advanced. During the first workshop the impact pathways of the project were identified and summarized in the following idea: "There is a considerable improvement in the sustainability of the farms and in the region with the application of adequate technologies, which have resulted in higher incomes, conservation of natural resources and improvement at the social level". Based on that, the actors proposed a set of activities to achieve the vision. In the next workshops, participants reflected on the results and progresses achieved so far using participatory methods and suggested changes for better results and impact. The project's strategies and activities had been changed to some extent, based on the lessons learnt.

Considering the impact pathways, members of the network elaborated an annual communication plan to effective disseminate the knowledge generated along the project, based on the work at the farm level and taking into account the aims of the different groups (farmers, technicians and institutions). During the last two years several activities, took place, according to the designed plan. Farm's meeting and field days were organized supported by the inter-institutional network. In November 2014, a field day was done in a farm with the objective of showing main results of the project. Almost 160 people had the possibility to listen about the changes implemented by a farmer in the field, the reasons for doing that and the results obtained. The evaluation showed that participants gained new ideas for their farms.

<table><caption>Table 1: After two years of implementation of redesign proposed, results at farm level.</caption><thead><tr><th>ATTRIBUT</th><th>DIAGNOSTIC CRITERIA</th><th>CRITICAL POINT</th><th>INDICATOR</th><th>UNIT / SCALE</th><th>Initial situation (av. 7 farms)</th><th>Intermediate situation (av.7 farms)</th></tr></thead><tbody><tr><td rowspan="3">Productivity</td><td>Productive efficiency</td><td>Low or upgradable production yields</td><td>Equivalent meat production (i.e. meat + wool)</td><td>kg ha<sup>-1</sup></td><td>99</td><td>123</td></tr><tr><td>Economic efficiency</td><td>Low or upgradable economic income</td><td>Net income</td><td>U$S ha<sup>-1</sup></td><td>70</td><td>98</td></tr><tr><td>Quality of life</td><td>High level of satisfaction with quality of life</td><td>Family satisfaction with quality of life</td><td>5 to 1 (*<sup>1</sup>)</td><td>4,6</td><td>4,6</td></tr><tr><td rowspan="2">Stability</td><td></td><td>Inadequate labor organization</td><td>N° of families that mention an improvement in labor organization during interviews/ 7 farms involved in the project (*<sup>2</sup>)</td><td>%</td><td>—</td><td>100</td></tr><tr><td>Productive stability</td><td>Low use of adequate production techniques</td><td>% implementation of an adequate technology set proposed (*<sup>3</sup>)</td><td>%</td><td>39</td><td>98</td></tr><tr><td rowspan="4">Reliability/ Adaptability/ Resiliency</td><td rowspan="2">Natural Resources conservation</td><td>Degraded natural grassland</td><td>Spring biomass of natural grassland</td><td>kg DMha<sup>-1</sup></td><td>1183</td><td>1868</td></tr><tr><td>Good level of biodiversity</td><td>Richness and diversity of birds</td><td>N° (Shannon index)</td><td>129 (3.71)</td><td>132 (3,86)</td></tr><tr><td>System fragility</td><td>Availability of family labor</td><td>Proportion of labor input provided by family</td><td>%</td><td>93</td><td>93</td></tr><tr><td>Diversification</td><td>Diversification of income sources</td><td>Number of income sources</td><td>N° (*<sup>4</sup>)</td><td>2.7</td><td>2.6</td></tr><tr><td rowspan="2">Self-reliance</td><td>Financial dependence</td><td>Low level of debt</td><td>Relation debt / patrimony</td><td>5 to 1 (*<sup>5</sup>)</td><td>5</td><td>5</td></tr><tr><td>Decision-making</td><td>Lack of medium and long term planification</td><td>Family worth and use of medium and long term planification</td><td>5 to 1 (*<sup>6</sup>)</td><td>2.1</td><td>3.9</td></tr></tbody></table>

## 4 Conclusions

The methodological approach is being effective in improving sustainability at farm level and contributing to regional development, where farmers, local institutions and researchers jointly define activities based on the results of the project.

*Acknowledgements.* This work would not have been possible to do without the contribution of the farmers and their families and the local actors. We wish to thank also to A. Albin, M. Benvenuto, I. Bortagaray, Ó. Blumetto, G. Cardozo, A. Castagna, P. Clara, L. Del Pino, F. García, J.C. Gilzans, C. Leoni, S. Montaldo, G. Quintans, M. Scarlato, M. Silvera, G. Tiscornia.

