideas to improve it (Tab. 1) thus contributing to step by step design (Meynard et al., 2012).

The ICS-JTN is now using the decisional model not only for the monitoring and analysis of the cropping system experiments, but also to accompany changes inside farms and to offer farmers strategic agronomical advices.

*Acknowledgements.* This work was funded under the Joint technology network for 'innovative cropping system' by the Ministry of Agriculture, Food and Forest - CASDAR. We thank all the participants of the JTN workshop of February 13th 2015 in Brittany - France, as well as M-H. Jeuffroy for her thorough re-reading of this article.

## References

* Deytieux V., Dumans P., Guérin O., Landé N., Minette S., Nolot J-M., Petit M-S., Reau R., Tordeur A., Vivier C., 2010, A multi-actors experimental network to assess innovative arable cropping systems. Agro2010 XIth ESA Congress, Agropolis International, Montpellier, France, 995-996.

* Le Gal P-Y., Mérot A., Moulin C-H., Navrete M., Wry J., 2009. A modelling framework to support farmers in designing agricultural production systems. Environmental Modelling & Software 25, 258-268.

* Meynard, J.-M., Dedieu, B., and Bos, A.P. (Bram), 2012. Re-design and co-design of farming systems. An overview of methods and practices. In: I. Darnhofer, D. Gibbon, and B. Dedieu, eds. Farming Systems Research into the 21st Century: The New Dynamic. Springer Netherlands, 405-429.

* Petit M.-S., Reau R., Deytieux V., Schaub A., Cerf M., Omon B., Guillot M.-N., Olry P., Vivier C., Piaud S., Minette S., Nolot J-M., 2012, Systèmes de culture innovants : une nouvelle génération de réseau expérimental et de réseau de compétences, Innovations Agronomiques 25, 99-123.
# Integrated effects of conservation agriculture in a crop-livestock system on western loess plateau, china

Lingling Li *±1*, Renzhi Zhang 2, Bill Bellotti 3 & Adam Komarek 4

## 1 Introduction

Conservation agriculture (CA) is now widely recognized as a viable option for practicing sustainable agriculture (Kassam *et al.* 2012). China is an ancient agricultural country, it has a long history of soil and water conservation practices. However, modern conservation agriculture is fairly new. The area under CA expanded rapidly from 45 million ha in 1999 to 111 million ha in 2009, with an growth rate of 6 million ha per annum (Kassam *et al.* 2009). Soil erosion on the Loess Plateau is the highest in China, and indeed among the highest in the world (Liu 1999). The semi-arid Loess Plateau is the most important region of rainfed agriculture in China. This paper discusses CA effects in a crop-livestock system on western loess plateau.

## 2 Materials and Methods

An experiment on different tillage systems was designed for a one-year one-crop rotation of spring wheat and field pea, and implemented from August 2001 in Dingxi, a typical semi-arid area on the Loess Plateau. The experimental design included six treatments, replicated four times, and included both phases of the wheat/pea rotation each year, the experiment was last from 2001 till this paper, it is still on going. The six treatments included traditional farmer practice (T, conventional tillage with crop residues removed) and a CA treatment (NTS, no tillage with crop residues retained).

To separate the effects of tillage and crop residues, these factors were combined factorially as follows:

* T, traditional tillage with crop residues removed

* NT, no tillage with crop residues removed

* TS, traditional tillage with crop residues retained

* NTS, no tillage with crop residues retained

The design also incorporated plastic mulch with and without tillage. The main aim of this research was to develop suitable CA practices for the rainfed Loess Plateau.

APSIM was used to simulate crop residue retention practices on a daily time step, crop, forage and soil-related processes and the influence of climate and management activities on these processes using local climate and soil data (Keating *et al.*, 2003). APSIM has been previously developed and tested for the spring wheat-field pea rotation in Dingxi (Chen *et al.*, 2008). We set up different simulations where the amount of crop residue retained after harvest changed incrementally by 10% from 0% to 100%.

We calculated the net economic value of the spring wheat-field pea rotation when different amounts of crop residues were retained on a per hectare basis. We therefore calculated the NPV associated with retaining different amounts of crop residues using different planning horizons, i.e. 3, 6, 10 and 20 years.

## 3 Results - Discussion

The research found that NTS considerably increased surface soil (0–10 cm) moisture at sowing. However, the different tillage patterns had no strong effect on total soil water storage (0–200 cm)(Table 1). Although NTS had no strong effect on soil bulk density and total porosity, the non-capillary porosity and aggregates under NTS greatly improved. Thus, soil saturation conductivity and soil infiltration increased, whereas soil and water erosion decreased, soil loss (sediment load) from erosion decreased by 62.4% (Table 2). Grain yield was generally higher under NTS than under conventional tillage over 12 year (Table 3).

<table><caption>**Table 1.** Soil water at sowing under different tillage practices (V%)</caption><thead><tr><th rowspan="2">Crop</th><th rowspan="2">Soil depth (cm)</th><th colspan="2">2002</th><th colspan="2">2003</th><th colspan="2">2004</th></tr><tr><th>T</th><th>NTS</th><th>T</th><th>NTS</th><th>T</th><th>NTS</th></tr></thead><tbody><tr><td rowspan="3">Wheat</td><td>0–5</td><td>10.2</td><td>19.5</td><td>7.0</td><td>12.4</td><td>15.7</td><td>20.2</td></tr><tr><td>5–10</td><td>15.2</td><td>20.1</td><td>9.6</td><td>13.5</td><td>19.9</td><td>21.1</td></tr><tr><td>10–30</td><td>20.1</td><td>21.5</td><td>15.3</td><td>16.2</td><td>19.9</td><td>20.2</td></tr><tr><td rowspan="3">Field pea</td><td>0–5</td><td>16.9</td><td>21.8</td><td>14.2</td><td>22.1</td><td>9.9</td><td>11.0</td></tr><tr><td>5–10</td><td>23.3</td><td>24.3</td><td>17.1</td><td>21.3</td><td>18.4</td><td>19.4</td></tr><tr><td>10–30</td><td>21.4</td><td>22.5</td><td>14.3</td><td>16.9</td><td>20.0</td><td>21.7</td></tr></tbody></table>

<footnote n="1">¹Gansu Provincial Key Laboratory of Aridland Crop Science / Faculty of Agronomy, Gansu Agricultural University, China</footnote>

<footnote n="2">²Faculty of Resource and Environment, Gansu Agricultural University, China</footnote>

<footnote n="3">³University of Western Sydney, Australia</footnote>

<footnote n="4">⁴International Food Policy Research Institute, Washington, DC 20006, USA</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: lill@gsau.edu.cn</footnote>
<table><caption>**Table 2.** Cumulated runoff, infiltration and sediment under different tillage systems (P < 0.05)</caption><thead><tr><td>Treatment</td><td>Rainfall (mm)</td><td>Cumulated runoff (mm)</td><td>Cumulated infiltration</td><td>Sediment (g m<sup>-2</sup>)</td></tr></thead><tbody><tr><td>T</td><td>85</td><td>53.10b</td><td>31.90b</td><td>27.77ab</td></tr><tr><td>NT</td><td>85</td><td>62.90a</td><td>22.10b</td><td>32.73a</td></tr><tr><td>TS</td><td>85</td><td>66.26a</td><td>18.74c</td><td>23.79b</td></tr><tr><td>NTS</td><td>85</td><td>44.85c</td><td>40.15a</td><td>14.89c</td></tr></tbody></table>

<table><caption>**Table 3.** Average grain yield of crops in 2002-2013 under different tillage systems (kg ha⁻¹)</caption><thead><tr><td>Rotations</td><td>T</td><td>NT</td><td>TS</td><td>NTS</td><td>TP</td><td>NTP</td></tr></thead><tbody><tr><td>Pea→wheat</td><td>1415</td><td>1367</td><td>1521</td><td>1686</td><td>1583</td><td>1660</td></tr><tr><td>wheat→Pea</td><td>1422</td><td>1361</td><td>1531</td><td>1786</td><td>1715</td><td>1637</td></tr></tbody></table>

APSIM modeling results showed that crop residue retention increased grain production, reduced forage production leading to smaller livestock flock sizes and increased family heating and cooking costs. The net effect was that retaining minimal crop residues lead to the highest profits when there was a three year planning horizon and full crop residue retention provided the highest profit when the planning horizon exceeded 10 years. This is because the benefits on crop residue renetion take time to be realised, but farmers discount the future. When the planning horizon was 3, 6, 10 and 20 years the average maximum NPV was achieved when 0%, 0%, 100% and 100% of crop residues were retained, respectively (Fig. 1). The average NPV for the different planning horizons was within 10% of the maximum average NPV for a wide range of crop residue retention practices (Fig. 1), indicating that crop residue retention has limited impact of economic outcomes, and adoption could be linked to other factors we have not directly observed. Our results were not very sensitive to changes in discount rates.

<figure><img src="image_220.png" /><figcaption>**Fig. 1.** Average net present value (NPV) of a spring wheat-field pea no-tillage rotation over different simulation periods with different crop residue retention rates and different planning horizons in Dingxi.</figcaption></figure>

## 4 Conclusions

14-years field study on the rainfed Loess Plateau showed that no till with stubble retention could improve surface soil water availability at sowing, improve soil quality and infiltration, alleviate erosion, and increase grain yield. No-till with stubble retention is the most productive and sustainable practice for the research area. APSIM modeling research indicated that providing financial incentives to retain crop residues during the initial transition years could be a policy option.

*Acknowledgements.* NSFC(31460337) provided financial support for the research.

## References

Chen, W., Robertson, M., Li, L., Bellotti, W., French, R. and Bell, R. (2008). Simulation of field pea growth and yield in diverse environments. in Unkovich, M. (ed.), 14th Agronomy Conference, Global Issues. Paddock Action.

Kassam, A., T. Friedrich, et al. (2012). Conservation agriculture in the dry Mediterranean climate. Field crops research, 132: 7-17.

Kassam, A., T. Friedrich, et al. (2009). The spread of Conservation Agriculture: justification, sustainability and uptake. International Journal of Agricultural Sustainability, 7(4): 292-320.

Keating, B.A., Carberry, P.S., Hammer, G.L. et al. (2003). An overview of APSIM, a model designed for farming systems simulation, European Journal of Agronomy, 18, 267-288.

Liu, G. (1999). Soil conservation and sustainable agriculture on the Loess Plateau: challenges and prospects. Ambio, 28(8): 663-668.
# A procedure to analyze multiple Ecosystem Services in apple orchards

Constance Demestihas<sup>*±1,2*</sup>, Daniel Plénet<sup>2</sup>, Michel Génard<sup>2</sup>, Dominique Grasselly<sup>1</sup>, Iñaki García de Cortázar-Atauri<sup>3</sup>, Marie Launay<sup>3</sup>, Nicolas Beaudoin<sup>4</sup>, Sylvaine Simon<sup>5</sup>, Marie Charreyron<sup>6</sup>, Marie-Hélène Robin<sup>7</sup> & Françoise Lescourret<sup>2</sup>

## 1 Introduction

In order to meet market requirements, current fruit production has been strongly intensified, thus running fossil energy reserves dry in some cases. Arboriculture faces the double task of providing acceptable fruit production levels and preserving natural resources. This duality can be analyzed with the concept of ecosystem services (ES), from which humans draw potential benefits (MEA 2005). This concept has already been used in agroecosystems but very rarely in orchard systems. Yet the perennial nature of orchards, the quasi systematic presence of cover-crops, the possible carbon sequestration in trees and soil, and the permanent provisioning of habitat to natural predators for pest regulation are all characteristics which make them interesting for ES analysis. The interactions between the functions underlying the services are multiple, very complex and strongly influenced by agricultural practices and pedoclimatic conditions. This leads to tradeoffs and synergies amongst ES. The study of these relations is necessary to adjust agroecosystem management towards specific multiple ES profile goals. Hereafter, we present an approach to analyze ES relations within an apple orchard, using indicators of ecosystem functions underlying ES.

## 2 General approach of multiple ecosystem services analysis in the case of apple orchards

The ‘cascade’ concept of (Haines-Young and Potschin 2009) causally linking biophysical structures, functions, services and benefits has inspired our analysis. Following the CICES (Common International Classification of Ecosystem Services classification (Haines-Young and Potschin 2013) we distinguished different ES groups within the apple orchard agroecosystem: fruit production (provisioning service), pest and disease control, climate regulation, water regulation (regulation services), soil fertility maintenance (maintenance services). Each of these services groups can be determined by services or disservices and more upstream, by ecosystem functions. Ecosystem functions or processes transform physical, chemical and biological elements in the soil-plant-atmosphere continuum within specific time scales and are impacted by agricultural management and pedoclimatic conditions. These transformations are precisely the dynamics creating complex synergies and tradeoffs relationships between ES. The conceptual scheme of Fig. 1 shows how these elements match up within an apple orchard, in close connection with the nitrogen and carbon biogeochemical cycles, as well as the water cycle. Quantifying flows is necessary to understand the relationships between all these transformations and may be subsequently of interest for service indicators.

<footnote n="1"><sup>1</sup> Centre Technique Interprofessionnel des Fruits et Légumes (C.T.I.F.L.), Centre de Saint Rémy</footnote>

<footnote n="2"><sup>2</sup> INRA, UR1115 Plantes et Systèmes de Culture Horticoles</footnote>

<footnote n="3"><sup>3</sup> INRA, US 1116, Agroclim</footnote>

<footnote n="4"><sup>4</sup> INRA Laon UR AgroImpact</footnote>

<footnote n="5"><sup>5</sup> INRA Gotheron, UE695</footnote>

<footnote n="6"><sup>6</sup> Station expérimentale La Pugère</footnote>

<footnote n="7"><sup>7</sup> INRA Auzeville UMR AGIR</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: constance.demestihas@paca.inra.fr</footnote>
<figure><img src="image_221.png" /><figcaption>Fig. 1. Conceptual scheme of ecosystem functions and services within an apple orchard framework</figcaption></figure>

In order to quantify a great deal of transformations within an apple orchard, two models were used: STICS (Brisson *et al.* 1998), a dynamic, generic and robust model, which aims at simulating the soil-crop-atmosphere system while considering the impact of agricultural practices and pedoclimatic conditions; IPSIM (Aubertot and Robin 2013), a qualitative aggregative modelling framework to predict crop injury profile as a function of cropping practices as well as the abiotic/biotic environment. STICS has been parametrized on apple tree using bibliography data and field collected data. We focused on specific output variables describing dynamically interest functions such as tree aboveground biomasses (fruit, leaves, stems), mineral and organic nitrogen in soil, net carbon sequestration in soil, greenhouse gases emissions or lixiviation. Some outputs can be appreciated as a function's state indicator (nitrate content at blooming), as a function's dynamic indicator (fruit biomass from blooming to harvest) or a service indicator (yield). Concerning the pest regulation service, a bibliographic review has been made in order to identify the key factors impacting the severity of crop injury by 3 major apple orchard pests (apple scab, rosy apple aphid and codling moth). Experts were then questioned on the same points in order to validate these factor impacts, which are mainly cultural practices, pedoclimatic conditions and landscape management. Model outputs are then to be selected as ecosystem service indicators in order to understand the impact of agricultural management on ES relations.

The third part of this approach deals with field data. We chose to work on two experimental apple orchard sites in south-eastern France, which are marked by different pedoclimatic conditions. Each one presents three different system managements ranging from conventional to organic management. Fruits, leaves and stem growth as well as their nitrogen and carbon content were regularly measured in order to assess specific parameters. Specific soil analyses were performed. Data on water content were registered by capacitive sensors or tensiometers. These field data are crucial to parametrize the STICS as well as IPSIM models and compare model outputs with field data. They can as well be used directly as functions or services indicators together with the model outputs.

## 3 Conclusion

Recent studies show increasing interest in agriculture seen through the concept of ES. This contributes to change agriculture-environment relations analysis, taking into consideration different stakeholders and most importantly linking natural resources management to agricultural management.

An apple orchard agroecosystem is necessarily impacted by its agricultural management and its pedoclimatic conditions. These impacts modify the agroecosystem resources by triggering different functions, thus creating synergies and trade-offs between ES. Modelling tools help consider the complex transformations and their interaction occurring in the agroecosystem and describe them with output variables, which can be used as ecosystem function or service indicators. Our approach enables to explicit the impact of some cultural practices linked to particular pedoclimatic conditions on the functions underlying the ES and, in this way, to analyze multiple ES relations. Working with models may also allow simulating fictive scenarios combining different agricultural practices and pedoclimatic conditions, thus searching for management options corresponding to various profiles of synergies and trade-offs between ES.

## References

* Aubertot, Jean-Noël, and Marie-Hélène Robin. 2013. "Injury Profile SIMulator, a Qualitative Aggregative Modelling Framework to Predict Crop Injury Profile as a Function of Cropping Practices, and the Abiotic and Biotic Environment. I. Conceptual Bases." *PloS one* 8(9):e73202. Retrieved October 5, 2014.

* Brisson, Nadine *et al.* 1998. "STICS: A Generic Model for the Simulation of Crops and Their Water and Nitrogen Balances. I. Theory and Parameterization Applied to Wheat and Corn." *Agriculture and Environment* 18:311–46.

* Haines-Young, R., and M. Potschin. 2009. *Methodologies for Defining and Assessing Ecosystem Services*.

* Haines-Young, Roy, and Marion Potschin. 2013. *Common International Classification of Ecosystem Services (CICES): Consultation on Version 4, August-December 2012*.

* MEA. 2005. *Ecosystems and Human Well-Being: Current State and Trends*. Washington D.C: Island Press.
# Intercropping grains, oilseeds and row crops with forage species to enhance cropping system sustainability

Aaron A. S. Mills *±<fnref n="1" />, Isabelle Breune <fnref n="2" />, Christine Noronha <fnref n="1" />, Judith Nyiraneza <fnref n="1" />, Gaétan Parent <fnref n="3" /> & A. Vernon Rodd <fnref n="1" />

## 1 Introduction

Producers currently require low-cost and effective management tools to mitigate losses incurred due to agroclimatological and socioeconomic variability. Increased diversity has repeatedly been shown to improve functioning and resilience in agricultural, ecological and economic systems (Symstad et al., 2003; Cardinale et al., 2012; Isbell et al., 2013). By increasing agricultural biodiversity through intercropping, production systems use nutrients more efficiently, soil erosion and water loss is reduced, there is improved biocontrol of pest species, soil organic matter increases, and there is an overall increase in the provision of agro ecosystem services. The practice of underseeding cereal crops with a subordinate forage species is common in Eastern Canada, and has repeatedly been shown to improve soil quality and increase yields of subsequent rotation crops (Carter et al., 2009; Hauggaard-Nielsen et al., 2012). Annual ryegrass intercropped in silage corn production can serve as a cover crop to reduce soil erosion (Malik et al., 2000) and suppress winter annual weeds (Shrestha et al., 2002). Intercropping with cover crops has been shown to increase biodiversity, reduce field runoff and increase soil nutrients for subsequent cash crops (Snapp et al., 2005). There has also been report of biofumigatory and enhanced biocontrol effects of intercropping (Iverson et al., 2014).