## References

Alvarez, S., Douthwaite, B., Thiele, G., Mackay, R., Córdoba, D. & Tehele, K. (2010). Participatory Impact Pathways Analysis: a practical method for project planning and evaluation. Development in Practice, Volume 20, Number 8, pp. 946-958.

Dogliotti, S.; García, M.C.; Peluffo, S.; Dieste, J.P.; Pedemonte, A.J.; Bacigalupe, G.F.; Scarlato, M.; Allaume, F.; Alvarez, J.; Chiappe, M. & Rossing, W.A.H. (2014). Co-innovation of family farm systems: A systems approach to sustainable agriculture. *Agricultural Systems*, **126**, 76-86.

Masera, O., Astier, M. & López-Ridaura, S. (2000). Sustentabilidad y manejo de recursos naturales; el marco de evaluación MESMIS. GIRA,México.

Rossing, W.A.H.; Dogliotti, S.; Bacigalupe, G.F.; Cittadini, E.; Mundet, C.; Mariscal Aguayo, V.; Douthwaite, B. & Alvarez, S. 2010. Project design and management based on a co-innovation framework. In: Building Sustainable Rural Futures: The Added Value of Systems Approaches in Times of Change and Uncertainty – IFSA 2010, Vienna, Austria, pp. 402-412.

<footnote>(*¹) 5: very satisfied, 4: moderately satisfied, 3: satisfied, 2: little satisfied, 1: not satisfied (*²) Emerged as critical point during monitoring interviews. Farmers mention less workload and task simplification (*³) Adequate technology set proposed: adjustment in stocking rate and sheep to cattle ratio, pasture allocation according biomass height and animal category; adjustment of the mating season, management according to body condition, ovarian activity diagnosis, pregnancy diagnosis, control of breastfeeding, fall weaning, preferential handling of the rearing, mate of heifers at 2 years and use of records.(*⁴) Sources: cattle, sheep, other animal productions, vegetable production, off-farm work (*⁵) 5: <0.05, 4:0.05- 0.1, 3: 0.1-0.15, 2: 0.15-0.2, 1: >0.2 (*⁶) 5:worth planification and use long-term plans, 4: worth planification and use medium-term plans, 3:worth planification and some areas planned, 2: worths planification and don't use plans, 1: not worth and don't use plans.</footnote>
# Production gaps in livestock grazing systems in Sierras del Este, Uruguay: magnitude, causes and strategies to reduce them.

Ignacio Paparambora *±1 & Raúl Gómez <fnref n="1" />

## 1 Introduction

In Uruguay beef cattle production is the main source of income in 24848 farms, covering an area of 11.5 million hectares, being 55% of those farms classified as family. The main productive orientation in family farming is cattle breeding, based on natural grasslands. Those systems have very low productivity: 70-80 kg of meat ha⁻<fnref n="1" /> (Berreta, 2003), calving rate of 62%, and only 50% of the heifers get pregnant with 2 years (DIEA, 2002). Natural grasslands are the main forage resource available in those systems, representing at least 60%. It has been reported that grassland productivity has been low, exists risk of erosion, loss of biodiversity and high presence of weeds (Boggiano, 2003).

Most of the studies in yield gaps analysis have been developed in cropping systems. In these studies the current levels of production are analyzed, potential and achievable yields are determined, considering defining, limiting and reducing factors (van Ittersum et al., 2013). In animal production this approach has been rarely used (Cortez-Arriola et al., 2014). However, it would be very adequate for studying livestock production systems, especially in the analysis of resource use efficiency. In livestock grazing systems the production gaps may be caused by an inadequate management of the forage (without considering time and space) and the herd. Consequently, forage production and utilization are affected (Carvalho et al., 2004). Based on this approach we want to answer which is the magnitude and which are the causes of the production gaps in livestock grazing systems in the region of Sierras del Este, Uruguay. This information is of great relevance to think the re-design of production systems, but looking for a diagnosis for action, since our purpose is to intervene on the causes that limit or reduce production levels.

This work is part of the extension and development project "Improving the sustainability of family farming", carried out by the National Institute of Agriculture Research and Plan Agropecuario of Uruguay with AgResearch of New Zealand, between 2014 and 2017. The aim of this project is to increase the productivity and quality of natural resources in family cattle farms.

## 2 Materials and methods

We are working in 22 focus farms throughout Uruguay, around each of which it has been formed a group of 8-12 farmers. A co-innovation approach (Dogliotti et al., 2014) is being followed. The approach involved characterization and diagnosis of the farm system's sustainability, followed by cycles of re-design, implementation, and monitoring of system evolution. In this paper we present the farm diagnosis and discuss the production gap of four focus farms located in the region Sierras del Este, Uruguay.

The four focus farms were selected with the support of local farmers organizations and extensionist. The main criteria for choosing the focus farms were: being family farmers, being livestock grazing systems with at least 60% of the area with natural grasslands, and showing interest in making changes in their farms. The characterization and diagnosis of the farms was done during 2014, while farm re-design and its implementation started during 2015 (to be developed until 2017).

Monthly visits during the first year were done to evaluate farm resource availability and productive results in addition to farmers objectives. To analyze the magnitude of the production gap, we compared the meat production levels of the four focus farms with those obtained in the seven case studies reported by a Co-innovation project in Rocha-Uruguay (Ruggia et al., 2014). The average gap was estimated as: [(average production obtained in the 4 focus farms - average production obtained in the Co-innovation project in Rocha after three years of work) / average production of Co-innovation project in Rocha after three years of work] * 100. We also compared other important indicators for cattle breeders systems: the forage allowance, cow's pregnancy rate and kg of weaning calf per breeding cow.

## 3 Results and discussion

The focus farms are representative of the predominant family livestock farms in the region. They have small land area, predominance of family labor, the main forage source is natural grassland and the production orientation is breeding. On average, productivity levels were low: meat production of 84 kg equivalent meat (meat + wool) ha⁻<fnref n="1" />, cow's pregnancy rate of 77%, and 98 kg of weaning calf per breeding cow (Table 1). This diagnosed situation was similar to the baseline reported by the 7 pilots farms of the Co-innovation project of INIA for the same region. These farms after

<footnote n="1">¹ National Institute of Agriculture Research, INIA, Uruguay.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: ipaparamborda@gmail.com</footnote>
three years of working with a co-innovation process achieved an increase in productive results reaching the following results: meat production of 122 kg equivalent meat per year ha⁻<fnref n="1" />, a pregnancy rate of 90% and 149 kg of weaning calf per breeding cow. Considering those production levels as achievable, the production gaps in our focus farms was estimated in 31%.

<table><caption>**Table 1.** Farm characteristics and productive results of each of the four focus farms, its average, and the average for the seven pilot farms of the CoInnovation project in Rocha, at the beginning and after three years of changing implementation.</caption><thead><tr><th>Farm</th><th>Total area (ha)</th><th>Stocking rate (LU)</th><th>Natural grassland area/ Total area (%)</th><th>Sheep-to-cattle ratio</th><th>Cow's pregnancy rate (%)</th><th>Meat yield (kg ha⁻<fnref n="1" />)</th><th>Weaning calf per breeding cow</th><th>Number of cows mated per year</th><th>Forage allowance (kg DM per kg LW)</th></tr></thead><tbody><tr><td>1</td><td>179</td><td>0.89</td><td>53</td><td>0.14</td><td>80</td><td>86</td><td>97</td><td>105</td><td>3.0</td></tr><tr><td>2</td><td>517</td><td>0.83</td><td>90</td><td>1.5</td><td>79</td><td>99</td><td>110</td><td>202</td><td>2.8</td></tr><tr><td>3</td><td>520</td><td>0.62</td><td>60</td><td>0.5</td><td>63</td><td>65</td><td>79</td><td>171</td><td>3.8</td></tr><tr><td>4</td><td>189</td><td>1.10</td><td>100</td><td>2.0</td><td>85</td><td>86</td><td>105</td><td>92</td><td>3.9</td></tr><tr><td>MSGF Este 2014¹</td><td>351</td><td>0.86</td><td>76</td><td>1.0</td><td>77</td><td>84</td><td>98</td><td>143</td><td>3.4</td></tr><tr><td>CoInn Rocha 2014²</td><td>240</td><td>0.84</td><td>84</td><td>1.4</td><td>90</td><td>123</td><td>149</td><td>101</td><td>6.1</td></tr><tr><td>CoInn Rocha 2012²</td><td>240</td><td>0.92</td><td>87</td><td>2.6</td><td>76</td><td>99</td><td>107</td><td>95</td><td>3.5</td></tr></tbody></table>

LU: livestock units. DM: dry matter. LW: live weight.