## 2 Materials and Methods

**Intercropping soybean and canola with forage species:** An experiment was conducted to measure the effects of underseeding (at the time of main crop planting) or overseeding (seeding into the main crop later in the season) forage species into either soybean or canola. Soybean production in Eastern Canada involves a late harvest leaving little time for seeding a winter cover crop. Therefore an intercropped forage species would serve to reduce soil erosion through the winter and help to build soil organic matter through time. Although the majority of canola varieties grown in Canada contain herbicide resistance traits, the potential to reduce the amount of chemical inputs to be replaced with inexpensive and sustainable option to improve soil, would be welcomed by producers. Both soybean (DH420, Sevita Int.) and canola (In Vigor L130, Bayer Crop Science Canada) were planted and underseeded with either cereal rye, red or white clover or an unseeded control. Overseeding was performed at soybean leaf drop using the same species. Additionally, all plots were split to receive either a herbicide application or an untreated water control. Both crop yield and biomass of the intercrop species were measured.

**Intercropping potatoes with brown mustard:** The larvae of click beetles (*Agroites sputator*), also known as wireworms, are becoming a major agricultural pest across the world. The long life cycle (five years for some species) combined with their wide host range and the lack of efficacious insecticides has made controlling this pest a challenge. Studies with brown mustard grown as rotation crop for two years showed a significant reduction in wireworm damage to the potato crop planted in the third year. A preliminary study was done in 2014 to determine the best time to seed brown mustard in a potato crop. Potato drills (6 x 4 m) were planted with var. Russet Burbank. Treatments consisted of planting an intercrop of brown mustard (var. Centennial) at one of five seeding dates, either 5, 6, 7, 9 or 12 weeks after the potatoes were planted. Treatments were designed to encompass the entire production cycle of the potato crop (pre-canopy, row closure and following desiccation). One week prior to the final seeding date (week 12), a desiccant was applied to the main crop to initiate senesence. Potato tubers were harvested and evaluated for yield and quality.

**Intercropping corn with ryegrass:** If harvested for grain, corn crops contribute to building soil organic matter levels if cut higher and a significant proportion of residue remains following harvest (Villamil et al., 2015). However if corn is harvested for feed as silage, more of the aboveground biomass is removed which may lead to soil degradation through time. One proposed solution is to overseed common ryegrass into a standing corn crop at the time of fertilizer top-dressing. This concept was tested using on-farm trials in St. Foy Quebec whereby several varieties of ryegrass were evaluated (English, Italian and Westerwold). Ryegrass seed was mixed with topdress fertilizer for application.

<footnote n="1">¹ Crops and Livestock Research Centre, Agriculture and Agri-Food Canada, 440 University Ave. Charlottetown, Prince Edward Island, Canada</footnote>

<footnote n="2">² Knowledge and Technology Transfer Office, Agriculture and Agri-Food Canada, 2828 Laurier Boulevard, Quebec, Quebec, Canada</footnote>

<footnote n="3">³ Soils and Crops Research and Development Centre, Agriculture and Agri-Food Canada, 2560 Hochelaga Blvd., Quebec, Quebec, Canada</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: aaron.mills@agr.gc.ca</footnote>
## 3 Results-Discussion

**Oilseed/forage:** For crop yields, soybean did not respond well to underseeding. As the season progressed, particularly in the red clover plots, soybean plants were visibly overcome by the clover. In all cases, underseeding resulted in significantly lower yields in soybean. Overseeding soybean on the other hand, resulted in slightly higher yields than the control for red and white clover. For canola there were no significant differences between total yield for the control and either underseeded or overseeded red and white clover showing a potential for underseeding canola, despite the crop having traits for herbicide resistance. Cover crop biomass measurements were taken post-harvest from the underseeded treatments. Red clover yields were higher in sprayed intercropped treatments than in treatments that lacked herbicide application for both canola and soybean.

**Potato/brown mustard:** Although the brown mustard was established and started growing at all seeding dates, growth was slowed due to a lack of light and moisture for the first four seeding dates. Planting following the application of a desiccant (week twelve) resulted in better growth of mustard, however, as a long season variety was used in this trial, the mustard did not have sufficient time to grow before potato harvest. Therefore some of the beneficial effects of the mustard on wireworm control were not observed. There were no significant effects of the intercrop on the main crop quality or yield.

**Corn/ryegrass:** There were no significant effects of intercropped ryegrass on the overall yield of corn for either ryegrass variety or seeding date. However, there were clear trends showing certain varieties of ryegrass which performed better than others. Westerwold varieties actually produced seed heads during the planting year. Seeding later than the 11-leaf stage resulted in poor ryegrass establishment due to shading, and it was determined that seeding at the time of topdressing (3-leaf stage), provided good establishment with no negative effects to the corn crop.

## 4 Conclusions

In all three experiments described above each one shows indications that intercropping may provide benefits to the cropping system as a whole. However in each case, more research is required to optimize seeding rates, seeding dates, and interactions between main crop and intercrop species. There is potential for the use of intercrop polycultures to mitigate some of the year-to-year variability. With each one of these systems, it is also necessary to develop a solid technology transfer protocol at the initiation of the research in order to facilitate the adoption of intercropping strategies on-farm.

## References

* Cardinale, B.J., Duffy, J.E., Gonzalez, A., Hooper, D.U., Perrings, C., Venail, P., Narwani, A., MacE, G.M., Tilman, D., Wardle, D.A., Kinzig, A.P., Daily, G.C., Loreau, M., Grace, J.B., Larigauderie, A., Srivastava, D.S. & Naeem, S. (2012). Biodiversity loss and its impact on humanity. *Nature* **486**, 59-67.

* Carter, M.R., Noronha, C., Peters, R.D. & Kimpinski, J. (2009). Influence of conservation tillage and crop rotation on the resilience of an intensive long-term potato cropping system: Restoration of soil biological properties after the potato phase. *Agriculture, Ecosystems and Environment* **133**, 32-39.

* Hauggaard-Nielsen, H., Johansen, A., Carter, M.S., Ambus, P. & Jensen, E.S. (2012). Strip cropping of alternating perennial grass-clover and annual rye-vetch intercrops when grown within an organic farming system. *Field Crops Research* **136**, 1-11.

* Isbell, F., Reich, P.B., Tilman, D., Hobbie, S.E., Polasky, S. & Binder, S. (2013). Nutrient enrichment, biodiversity loss, and consequent declines in ecosystem productivity. *Proceedings of the National Academy of Sciences of the United States of America* **110**, 11911-11916.

* Iverson, A.L., Marín, L.E., Ennis, K.K., Gonthier, D.J., Connor-Barrie, B.T., Remfert, J.L., Cardinale, B.J. & Perfecto, I. (2014). Do polycultures promote win-wins or trade-offs in agricultural ecosystem services? A meta-analysis. *Journal of Applied Ecology* **51**, 1593-1602.

* Snapp, S.S., Swinton, S.M., Labarta, R., Mutch, D., Black, J.R., Leep, R., Nyiraneza, J. & O'Neil, K. (2005). Evaluating cover crops for benefits, costs and performance within cropping system niches. *Agronomy Journal* **97**, 322-332.

* Symstad, A.J., Chapin, I., F.S., Wall, D.H., Gross, K.L., Huenneke, L.F., Mittelbach, G.G., Peters, D.P.C. & Tilman, D. (2003). Long-term and large-scale perspectives on the relationship between biodiversity and ecosystem functioning. *BioScience* **53**, 89-98.

* Villamil, M.B., Little, J. & Nafziger, E.D. (2015). Corn residue, tillage, and nitrogen rate effects on soil properties. *Soil and Tillage Research* **151**, 61-66.
# Origins of the performance gaps in innovative cropping systems under experimental assessment

Caroline Colnenne-David <sup>±1,2</sup>, Gilles Grandeau <sup>1,2</sup>, Véronique Tanneau <sup>1,2</sup>, Marie-Hélène Jeuffroy <sup>1,2</sup> & Thierry Doré <sup>1,2</sup>

## 1 Introduction

According to the evolution of the economic context and new agricultural issues, innovative cropping systems (ICSs) that take into account current on-farm constraints need to be proposed. The objective of our project was to design, by prototyping (Vereijken, 1997; Reau & Doré, 2008), ICSs meeting different environmental and economic quantified objectives and to assess them in a long-term field experiment. Those ICSs were designed for winter wheat-based rotations in the Paris basin area, in France. The objective of this paper is to identify, after the first complete rotation, the reasons why some of the goals assigned to the ICSs were not reached, and why some environmental characteristics evolved differently than expected.

## 2 Materials and Methods

In 2008, four ICSs were designed by prototyping to satisfy various environmental constraints. Three out of the four systems had to meet simultaneously three quantitative objectives: (1) to satisfy a major environmental constraint, which represents a major break regarding objectives to be reached by current cropping systems (the banning of pesticide use (No-Pest), reducing fossil energy consumption by 50% (L-EN), or reducing C balance by 50% (L-GHG); (2) to satisfy a wide range of environmental criteria with specific quantitative targets; and (3) to produce the maximum possible yield given the constraint and the environmental targets. A fourth system (PHEP) was designed to reach environmental and production goals with no major constraints. Compared to conventional systems of cereal-based rotations, those ICSs differ largely on the crops of the rotation and on their crop management (including soil tillage, crop protection, fertilization, genotype choices, the use of cover crops, etc.). The crop management systems were defined as a set of decision rules (Debaeke *et al.*, 2006).

The designed candidate systems were assessed *ex ante* with different tools and models (Colnenne-David & Doré, 2014). The GES'TIM database (2010) and IPPC coefficient (2007) were used to calculate GHG emissions as well as energy consumption. The SIMEOS® tool (v. 2010), based on the AMG model (Andriulo *et al.*, 1999), and the Indigo® tool (Bockstaller *et al.*, 2008) were used to calculate respectively C sequestration and several agro-ecological indicators.

Since 2008, the most promising prototype systems have been *ex post* assessed in a long-term field trial, in farming conditions, located in Grignon, France (N 48.84, E 1°57) on 6.5 ha. Three replications of each system have been randomly distributed. Results are analyzed here over a complete crop rotation (i.e. five crops for the PHEP and the L-EN cropping systems, six crops for the L-GHG and the No-Pest systems). The environmental performances (i.e. energy consumption, GHG emissions, C sequestration and various environmental criteria) of the ICSs, based on real practices performed in this trial since 2008, were calculated with the tools and models used during the prototyping step (i.e. *ex ante* assessment). Crops were harvested at maturity with a farmer combine.

## 3 Results – Discussion

In the L-EN system, the energy constraint was nearly fulfilled, while in the L-GHG system the C balance constraint was not satisfied (Table 1).

<table><caption>**Table 1.** Results of total energy consumption (MJ ha⁻¹year⁻<fnref n="1" />) and C balance (kg CO₂eq.ha⁻¹year⁻<fnref n="1" />) calculated over a complete rotation for the ICSs</caption><thead><tr><th></th><th>energy consumption (MJ ha<sup>-1</sup>year<sup>-1</sup>)</th><th></th><th>C Balance (kgCO<sub>2</sub>eq.ha<sup>-1</sup>year<sup>-1</sup>)</th><th>Carbon sequestration (kgCO<sub>2</sub>eq.ha<sup>-1</sup>year<sup>-1</sup>)</th><th>GHG emissions (kgCO<sub>2</sub>eq.ha<sup>-1</sup>year<sup>-1</sup>)</th></tr></thead><tbody><tr><td>L-EN</td><td>5 201 (+/-502)</td><td>L-GHG</td><td>1 202 (+/-86)</td><td>-149 (+/-117)</td><td>1 052 (+/-183)</td></tr><tr><td>PHEP</td><td>7 755 (+/-711)</td><td>PHEP</td><td>1 188 (+/-270)</td><td>-117 (+/-150)</td><td>1071 (+/-145)</td></tr><tr><td>L-EN-PHEP)</td><td></td><td><sup>‡</sup>HG-PHEP) PHEP</td><td></td><td></td><td></td></tr><tr><td>PHEP</td><td>-33%</td><td></td><td>+1%</td><td>+27%</td><td>-2%</td></tr></tbody></table>

<footnote n="1">¹ INRA UMR 211 Agronomie, 78850 Thiverval-Grignon, France</footnote>

<footnote n="2">² AgroParisTech UMR 211 Agronomie, 78850 Thiverval-Grignon, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: caroline.colnenne@grignon.inra.fr</footnote>
As the energy and the C balance constraints are expressed in reference to the PHEP system, the performance of this system plays an important role in the assessment results. In the PHEP system plots, low N fertilization, due to high organic matter content in the soil of the experimental field, allowed high performance of this PHEP system, which contributed to explain why the energy and the C balance constraints were not totally met in the L-EN and the L-GHG systems respectively. All the ICSs satisfy environmental criteria in terms of crop biodiversity, NH₃ and N₂O pollutions, pesticide use and energy consumption. Over the 2009-2014 period, yields nearly reached the goals for all these ICSs. However, there are variations depending on (i) the crops: whatever the cropping system, cereal (except maize), oilseed rape and hemp crop yields were systematically higher than expected, faba bean and flax yields were regularly lower, (ii) the pest pressure: in the No-Pest system, a sharp decrease (-90%) of faba bean yield in 2011 was due to black aphid attacks, and (iii) time: in the L-GHG system, yields were as high as expected during the four first years of the rotation and lower than expected during the two last years of the rotation.

In order to explain why some objectives assigned to the ICSs were not satisfied and some unexpected evolutions of the environment observed in several plots, main agronomical practices were analyzed and ranked in four groups: (1) some agronomical strategies may not be suitable to reach the goals, (2) some practices could not satisfy simultaneously a multiplicity of objectives, (3) some planned practices may not be appropriate in the context of the field-trial conditions, and (4) an unpredicted evolution of the agrosystem, following agronomical practices proposed, occurred. In table 2, one example, collected from the field trial, is presented from each of these four groups.

<table><caption>Table 2. Classification of the agronomical practices and examples collected in the ICSs.</caption><thead><tr><td>Classification</td><td>Examples collected in the ICSs</td></tr></thead><tbody><tr><td>Some agronomical strategies may not be suitable to reach the goals.</td><td>In the L-GHG ICS, forbidden ploughing did not allow to increase C sequestration as expected. After the first rotation, evolution of C sequestration was -149kgCO<sub>2</sub> ha<sup>-1</sup>year<sup>-1</sup> instead of +87kgCO<sub>2</sub> ha<sup>-1</sup>year<sup>-1</sup> expected during the ex ante assessment step.</td></tr><tr><td>Some practices could not satisfy simultaneously a multiplicity of objectives.</td><td>In the No-Pest ICS, it did not appear possible to satisfy both the constraint (i.e. no pesticide use) and the environmental criteria regarding soil organic matter. Within this system, the restitution of small amounts of organic matter, due to low yields achieved in no pesticide use conditions, combined with regular ploughings, necessary to manage weed populations, had an adverse effect on C sequestration. After the first rotation, evolution of C sequestration were -560kgCO<sub>2</sub> ha<sup>-1</sup>year<sup>-1</sup> and -117kgCO<sub>2</sub> ha<sup>-1</sup>year<sup>-1</sup> respectively in the No-Pest and the PHEP ICSs, the last ICS being considered as a reference in the long-term field trial.</td></tr><tr><td>Some planned practices may not be appropriate in the context of the field-trial conditions.</td><td>In the L-GHG ICS, in order to produce high amount of aerial biomass, cover crops were sown just after harvest in summer. Over the period 2009-2014, according to very dry conditions in summer in Grignon, cover crops did not grow three years out of six.</td></tr><tr><td>An unpredicted evolution of the agrosystem, following practices proposed, occurred.</td><td>In no till conditions, i.e. the L-EN and the L-GHG ICSs, higher weeds development than expected were observed and led to mow two plots of oilseed rape in 2014.</td></tr></tbody></table>

All agronomical practices need to be analyzed similarly in order to reinforce the relevance of this classification and to validate it. Moreover, a complementary investigation should be implemented in the same way with decision rules defining the crop management, in order to help to design an evolution of the innovative cropping systems.

## 4 Conclusions

The results gathered during the first complete rotation highlighted gaps between measurements and goals and a first classification of the agronomical practices allowed to identify the major sources of disparities. These knowledge could be used both to re-design the ICSs which do not satisfy their objectives, over a second prototyping design loop, or to design innovative systems defined with other goals to reach. However, a more complete agronomic diagnostic will be necessary to identify and to rank all the causes which explain discrepancies between measurements and objectives in order to reinforce the classification of agronomical practices in these four different groups. Moreover, these "cropping system" experiments should contribute to the learning in terms of design processes and cropping system management.

## References

* Andriolo A., Mary B. & Guerif J. (1999). Modelling soil carbon dynamic with various cropping sequences on the rolling pampas. *Agronomie*, **19**, 365-379.

* Bockstaller C., Guichard L., Makowski D., Aveline A., Girardin P. & Plantureux S. (2008). Agri-environmental indicators to assess cropping and farming systems. A review. *Agronomy of Sustainable Development*, **29**, 139-149.

* Colnenne-David C. & Doré T. (2014). Designing innovative productive cropping systems with quantified and ambitious environmental goals. *Renewable Agriculture and Food Systems*. doi:10.1017/S1742170514000313.

* Debaeke P., Nolot J.M. & Rafaillac D. (2006). A rule-based method for the development of crop management systems applied to grain sorghum in south-western France. *Agricultural systems*, **90**, 180-201.

* GESTIM (2010). Ministère de l'agriculture, de l'alimentation, de la pêche, de la ruralité et de l'aménagement du territoire. Réf. 0933103. IPCC. Climate Change (2007). The physical science basis. *UK: Cambridge University Press*, pp 1009.

* Reau R., & Doré T. (2008). Systèmes de culture innovants et durables : quelles méthodes pour les mettre au point et les évaluer ? *Educagri Editions*, Dijon, France, 175pp.

* Vereijken P. (1997). A methodological way of prototyping integrated and ecological arable farming systems (I/EAFS) in interaction with pilot farms. *European Journal of Agronomy*, **7**, 235-250.
# Design, experimentation and assessment of four protected vegetable cropping systems adapted to different food systems

Amélie Lefèvre *±<fnref n="1" />*, Chloé Salembier <fnref n="1" />, Benjamin Perrin<fnref n="1" />, Claire Lesur-Dumoulin<fnref n="1" /> & Jean-Marc Meynard <fnref n="2" />

## 1 Introduction

The objective of the French program Ecophyto is to reduce the farming dependency on pesticides without affecting their economic performances. This is a great challenge in vegetable production given that (i) it concerns a wide range of crop species, pests and pathogens, (ii) biotic and abiotic components evolve quickly, especially under greenhouse conditions, (iii) as vegetables are commercialized as fresh products, failure to control pests and pathogens has a direct impact on producers' incomes. Integrated pest management (IPM) strategies are largely dependent on production objectives and resources available on farm (Navarrete et al., 2014). Here we report the design, experiment and assessment of four cropping systems prototypes suited to different food systems in the French Mediterranean basin.

## 2 Materials and Methods

We followed a step-by-step design and assessment framework involving four iterative main steps. Iterations aim at accumulating learning and enable continual improvements of the cropping systems (Meynard et al., 2012).

*Step 1: Diagnosis.* We analyzed how differences between food systems affect farm and field management based on the overall characteristics of the national and regional productive contexts. Following this diagnosis, four productive contexts are step-by-step specified as general frameworks to guide the design process.