Which are the causes of current production levels and the production gaps?. There is lack of planning in the production systems and an imbalance between forage production and animal requirements. Forage allowance, an instantaneous measure of the forage-to-animal relationship, was on average low in the 4 focus farms (3.4 kg dry matter kg live weight<fnref n="1" />). Consequently, exist low levels of consumption, higher energy costs on grazing, leading to breeding cows to be at many times of the year in negative energy balance (Carvalho et al., 2004). Moreover no management practices for breeding were used in the cattle herd, such as: differential feeding management according to body condition and physiological state, mating period setting, suckling control, final weaning in March and diagnosis of ovarian activity and pregnancy, differential feeding management of the female calves in the first winter.

A successful co-innovation process involving farmers in the same region and with the same problems, resulted in important improvements of system functioning within three years, providing specific technical information to re-design the farm systems and elements to think about the possible trajectories for change, inspiring to apply this knowledge to reduce production gaps in other farmers. In this sense, although the current gap is around 31%, there are alternatives to reduce it. This requires re-thinking the systems globally working together extensionists and farmers, redesigning them based on a different organization of the resources that are available.

## 4 Conclusions

There is an important production gap in family livestock farms under study. They are reaching lower results than the possibilities ones to achieve with the available resources. As reported by national research this gap can be reduced. The challenge is to generate changes in management practices without adding external inputs based on systems approach and with active participation of farmers.

## References

* Berretta, E. (2003). Perfiles por País del Recurso Pastura / Forraje. FAO, Roma, Italia. 31p. Consultado en la web: http://www.fao.org/ag/AGP/AGPC/doc/Counprof/PDFfiles/Uruguay_Spanish.pdf

* Boggiano, P. (2003). Informe de consultoria; subcomponente manejo integrado de pradera. Proyecto combinado GEF/IBRD Manejo integrado de ecosistemas y recursos naturales en Uruguay. Componente Manejo y conservación de la diversidad biológica. MGAP, Montevideo, Uruguay. 72 p.

* Carvalho, P.C.F., Canto, M.W. & Moraes, A. (2004). Fontes de perdas de forragem sob pastejo: forragems e perde? In: Pereira, O.G., Obeid, J.A. & Fonseca, D.M. (Eds.). II Simpósio Sobre Manejo Estratégico da Pastagem, 2, 2004, Viçosa. Anais. Viçosa, Suprema Gráfica e Editora Ltda.. p.387-418.

* Cortez-Arriola, J., Groot, J., Améndola Massiotti, R., Scholberg, J., Mariscal Aguayo, V., Tittonell, P. & Rossing, W.A.H. (2014). Resource use efficiency and farm productivity gaps of smallholder dairy farming in North-west Michoacán, Mexico. *Agricultural Systems*, **126**, 15-24.

* Dogliotti, S., García, M.C., Peluffo, S., Dieste, J.P., Pedemonte, A.J., Bacigalupe, G.F., Scarlato, M., Alliaume, F., Alvarez, J., Chiappe, M. & Rossing, W.A.H. (2014). Co-innovation of family farm systems: A systems approach to sustainable agriculture. *Agricultural Systems*, **126**, 76-86.

* Ministerio de Ganadería Agricultura y Pesca. (2012). Datos preliminares del censo agropecuario 2011. DIEA, Montevideo, Uruguay. Disponible en: http://www.mgap.gub.uy/portal/hgxpp001.aspx?7,5,27,O,S,0,MNU;E;2;16;10;6;MNU

* Ruggia A., Scarlato, S., Cardozo, G., Blumetto, O. & García, F. (2014). Co-innovation in family beef-cattle systems in the East of Uruguay III: Impact on farming systems sustainability. Anais do X Congresso da Sociedade Brasileira de Sistemas de Produção, 14 a 17 de Maio, Foz de Iguazu, Brasil.

* Van Ittersum, M.K., Cassman, K.G., Grassini, J.W., Tittonell, P. & Hochman, Z. (2013). Yield gap analysis with local to global relevance – a review. *Field Crops Research*, **143**, 4–17.

<footnote n="1">¹Average Project "Improving the sustainability of family farming" year 2014.</footnote>

<footnote n="2">²Average Co-innovation project in Rocha (Ruggia et al., 2014) years 2012 and 2014.</footnote>
# Herbage allowance a management tool for re-design livestock grazing systems: four cases of studies

Geronimo Cardozo *<fnref n="1" />, Martin Jaurena <fnref n="2" /> & Martin Do Carmo ³

## 1 Introduction

Native grasslands represent the largest agro-ecosystem in the Campos biome region, and provide valuable economic and ecosystem services, but they are critically threatened by changes in land use (Overbeck *et al.* 2007). Animal production limitations in these ecosystems are mainly related to grazing management and its interaction with climatic variability. Low income and degradation of natural grassland by over stocking rate is the major problem of the livestock farmers in Uruguay. Traditional management is not quantitative and over or under stocking, has limited both pasture and animal production. Under experimental conditions, management of herbage allowance (HA) has proved to be an effective tool to increase animal performance without increase in cost of production. Stocking rate is a poor indicator of grazing intensity, in contrast to HA, because it gives no information of feed availability. Herbage allowance measured as kg of forage dry matter (DM) per kg of animal live weight (LW) integrates both animal demand and feed availability (Sollenberger *et al.*, 2005). For Campos grasslands, with the management of herbage allowance it is possible to control herbage growth, individual and per ha animal productivity (Do Carmo *et al.*, 2013; Soca *et al.*, 2013; Nabinger *et al.*, 2000). At farm level the use of HA to manage the grazing pressure in each paddock should improve both animal and pasture production. Optimization of stocking rate to maximize animal production should combine pasture resources and animal demand of each type for each season. The objective of this work was to test HA control on 4 commercial grazing systems evaluating the impact on animal production by changing stocking rate management in time and space. To implement the changes in each farm, we working with a participatory approach that considers the particularities of each producer, co-innovation approach (Dogliotti *et al.*, 2014).

## 2 Materials and Methods

The study was conducted in 4 farms in Uruguay, each one in a different region (Farm 1: 31°38′53″ S 56°31′10″ W, Farm 2: 32°35′46″ S 56°07′10″ W Farm 3: 32°38′47″ S 54°42′38″ W, Farm 4: 33°42′42″ S 55°26′46″ W) from November 2012 to current days. The sizes of these farms are between 465 to 2200 ha and the proportion of improved grasslands is less than 5%, the native grassland is the main feed resource. Three of them combine cattle and sheep mixed grazing. Farm 1 is an open system that has cow-calf system and sale fattened steers but part of them was bought out of the system, also sale cows and growing steers. Farm 2, is a closed system that sale fattened steers but does not bought out of the system, thus has all the sub systems inside the farm. Farm 3, has a cow-calf systems and also sale ‘pastoral services’ to livestock owners. Farm 4, has all systems combined with a strong commercial approach looking for the buy and sell price difference in livestock to make the best price difference, this strategy difficult our main ‘technical’ approach to be implemented. In each farm, we identified the area of technical problem, and with the constraints of each farmer we worked together to try to solve them. The main problems detected were: low herbage production in Farm 1, low general stocking rate in Farm 2, low cow-calf productivity in Farm 3 and relatively low animal production ha⁻<fnref n="1" /> in Farm 4. Adjustment of herbage allowance was a major target to solve these problems, and do that monthly was the center of the work due to the high environment variability (Wheeler *et al.*, 1973) in Campos grassland. Due to different farms sizes we controlled closely only part of the systems, from 18% in farm 2 to 50% in farm 1, but working with the most demanding process, like fattening and cow’s pregnancy. Herbage mass was measured by "comparative yield method" (Haydock & Shaw, 1975) and stocking rate by monthly or seasonal weight of animals, both used to reach our HA reference value for each season and animal category. Uruguayan rainfall average is around 1200 mm y⁻<fnref n="1" /> distributed equally around the year, but with high variability between years. The rainfall was above the average in 2012 and 2014 when rainfall average was 1466 and 1800 respectively; in 2013 was an average year, but spring and summer rainfalls were above the average. Animal performance was measured as pregnancy rate and calf weight at weaning for cow-calf systems and meat production ha⁻<fnref n="1" /> for growing and fattening systems.