*Step 2: Design.* For each productive context, and particularly for the management of both air- and soil-borne pests and pathogens, main agroecological and management processes were targeted at the short and/or longer term. These processes are thought to affect different levels of the multi-trophic chain at the scale of the greenhouse and its immediate surroundings (Messelink et al., 2014). We then identified IPM tools that can involve the cropping sequences and/or IPM decision rules at crop scale, to stimulate or, at the least, not disturb the targeted processes. These tools were used to design a global strategy, i.e. the way practices are used, combined and adapted for each of the four contexts.

*Steps 3 and 4: Experimentation and assessment.* An on-station system experiment (Debaeke et al., 2009) of the prototypes started in April 2013 in the Roussillon region (South of France). Experimental features (e.g. field size) were chosen to be close to those used in commercial farms; for example crops are implemented under 400m<fnref n="2" /> unheated greenhouses (silt sandy soil). IPM implementation was supported by weekly qualitative inspections of the crops. Specific quantitative and localized measures were used to describe the evolution of pests, pathogens and beneficial organisms (released or native). Cropping systems were assessed and, when possible, compared to improve the prototypes at every step of the process.

## 3 Results and Discussion

### Diagnosis: differences between food systems affect farm and field management

We identified that long or short commercialization supply chains (LSC or SSC) and organic or conventional farming (OF or CF) certification schemes highly influenced crop management strategies. LSC systems strictly comply with marketing standards: specific categories depending on fruit size and homogeneity, superficial defects and firmness. In contrast, SSC systems accept some superficial defects and give priority to organoleptic quality (mainly resulting from cultivar choice and harvesting ripeness). SSC systems tend to produce relative small quantities of a wide range of products, which encourage the simplification of crop management strategies (time schedule optimization and input reduction). SSC systems also tend to use land more intensively than LSC systems. LSC usually relies on crop specialization which means for example, in our region that lettuce has a major place in the cropping sequence.

### Cropping systems design: examples of main targeted agroecological processes and link with IPM strategies

Each prototype was designed to target a specific certification scheme and supply chain. The strategy for the LSC-CF system relies on introducing crop diversity in summer and on regular soil solarization to prevent soil-borne issues. Aerial pests are mainly managed at the crop cycle time scale with short-term effects tools such as biocontrol (e.g. releases of biological control agents). To prevent damage and losses, biocides may be used giving priority to products with the least toxicity on beneficials. In LSC-OF, in addition to regular soil solarization, the cropping sequence involves more botanical diversity in summer and winter to prevent soil-borne diseases. When relevant, crop schedule and harvest date are adapted to reduce damage and income losses. Biocontrol is the main tool used for summer aerial pest

<footnote n="1">¹ INRA SAD UE0411 Domaine expérimental Alénya Roussillon, Alénya, France</footnote>

<footnote n="2">² INRA SAD UMR 1018 SAD APT, Thiverval-Grignon, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Amelie.Lefevre@supagro.inra.fr</footnote>
management in LSC-OF, with priority being given to preventive practices, for example to guarantee an early migration and installation of predators or parasitoids into the greenhouse. SSC-OF and SSC-CF strategies rely on cumulative processes and mid-term effects for air- and soil-borne pests and pathogens since they involve natural regulation process and a less intensive management (labour and inputs dedicated to the crops). In SSC systems, the soil is never solarized: soil-borne pathogens are managed through a higher diversity of cultivars and species along the cropping sequence combined with accurate practices for the enhancement of the soil microbial activity. Intercropping is tested to reduce the impacts and damages on plants from soil-borne and aerial pests and pathogens. These systems also enhance migration of indigenous beneficial insects with a relevant management of the surroundings, through implementation and management of semi-natural habitats. Biocides can be sprayed to regulate an explosive situation (chemicals if needed as a last resort only in SSC-CF) using the IPM decision rules.

## Two examples of IPM strategies implemented in 2014: LSC-OF and SSC-CF cucumber crop

A long, smooth type cultivar of cucumber was planted in the LSC-OF system in late April. In contrast, a short, thorny cultivar of cucumber was planted in late March in intercropping with sweet pepper and French bean in the SSC-CF system. According to the target and the practice, IPM interventions were implemented systematically or based on weekly inspections and decision rules. For example, in LSC-OF, if whiteflies infestation was poorly controlled by macro-organisms, *Verticillium lecanii* (Z.) could be sprayed to prevent sooty mould damage. Regarding the agroecosystem state (see the biotic pressure in Figure 1) and the practices actually implemented, some pre-defined decision rules were modified on the way. In the LSC-OF system, macro-organisms were released four times to prevent the development of the main pests (whiteflies, thrips and phytophagous mites). Localized spraying of sulfur was done twice to deal with disease development and one microorganism (*Verticillium lecanii*-m) was used.

<figure><img src="image_222.png" /><figcaption>**Fig. 1.** Evolution of the biotic pressure on cucumber crops in the LSC-OF and SSC-CF systems.</figcaption></figure>

Total and commercial LSC-OF yields were of 32.7 and 23.2 fruit m⁻² respectively (three harvests per week over 10 weeks). The non-commercial part resulted from biotic damages, abiotic stresses (weak nutritional status) and commercial factors (fruit size). In SSC-CF system, contrary to what was expected (Ratnadass et al., 2012), beneficials colonized the crop lately. Therefore, to avoid major income losses due to a general strong pest's infestation, macro-organisms had to be released several times (6). Diseases were controlled with localized applications of sulfur and a general chemical fungicide. Total and commercial yields were of 68.3 and 67 fruit m⁻² respectively (three harvests per week over 13 weeks). The SSC-CF short, thorny type of cucumber, chosen to satisfy consumer demands, fulfilled the requirements with a long harvest period, a low non-commercial part and a satisfying total productivity.

## 4 Conclusions

During the first 18 months of this experiment, global strategies were implemented and assessed particularly according to the agroecological processes targeted. We identified that in addition to decision-making issues, more knowledge production ways should be explored (see Cardona et al., 2015) to be able to actually implement the ecological pathways for reducing the impacts of pests via the plants diversity. Future research will focus particularly on cumulative processes and assessment on a long-term scale. Assessment of prototypes will involve particular discussions with producers, technicians and scientists within a participatory research program.

Acknowledgements. The 4SYSLEG project is funded by the Dephy EXPE Ecophyto action plan of the French Ministry of Agriculture, Agrifood and Forestry and by the French National Agency for Water and Aquatic Environments (ONEMA).

## References

* Cardona, A., Lefèvre, A. and Salembier, C., (2015) Open-up the (co)design process of farming systems: a reflexive analysis. 5th International Symposium for Farming Systems Design, September 7-10 2015, Montpellier, France.

* Debaeke, P., Munier-Jolain, N., Bertrand, M., Guichard, L., Nolot, J-M., Faloya, V. and Saulas, P., (2009) Iterative design and evaluation of rule-based cropping systems: methodology and case studies. A review. *Agronomy for Sustainable Development, 29 (1)*, 73-86.

* Messelink, G J., Bennison, J., Alomar, O., Ingegno, B L., Tavella, T., Shipp, L., Palevsky, E. and Wackers, F L., (2014) Approaches to Conserving Natural Enemy Populations in Greenhouse Crops: Current Methods and Future Prospects. *Biocontrol, 59 (4)*, 377-93.

* Meynard, J.M., Dedieu, B. and Bos, A.P., (2012) Re-design and co-design of farming systems. An overview of methods and practices. (2012) In : Farming Systems Research into the 21st century: The new dynamic. Ika Darnhofer, David Gibon, Benoît Dedieu, Editors, Springer, 407-432.

* Navarrete, M., Dupré, L. and Lamine, C. (2014) Crop management, labour organization, and marketing: three key issues for improving sustainability in organic vegetable farming, *International Journal of Agricultural Sustainability* (DOI: 10.1080/14735903.2014.959341)

* Ratnadass, A., Fernandes, P., Avelino, J. and Habib, R. (2012) Plant species diversity for sustainable management of crop pests and diseases in agroecosystems: a review. *Agronomy for Sustainable Development, 32 (1)*, 273-303.
# Farming systems design to facilitate transition toward low input agriculture

Marie Thiollet-Scholtus *±1 & Xavier Coquil 2

## 1 Introduction

Can the design of innovative farming systems facilitate transition of rural territory to more sustainable agriculture? We question the relevance of design to support and facilitate the transition towards more self-sufficient and more environmental friendly farming systems. That question is asked to two experiment in the Northern East of France: the first experiment is dedicated to step by step design of autonomous mixed crop dairy systems (self-sufficient, and converted to organic farming) in the Lorraine region; the second experiment is dedicated to the design of organic vineyards in the Alsace Region. Staudenmaier (1985) distinguishes three types of design according to their distance to the uses that are made it in the current world: the invention refers to the creative act and the creation of something that did not exist before; Innovation refers to the processes of acquisition and diffusion of novelty; project management refers to an organizational process of design. Farming System Design is mainly focused on invention of technical or organizational alternatives for the future. The invention of such alternatives relegates innovation to questions of social acceptability of innovation taking the risk of inadequation according to users' current needs and desires (see for example Temple et al., 2011). The two experiments presented in this paper postulate that adhesion to the real work of farmers is necessary to think farming system design as a source of innovation in agricultural areas.

## 2 Materials and Methods

Step by step design of autonomous mixed crop-dairy farming systems (organic) takes place in the INRA experimental station of ASTER-Mirecourt since 2004. Two agricultural systems are designed from the natural properties of the land without using chemical or organic input (Coquil et al., 2009; Coquil et al., 2014b). Former experience of the staff was built according to conventional farming in mixed crop dairy systems. Design of vineyards takes place in two vineyards owned by private wineries: both farms are converted to organic farming since middle nineties' and to biodynamic since 2008 and 2009 respectively. Experimental vineyards were designed according to winegrowers' goals: the first one wished to reduce its fungicide treatments and especially reducing the amount of copper/ha/year by adding mixed essential oils of citrus fruits. The second winegrower wanted a solution to combine improvement of vineyards vigor, management of weeds and providing an unfavorable environment for fungal diseases extension, by changing the field soil cover management. Design of vineyards used designing methods presented in Metral et al., (2012) and in Thiollet-Scholtus et al. (2013).

## 3 Results - Discussion

Design of autonomous and organic mixed crop dairy systems is done step by step in ASTER-Mirecourt. Scientists of the experimental station initiated this design. This design forced all experimenters to deeply transform their work in terms of practices but also in terms of values. Thus, step-by-step design was managed as a corporate project to facilitate efficient experience of the experimenters working in a new framework of activity: farmers standards and values to which they refer are not the same in 2014 compared to the pre-transition period. The resources mobilized by the experimenters to develop efficient practices are the object of discussions with farmers interested in that kind of transition towards more sustainable dairy systems (Coquil et al., 2014a). These exchanges allow moving towards more efficient and autonomous systems, so that farmers are able to make their own path and develop, in turn, experience. Prior to these exchanges, we demonstrate the technical, environmental and economic efficiency of the designed dairy systems so that farmers can give them credit. From this evidence, nearly 800 visitors (mainly farmers and advisors) a year visit the experimental station of ASTER-Mirecourt.

The question of assessing the technical suitability of designed alternatives is also a prerequisite for winegrowers interested in implementing low input practices on their vineyard. The originality of this device lies on the fact that winegrowers goals have been traduced and set in an experimentation on their own vineyards with scientists and technical staff from INRA in 2013. Winegrowers are responsible of the project management. Scientists and technical staff from INRA make available technical and human resources to equip this design and to assess reliably and accurate biotech, environmental and socio-economic results of designed vineyard systems since 2014.

<footnote n="1">¹INRA-SAD-UR-0055 ASTER. 28, rue de Herrlisheim F-68021 Colmar, France</footnote>

<footnote n="2">² INRA-SAD-UR-0055 ASTER, 662, avenue Louis Buffet, F-88500 Mirecourt, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: marie.thiollet-scholtus@colmar.inra.fr</footnote>
The main difference between the two approaches returns us to innovation: the design experiment in private vineyards assures the strong adherence of the 2 concerned winegrowers to experimental design of the systems and to the assessment of the systems. But diffusion of results of these experiments is strongly influenced by the socio-professional networks of these two winegrowers. Step-by-step design of mixed crop dairy systems in an INRA station does not a *priori* ensure the production of knowledge in adherence with the concerns of farmers. The new mixed crop-dairy systems ensure the production of knowledge in adherence with experimenters needs and concerns. However, exchanges with farmers are built according to resources used by experimenters to develop efficient way of working in designed systems. Thus, knowledge is contextualized in experimenters’ daily work. In this step-by-step design, farmers interested in designed systems, come to visit the INRA experimental farm: the INRA appurtenance might be an advantage, as it is seen by farmers, as a guarantee in terms of assessment, and it makes access easier for farmers from different backgrounds as INRA station is less marked by farmers standards, values and political position than a private farm.

## 4 Conclusions

Design of farming systems might have a large contribution to transition of rural territories to sustainable agriculture: knowledge must be produced in adherence with farmers’ concerns and goals. The design methodologies and design locations can be different, but it seems necessary, however, to consider socio-professional networks of farmers and managers of experimental stations to facilitate the innovation process.

*Acknowledgements.* The authors would like to thank INRA Aster-Mirecourt and Colmar team for their contributions.

## References

Coquil, X., Blouet, A., Fiorelli, J.L., Bazard, C., Trommenschlager, J.M., 2009. Conception de systèmes laitiers en agriculture biologique : une entrée agronomique. Productions Animales 22, 221-234.

Coquil, X., Béguin, P., Dedieu, B., 2014a. Transition to self-sufficient mixed crop-dairy farming systems. Renewable Agriculture and Food Systems 29, 195-205.

Coquil, X., Fiorelli, J.L., Blouet, A., Mignolet, C., 2014b. Experiencing Organic Mixed Crop Dairy Systems: A Step-by-Step Design Centred on a Long-term Experiment. In: Springer (Ed.), Organic farming, prototype for sustainable agricultures, pp. 201-217.

Metral, R., Lafond, D., Gary, C., Merot, A., Metay, A., and Wery, J. (2012). How to design and experiment new cropping systems with low pesticide inputs for perennial crops: framwork development and application to vineyards. In "ESA2012" (F. L. Stoddard and P. Mäkelä, eds.), Vol. 1, Helsinki, Finland

Staudenmaier, J.M., 1985. Technology's Storytellers. MIT Press, Cambridge, Massachusets. 282.

Temple, L., Bakry, F., and Marie, P. (2011). Innovations sociales pour éliminer les pesticides dans les bananeraies : les conditions de mobilisation du travail. In "Le travail en agriculture : son organisation et ses valeurs face à l'innovation." (P. Béguin, B. Dedieu and E. Sabourin, eds.), pp. 115-131. L'Harmattan, Paris.

Thiollet-Scholtus, M., Ley, L., Bockstaller, C., Delière, L., Forget, D., Metral, R., and Lafond, D. (2013). Design and assessment of new low-input viticulture systems. In "GiESCO", Vol. 1, Porto, Portugal.
# System approach farming reduces the carbon footprint of crop production

Yantai Gan *±<fnref n="1" />, Chang Liang<fnref n="2" />, Qiang Chai<fnref n="3" />, Reynald Lemke<fnref n="4" />, Con Campbell<fnref n="5" /> & Robert Zentner<fnref n="1" />

## 1 Introduction

Conventional farming systems heavily rely on various inputs such as fuel, inorganic fertilizers, pesticides, labor and equipment to increase productivity. This approach, albeit effective in meeting the primary goals, often ignores its effect on environmental sustainability and long-term systems viability. It is unclear whether an integrated system approach can be developed for farming that is to increase crop productivity while at the same time, reducing negative environmental impacts and enhancing systems sustainability. In a 25-yr (1985-2009) field study, we evaluated the outcomes of using integrated systems approaches in farming and quantified the impacts of system approach farming on agricultural productivity and environmental footprints.

## 2 Materials and Methods

The study was carried out at the Semiarid Prairie Agricultural Research Center of Agriculture and Agri-Food Canada, Swift Current (50°17"N, 107°48"W), Saskatchewan. The following four key farming practices were identified and integrated together to form a "Suite" of farming strategies: (i) use of grain legumes to fix atmospheric N₂ into plant-available N and to replace a portion of inorganic N fertilizer; (ii) use of annual soil tests to determine soil nutrients that are available for crops, (iii) adequately applying fertilizers to meet the plants" requirements and avoid over- or under-fertilization; and (iv) adoption of more intensified crop rotation systems to sequester more CO₂ from the atmosphere to offset carbon emissions associated with crop inputs. This "Suite" was performed on four commonly-adopted cropping systems, namely (a) fallow-flax (*Linumusitatissimum*)-wheat (abbreviation, FFlxW), (b) fallow-wheat-wheat (FFW), (c) continuous wheat (ContW), and (d) lentil (*Lens culinaris*)-wheat (LentW). During the 25-yr period, crop biomass, yield, various inputs (fertilizers, pesticides, labor, energy, fuel, etc), and carbon emissions associated with the inputs and from the soils were determined each year in each plot. Crop productivity and climate consequences associated with the adoption of the "Suite" were determined.

## 3 Results–Discussion

The 25 years were categorized into dry, normal, and wet years based on water availability (Table 1). Using the integrated "Suite" of farming practices, the four cropping systems all resulted in negative (desirable) carbon footprints, averaging -223 kg CO₂ eq per tonne of grain in dry years, -176 in normal years, and -129 in wet years. These negative carbon footprint values indicated that for each tonne of wheat grain produced a **net** amount of 27 to 377 kg of CO₂ equivalents was captured from the atmosphere. Among the four systems evaluated, the LentW rotation was most favorable with the most negative carbon footprint at -377 kg CO₂ eq per tonne of grain.

Soil organic carbon (SOC) in those systems increased gradually over years, with the greatest increase occurring between 1993 and 1999 (Figure 1). The LentW system gained an average 1039 kg CO₂ eq ha⁻<fnref n="1" /> through soil carbon sequestration annually, which was 26% more than the gain for ContW, 56% more than for FFW, and 62% more than for FFlxW. In the legume-cereal system, lentil plants fixed N₂ from the atmosphere (data not presented here) and the increased N availability enhanced plant biomass accumulation, leading to more carbon sequestered into soils.

In the Lentil-wheat system, the wheat produced a similar amount of grain as wheat in the continuous wheat system, averaging 1860±150 kg ha⁻<fnref n="1" /> yr⁻<fnref n="1" />, but the former did so with 29% less N fertilizer supplied because of the lentil fixing atmospheric N through symbiosis. Consequently, fertilizer N use efficiency for wheat in the LentW system averaged 80% greater than for ContW in dry years, 97% greater in normal years, and 36% greater in wet years.

Legume-rhizobial associations are well known to be an effective solar-driven N₂-fixing system in which atmospheric N₂ is transformed into ammonia to provide a large portion of the N requirements for plant growth (Jensen et al. 2012). A portion of the fixed-N remains in the crop roots, nodules, and in the soil rhizo deposits contributing to the N pools in the soil and benefiting subsequent crops (Gan et al. 2014). It is clear that the use of grain legumes to replace the summer fallow phase of the rotation is one of the key components of integrated systems for obtaining a reduced or even negative carbon footprint in crop production.