## 3 Discussion

Farm 1, changed the spatio-temporal management of cows and fattening steers, by changing paddocks assigned to each one in each season and by management of seasonal HA, this system implies that during the grassland growing season the fattening steers are in the best paddocks for this season at HA of 6 kg DM kg LW⁻<fnref n="1" /> or more and growing steers and

<footnote n="1">¹ Trenta y Tres Experimental Station, National Institute of Agricultural Research (INIA), Uruguay</footnote>

<footnote n="2">² TacuarembóExperimental Station, National Institute of Agricultural Research (INIA), Uruguay</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: martindocarmocorujo@gmail.com</footnote>
cows assigned to other paddocks. However lactating cows are also with 5 kg DM kg LW⁻¹ and suckling restriction (Quintans *et al.*, 2010) was applied to enhance pregnancy probability. In winter season when the herbage growth is minimum pregnant cows (at HA of 3,0 kg DM kg LW⁻¹) were allocated to the best paddock of the previous growing season that accumulated herbage during the fattening period. Although the spring-summer rainfall was above the average in the last three years, we measured an increment of the animal production from 90 to 110 kg ha⁻¹, and from 110 to 120 kg ha⁻¹ comparing year 2012/13 with 2013/14 and 2014/15.

Farm 2, changed both spatio-temporal use of paddocks and HA by cows and fattening steers to enhance the resource use efficiency. This process involved “cultural” change because by tradition some paddocks were paired to animal category, e.g. paddocks for cows and paddock for fattening all around the year. At these paddocks the stocking rate was increased based on herbage mass measurements, relative to the "traditional management" of the farmer. Live weight production ha⁻¹ increased from 111 to 196 kg ha⁻¹, without reducing individual animal performance. HA was managed between 3 to 5 in spring to 7 or 8 in winter, and taking into account the herbage mass also to define HA value due to its influence on herbage intake (Wales *et al.*, 1999). However because general stocking rate of the farm was not increased immediately, net income was not increased, and live weight gain per animal was already high, nevertheless 800 ha from the 2200 ha of the farm were remained ungrazed in the last summer, showing the potential to increase general stocking rate without increase in economic risk or food outside the farm, with a close control of HA only in 18% of the farm.

In Farm 3, initially the stocking rate was relatively high to the herbage mass present, (HA was 4.5 kg DM kg LW⁻¹ with herbage mass of 950 kg DM ha⁻¹) and we cannot diminish due to financial constraints. Changing the spatial allocation of the most (lactating cows) and less (cows non-pregnant non-lactating, growing females, recently pregnant cows) food demanding animals we modified the HA for lactating cows during the first breeding season (reaching HA of 6.2 kg DM kg LW⁻¹) and also we added suckling restriction to increase pregnancy probability (Quintans *et al.*, 2010). Compared with the previous years pregnancy rate increased from an average of 70% during 2012 to 88% and 90% for year 2013 and 2014, and calf weight measured in May 2013 was 155 kg and 188 kg in May 2014. Comparing pregnancy rate with the average of the farmers of his region, that report pregnancy rate by themselves, the increment was 7% and 18% for 2013 and 2014 respectively.

In Farm 4, the management focus were the growing steers, similar to Farm 2, however the commercial strategy of the farm, and the constraint to management of livestock, limit the potential increase, anyway HA was adjusted in late autumn to 6.0 kg DM kg LW⁻¹ and was 4.4 kg DM kg LW⁻¹ in average along the year. Live weight production was increased 20% from 100 to 120 kg ha⁻¹ for 2013/14.

## 4 Conclusions

Along two years of the project, in most cases, HA use has been widespread from paddocks to almost of the systems. The improvement of reproductive and productive results showed how this tool could be useful to sustainably systems re-design, as long as the manager (or farmer) incorporates the tool in his decisions. This tool and the approach (co-innovation) to work with farmers contributed to assess the impact of herbage allowance to adjustments on stocking rate. The increase of livestock productivity was based on native grasslands without additional needs of inputs, specifically by managing the plant-animal interactions at different spatio-temporal levels. Nevertheless, we should confirm these results in different climatic years, and we need to develop predictive tools to reduce the too time-consuming herbage mass measurements in order to simplify the incorporation of HA management technologies in farms.

## References

* Do Carmo, M., Carriquiry, M. & Soca, P. (2013). Effect of forage allowance on native pasture traits, stocking rate and beef cow body condition. *Proceedings of the 22nd International Grassland Congress*, Sydney, Australia. p 555-556.