<footnote n="1">¹ Agriculture and Agri-Food Canada, Research Centre, Swift Current, SK, S9H 3X2, Canada</footnote>

<footnote n="2">² Environment Canada, Gatineau, Québec, K1A 0H3, Canada</footnote>

<footnote n="3">³ Gansu Provincial Key Laboratory for Aridland Crop Science, Gansu Agricultural University, Lanzhou, China</footnote>

<footnote n="4">⁴ Saskatoon Research Centre, Agriculture and Agri-Food Canada, Saskatoon, SK, S7N 0X2, Canada</footnote>

<footnote n="5">⁵ Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario, K1A 0C6, Canada</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: yantai.gan@agr.gc.ca</footnote>
In each year, fertilizers were applied to the crops based on annual soil test; this ensured the best estimate between N supplies and N requirements by crop plants. Consequently, N surplus (defined as N input minus total N uptake by crop plants, with the assumption of soil N state remaining unchanged in a given crop season) was near zero in most years. In the study period, wheat crops received an annual N fertilizer rate between 3.7 and 69 kg N ha⁻¹; this was based on soil test recommendations each year. Our sensitivity tests revealed the N₂O emissions were 116 kg CO₂ eq. ha⁻¹ with N fertilizer application based on annual soil tests, whereas the N₂O emission value was increased to 158 kg CO₂ eq. ha⁻¹ if a blanket rate of N fertilizer had been, otherwise, used without soil tests. The former approach reduced N₂O emissions by 27% annually compared to the latter. As a result, the soil-testing approach lowered the annual carbon footprint of wheat by an average of 7%. These results clearly indicate (i) the benefits of soil testing for crop fertilization over a blanket rate of fertilization, and (ii) the benefits of using legume-cereal rotation over wheat monoculture systems, in reducing carbon emissions and lowering carbon footprints.

<table><caption>**Table 1.** Carbon footprint of various cropping systems in dry, normal, and wet growing seasons.</caption><thead><tr><th rowspan="2">Cropping system</th><th colspan="4">Carbon footprint</th></tr><tr><th>Dry</th><th>Normal</th><th>Wet</th><th>Mean</th></tr><tr><td></td><td colspan="4">kg CO<sub>2</sub>eqt<sup>-1</sup> of grain</td></tr></thead><tbody><tr><td>FFlxW</td><td>-3</td><td>-49</td><td>-5</td><td>-27</td></tr><tr><td>FWW</td><td>-168</td><td>-166</td><td>-109</td><td>-164</td></tr><tr><td>ContW</td><td>-148</td><td>-167</td><td>-154</td><td>-151</td></tr><tr><td>LentW</td><td>-570</td><td>-322</td><td>-249</td><td>-377</td></tr><tr><td>Mean</td><td>-223</td><td>-176</td><td>-129</td><td>-146</td></tr><tr><td>LSD(0.05)</td><td>256</td><td>48</td><td>59</td><td>79</td></tr><tr><td>P-value</td><td>0.02</td><td>&lt; 0.01</td><td>&lt; 0.01</td><td>&lt; 0.01</td></tr></tbody></table>

Annual precipitation was 105-210, 211-340, and 341-421 mm, in dry, normal, and wet years, respectively.

The four systems are (1) fallow-flax (*Linumusitatissimum*)-wheat (FFlxW), (2) fallow-wheat-wheat (FWW), (3) continuous wheat (ContW), and (4) lentil (*Lens culinaris* Medikus)-wheat (LentW).

<figure><img src="image_223.png" /><figcaption>**Fig. 1.** Changes of soil organic carbon (SOC) during the 1979-2009 period in different cropping systems. The line bars are the standard error of the means.</figcaption></figure>

## 4 Conclusions

Some key individual farming practices can be identified, selected, and integrated together to form a “Suite” of farming strategies. This “Suite” can allow the increase of crop productivity, while at the same time, substantially reducing the carbon footprints of crop production. With well-designed system approaches, field crops can actually convert more CO₂ from the atmosphere than is actually emitted during its production.

*Acknowledgements.* The funding was provided by Saskatchewan Pulse Growers through Pulse Cluster program of AAFC.

## References

* Jensen, E.S., Peoples, M.B., Boddey, R.M., Gresshoff, P.M., Henrik, H.N., Alves, B.J.R., Morrison, M.J., 2012. Legumes for mitigation of climate change and the provision of feedstock for biofuels and biorefineries. A review. Agronomy for Sustainable Development, 32, 329-364.

* Gan, Y., Liang, C., Chai, Q., Lemke, R.L., Campbell, C.A., Zentner, R.P., 2014. Improving farming practices reduces the carbon footprint of spring wheat production. Nature Communications 5, 5012.
# Open-up the (co)design process of farming systems: a reflexive analysis.

Aurélie Cardona ±<fnref n="1" />, Amélie Lefèvre <fnref n="2" /> & Chloé Salembier *<fnref n="2" />

## 1 Introduction

Participatory research methods are increasingly used in the agricultural research for several years all over the world. In this perspective, participative methodologies are developed to design and assess innovative farming systems (Cerf et al., 2012). However, as there is a lack of scientific debate about the design methodologies and a lack of consideration of end-users in the agronomic research (Prost et al., 2012); the objectives of these participatory methodologies are not always extremely clear, such as their conditions of implementation (Neuberg, 2000). What is the interest of using participatory methods for designing farming systems? What is expected by the agronomists? What challenges are they facing during the implementation of this participatory design process? What could be the place of a social scientist in this process, how does he take part in the action? How do interactions between agronomists and social scientists feed the evolution of a participatory research program?

To address these questions we developed a reflexive analysis of a research program using participatory methods, involving both social scientists and agronomists, as it has already been done (Eshuis & Stuiver, 2005; Lyon et al., 2010). This communication will show why and how agronomists working on a system experiment on station chose to formally involve technicians, farmers, specialized scientists and extension agents in their design process, how the interactions between social scientists and agronomists fed the evolution of the participative research program and finally to explain why involving all these people doesn't necessary mean that this is a "co-design" process.

## 2 Materials and Methods

The 4SYSLEG project aims at producing scientific and operational knowledge on pesticide-free practices in protected vegetable crops. To reach this objective and make sure that this knowledge would be shared and used (by producers, scientists), we developed a pluri-disciplinary and reflexive approach, involving social scientists and agronomists to guide the participative design method developed in the 4SYSLEG project. Agronomists developed a step by step participative design methodology (Meynard et al., 2012) to produce and assess innovative prototypes of cropping systems. The participative program can be declined in three main points: (i) Producers or experts (scientists, extension agents) are punctually contacted at different stages of the project. These exchanges contribute to clarify the set of goals and constraints built for each cropping system experimented (first step of an iterative design process; Debaeke et al., 2009), and to collect knowledge to understand biological processes or manage the crops experimented. (ii) Design workshops are implemented one day every semester gathering researchers, technicians, farmers and extension agents to explore in collective hitherto unseen way to design innovative practices, cropping systems and management strategies (e.g. intercropping systems). (iii) Tracking of on-farm innovations developed by producers consists in looking out for innovations designed by farmers and analyzing them to acquire empirical references useful to guide the design process. The prototypes are then experimented on station during the design process and combine practices to reduce pesticides use while maintaining socio-economical performances expected in different sets of productive contexts.

In the same time, social scientists realized interviews with technicians and researchers (15) of the experimental station where takes place the 4SYSLEG project. The interviews aimed at analyzing the context of development of the participative approach and focused on the transformation of the experimental activities, the research themes and the nature of partnerships within the experimental station. A social scientist also made observations of the participative design process and frequently asked agronomists to explicit their choices to maintain the reflexive dynamic. On the basis of these interviews, observations and regular interactions, social scientists are both observers and actors of the participative process. They contribute to formalize the participative method and its evolution through "live feedback" or retrospective analysis which help to integrate social dimensions in the management of the design process.

## 3 Results – Discussion

This reflexive approach explains the questions who led agronomists to use a participative design approach and the challenges they faced. The two main questions leading them to get involved in a participative approach are: How to design and assess innovative systems responding the challenge of pesticide use reduction at the cropping system scale while considering productive constraints? How to design innovative systems facing the lack of scientific knowledge on market gardening systems? This is to address these questions that agronomists developed a step by step design methodology, based on the capitalization of learnings to make evolve the prototypes experimented. As this method

<footnote n="1">¹ INRA SAD, UR767 Ecodéveloppement, INRA PACA, Avignon, France</footnote>

<footnote n="2">² INRA SAD, UE0411 Domaine expérimental Alénya Roussillon, Alénya, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: aurelie.cardona@avignon.inra.fr</footnote>
maintain open the realm of the possible in terms of design choices, it allows identifying and integrating, in the project, step by step, new research questions and knowledge (coming from the experiment or from partners).

This is also to guide this process that they organized recurrent workshops to gather a fixed collective of stakeholders having experiences and knowledge complementary to the project's carriers. Working with this collective allowed the agronomists to go over "concepts", to face it to "farmer's realities" (normative recommendations, logistic or economic constraints on farm, technical uncertainty). It invited agronomists to consider in the design multiple dimensions of farming systems and allowed producing prototypes of systems more innovative and operational. During these workshops, prototypes and results of the experiment are discussed within the group, which allows bringing scientific results closer to the "practice" and identifying new gaps in scientific knowledge. To fill these gaps (also identified through the experiment) on specific practices or combination of practices (decisional or biological process), agronomists collaborate with specialized scientists, extension agents but also with farmers, knowing to develop innovations on their farms (Goulet et al., 2008).

However, while implementing this participative design process, agronomists faced two new challenges. The first was to develop design methods hinging on participative research and system experiment and to qualify the kind of knowledge produced by these design methods. Social science can contribute saying that the extension agents and farmers provide "experiential knowledge" about the coordination of different "socio-technical growth factors" within specific localities especially useful for systemic approach (Stuiver et al., 2004). This "experiential knowledge" is mixed with scientific knowledge. After that, agronomists deal with on-station experimental advantages (e.g. taking risks is more possible than in on-farm experiments) and constraints (e.g. dealing with the overall activities to coordinate in the station). The designed systems are then experimented on station and discussed within the group of participants. Even if the agronomists finally stay the only decision-makers and monitors of the experimentation, we can consider this experience as a step towards "experiential science" (Baars, 2011).

The second challenge was to build and maintain a participative program where scientific and nonscientific interests meet. In this case, as social scientists and agronomists regularly exchanged, the participative design process is the result of a combination between agronomists' needs (e.g. knowledge on intercropping systems) and sociological hypothesis and analysis (e.g. about the interactions between the various actors and about the science-society relationship). These exchanges help the scientists to be more comfortable with the fact that the design process is a dynamic learning process requiring modifications as work progresses and different participants learn or change their expectations – as it is often the case during participatory programs (Cardoso et al., 2001). In our case, the design process has been constructed and evolved following the nonlinear "design-experiment-assessment process" where the system experiment appeared to be an interesting "intermediary object" to build and maintain interactions with partners, as it provides results and offers a concrete way to valorize the knowledge exchanged.

## 4 Conclusions

This reflexive analysis of a co-design process, explains how working with partners allowed the agronomists to bring their work closer to farmer's realities while capitalizing scientific and operational knowledge to design and manage the innovative cropping system experimented. It also shows how interactions between agronomists and social scientists generated a dynamic learning process which contributed to the evolution of the program. This interdisciplinary analysis finally outlines the characteristics of a non-linear and dynamic participative design method opening the door to an "experiential science", which could be the future of the knowledge production on research experimental station.

*Acknowledgements.* We gratefully acknowledge financial support from the SMACH INRA program.

## References

Baars T. (2011). Experiential Science; Towards an Integration of Implicit and Reflected Practitioner-Expert Knowledge in the Scientific Development of Organic Farming. *Journal of Agricultural and Environmental Ethics*, **24-6**, 601-628.

Cardoso I.M., Guijt I., Franco F.S., Carvalho A.F., Ferreira Neto P.S. (2001). Continual learning for agroforestry system design: university, NGO and farmer partnership in Minas Gerais, Brazil. *Agricultural Systems*, **69-3**, 235-257.

Cerf M., Jeuffroy M.-H., Prost L., Meynard J.-M. (2012). Participatory design of agricultural decision support tools: taking account of the use of Situations. *Agronomy for Sustainable Development*, **32-4**, 899-910.

Debaeke, P., Munier-Jolain, N., Bertrand, M., Guichard, L., Nolot, J-M., Faloya, V. and Saulas, P. (2009). Iterative design and evaluation of rule based cropping systems: methodology and case studies. A review. *Agronomy for Sustainable Development*, **29** (1), 73-148.

Eshuis J., Stuiver M. (2005). Learning in context through conflict and alignment: Farmers and scientists in search of sustainable agriculture. *Agriculture and Human Values*, **22-2**, 137-148.

Goulet F., Pervanchon F., Conneau C., Cerf M. (2008). Les agriculteurs innovent par eux-mêmes dans leurs systèmes de culture in Reau R. et Doré T., *Systèmes de culture innovants et durables : quelles méthodes pour les mettre au point et les évaluer* ? *Educatrî, Dijon*, 53-69

Lyon A., Bell M., Croll N.S., Jackson R., Gratton C. (2010). Maculate conceptions: power, process, and creativity in participatory research. *Rural Sociology*, **75-4**, 538-559.

Meynard J.-M., Dedieu B., Bos A.P.(2012) Re-design and co-design of farming systems. An overview of methods and practices, in I. Darnhofer, D. Gibbon, B. Dedieu. *Farming Systems Research into the 21st Century: The New Dynamic*, 405-429.

Neubert D. (2000). A new magic term is not enough. Participatory approaches in agricultural research. *Quarterly Journal of International Agriculture*, **39-1**, 25-50.

Prost L., Cerf M., Jeuffroy M.-H. (2012) Lack of consideration for end-users during the design of agronomic models. A review. *Agronomy for Sustainable Development*, **32-2**, 581-594.

Stuiver M., Leeuwis C., van der Ploeg J.D. (2004). The power of experience: farmers' knowledge and sustainable innovations in agriculture in Wiskerke, JSC; Ploeg, JD *Seeds of transition: essays on novelty production, niches and regimes in agriculture*, 93-118.

Vinck D. (2009). De l'objet intermédiaire à l'objet-frontière. Vers la prise en compte du travail d'équipement. *Revue d'anthropologie des connaissances*, 3, 1-1, 51-72.
# Design of innovative orchards: proposal of an adapted conceptual framework

Sylvaine Simon *±¹, Magalie Lesueur-Jannoyer ², Daniel Plénet ³, Pierre-Éric Lauri ⁴ & Fabrice Le Bellec ⁵

¹ INRA, UE695 Gotheron, 26320 Saint-Marcel-lès-Valence, France

² CIRAD, UPR HORTSYS, 97285 Le Lamentin cedex 2, France

³ INRA, UR1115 Plantes et Systèmes de culture Horticoles, 84914 Avignon cedex 9, France

⁴ INRA, UMR AGAP, 34398 Montpellier cedex 5, France

⁵ CIRAD, UPR HORTSYS, 97455 Saint-Pierre cedex, France

* Speaker

± Corresponding author: sylvaine.simon@avignon.inra.fr

## 1 Introduction

System experiments are a developing approach to address complex questions such as the design and management of sustainable cropping systems. If the general framework of system experiments is well documented and adapted to iteratively design annual cropping systems (Debaeke et al., 2009), specificities of other crops are not always considered with possible limits at using the approach. As perennial and multi-layer systems that produce fresh fruit, orchards are complex agroecosystems that require specific design and management over space and time. The present work analyzed two contrasted system experiments aiming at decreasing pesticide use in temperate (apple) and Mediterranean/tropical (citrus) fruit productions. Our aim was to examine similarities and differences, and to propose a conceptual framework for designing innovative orchards.

## 2 Materials and methods

A crossed analysis was used to identify general aims, methodology used to design, type of levers combined to decrease pesticide use and related processes, and nature of outputs of the two studied system experiments (Table 1, Fig. 1).

<table><caption>Table 1. Main outlines of the two studied experiment systems.</caption><thead><tr><th></th><th>Apple (Simon et al., 2011)</th><th>Citrus (Le Bellec et al., 2012)</th></tr></thead><tbody><tr><td>Research program</td><td>BioREco</td><td>ECOFRUT followed by Agrum'Aide</td></tr><tr><td>Location</td><td>South-East France</td><td>Reunion Island (France)</td></tr><tr><td>Orchard type</td><td>System experiment</td><td>Growers'orchard network</td></tr><tr><td>Study period</td><td>2005-2015</td><td>2010-2018</td></tr><tr><td>General aim / target pests</td><td>Pesticide use decrease / insects, diseases, weeds</td><td>Pesticide use decrease / insects and weeds</td></tr><tr><td>Methodology to design orchards</td><td>Mix of prototyping & step by step approach based on scientific knowledge and experience</td><td>Intory approach involving growers, advisors, scientists; Step by step approach</td></tr><tr><td>Main levers used to decrease pesticide use (see also Fig. 1)</td><td>Low-susceptibility cultivar & combining of several methods</td><td>Weeds management with or without introduction of cover crop</td></tr><tr><td>Main processes at stake and practices to control pests</td><td>Bottom-up and top-down processes & direct measures</td><td>Bottom-up and top-down processes & direct measures</td></tr><tr><td>Main outputs</td><td>Important pesticide decrease(45-60%) when all levers are combined& information of damage risk available</td><td>Important pesticide decrease (50 % and more) when the growers are implicated as co-designers of the cropping system http://cosaq.cirad.fr/projets/agrum-aide</td></tr></tbody></table>

<figure><img src="image_224.png" /><figcaption>Fig. 1. Main levers combined to (re-)design apple and citrus orchard systems to manage orchard pests. Levers refer to broad non-exclusive groups of levers according to their nature and/or mode of action; Orchard reconception combines all types of levers.</figcaption></figure>
## 3 Results – Discussion: Spatial and temporal design of innovative orchards towards more sustainability

This cross-analysis outlined (Table 2) three main aspects:

- The young unproductive stage of the orchard that can last for 2 to 5 years according to fruit species and cultivar requires careful management as the development and yield of the adult tree is building up during this stage.

- The permanency of the crop constrains the management of soil fertility: some practices are no more possible (e.g. legume crops in the rotation), fertilizing and ground cover management differentiate between tree rows and alleys.

- The longevity of the crop constrains decision making: a pest can be damageable in the present but also in the following seasons as many serious pests can complete their lifecycle in the orchard and build up important populations or inoculum across years. This is especially true in tropical areas where there is no dormant season. Conversely, the permanency of the orchard habitats facilitates the planting or sowing of plant assemblages (e.g., ground covers, lining hedgerows) to enhance conservation biocontrol and/or compete weeds, provided non-disruptive practices are applied.