* Dogliotti, S., García, M.C., Peluffo, S., Dieste, J.P., Pedemonte, A.J., Bacigalupe, G.F., Scarlato, M., Alliaume, F., Alvarez, J., Chiappe, M. & Rossing, W.A.H. (2014) Co-innovation of family farm systems: A systems approach to sustainable agriculture. *Agricultural Systems*, **126**, 76-86.

* Haydock & Shaw (1975). The comparative yield method for estimating dry matter yield of pasture. *Australian Journal of Experimental Agriculture and Animal Husbandry*, **15**, 663-670.

* Nabinger, C., de Moraes, A. & Maraschin, G.E. (2000). Campos in southern Brazil. In: Grassland ecophysiology and grazing ecology. CABI Publishing. p 355-376.

* Overbeck, G.E., Muller, S.C., Fidelis, A., Pfadenhauer, J., Pillar, V.D., Blanco, C.C., Boldrini, I.I., Both, R., Forneck, E.D. 2007. Brazil's neglected biome: the South Brazilian Campos. *Perspectives in Plant Ecology, Evolution and Systematics* **9**:101-116.

* Soca, P., Carriquiry, M. & Do Carmo, M. (2013). Forage allowance and cow genotype, tools to increase animal production in native pastures. *Proceedings of the 22nd International Grassland Congress*, Sydney, Australia. p 579-580.

* Sollenberger, L.E., Moore, J.E., Allen, G.A. & Pedreira, C.G.S. (2005). Reporting forage allowance in grazing experiments. *Crop Science*, **45**, 896-900.

* Quintans, G., Banchero, G., Carriquiry, M., Lopez-Mazz, C. & Baldi, F. (2010). Effect of body condition and suckling restriction with and without presence of the calf on cow and calf performance. *Animal Production Science*, **50**, 931-938.

* Wales W.J., Doyle, P.T., Stockdale, C.R. & Dellow, D.W. (1999) Effects of variations in herbage mass, allowance, and level of supplement on nutrient intake and milk production of dairy cows in spring and summer. *Australian Journal of Experimental Agriculture*, **39**,119-130.

* Wheeler, J.L., Burns, J.C., Mochrie, R.D. & Gross, H.D. (1973) The choice of fixed or variable stocking rates in grazing experiments. *Experimental Agriculture*, **9**, 289-302.
# Participatory Design of Livestock Systems: Explore, Experiment, Innovate (case study in Burkina Faso)

Jéthro DELMA B. *±1,3, Eric VALL 2, Hassan B NACRO 3 & Valérie BOUGOUMA-YAMEOGO 3

## 1 Introduction

In mixed farming systems in western Burkina Faso, more than 80 % of the farmers have in mind a familial breeding project (animal traction, fattening cattle or sheep, milking production, poultry, pigs, etc.). However, more than half of the projects miscarried before starting due to a lack of preparation. Moreover, significant technical weaknesses mainly related to food management are observed in the project implemented (main expenditure item). This lack of control affects the profitability and the sustainability of the projects. Strengthening the capacity of farmers in the development and implementation of aviable breeding project viable is an important research and development issue. The objective of this study is to present a Participatory Design of Livestock Systems (PDLS) approach tested in Burkina Faso. The main hypothesis is that the PDLS approach allows farmers to strengthen their capacity to formulate more realistic projects and change their way of thinking and planning the feeding of the livestock.

## 2 Materials and Methods

The study involved six farmers carrying breeding projects (2 animal traction (AT), 2 fattening production units (FP), 2 milk production units (MP)). The PDLS approach is inspired by the work on action research in partnership (Vall and Chia, 2014) and design of innovative agricultural systems (Meynard et al., 2012). It aims to help the farmer to make a successful breeding project by adjusting it gradually, at the same time as he learns how to control it, is convinced of its interest, and gradually reorganized the work and the resources of production. This is a participatory and progressive approach including 4 phases: (i) diagnosis by analyzing the initial project of the farmer (P0: identification of goals, strengths and weaknesses, etc.); (ii) research of improvement options (during which the farmer and the adviser identify possible solutions to the causes of the weak points of the project, and then choose the options that best suit to the objective and situation of the farmer; P1: project in phase of design); (iii) implementation and adjustment, where the farmer and the adviser follow the implementation of the project and if necessary adjust it to optimize the effects required by the farmer (PF: project carried out) and (iv) participatory assessment of the project and its consequences on the future of the farm(Fig 1).