<table><caption>**Table 2.** Specificities of the design and management of orchard systems compared to rotational systems.</caption><thead><tr><td></td><td>Rotational cropping systems</td><td>Orchard systems</td></tr></thead><tbody><tr><td>Cropping system design</td><td>One-layer annual crops, no woody crop</td><td>Tree rows; arboreal and herbaceous layers; conservative design, i.e. treedensity, cultivar</td></tr><tr><td>Cropping system lifetime</td><td>Seasonal crops in the rotation with some exceptions (e.g. alfalfa)</td><td>Pluriannual young stage constrains the orchard potential yield</td></tr><tr><td>Fertilization and soil management</td><td>Whole-field fertilizer supply</td><td>Alley/tree row differentiation for fertilization; no tillage/ploughing in the alley</td></tr><tr><td>Soil cover and crop management</td><td>Successive crops</td><td>Permanent ground cover in the alleys and sometimes in the tree row; permanent trees with complex architecture</td></tr><tr><td>Weed management</td><td>Long-term decisional management</td><td>Long-term decisional management</td></tr><tr><td>Pest and disease management</td><td>Pest/disease lifecycle broken by crop sequences</td><td>Possible increase in population/inoculum across years; pest damage can affect yield in the following years (e.g. aphids affect growth)</td></tr><tr><td>Natural enemies management</td><td>Scarce permanent resources and habitat unless semi-natural habitats are present</td><td>Management of within-, peri- and extra-orchard permanent resources and habitats</td></tr></tbody></table>

## 4 Conclusions

Because of their longevity, orchards permit to foster both bottom-up and top-down processes in the food chain. Therefore, they offer opportunities to redesign the cropping system within space and time (Fig. 2), and to enhance ecosystem services such as pest and weed control through the management of cultivated and companion plants (Thies et al., 2003; Tscharntke et al., 2007). In such perennial cropping systems requiring intensive and long-term management, interactions among the orchard life stages, spatial and functional dimensions and practices need to be explicitly considered to optimize the efficiency of the system as a whole. Such complexity in the re-design requires knowledge from many stakeholders in the food system (growers, advisors, scientists...). Co-design also requires more and renewed interactions among these stakeholders with the aim of building capacity in participatory approach appropriation, and for growers in the design of their own orchard and decisional system (Le Bellec et al., 2012; Lauri, 2014).

<figure><img src="image_225.png" /><figcaption>**Fig. 2.** General framework: main dimensions to consider when designing orchard systems.</figcaption></figure>

## References

- Debaeke, P., Munier-Jolain, N., Bertrand, M., Guichard, L., Nolot, J.M., Faloya, V. & Saulas, P. (2009). Iterative design and evaluation of rule based cropping systems: methodology and case studies. A review. *Agronomy for Sustainable Development*, **29**, 73–86.

- Lauri, P.E. (éd.) (2014). Conception de systèmes horticoles innovants. Bases biologiques, écologiques et socio-économiques. Formasciences, INRA, Paris.

- Le Bellec, F., Rajaud, A., Ozier-Lafontaine, H., Bockstaller, C. & Malézieux, E. (2012). Evidence for farmer's active involvement in co-designing citrus cropping systems using an improved participatory method. *Agronomy for Sustainable Development*, **32**, 703–714.

- Simon, S., Brun, L., Guinaudeau, J. & Sauphanor, B. (2011). Pesticide use in current and innovative apple orchard systems. *Agronomy for Sustainable Development*, **31**, 541–555.

- Tscharntke, T., Bommarco, R., Clough, Y., Crist, T.O., Kleijn, D., Rand, T.A., Tylianakis, J.M., van Nouhuys, S. & Vidal, S. (2007). Conservation biological control and enemy diversity on a landscape scale. *Biological control*, **43**, 294–309.

- Thies, C., Steffan-Dewenter, I., Tscharntke, T. (2003). Effects of landscape context on herbivory and parasitism at different spatial scale. *OIKOS*, **101**, 18–25.
W4. Farms in transition to organic agriculture or agroecology

Chair: Jacques Wery, Montpellier SupAgro
# Farmer's proximity to organic farming in two French cashcrop regions: focus on technical practices,commercial strategies and professional networks

Caroline Petit *±1*, Pauline Leblanc 2, Julia Sicard 3, Catherine Mignolet 4 & Fabienne Barataud 4

## 1 Introduction

In developed countries and particularly in France, organic farming (OF) currently enjoys unprecedented support from consumers and from national and, in many cases, regional public agencies that wish to develop OF, especially for environmental reasons. Over the last few years there has been increasing concern about the transition towards and conversion to OF (Lamine & Bellon, 2009). Depending on the case, this transition is said to be progressive, rapid or abrupt, compared to a given conventional system. Different types of proximity to OF thus exist between farms. Our aim was to characterize the processes of transition by analyzing the extent of changes between conventional and organic systems, with a focus on technical practices, commercial strategies and professional networks.

## 2 Materials and Methods

Regarding the general research question, we assumed that the proximity to OF is developing variously on farms. Technical proximity to OF was evaluated by means of a scoring system with principle-based indicators on the cropping system (Petit & Aubry, 2014). The scoring system enabled us to obtain a final score out of 20, which could be divided into cropping plan / crop sequence sub-scores and crop management sequences sub-scores. This matrix was designed to assess the gap between practices in conventional farming and those in organic farming. The lower the score, the more the technical system presents a configuration close to those typical of OF. Following the scoring process, a typology of technical proximity to OF was produced. This typology was based on patterns of cropping systems and on sub-scores attributed for each farmer.

The analysis of commercial proximity to OF was based on four relevant criteria concerning the organizational dimension of markets for organic produce (presence of crops under quotas or contracts; opportunities for organic outlets; storage capacity on the farm; and commercialization in short supply chains) (Petit & Aubry, op cit).

The analysis of the professional network was based on four relevant criteria concerning opportunities for technical exchanges and the characterization of neighbors (direct or indirect access to organic farming advice; collective events such as training or tours of fields; presence of organic farmers in the neighborhood; contact with organic farmers as part of labor or equipment management).

The study was based on the empirical results of surveys on cash crop farmers in two French regions, Île-de-France and Lorraine. The analytical framework to evaluate technical proximity was initially developed in the context of the Île-de-France region and then applied in Lorraine. Interviews were held with 52 farmers in the two regions, between 2010 and 2012 in Île-de-France and in 2014 in Lorraine. The sample of farmers surveyed is described in Table 1 according to their position with regard to OF: conventional not considering conversion to OF, and conventional considering conversion to OF. The latter group refers to farmers who don't exclude a conversion to OF in the medium or long term and often get in touch with Chambers of Agriculture to ask questions about OF.

<table><caption>Table 1. The sample of farmers surveyed.</caption><thead><tr><td></td><td>Conventional not considering OF</td><td>Conventional considering OF</td><td>Total</td></tr></thead><tbody><tr><td>Île-de-France region</td><td>12</td><td>4</td><td>16</td></tr><tr><td>Lorraine region</td><td>21</td><td>15</td><td>36</td></tr><tr><td>Total</td><td>33</td><td>19</td><td>52</td></tr></tbody></table>

## 3 Discussion

Results of scores obtained by each farmer in the two regions are presented in the form of a scatter graph (Fig. 1). A majority of farmers have high cropping plan/crop sequence sub-scores and high crop management sub-scores. Regarding the breakdown of overall scores, we proposed four degrees of technical leaps to reach OF. The distribution of farmers across the four types of technical leaps is expressed in percentages for the two categories of farmers and the two regions (Fig. 2). The first key result is the absence of STL in the Île de France region compared to the Lorraine region. We then observed a higher proportion of farmers considering OF with MTL-CrP in the two regions. This means that interests for OF tend to lead farmers to rethink especially cropping plans and to apply structural changes. A more surprising result is

<footnote n="1">1 INRA UMR 1048 SADAPT / Agroparistech</footnote>

<footnote n="2">2 Coop de France Lorraine</footnote>

<footnote n="3">3 CGA de Lorraine</footnote>

<footnote n="4">4 INRA UR 055 SAD ASTER Department, Institute, Faculty, University</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: caroline.petit@agroparistech.fr</footnote>
the high proportion of farmers not considering OF farmers with a STL, in Lorraine. Forms of technical proximity can then occur independently of the interest for OF.

<figure><img src="image_226.png" /><figcaption>Fig. 1. Individual scores for farmers not considering OF (a) and considering OF (b).</figcaption></figure>

<figure><img src="image_227.png" /></figure>

STL: Small Technical Leap (low cropping plan score and low crop management sequences score)

MTL-CrP: Medium Technical Leap section cropping plan/crop sequence (low cropping plan score and high crop management sequences score)

MTL-CrM: Medium Technical Leap section crop management sequences (high cropping plan score and low crop management sequences score)

LTL: Large Technical Leap (high cropping plan score and high crop management sequences score)

<caption>Fig. 2. Typology of technical leaps and distribution of farmers, in percentages within the four types.</caption>

Along with the typology of technical leaps, we focused on professional networks (Lorraine region) and commercial strategies (Île de France region). Small, medium and large professional network and commercial leaps (respectively SPNL, MPNL, LPNL and SCL, MCL, LCL) were defined (Table 2).

<table><caption>**Table 2.** Cross-comparison of types of technical leaps and professional network and commercial leaps. For each case, results expressed in % of farmers not considering OF / % of farmers considering OF</caption><thead><tr><th rowspan="2"></th><th colspan="3">LORRAINE</th><th colspan="3">ÎLE DE FRANCE</th></tr><tr><th>SPNL</th><th>MPNL</th><th>LPNL</th><th>SCL</th><th>MCL</th><th>LCL</th></tr></thead><tbody><tr><th>STL</th><td>14/7</td><td>0/0</td><td>5/0</td><td>0/0</td><td>0/0</td><td>0/0</td></tr><tr><th>MTL</th><td>0/13</td><td>14/27</td><td>24/27</td><td>0/0</td><td>42/25</td><td>25/50</td></tr><tr><th>LTL</th><td>0/0</td><td>5/27</td><td>38/0</td><td>0/0</td><td>8/25</td><td>25/0</td></tr></tbody></table>

The first key result is that the combination of large technical, commercial and professional network leaps always steams from the fact of not considering OF farmers. Apart from those cases, we observed a gradient of scope of the leaps and consideration for OF. No clear correlation appears. This result can be interpreted as a disconnection between impediments, which are expressed by leaps, and motivations, which are expressed by consideration for OF.

## 4 Conclusions

Our results in two French regions, based on shared frameworks, reveal different patterns of proximity to OF. Regarding the transition to OF processes, discussion is underway about the necessity to remove the obstacles and simultaneously to enhance farmers' motivations. Part of the study was also dedicated to the analysis of motivations for and resistances to conversion to OF in our sample of conventional farmers and compared to a group of organic farmers in the same regions. Work is in progress on the comparison of types of leaps and types of motivation/impediment.

*Acknowledgements.* We would like to sincerely thank all the farmers surveyed, and Liz and Nonta Libbrecht for the English editing. This work was supported by the Region Île-de-France under grant DIM ASTREA and by the Region Lorraine, DRAAF and Agence de l'Éau Rhin-Meuse.

## References

* Lamine, C. & Bellon, S. (2009). Conversion to organic farming: a multidimensional research object at the crossroads of agricultural and social sciences. A review. *Agronomy for sustainable development*, 29, 97-112.

* Petit, C. & Aubry, C. (2014). Technical and commercial change during transition to organic farming: towards a methodological approach based on the scope of the leaps forwards. In: Proceedings of the 11th European IFSA Symposium. Farming systems facing global challenges: capacities and strategies. 2014.
# Conversion towards organic farming leads to a complexification of the farming system management: application to vineyard systems

Anne Merot *±1

## 1 Introduction

In the current context of climatic, socio-economic and environmental changes, farmers have to modify their farming systems to reduce environmental impacts while still assuring profitability at farm scale. Organic farming appeared to be an interesting production mode to face these changes. In few farms simple adjustments can be sufficient to operate conversion but in most of farms changes in the crop management sequence are not obvious and can lead to deep evolutions (Lamine & Bellon, 2009). Advisers worry about farm sustainability when converting to organic farming without enough preparation. A state of art of the scientific literature showed that not much attention has been given to the conversion period from conventional to organic farming.

Thus in this work, we analyse technical and organizational changes associated to the conversion and showed that conversion towards organic farming for vineyards systems often leads to a complexification of the cropping system management.

## 2 Materials and Methods

The work was carried out in Southern France in the Languedoc region. The Languedoc-Roussillon is the larger vineyard area in France. In 2010 it represented 30 % of the total French vineyard (Agreste, 2010). In 2011 (Agence Bio, 2011), 19 907 ha of organic vineyards corresponding to 1199 farms were inventoried in Languedoc-Roussillon. 57% of these 19 907 ha were still in conversion towards organic farming. 16 farms were interviewed from 2009 to 2012 the year before conversion towards organic farming and the first year labelled. The survey was conducted at the vineyard scale. The sample of farms interviewed was based on a 3-key classification of the farms on the study area (Merot et al., 2008): we selected on four soil and landscape zones highly representative of the region. The type of commercialization was also taken into account (wine-growers in cooperative or individual cellars) and the vineyard area. We collected data on production factors, management practices and associated indicators. A characterization of the changes operated in relation to the organic label was performed.

## 3 Discussion

Results showed that the conversion towards organic farming resulted in an increase of the farming system complexity. Considering the whole set of farms interviewed, the frequency of changes varied largely from one agricultural practice to another but most of agricultural practices were finally impacted by the conversion. It was interesting also to remark the diversity of changes between farms. The intensity of changes was less important in some farms.

We noted that the number of operations was enlarged (+ 15 %). Most particularly soil management was intensified (+ 25% of traffic) and the phytosanitary traffic was higher (+ 14%). The changes observed concerned also the number of crop management sequences (for 8/16 farms - fig. 1).

<footnote n="1">¹ UMR System, INRA E&A</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: anne.merot@supagro.inra.fr</footnote>
<figure><img src="image_228.png" /><figcaption>**Fig. 1.** Evolution of the number of crop management sequences before (conventional) and after conversion (organic) towards organic farming.</figcaption></figure>

Whereas the number of fields in production stayed relatively stable in most of farms surveyed, the number of crop management sequences increased for 50% of vineyards studied.

The number of indicators used for technical decision making was also higher after the conversion towards organic farming (5.5 indicators in average in conventional farming and 6.7 in organic farming). These changes impacted directly production factors so as labor and equipment and the farming system organization leading to a complexification of this organization. To compensate this complexification, two strategies were identified depending on the type of commercialization. Wine growers in cooperative decided to maintain the vineyard area and increased labor whereas individual cellars tended to reduce the number of fields to limit the increase of crop management sequences and labor.

## 4 Conclusions

These results suggested that conversion towards organic farming implied more than a substitution of products: It was more a complete re-organization of the farming system management in most of farms surveyed. In fact, the label gives the list of pesticides not forbidden. But in reality, organic farming conversion is never an addition of substitutions. It could begin with addition of substitutions. But after one or two years, farmers have to face changes in organization and in labour so that they have to re-organize their vineyard system. Converting to organic farming lead to a complexification of the decision-making: field heterogeneity is taken more deeply into account and crop management sequences are more numerous. Behind this change, there is also an adaptation of the field indicators used for decision-making. Adapting indicators to analyse, manage and assess the conversion is therefore one essential issue for supporting conversions.

## References

* Agence bio (2011). "L'agriculture biologique - Chiffres clés - Edition 2011," La Documentation Française, Paris, France.

* Agreste (2010). "Languedoc-Roussssilon - premières tendances viticulture" Service de la statistique et de la prospective - Ministère de l'agriculture de l'agroalimentaire et de la forêt, Paris, France. 4 pp.

* Lamine, C., Bellon, S. (2009). Conversion to organicfarming: amultidimensionalresearchobject at the crossroads of agricultural and social sciences.A review. Agronomy for Sustainable Development, 29 : 97-112.

* Merot, A., Bergez, J.-E., Capillon, A., Wery, J. (2008).Analysing farming practices to develop a numerical, operational model of farmers' decision-making processes : An irrigated hay cropping system in France. Agricultural Systems 98 (2): 108-118.
# A transdisciplinary approach to structure knowledge gathering on organic farming systems: evaluation of organic farm strategies in the case of Flanders

Fleur Marchand *±1,2, Jo Bijttebier 1, Jef Van Meensel 1, Matthias Strubbe 1 & Ludwig Lauwers 1,3

## 1 Introduction

Decision making or adapting farm strategies to environmental and social demands on organic farms faces specific fundamental problems. First, gathering of farm specific data and knowledge is limited due to the small number of farms within each production type. Second, the diversity of organic farms causes difficulties in comparing these individual data. Moreover, as processes on organic farms are more complex and less controlled than on conventional ones, strategy design at farm level needs to account for this specificity and complexity. These problems call for new ways of structuring farming system knowledge for strategy design and strategic choices. Literature provides two key issues in overcoming these problems. First, a system approach delivers tools to understand the complex interactions within and between farming systems (Darnhofer et al., 2012). Second, a transdisciplinary co-production of knowledge is recommended and includes societal actors in the knowledge co-production process (Aeberhard & Rist, 2009). Such an approach should be able to assemble the needed expert and tacit knowledge from different stakeholder groups (farmer, advisors, farm networks, research and educational institutions). As a result, this paper aims to contribute to the aforementioned shortcomings by describing a transdisciplinary and system approach to structure knowledge for strategic choices in organic farming.

## 2 Materials and Methods

We define the transdisciplinary co-production of knowledge as the collection and analysis of information on the organic farming system involving scientists, farmers, advisors, farm network representatives and educational institutions in all phases of the research process (De Ridder et al., 2007) to set up a learning process that encourages implementation of the outcomes. During the transdisciplinary process, we searched for techniques that include system thinking and explored both quantitative and qualitative techniques because a mixed methods approach can provide strengths that offset the weaknesses of each type of research (Creswell & Clark, 2011). Using both numbers and words, combining inductive and deductive thinking, is highly suitable for solving complex problems. The combination of these techniques resulted in a framework (Fig. 1) that is able to structure knowledge gathering on organic farming leading towards a better understanding of the complex organic farming system.

<figure><img src="image_229.png" /><figcaption>Fig. 1. Overall framework to structure knowledge gathering on organic farming systems.</figcaption></figure>

<footnote n="1">¹ Social Science Unit, Institute for Agricultural and Fisheries Research, Merelbeke, Belgium</footnote>

<footnote n="2">² Ecosystem Management Research Group and IMDO, University of Antwerp, Belgium</footnote>

<footnote n="3">³ Department of Agricultural Economics, Ghent University, Belgium</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: fleur.marchand@ilvo.vlaanderen.be</footnote>
Since each farming sector has its own context, problems and farming practices, we set up a different process for different sectors (suckler cow farms, dairy farms and arable farms). From the gradual nature of these implementation processes, we could develop an overall framework (Fig. 1) consisting of three main phases (P1-P3). Throughout a first phase (P1), dominant management features are captured during organic network meetings, through observations and participation in discussion groups with farmers making use of mainly qualitative techniques (coding of observations and interview notes). Second (P2), a farm scan is developed and used to structure quantitative and qualitative information on these main system features in a collaboration between advisors, experts and researchers (Bijttebier et al. 2015). In a third phase (P3), the focus is defining the relation between the (dominant) management features in which techniques as cognitive mapping and farm modelling are relevant.

## 3 Results-Discussion

The implementation of the framework for the three cases (suckler cow farms, dairy farms and arable farms) revealed important issues (Table 1) with respect to the effectiveness of the process, the gradual data gathering nature and convergent attention points. First, depending on the identity of the initiators, the initial questions from the stakeholders and the involvement of the farmers, the course of the process is very different. Within each sector, the phases were carried out not through a linear process and not even an iterative one. Instead, depending on the questions and needs of the farmer groups, the phases were succeeded organically. For example, in the case of dairy farms, we started with P2 as the question of production cost was urgent for the farmers at that time. Furthermore, the phases interact highly when additional knowledge is gained. In P3, cognitive maps reveal insights in the main elements of a system and the links and tradeoffs between them. This interacts highly with P1 where dominant features were determined through observations and interviews. However, working within this framework structured all relevant information.

<table><caption>Table 1. Implementation for the farm sectors: suckler cow farms, dairy farms and arable farms.</caption><thead><tr><th></th><th>suckler cow farms</th><th>dairy farms</th><th>arable farms</th></tr></thead><tbody><tr><td>initiators</td><td>farmers</td><td>advisors</td><td>advisors</td></tr><tr><td>initial question</td><td>data sharing with respect to technical and economic performance on the farms</td><td>calculation of cost for the production of 1 liter milk</td><td>quantifying indicators</td></tr><tr><td>short evaluation</td><td>good and efficient process, farmers put forward questions, deliver data easily and search for solutions</td><td>slow and less efficient process, farmer involvement is difficult, advisors are needed as moderator/mediator</td><td></td></tr><tr><td>Time frame 1</td><td>P1: dominant features</td><td>P2: specify feature production cost</td><td>P1: dominant features</td></tr><tr><td>Time frame 2</td><td>P3: model development</td><td>P1 &amp;3: relation between dominant data features through the use of a model</td><td>P2: specify features with scan</td></tr><tr><td>Time frame 3</td><td>P2: specify features with scan</td><td>P3: discussion on relations between features</td><td>P3: cognitive mapping (advisors)</td></tr><tr><td>Time frame 4</td><td>P3: cognitive mapping (advisors&amp;farmers)</td><td>P3: cognitive mapping (advisors&amp;farmers)</td><td>P3: cognitive mapping (farmers)</td></tr></tbody></table>

Second, although the approach was set up separately for the three cases, outcomes converge to common key features of major importance. These vary from technical and biophysical characteristics such as optimizing crop rotation to characteristics related to sales and logistics. As such, insights in common attention points may incite cooperation and learning between these farming sectors and novel strategy search within the organic farm system. Furthermore, the transdisciplinary stakeholder group also suggest, based on the cognitive maps and the quantitative farm models to further model and simulate system changes when a new farm strategy is considered on the organic farm. This phase is not included in the framework yet, as we have no empirical evidence or experience for this possible phase so far.

## 4 Conclusions

Through a transdisciplinary research approach, we were able to develop a framework to structure knowledge gathering and sharing on organic farming systems. The implementation through three cases occurred organically and very diverse. However, the common framework provides a tool for advisors and researchers to guide the knowledge structuring, depending on the farmers' questions, within different sectors towards the same system approach. This approach can be used to structure and improve knowledge transfer during network meetings and finally to support farmers decision making when adapting their strategies to fast changing socio-ecological demands. This approach should make learning on common key features between sectors possible and even lead towards cooperation in the long term.

## References

* Aeberhard, A. &Rist, S. (2009). Transdisciplinary co-production of knowledge in the development of organic agriculture in Switzerland. *Ecological Economics*, **68**, 1171-1181.

* Bijttebier, B., Lauwers, L. & Marchand, F. (2015). Structuring data gathering on organic farms: the transdisciplinary development and use of a farm scan within a broader methodological framework. *Paper on the 5th International Symposium for Farming Systems Design*, Montpellier, France.

* Creswell, J. & Clark, V. (2011). Designing and conducting mixed methods research. Sage, London, UK.

* Darnhofer, I., Gibbon, D. & Dedieu, B. (2012). Farming systems research into the 21st Century: the new dynamic. Springer, Dordrecht, The Netherlands.

* de Ridder, W., Turnpenny, J., Nilsson, M. & von Raggamby, A. (2007). A framework for tool selection and use in integrated assessment for sustainable development. *Journal of Environmental Assessment Policy and Management*, **9**, 423-441.
# Co-design of organic farming systems on the Canadian Prairies

Martin Entz<sup>*±1</sup></sub>, Joanne Thiessen Martens<sup>1</sup>, Gary Martens<sup>1</sup>, Michelle Carkner<sup>1</sup>, Derek Lynch<sup>2</sup>, Mark Kopecky<sup>3</sup> & Kristen Podolsky<sup>4</sup>

## 1 Introduction

Functional organic farming systems require diverse rotations and effective nutrient recycling, plus biological integrity for pest management. Current Canadian farming systems, on the other hand, employ simple rotations and rely on agrichemicals for nutrient and pest management. Therefore, transition from conventional to organic farming involves not only a transition to fewer agrichemicals, but farming system redesign. Most extension and outreach services available to farmers in Canada are aimed at individual components of specialized farming system, and are not serving farmers interested in farming system redesign.

Canada has invested in organic agriculture research for over 10 years, and research institutes have generated a wide range of information applicable to organic farmers. However, steps to place this new information into a whole farming system context have been lacking. The objective of this project is to link new academic research on organic farming methods with farmer practice in order to facilitate improved farm system design for organic production.

## 2 Materials and Methods

This project employes a co-operative innovation model aimed at linking academic research with farmer experience for new cropping system designs in organic agriculture. The model (Fig. 1) begins with a detailed farm scan by the Institute after initial contact between farmer and research team. Information in the scan includes land base, crop production, soil health, machinery inventory, labour resources, etc. This information is analyzed by the institute with a view to identifying gaps in farm integrity and resilience. Gap analysis results are then presented to the farmer, after which scenarios to overcome challenges are developed. The goal is to consider a number of different scenarios and discuss and rank their relative strengths and weaknesses. Cooperative scenario development is conducted in two phases: Phase I involves "kitchen table" discussions between the research team and individual farmers or farm families. Phase II involves a group of farmers from the region sharing and critiquing each others' scenarios and learning how recent research results can assist them. The next phase involves planning and carrying out selected scenarios in on-farm trials. These trials are conducted by the farmer or together with the institution. Results from on-farm testing are shared between farmers and the institute after the growing season, and again among local farm groups. The entire process is designed to be iterative and to work with individual farmers for a minimum of three years.

<figure><img src="image_230.png" /><figcaption>**Fig. 1.** Co-operative innovation model used by the Natural Systems Agriculture Lab at the University of Manitoba.</figcaption></figure>

<footnote n="1"><sup>1</sup> University of Manitoba, 222 Dafoe Rd, Winnipeg, Manitoba R3T 2N2, Canada</footnote>

<footnote n="2"><sup>2</sup> Dalhousie University, Canada, Truro, Nova Scotia, Canada</footnote>

<footnote n="3"><sup>3</sup> Organic Valley, Wisconsin, USA</footnote>

<footnote n="4"><sup>4</sup> Manitoba Pulse Growers Association, Carman, Manitoba, Canada</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: m_entz@umanitoba.ca</footnote>
This model is being used in two projects: 1) Transition to organic soybean production (9 farms); and 2) Optimizing productivity of long-term organic farmers by improve nutrient cycling (12 farms). Farm visits were conducted between June and October, 2014. University staff spent half days getting a personal tour of the farm and a detailed inventory of farm resources. On-line information supplemented farm data, though in the nutrient optimization project, plant and soil samples were also collected for future nutrient analysis. All information is organized in a database. In the optimization project, we are exploring the use of different models (eg., Integrated farming systems model, APSIM, etc) to help better describe and understand macronutrient balance.

In the soybean project, a graduate student (M. Carkner) is evaluating 15 non-GMO soybean varieties in replicated tests on 5 of the case farms. Soybeans are relatively new to this production region and suitable varieties for organic production are limited. The on-farm soybean experiments allow direct input of local information into farmers' decision making. The varieties are grown under typical farm weeding vs hand weeded conditions thereby providing farmers with information on the relative weed competitiveness of different varieties.

**3 Results – Discussion**

**Soybean project:** Gap analysis was conducted in autumn, 2014 and researchers presented results along with some ideas for improvement to individual farmers in December, 2014. Major weaknesses were weed management, specifically lack of suitable equipment and lack of rotation diversity to ensure acceptable weed control. Co-operative scenario development started soon after the initial farm scan. A meeting was held in March, 2015 once each farmer had developed some options to overcome their constraints. At the meeting, each farmer presented their organic soybean production plan including prior crop in rotation, seeding details, tillage management, and specific weed control interventions. The graduate student presented variety test results. Several longer season varieties were damaged by frost in the study, something that farmers took careful note of. Farmers then worked in groups of 3 or 4, and together with a field researcher/facilitator, discussed new options based on responses to each others ideas and questions.

Farmers learned new things from each other – these were recorded. For example, experienced farmers stressed the role of perennial forages for Canada Thistle and wild oat control, while farmers with previous row crop experience explained equipment details. Farmers had heard of flaming for weed control but none had any direct experience. They expressed a strong interest in this practice, especially since several wet springs have made tillage less than effective in early season weed control. All farmers will grow organic soybeans in 2015 and some will transition additional land for future organic soybean production. All farmers are trying new techniques in 2015, based on what they learned in the program. Staff from the institution will visit the farms at least once during the season and be available for questions (phone/email). A follow up meeting will be held in November, 2015.

**Organic optimization project:** A main goal in this project is to construct whole farm nutrient budgets in order to identify nutrient limitations to organic farm productivity. This involves a simple mass balance; therefore grain samples from the 2014 crops are being tested for nutrient concentrations to determine removal. Assessment of soil nutrient stocks are being tested using a novel approach where aboveground biomass and nutrient concentration in the „soil- building” crop (typically an annual green manure or perennial forage) is tested in order to measure soil nutrient supplying power. This technique is deemed more suitable than standard soil testing as used in previous on-farm surveys (Entz et al. 2001). Early results from this test indicate that tissue phosphorous concentrations range from sufficient to seriously deficient. Gap analysis is currently underway for the optimization project and farm consultations are planned for May to July, 2015.

**4 Conclusions**

One early conclusion is that farmers are eager to be involved; they are learning a great deal; and they are starting to see possibilities for farming system redesign. Working with other farmers appears very important to participants, though they also appreciate the institute's involvement. Early indications are that farm system design changes for improved weed management in soybean are related to equipment resources, farmer experience, and crop rotation for control of perennial weeds. Farm system design changes for optimizing productivity of long-term organic systems, especially where soil phosphorous levels are low, regard livestock integration and changes to livestock management (eg., more grazing than haying, which removes more nutrients from land).

*Acknowledgements.* We gratefully acknowledge our funders: Growers International Organic Sales, OrganicValley, Agriculture and AgriFood Canada, the Manitoba Pulse Growers Association and the Manitoba Government.

**References**

Entz, M.H, Guilford, R, & Gulden, R.H. (2001). Crop yield and soil nutrient status on 14 organic farms in the eastern portion of the northern Great Plains. *Canadian Journal of Plant Science* **81**: 351-354.
# Decision making processes and factors driving apple protection strategies at farm level

Solène Pissonnier 1,2, Claire Lavigne 2, Jean-François Toubon 2 & Pierre-Yves Le Gal 1

## 1 Introduction

Apple growers apply numerous chemical treatments to protect fruits from both pests and diseases. Alternative methods exist but their efficiency on pest control can be limited, while public regulations, retailers, supermarkets and consumers are increasingly demanding regarding fruit sanitary quality, both on national and export markets (Simon et al., 2011; Drogué and DeMaria, 2012). Few pesticide residues, no symptom of diseases, standardized visual aspect and high nutrient quality are then included in contract specifications putting pressure on apple growers and their first buyers to fulfill all these requirements (Simon et al., 2010). In that context encouraging transitions towards sustainable practices requires understanding decision making processes and factors that drive growers' design and implementation of crop protection strategies at farm level, where trade-offs have to be made regarding allocation of resources between farm activities. This ongoing study is based on semi-qualitative surveys of 35 apple farms. It characterizes the diversity of their protection strategies, according to their natural environment, their own resources and their marketing strategy, and identifies the decision-making processes and factors that drive this diversity.

## 2 Materials and Methods

The study has been carried out in South-East and Center-West of France. Orchards of the South-East region are more susceptible to insects due to a hot and dry climate, while the wetter climate of the Center West region makes them more susceptible to fungal diseases. Interviewed growers were selected in order to have a diversified sample regarding their main market channel, i.e. Apple Grower Cooperatives (AGC) and self-sellers, their farm circumstances and their protection practices. One AGC per region was studied and 10 to 13 members were interviewed per AGC. Twelve self-seller growers were also met per area. AGC growers were selected based on the diversity of protection practices observed in the AGC database storing the crop protection treatments applied by all their members every year. Self-sellers were contacted based on local networks and selected for their diversity of both context and assumed practices. Individual semi-structured interviews were conducted to better understand growers' protection strategies and practices. Technicians supporting growers in their protection management were also interviewed in order to understand the way they provide advices and their relationship with growers. Protection practices were analyzed based on the whole annual set of treatment records per grower. Practices were differentiated according to the type of products used rather than the number of sprays, which is linked to the annual weather context. Six protection strategies were identified based on the following orchard management variables: selection of market segment, selection of planted varieties, selection of restricted pesticides, choice of alternative methods to pesticides, objectives of fruit quality.

## 3 Results

**A set of common factors driving decision making processes:** All the growers take their protection decisions according to a common framework including public regulations, private requirements, and climate. Public regulations define the authorized products, doses, mixes, pre-harvest intervals, maximum number of applications of a given product, and width of untreated areas. Every grower has to adapt his strategy to the public regulation rules and may be controlled in that respect by public officers. Private requirements imposed by buyers, especially supermarket chains and exporters, add specifications possibly stricter than public regulations, for instance regarding accepted number and quantities of pesticide residues. Requirements depend on the marketing channel or firm, but all the growers interviewed had contracted specific requirements with a given body such as AGC, organic certification agency or regional council (e.g. Sud Nature in Languedoc-Roussillon region). Daily climatic conditions also determine a set of decision rules such as disease control in relation with rainfall or treatment triggering in relation with wind speed.

**But yet a diversity of protection practices in the same framework:** Both AGC treatment databases and growers' interviews highlight the large diversity of practices encountered in a shared decision-making framework. A gradient arises from growers trying to avoid toxic pesticides and managing their orchard only with natural products to growers using only and frequently synthetic products. As a result application of pesticides varies largely in each treatment group (Table 1). This diversity within a same set of private specifications and climatic context is linked to each grower's own choices regarding the design and implementation of his protection strategy.

<table><caption>**Table 1.** Range of pesticide use according to the type of treatment in a given GO (# of copper/mancozeb-captan/sulphur based treatments over total # of fungicides, and # of bio-insecticides over total # of insecticides)</caption><thead><tr><th>Treatment group</th><th>Treatment product</th><th>Range of treatment per farm (%)</th></tr></thead><tbody><tr><td rowspan="3">Fungicide</td><td>Copper-based</td><td>0-44</td></tr><tr><td>Sulphur-based</td><td>0-56</td></tr><tr><td>Mancozeb/Captan-based</td><td>0-62</td></tr><tr><td>Insecticide</td><td>Bio-product</td><td>0-72</td></tr></tbody></table>

<footnote n="1">¹ CIRAD, UMR Innovation, F-34398 Montpellier, France</footnote>

<footnote n="2">² INRA, UR 1115 PSH, Plantes et Systèmes de culture Horticoles, F-84000 Avignon, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: solene.pissonnier@cirad.fr</footnote>
## ... Leading to six protection strategies: Six protection strategies were identified corresponding to specific combinations of five variables (Table 2). The growers adopting bio-ecologic (S1) and ecologic (S2) strategies aim to reduce chemicals use as much as possible in order to protect consumers and workers' health and the environment. In that respect they try to reach a balance between pests and natural enemies and to only use organic products and pesticide alternative methods such as releases of natural enemies and implementing bird nest boxes. Most of these practices are based on orchard observations. Since their apple yields are usually low (around 20t/ha), these growers target niche markets where apple is sold at higher prices than usual (1 to 2€/kg), such as short chain. They also valorize damaged apple as juice and they diversify their production with other crops to satisfy their customers. But since their incomes are not high enough to hire workers, they show a high workload seen as a major constraint for extending their activities. Compared to S2, S1 growers have self-imposed bans of authorized organic products that they think environmentally unsuitable because their formulation is based on toxic molecules for other insects. They experiment new protection practices such as biodynamic ones ahead of research institutes and are considered as information source.

The growers adopting the combined strategy (S3) aim to reach a trade-off between their income objective and their will to evolve towards sustainable practices. As such they try to achieve a high yield and commercial quality (premium fruits) to secure their income, by using both conventional and organic methods. They generalize alternative methods on their whole orchard, to jointly protect consumers, workers' health and environment. Most of these practices are based on orchard observations. All of them except one belong to AGC and their selling price is low, around 0.5€/kg with an average yield of 44t/ha. They work with several information sources and with AGC technicians.

The financial strategy (S4) is adopted by young producers or growers who have faced financial difficulties in the past few years. As AGC members they get low selling prices, 0.3€/kg. Thus they try to improve their financial situation by minimizing technical and economic risks, i.e. reaching high yields (e.g. 60-80t/ha for Golden) and high commercial fruit quality (premium category) while reducing production costs. As such they avoid using natural treatments like copper or bio-insecticides since they do not trust their pest control efficiency which could negatively impact their economic results. They are also careful with the number of sprays they apply in order to reduce costs. They are supported by AGC technicians to find trade-offs between their objectives of respectively high production and low protection costs.

The growers adopting the risk-limited strategy (S5) aim to maintain their good economic situation by reaching high yield and maximizing premium quality every year. They grow high valued cultivars like Pink Lady® and they combine apple production with other activities on the farm or outside the farm. The apple orchard is seen as a way to increase the farm profits independently of environmental impacts. They follow technician advice only (from AGC or agricultural chambers) in order to be sure to respect public regulation. As a consequence, their practices are influenced by technicians' advice. Their treatments are based on synthetic pesticides.

Compared to S5, the no-risk strategy (S6) regroups growers who aim not only to maintain but to maximize their profits by achieving the highest yields and quality. They are AGC growers with a strong link with their cooperative. Their focus is more on how to reduce production costs at a farm scale and maximize labor force and equipment efficiency, than changing their protection strategy.

<table><caption>**Table 2. Characterization of the six protection strategies**</caption><thead><tr><th></th><th>Growers (n)</th><th>% of surface with resistant cultivar</th><th>specific requirements contracted</th><th>Product self-restrictions</th><th>Alternative methods to pesticides</th><th>Effects of fruit quality*</th></tr></thead><tbody><tr><td>S1: bio-ecologic</td><td>4</td><td>&gt;50%</td><td>100% Organic or Biodynamic</td><td>Yes</td><td>4</td><td>A+G+S</td></tr><tr><td>S2: biologic</td><td>2</td><td>&gt;50%</td><td>100% Organic</td><td>No</td><td>4</td><td>A+G+S</td></tr><tr><td>S3: combined</td><td>10</td><td>5-50%</td><td>5-50% organic/BabyFood</td><td>Yes</td><td>At least 2</td><td>A+C+G+S</td></tr><tr><td>S4: financial</td><td>6</td><td>&lt;25%</td><td>No</td><td>No</td><td>0-1</td><td>C</td></tr><tr><td>S5: risk-limited</td><td>10</td><td>0%</td><td>No</td><td>No</td><td>0-1</td><td>C</td></tr><tr><td>S6: no risk</td><td>3</td><td>0%</td><td>No</td><td>No</td><td>1-2</td><td>C</td></tr></tbody></table>

## 4 Discussion and conclusion

Although growers located in a similar area and AGC face common buyer specifications and weather conditions they show a diversity of protection strategies depending on a range of management components: the farm financial situation, selling prices according to their marketing strategy, their technical environment and information sources, their specialization in apple production. Formalizing the farm set of structural and management characteristics that drive growers' decision making processes is still in progress. Growers' own knowledge and values may also play a part in the strategy implemented. For instance, agroecological methods require new technical skills and some ecological consciousness. This complex combination of factors finally makes each farm a specific case that should require targeted support in order to evolve towards more agroecological protection strategies and practices.

## References

* Drogué S., DeMaria F. (2012). Pesticide residues and trade, the apple of discord? *Food Policy* 37, 641-649.

* Simon S., Bouvier J.-C., Debras J.-F., Sauphanor B. (2010). Biodiversity and pest management in orchard systems. A review. *Agronomy for Sustainable Development* 30, 139-152.

* Simon S., Brun L., Guinaudieu J., Sauphanor B. (2011). Pesticide use in current and innovative apple orchard systems. *Agronomy for Sustainable Development* 31, 541-555.

<footnote>*A: Agronomic; C: Commercial; G: Gustative; S: Sanitary</footnote>
# Production2020: designing and assessing sustainable farming systems in Switzerland

Martin Braunschweig<sup>*±1</sup>, Andreas Roesch<sup>1</sup>, Maria Bystricky<sup>1</sup>, Thomas Nemecek<sup>1</sup> & Gérard Gaillard<sup>1</sup>

## 1 Introduction

With the introduction of integrated production, the proof of ecological performance (PEP) and the ethology programmes, developments in Swiss agriculture in the 1990's were well regarded, and were met with high credibility ratings by consumers. In terms of ecology and animal welfare, Swiss agriculture achieved an internationally leading position. Although the Swiss agricultural system has proven its worth in principle, however, in recent years it has also shown its limitations. A comprehensive analysis of farming systems in Switzerland showed that several benchmarks were not achieved. For instance, the nitrogen load is still too high and the quality of ecological compensation areas should be improved (Herzog *et al.*, 2008). Furthermore, in intensive production regions pesticide concentrations are above water quality standards (Moschet *et al.*, 2014). In general, the sustainability development process of Swiss farming systems has stagnated for the recent years. Moreover, the Swiss agricultural production system shows high variability among groups of comparable farms in various aspects such as environmental impacts, energy use, and earnings from farming, indicating a considerable potential for improvement. In this context and considering a multitude of challenges facing the agricultural sector, Agroscope has launched a research initiative named "Production2020". It aims at the development of a seminal knowledge system for the design and management of agricultural systems in Switzerland.

## 2 Main Principles

The initiative is based on seven main principles and focuses on the improvement of sustainability of Swiss agricultural production systems:

1. Consideration of all three dimensions of sustainability,

2. Focus on primary production,

3. Action at farm-manager level,

4. Embedding primary production in the entire value chain with optimal use of resources,

5. Consideration of the entire life cycle,

6. Striving towards impact goals, and

7. Strengthening the personal responsibility of the farmers.

The first main principle emphasizes that all three dimensions of sustainability, economic, environmental and social, should be equally integrated in a design for sustainability. The present-day confrontation between economy and ecology must be resolved and the public service compensations for environmental performance should be reduced to a minimum based on effectiveness. The second principle refers to the specific challenges in food production and it is therefore strategically crucial to focus on primary production rather than on the other services of agriculture. The third principle indicates that the farmer is the main actor who ultimately decides and implements the actions. We have to keep in mind that farmers see themselves first of all as agricultural producers and not as forest rangers, hoteliers or landscape gardeners. Alternative management options to the predominant family farming may also be considered in order to remain flexible with respect to organisation forms for food production. The fourth principle indicates that primary production cannot be designed and managed independently of the value chain, bearing in mind product choice and product quality. Production must be adapted to the market guaranteeing demand by the food sector up to the consumer, and by the citizen as a taxpayer. In this regard, a reasonable use of natural resources is of common interest. The design for sustainability takes place along the entire life cycle of the observed systems, which is emphasized by the fifth principle. There should be no shifting of a sustainability burden from primary production to the upstream or downstream value-added stages, and vice-versa. Neither should there be a one-sided improvement of a sustainability criterion at the expense of other criteria not considered. The sixth principle is based upon the DPSIR framework, which describes causal links from the driving force to pressure, state, impact and the response. The intention of the seventh principle refers to the personal responsibility of farmers so that they can act meaningfully by using the best options for their business rather than limiting their creativity by externally imposed commands or prohibitions.

<footnote n="1"><sup>1</sup>Life Cycle Assessment, Institute for Sustainability Sciences ISS, Agroscope, Federal Department of Economic Affairs, Education and Research EAER</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: martin.braunschweig@agroscope.admin.ch</footnote>
## 3 Initiation of the Research Initiative

Based on these seven principles, research activities have been initiated in order to develop, test and provide possible courses of action for progressing towards next-generation agricultural systems.

The research initiative is organised into nine work packages:

* WP1: Lead of the research initiative

* WP2: Strategy definition

* WP3: Sustainability assessment and design

* WP4: Options for action in plant production

* WP5: Options for action in animal husbandry

* WP6: Options for action in management

* WP7: Pilot farm network

* WP8: Institutional integration

* WP9: System integration and synthesis

It is essential for Agroscope to place this research initiative into the European context, where similar research efforts have already been initiated. The development and implementation of indicators for a sustainability assessment of Swiss agriculture system is new and key for this initiative. It will include indicators for resource efficiency, climate, nutrient management, ecotoxicity, biodiversity, soil quality, animal welfare, social factors including landscape aesthetics, and the economic short- and long-term situation of the farm. A main focus of the initiative lies on elaborating production systems and closing research gaps in order to provide additional options for action to farmers. A prerequisite of these options for action is that they are successfully tested and verified under practical conditions. A network of farms will be established to involve farmers in performing applied research under practical conditions and to receive feedback about these options for action on farm level. Farmers can then select appropriate options for action for their production needs to achieve the best possible sustainability assessment. The outcome of the sustainability assessment will be integrated into agricultural policy instruments. The initiative is guided by a scientific advisory group comprised of leading international experts in their fields ensuring a high level of research quality. We performed a stakeholder analysis to retrieve information about the challenges and opportunities of the Swiss agriculture and food sector. For this purpose we developed a questionnaire to interview stakeholders to explore their priorities and requirements. We interviewed 12 stakeholders including retailers, federal offices, non-governmental organisations, representatives of major food labels, farmers' associations, public institutions and industry representatives.

## 4 Preliminary Results from the stakeholder process

The analysis of the stakeholder interviews is still on going. However, first results were deduced from this analysis by identifying four main topics. A major requirement is to investigate and establish site-specific farming systems. This takes into account that sustainable food production is adapted to a specific location within a region. A second thematic priority is an effectively and efficiently producing agricultural and food sector. An effective agricultural production conforms to the criteria of sustainability and high-quality food by continually increasing the output/input ratio. A third important issue is to position Swiss food production in the context of a globalised world with direct competition with other producers and processors especially in the neighbour European countries. This deals with the question of how much and what kind of food should be home-produced and how much food should be imported, and which degree of self-sufficiency should be achieved in Switzerland. A fourth major topic is the perspective of the profession of the farmer in the future. Farmers have to meet high requirements, and the pressure on prices reduces margins for their products, which makes farming less attractive. In the light of this, basic information and possible solutions of technical nature as well as in management will be established for a seminal agricultural production.
Within these four major topics, current research activities from different disciplines are now being scanned for potential links between disciplines. In order to cover the remaining research gaps, new project proposals will be developed to provide knowledge for the current challenges of Swiss agriculture in the frame of the four major topics identified. At the time of the conference, our experiences and first results of this comprehensive initiative to further Swiss agricultural systems will be reported.

## References

* Moschet, C., Wittmer, I., Simovic, J., Junghans, M., Piazzoli, A., Singer, H., Stamm, C., Leu, C. & Hollender J. (2014). How a complete pesticide screening changes the assessment of surface water quality. *Environmental Science & Technology, 48*, 5423-5432.

* Herzog, F., V Prasuhn, V., Spiess, E., & Richner, W. (2008). Environmental cross-compliance mitigates nitrogen and phosphorus pollution from Swiss agriculture. *Environmental Science & Policy, 11*, 655-668.
# Projections to Latent Structures (PLS) to evaluate farming system effects on agro-ecosystem services: Changes after transition from conventional to organic farming system

Libère Nkurunziza *±<fnref n="1" />, Håkan Marstorp <fnref n="1" />,<fnref n="2" />, Iman Raj Chongtham <fnref n="1" />, Kristin Thored <fnref n="1" />, Ingrid Öborn <fnref n="1" />,<fnref n="3" />,
Göran Bergkvist <fnref n="1" /> & Jan Bengtsson <fnref n="4" />

## 1 Introduction

Farm management strategies impact targeted functions and services, such as crop productivity, soil fertility, nutrient cycling, weed dynamics, biodiversity, etc. The dynamic and diverse decisions on farm management, the diversity of the desired outcomes and the various ways to assess them require evaluation techniques that can handle many variables at time. To account for multi-factorial and multivariate dimensions of agro-ecosystems, the use of multivariate modeling for the evaluation of crop production systems have been recommended (Kenkel et al., 2002; Bianconi et al., 2013; Schipanski et al., 2014). One of the main advantages of the multivariate methods is that they can inform on major contributing factors to the variation of multiple functions and services from an agro-ecosystem. Doing so, the provided knowledge allows fine-tuning the factors of high importance under specific circumstances. One such method is Projections to Latent Structures (PLS, an extension of principal component analysis; also known as partial least squares, Eriksson et al., 2006). We used data from 36 farms to examine how historical and current management strategies and time since transition from conventional to organic farming systems (CFS to OFS) affected soil carbon (C) and nitrogen (N), and barley performance in two agro-ecological zones in Sweden.

## 2 Materials and Methods

Two study regions, Scania in Southern Sweden and Uppland in East-Central Sweden, were chosen. The regions differ in climate, soil parent material and landscape, but have similar extension services and national policy. In each region, eighteen farms were selected, in three groups of six farmers: old OFS converted before 2006, new OFS converted after 2006, and CFS. With PLS, (Eriksson et al., 2006), we explored the effects of management practices 1) on spring barley performance, 2) soil characteristics, and 3) the effects of soil characteristics on barley performances. For each region, we constructed X-Y matrices of managerial data retrieved from interviews and collected barley and soil data. For the two first models, we used approximately 100 management options over the period 2009-2012 (answers on 40 interview questions), the time since transition and a landscape complexity index in the X-matrix. The Y-matrix, soil and crop data, were collected from the same field of each farm in 2012, always in spring barley. In the third model, soil data was the X-matrix and spring barley data the Y-matrix. Barley data included biomass and N-concentration at different growth stages, and grain yield. Soil data included mineral-N (NO3-N, NH4-N), total carbon (tot-C) and nitrogen (tot-N), pH and texture. Each farm was considered as an observation and mean values of the measured crop and barley data were used. We used a filter method with the variable importance in the projection (VIP) for variable selection (Mehmood et al., 2012). After the first analysis, all VIPs less than 1 were eliminated and then, after a second model run, VIPs with a low-bound below zero were eliminated according to the jack-knife bootstrapping method in the model cross-validation (Afanador et al., 2013). PLS analyses were performed with the software SIMCA-P V 13.0 (Umetrics, Umeå, Sweden).

## 3 Results and Discussion

One or two principal components (PC) gave the best models, with goodness of fit (R²Y) ranging from 0.46 to 0.57 and from 0.16 and 0.30 for two PC and one PC, respectively. This indicates that the overall models, based on R²Y as a main criterion to evaluate the explained variation of all the response variables (Eriksson, 2006), were rather good. Model 1 had the best fit in Uppland and model 3 in Scania (Table 1). The cross-validation of the three models showed a prediction fit ranging from 0.26 to 0.40 (Table 1). The prediction of soil characteristics were rather difficult to model for both regions. The individual responses to management practices were better predicted for barley performances (Fig. 1a) than for soil characteristics (Fig. 1b). Long-term effects such as such as tot-C, tot-N and pH and humus content were the most difficult to predict (low R²Y and Q<fnref n="2" /> in Fig. 1b).

Time since transition, the proportion of grass/clover ley in the farming system and the techniques used to spread organic fertilizers were the most contributing factors to biomass, grain yield of barley and soil mineral N. The latter was also associated to higher crop performances. This suggests that, where mineral N-fertilizer is being reduced when converting

<footnote n="1">¹ Dept. Crop Production Ecology, Swedish University of Agricultural Sciences (SLU), P.O. Box 7043, SE-75007 Uppsala, Sweden</footnote>

<footnote n="2">² Dept. Soil and Environment, SLU, P.O. Box 7014, SE-75007 Uppsala, Sweden</footnote>

<footnote n="3">³ World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, P.O. Box 30677-00100 Nairobi, Kenya</footnote>

<footnote n="4">⁴ Dept. Ecology, SLU, P.O. Box 7043, SE-75007 Uppsala, Sweden</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: libere.nkurunziza@slu.se</footnote>
to OFS, more and well spread organic fertilizers will be needed to keep up biomass production at similar level as of CFS. Higher grain N concentrations were associated with OFS, probably because of the lower dry matter yield. The greater difficulty to model soil characteristic than barley performances could be explained by the fact that farm decisions are evaluated, in first place, by the outcome in terms of crop performances even though the latter depend on soil characteristics. Soil characteristics such as N can be amended with additional mineral N and we kept both CFS and OFS in the analyses to allow the comparison of both systems.

<table><caption>**Table 1.** Summary of the three models predicting crop performances and soil characteristic for respective agro-ecological zones. The initial explanatory variables of each model, the selected significant variables of importance in the model, the number of principal components (PC), the cumulated goodness of fit (R²X, R²Y) and the model cumulated goodness of prediction (Q²) are given</caption><thead><tr><th>Models</th><th>Initial variables</th><th>Selected variables</th><th>No of PC</th><th>R<sup>2</sup>X (cum)</th><th>R<sup>2</sup>Y (cum)</th><th>Q<sup>2</sup> (cum)</th></tr></thead><tbody><tr><td colspan="7">Uppland</td></tr><tr><td>1: Management ---&gt; Crop</td><td>86</td><td>25</td><td>2</td><td>0.59</td><td>0.57</td><td>0.34</td></tr><tr><td>2: Management----&gt;Soil</td><td>28</td><td>5</td><td>1</td><td>0.64</td><td>0.30</td><td>0.01</td></tr><tr><td>3: Soil ----&gt; Crop</td><td>17</td><td>11</td><td>2</td><td>0.93</td><td>0.46</td><td>0.40</td></tr><tr><td colspan="7">Scania</td></tr><tr><td>1: Management ---&gt; Crop</td><td>101</td><td>12</td><td>1</td><td>0.55</td><td>0.24</td><td>0.16</td></tr><tr><td>2: Management----&gt;Soil</td><td>32</td><td>8</td><td>1</td><td>0.36</td><td>0.16</td><td>0.04</td></tr><tr><td>3: Soil ----&gt; Crop</td><td>17</td><td>6</td><td>2</td><td>0.78</td><td>0.51</td><td>0.26</td></tr></tbody></table>

<figure><img src="image_231.png" /><figcaption>**Fig. 1.** Example of the cumulated goodness of fits (R²Y) and the goodness of predictions (Q²) for individual responses of management practices in Uppland: a) model 3 and b) model 2.</figcaption></figure>

## 4 Conclusion

The PLS allowed to analyse the effects of management practices covering many years on barley performances and soil characteristics. Nitrogen content in barley grain increased with longer time since transition to organic farming, but with lower yield. We relatively well modelled barley performance, but it remained challenging to model the long-term management effects on soil characteristics, such as total-N and -C. The most important factors affecting the targeted ecosystem services were selected, for example the organic matter spreading techniques and leys proportion on the farm, and their manipulation can contribute to the design of better farming systems.

*Acknowledgements.* This work was supported by the Swedish Research Council FORMAS. We thank Fatima El Khosht for data collection and the farmers for their willingness to share the information on the management practices and the use of their fields for oil and crop data collection.

## References

Afanador, N.L., Tran, T.N., Buydens, L.M.C. (2013). Use of the bootstrap and permutation methods for a more robust variable importance in the projection metric for partial least squares regression. Analytica Chimica Acta **768**, 49-56.

Bianconi, A., Dalgaard, T., Manly, B.F.J., Govone, J.S., Watts, M.J., Nkala, P., Habermann, G., Huang, Y.B., Serapiao, A.B.S. (2013). Methodological Difficulties of Conducting Agroecological Studies from a Statistical Perspective. Agroecoly and Sustainable Food Systems. **37**, 485-506.

Eriksson, L., Johansson, E., Katteaneh-Wold, N., Trygg, J., Wikström, C., Wold, S. (2006). Basic principles and description of MVDA methods. Multi- and Megavariate data Analysis. Part I: Basic principles and Applications. Umetrics Academy, Umeå, Sweden, p. 425.

Kenkel, N.C., Derksen, D.A., Thomas, A.G., Watson, P.R. (2002). Multivariate analysis in weed science research. Weed Science. **50**, 281-292. Mehmood, T., Liland, K.H., Snipen, L., Saebo, S., 2012. A review of variable selection methods in Partial Least Squares Regression. Chemometrics Intelligent Laboratory Systems. **118**, 62-69.

Schipanski, M.E., Smith, R.G., Gareau, T.L.P., Jabbour, R., Lewis, D.B., Barbercheck, M.E., Mortensen, D.A., Kaye, J.P. (2014). Multivariate Relationships influencing crop yields during the transition to organic management. Agriculture, Ecosystem and Environment. **189**, 119-126.
# Characterizing agroecological farming systems by combining the resilience and ESR framework

Laura Schotte *±<fnref n="1" />, Erwin Wauters <fnref n="1" />,<fnref n="2" /> & Fleur Marchand <fnref n="1" />,<fnref n="3" />

## 1 Introduction

Industrialized agriculture depends on increasingly scarce external inputs like water, fuel and artificial fertilizers. Furthermore, literature describes negative externalities and a lack of food security and sovereignty associated with this type of agriculture. As a response to these urging issues, an expanding academic field suggests agroecology as a possible solution (Altieri, 1995; Holt-Giménez et al., 2013; Khumairoh et al., 2012). Altieri (1995) defines agroecology as the use of ecological concepts and principles for the design and management of sustainable agroecosystems where external inputs are replaced by natural processes. The concept covers three aspects; a scientific discipline, a set of principles and practices and a social movement (Silici, 2014). The scientific discipline involves the holistic study of agroecosystems. Agroecological principles and practices enhance resilience and ecological, socio-economic and cultural sustainability of farming systems. As a movement it seeks a new way of considering agriculture and its relationship with society.

In this paper, we focus on the way agroecology can be studied and represented. To do so, we use the case of Flanders, where agriculture is highly specialized and export-oriented. We want to analyze why and how farmers apply agroecological principles and which factors influence their decisions. As agroecology focusses on interaction and relationships within the agroecosystem, we need a systems approach to perform this analysis. However, a clear approach in literature is lacking. Therefore, we developed a conceptual framework to tackle the aforementioned questions.

## 2 Materials and Methods

We started this research with an extensive literature research, focusing on agroecology from different perspectives. This resulted in an overview of possible frameworks suited for systems analysis. Through interdisciplinary discussions and feedback from a transdisciplinary sounding board, we selected and combined the most promising frameworks for studying agroecosystems and their evolution towards sustainability.

## 3 Results – Discussion

To perform a system analysis taking into account different levels of the farming and food system and their interactions, we developed a conceptual framework, consisting of a combination of three existing frameworks. This combination is represented below (Fig. 1).

<figure><img src="image_232.png" /><figcaption>**Fig. 1.** Conceptual framework – combination efficiency-substitution-redesign (ESR), resilience and multilevel perspective (MLP).</figcaption></figure>

<footnote n="1">¹ Social Sciences Unit, Institute for Agricultural and Fisheries Research (ILVO)</footnote>

<footnote n="2">² Department of Veterinary Sciences, University of Antwerp</footnote>

<footnote n="3">³ Ecosystem Management Research Group and IMDO, University of Antwerp</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: laura.schotte@ilvo.vlaanderen.be</footnote>
Central in Fig. 1 is the efficiency-substitution-redesign framework (E-S-R) (Bellon et al., 2010; Chantre et al., 2014; Lamine, 2011), used for analyzing practices at farm level. This framework differentiates between three production stages during the transition to more sustainable agriculture. Efficiency stands for improving input efficiency, but without reducing farm dependence on external inputs. Substitution implies that chemical inputs are substituted with organic ones and alternative practices are implemented, but without (greatly) modifying the basic system structure. Redesign occurs when the farm system is redesigned on the basis of a new set of ecological processes and works as a functioning agroecosystem.

The resilience framework is used to analyze how farmers assure resilience of their farming system within the broader food system. This framework is derived from the work of the Resilience Alliance and Stockholm Resilience Centre, among others (Darnhofer et al., 2010a; Folke et al., 2010; Olsson et al., 2014; Rist et al., 2014). Darnhofer et al. (2010a) use resilience thinking to assess a farm's sustainability. Because agroecological farming systems are characterized as production systems which are locally embedded and adaptable to ensure continuity in farm income, these systems are assumed to be resilient. Through the combination of the ESR and resilience framework, it's possible to identify if the three different production phases show differences in their strategies to strengthen resilience. If so, characterizing farming systems by their resilience strategies (Darnhofer, 2010) can be an approach for distinguishing more agroecological systems from more conventional ones. We can also identify how strategies evolve when moving from one stage to another by analyzing farms who changed agricultural practices over time.

To identify the influencing factors on the strategy choices, we use the multilevel perspective framework (MLP) (Paredis, 2009). These factors can occur on landscape, regime, farm or niche level, with each level having a different impact on the strategies applied. Factors can also occur during different time spans, ranging from short and sudden shocks (e.g. market collapse) to constantly present stress factors (e.g. climate change).

By combining these three frameworks we constructed a systems approach for analyzing agroecology, taking into account as well ecological, social and economic aspects and their interrelation. The frameworks are well described in scientific literature – see references above – and have proven their relevance in (agro)ecosystem research. The innovatory aspect of this research is their combination and the application to agroecology. In the near future this conceptual framework will be used to characterize the livestock farming systems in Flanders, during which it will be tested and adapted if necessary.

## 4 Conclusions

This conceptual framework provides a holistic way of analyzing and representing agroecosystems, contributing to the scientific pillar of agroecology. It takes into account different levels from the food system, the interaction between ecological, social and economic aspects and their influence on farm level practices. Through the actual application of this framework agroecosystems can be characterized by their strategies for strengthening resilience. Combined with the study of evolutions in strategies and the influencing factors this may generate interesting results for upscaling agroecology in practice.

## References

* Altieri, M. A. (1995). Agroecology – The Science of Sustainable Agriculture. 2nd ed. Westview Press, London.

* Bellon, S., Desclaux, D. & Le Pichon, V. (2010). Innovation and research in organic farming: A multi-level approach to facilitate cooperation among stakeholders. Paper presented at the IFSA Congress, Boku, Austria.

* Holt-Giménez, E., Altieri, M.A. (2013). Agroecology, Food Sovereignty, and the New Green Revolution. *Agroecology and Sustainable Food Systems, 37*, 90–102.

* Khumairoh, U., Groot, J.C.J., Lantinga, E.A. (2012). Complex agro-ecosystems for food security in a changing climate. *Ecology and Evolution, 2*(7), 1696–1704.

* Chantre, E. & Cardona, A. (2014). Trajectories of French Field Crop Farmers Moving Towards Sustainable Farming Practices: Change, Learning and Links with the Advisory Services. *Agroecology and Sustainable Food Systems, 38*, 573–602.

* Lamine, C. (2011). Transition pathways towards a robust ecologization of agriculture and the need for systems redesign. Cases from organic farming and IPM. *Journal of Rural Studies, 27*, 209–216.

* Silici, L. (2014). Agroecology – What it is and what it has to offer. IIED Issue Paper. IIED, Londen.

* Darnhofer, I. (2010). Strategies of Family Farms to Strengthen their Resilience. *Environmental Policy and Governance, 20*, 212-222

* Darnhofer, I., Fairweather, J. & Moller, H. (2010a). Assessing a farm's sustainability: insights from resilience thinking. *International Journal of Agricultural Sustainability, 8*(3), 186–198.

* Folke, C., Carpenter, S.R., Walker, B., Scheffer, M., Chapin, T. & Rockström, J. (2010). Resilience Thinking: Integrating Resilience, Adaptability and Transformability. *Ecology and Society, 15*(4), 20.

* Olsson, P., Galaz, V. & Boonstra, B.J. (2014). Sustainability transformations: a resilience perspective. *Ecology and Society, 19*(4), 1.

* Rist, L., Felton, A., Nyström, M., Troell, M., Sponseller, A., Bengtsson, J., Österblom, H., Lindborg, R., Tidäker, P., Angeler, D.G., Milestad, R. & Moen, J. (2014). Applying resilience thinking to production ecosystems. *Ecosphere, 5*(6), 73.

* Paredis, E. (2009). Socio-technische systeeminnovaties en transities: van theoretische inzichten naar beleidsvertaling. Working paper n°10, Mei 2009.
# Modelling Adaptive Decision-Making of Farmer: an Integrated Economic and Management Model, with an Application to Smallholders in India

Marion Robert *±1*, Alban Thomas 2 & Jacques-Eric Bergez 1

## 1 Introduction

Farmers plan their cropping systems and farm operations depending on their expectations on economic, social and environmental contexts, and they make tactical adjustments to face uncertain rainfall, variable market prices, and changing resource availability. We use modeling to represent a farming system and we define its adaptation opportunities to drought events, delay in monsoon onset, limited irrigation water and market prices fluctuation in the Berambadi watershed, Karnataka, India. It is represented as three interacting sub-systems: the agent system, the operating system and the biophysical system (Martin-Clouaire & Rellier 2009; Le Gal et al. 2010). We focus on the agent system representation. Once the objectives and farm representation of the farmer have been identified, we use a double loop process to combine a proactive economic model with a reactive farm management model to simulate the planning and adapting process of the farmer under water constraint.

## 2 Materials and Methods

A case-based survey provided specific information on 27 farmers. Farmers were asked to detail their farm practices and management decisions. Changes and adaptation of their practices when facing downside climatic and price conditions were discussed to bring out adaptation decision-rules.

Individual farm representation were first formalized with a Belief-Desire-Intention architecture (Bratman 1987) and conceptualized with UML object diagrams. Genericity was allowed by introducing expert knowledge. An ontology was built with UML class diagrams.

Decisions on irrigation equipment and infrastructure investments were optimized using a stochastic dynamic programming approach. Our economic model described the strategic decision of the farmer to upgrade his irrigation capital stock (e.g., borewells, pumps) in order to optimize access to irrigation water.

Cropping system decisions and management practices were represented with UML sequence diagrams. Our farm management model used decision-rules to allow tactical adaptations in the crop choice and operational flexibility in the daily crop management tasks.

The agent system and its interactions with the biophysical system of crop growth and ground water level were implemented within the RECORD modeling platform (Bergez *et al.* 2013).

## 3 Results - Discussion

The conceptual approach is presented in Fig. 1. AMBHAS model (http://ambhas.com) provides information on the ground water (GW) volume available to the farmer for irrigation (1). Considering possible investments in irrigation equipment at the beginning of the year (e.g. dig a new borewell, buy a new pump, rebore a borewell), a converter combines the GW with other water resources (tank, canal, river), and irrigation equipment, and predicts the total available water for irrigation on the farm per irrigation investments (2).

Based on rainfall and crop price expectations, farm resources (equipment, labor, manure, and production techniques) (3), water available (2), the *tactical season bele* model provides to the *tactical season farm* model a matrix of crop yields per bele (i.e. per plot) and irrigation investment levels (4). Crops considered include any suitable crops that respect a crop rotation constraint, a preceding effect constraint, and a crop return time constraint. The *tactical season farm* model then selects the optimal cropping system that maximizes farmer's income for each irrigation investment level (5). The *strategical year farm* model selects the irrigation investment at the beginning of the year associated with the highest income. The *economic model* (*tactic season bele* + *tactic season farm* + *strategic year farm*) returns this value to the *farmer model* (6) and updates the available water volume on the farm at the beginning of the *Kharif season* that is sent to the second loop within the management model (7).

In the *tactical part* of the management model, decision rules (8) allow the farmer to adapt his cropping system to actual rainfalls and prices at the beginning of the season (9). Once crop choice is made, the *operational management model* applies production techniques (10) to trigger the daily farm operations. This model strongly interacts with a resource manager module (10') and crop model STICS (Brisson *et al.* 2003) (10') as a loop where rainfall, soil moisture, weed

<footnote n="1">¹INRA, UMR 1248 AGIR, F-31326 Castanet-Tolosan, France</footnote>

<footnote n="2">² INRA, UMR 1081 LERNA, F-31000 Toulouse, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: marion.robert@toulouse.inra.fr</footnote>
and pest pressures, and crop stage are checked before each operation (10). After each irrigation or rainfall event, STICS sends the water abstracted and drainage to AMBHAS (11).

<figure><img src="image_233.png" /><figcaption>**Fig. 1.** Conceptual representation of the farm system, information flux between sub-systems and double loop in the agent model (loop 1 as continuous dark line in economic model, loop 2 as dotted line in management model).</figcaption></figure>

## 4 Conclusions

The double-loop process used in our approach models the sequential and continuous aspect of the farmer's decision-making process. As time passes and more information become available, the farmer is able to adjust his strategical choices (economic model and optimization) and adapt his farming practices (management model and tactical and operational decision-rules) to his changing environment.

This farm system design helps in modelling farm practices in a context of water scarcity and climate change and their impacts on the ground water level evolution.

*Acknowledgements*. The authors wish to thank all contributors to the AICHA project and RECORD platform. The AICHA project is funded by CEFIPRA and ACCAF INRA program.

## References

Bergez, J., Chabrier, P. & Gary, C., 2013. An open platform to build, evaluate and simulate integrated models of farming and agro-ecosystems. *Environmental Modelling & Software*, 39, pp.39–49.

Bratman, M., 1987. *Intention, Plans, and Practical Reason* Harvard Un., Cambridge.

Brisson, N. et al., 2003. An overview of the crop model STICS. *European Journal of Agronomy*, 18, pp.309–332.

Le Gal, P., Merot, A. & Moulin, C., 2010. A modelling framework to support farmers in designing agricultural production systems. *Environmental Modelling & Software*, 25(2), pp.258–268.

Martin-Clouaire, R. & Rellier, J., 2009. Modelling and simulating work practices in agriculture - Google Search. *International Journal of Metadata, Semantic and Ontologies*, 4(1/2), pp.42–53.
# The viability of small islands agro-systems: the case of the French West Indies

Angeon V. *±1,2*, H. Ozier-Lafontaine <fnref n="3" />, S. Bates <fnref n="4" />,<fnref n="9" />, E. Chia <fnref n="5" />, A. Desilles <fnref n="9" />, J.-L. Diman <fnref n="3" />, P. Andres-Domech ⁶,<fnref n="9" />, M.-H. Durand <fnref n="7" />,<fnref n="9" />, A. Fanchone <fnref n="2" />, A. Larade <fnref n="8" />, G. Loranger-Merciris <fnref n="3" />, B. Merlot <fnref n="3" /> & P. Saint-Pierre <fnref n="9" />

## 1 Introduction

Farm modeling has a long history from mainly microeconomic approaches looking to maximize an agricultural income to bio-economic models that take into account the functioning of various biological processes and to probabilistic models or simulation-based approaches that grasp the market or production uncertainties. Nowadays agriculture is required to provide income and food security, to preserve environmental resources and cultural identities while being able to adapt to climatic change or to abet its mitigation. The aim of this study is to conceive a new way of farm modeling providing the sequential and simultaneous decisions to undertake on different parcels of a farm in order to abide by environmental and economic concerns. This research is applied to the case of the French West Indies, small tropical islands, where a major challenge for the sustainability of agricultural systems is to preserve the quality of soils. Soil functioning and its interactions with the various agricultural practices are highly complex. We resort to GISQ (Global Indicator of Soil Quality, Velasquez et al., 2007) a synthetic and recognized global indicator of soil quality since the evolution of its value depending on crops and agricultural practices can be known for a variety of tropical soils. A farm is composed of several parcels with different GISQ values on which crops or livestock with semi-annual, annual, semi-perennial or perennial cycles and specific production costs can be implemented. A farmer may also decide between two practices: intensive agriculture with high level of inputs (fertilizers, pesticides, enclosed breeding ...) or reasoned agriculture with limited plant treatments. All these farmer's choices made successively at different times on each parcel determine the evolution of the farmer's global income and of the soil quality in each parcel. We used viability theory to obtain the *viable controls* that can be implemented at each date of the end of a production cycle on a given parcel. These *viable controls* (choice of a new production on a given parcel along with a practice and eventually a soil treatment) guarantee the preservation of the soil quality on each parcel and a minimum global income to the farmer. The exercise spans on all a farmer's active life with the objective to reach a given level of GISQ on all the parcels when the farm will be transmitted to the next generation.

## 2 Materials and Methods

Viability theory is a set valued mathematical analysis designed for the control of dynamical systems with constraints and submitted to uncertainties (Aubin *et al.*, 2011). Unlike the usual approaches based on simulations techniques, viability theory relies on an inverse approach allowing to know the set of all the current states variables for which there exist controls such that the evolution of the states variables governed by these controls always comply with a set of constraints. The main drawback is the huge memory needed for the calculus of sets which limits the number of possible state variables, and, for the time being, the absence of a general and friendly viability calculus software. A software, specially designed for the purpose of this study, is presently in development. A mathematical and computational challenge is the handling of different durations for the cycles of various agricultural productions implemented on the different parcels of a farm. Some productions may span over years (e.g. bananas) while other can be produced in one month (e.g. salads). This is solved by introducing a time variable with the smaller unit (the month) and for each parcel an indicator of the remaining time before the end of a cycle in order to have the dates of possible changes of production which fatally increases the number of state variables. Currently, calculus have been done for a farm with only one parcel but the obtained results are more focused on the problem of long term soil management (sequential choice of production in order to achieve a satisfying value of GISQ at the end of the exercise while being profitable) than on a farm management (sequential choice of several productions simultaneously implemented with environmental and economic objectives).

<footnote n="1">¹ Université des Antilles-Ceregmia</footnote>

<footnote n="2">² INRA-UR 143 Unité de Recherche en Zootechnie</footnote>

<footnote n="3">³ INRA-UR 1321 ASTRO Agrosystèmes Tropicaux</footnote>

<footnote n="4">⁴ PSL Research University (Paris-Dauphine)-LEDa</footnote>

<footnote n="5">⁵ INRA-UMR Innovation ⁶AgroParisTech-LEF</footnote>

<footnote n="7">⁷ IRD-UMR GRED</footnote>

<footnote n="8">⁸ AgroParisTech-UMR Métafort</footnote>

<footnote n="9">⁹ LASTRE</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Valerie.Angeon@martinique.univ-ag.fr</footnote>
The data have been obtained from field research carried out for this research and from literature. For each possible production and practice we collected the costs per hectare and per month (various agricultural inputs, workforce, depreciation charges...), the subsidies (possibly attached to the quantity produced, the surface implemented or a onetime incitement for a given production) and the sale prices per ton. For each production, the quantities produced per hectare depend on the practice chosen and on the value of the GISQ at the beginning of the cycle. These parameters are given by the field experts. The evolution of the GISQ value depends on the production and practice that are implemented as well as on the GISQ value at the beginning of the production cycle. We considered that the GISQ decrease at the end of a production cycle is mainly determined by the percentage of exported biomass relatively to the total biomass produced which is characteristic of each combination of crop and practice. At the end of an agricultural season, soil amendment can be introduced to enhance the GISQ value before the beginning of a new campaign. It is possible to consider any number of production, they are only limited by the possibility to document the required parameters.

The control variables are the choices of production, practice and amendment. The system is depicted by three state variables: the GISQ (I), the time (t) and an indicator of economic performance (W). Earnings of each agricultural campaign are the difference between sales and all the expenditures occurred during the cycle. Economic performance is the sum of earnings cumulated all along a given period of time. It is required that at the end of the exploitation, the GISQ be over a target value considered as desirable. In viability theory this translates in the computation of a *capture basin of a target*. This capture basin of a target provides all the triples (I,t,W) of initial values of GISQ, exploitation period and economic performance for which there exists, from all the available range of productions and practices, at least one farm management suitable to restitute a soil with the desired quality. Furthermore, for a given period of exploitation, the software provides the best economic performance that can be obtained with a viable initial value of GISQ along with all the control rules that can be possibly or necessarily applied at each decision time in order to obtain both the desired soil quality and the best economic performance in final time. These optimal decisions may be reduced to a unique sequence of productions and practices imposed to the farmer but not necessarily, several optimal farm managements can lead to the same value of best economic performance and wished soil quality. In this case, it is possible to select a preferred pattern by defining some choice priorities in the range of possible productions and agricultural practices.

## 3 Results - Discussion

Six productions have been documented (plantain banana, tomato, yam, goat breeding, cattle breeding and fallow) for two types of agricultural practice (intensive and reasoned). The exploitation period spans on a maximum of 40 years.

<figure><img src="image_234.png" /><figcaption>Fig. 1. Examples of farm conducts</figcaption></figure>

Figures on the left and center give examples of farm conducts guaranteeing the desired soil quality and the best economic performance at the end time when starting from either good or bad values of GISQ and with 40 or 20 years for the accomplishment. For the three cases, it is by beginning with a period of fallow that the best economic performance is achieved. The target level of GISQ is reached sooner when the initial GISQ level is higher. When 40 years are at disposal to realize the objective of soil restoration it is then more profitable to undertake an “intensive fallow” during the first years and an “extensive fallow” afterwards. This conduct must not be used if the time at disposal is only of 20 years since a better economic performance will be obtained with a succession of “extensive fallow” only. These trajectories are depicted on the right figure that shows the graph of the best economic performance as a function of time to elapse, spanning from 1 to 40 years, and of values of initial GISQ.

## 4 Conclusions

This one parcel farm model can also offer a new perspective to the “soil value evaluation” problem. As shown on the right figure, restoring the soil quality in only 20 years is possible but with a negative “best economic performance”. In this case, the “best economic performance” indicates the minimal cost that would be necessary to invest from the starting date to be able to restore the soil quality in only 20 years. Computations for a several parcels farm are currently in development.

## References

* Aubin, J.-P., Bayen, A. & Saint-Pierre P. (2011). Viability Theory, new directions, Springer
* Velasquez E., Lavelle P. & Andrade M. (2007). GISQ, a multifunctional indicator of soil quality, *Soil Biology & Biochemistry*, 39, 3066–3080