<figure><img src="image_185.png" /><figcaption>**Fig. 1.** Participatory Design of Livestock Systems (PDLS)</figcaption></figure>

In this paper, we will focus ourselves on two points: i) improving the control of herd feeding and, ii) learning that underlie this control.

## 3 Discussion

The results obtained (fig 2) show that the PDLS method allowed in some cases (AT1, AT2 and MP2) to reduce deficits in fodder unit (FU) and digestible crude proteins (DCP), and in other cases (FP1 and MP1) to reduce food waste (in case of diet including cotton seedcake often distributed in large quantities). However in the case FP2, PDLS has

<footnote n="1">¹ Centre International de Recherche-développement sur l'Elevage en zone Subhumide (CIRDES), 01 BP 454, Bobo-Dioulasso, Burkina Faso</footnote>

<footnote n="2">² Centre International de Recherche Agronomique pour le Développement (CIRAD), UMR Selmet, F-34398 Montpellier, France</footnote>

<footnote n="3">³ Institut du Développement Rural (IDR), Université Polytechnique de Bobo-Dioulasso (UPB), 01 BP 1091 Bobo-Dioulasso, Burkina Faso</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: delmabjethro@yahoo.fr</footnote>
reduced waste in DCP, but at the expense of covering in fodder unit (FU). However in the FP2 cases, PDLS has reduced waste in DCP, but at the cost of inadequacy in FU.

<figure><img src="image_186.png" /><figcaption>**Fig. 2.** Evolution of the coverage of the herd needs (FU and DCP) over the phases of the project breeding (P0: initial project; P1: project in phase of design; PF: project carried out) for the three types of breeding projects (TA: Animal traction; FP: fattening production unit; MP: milk production unit)</figcaption></figure>

We can explain these improvements of the control of the animal feeding by learning induced by the PDLS approach. At this stage of the study, and subject to validation (in progress), we assume that these learnings are of different nature according to the phase of the PDLS approach:

* Diagnosis and Co-design Phase (P0 and P1): learning in terms of feasibility (technical, economic) and planning (forecasting forage crops, food stocks ...);

* Implementation Phase (P1): innovative practices (forage crops, storage fodder and food techniques...), feeding management (adjusting supply to needs), technical and economic management of the project (recording of expenses and revenue), monitoring of the project (farmer involvement);

* Assessment phase: for some of the farmers a better ability to projected themselves into the future and to develop more ambitious projects (increase of herd size and forage crops), to change their breeding model(complete stabling of cows for the milk production unit).

In the PDLS, partnership and permanent dialogue between the farmer and the advisor stimulate learning and help the farmer to design a more realistic project technically and economically as well (Meynard et al., 2012; Vall and Chia, 2014). It leads the farmer to question his breeding project to identify the points of weaknesses and to reflect on possible solutions. Finally, it develops technical and operational skills (concerning animals breeding and management) and strategic skills (ability to formulate more realistic project, and to change the production model)(Kolb, 1984; Argyris and Schön, 2002).

## 4 Conclusions

In the PDLS approach, the permanent interaction between the farmer and the adviser stimulates critical reflexion on breeding projects during the phase of diagnosis and co-design. This improves the viability of the project (reduction of food waste, optimizing costs, reducing losses of working time). Moreover, the heuristic nature of the approach increases the chances of the success of the project and the understanding of the practices and strategies of farmers as well. PDLS can be used as an advice tool for breeding. It enables the design and implementation of more realistic and better adapted breeding projects to the needs of animal production.

*Acknowledgements.* This study was supported by funding from DP-ASAP, CORAF project and a doctoral grant IADR / CIRAD. The authors thank the farmers of Founzan Department, Gombéledougou Koumbia and those who allowed us to work with them throughout the study.

## References

* Argyris Ch., Schön D. (2002). Apprentissage organisationnel. Théorie, méthode, pratique. Bruxelles, De Boeck Université.

* Chia E., 2004. Principes, méthodes de la recherche en partenariat : une proposition pour la traction animale. Revue d'élevage et de médecine vétérinaire des pays tropicaux, 57 (3-4): 233-240.

* Kolb, D.A. 1984. Experiential Learning: Experience as the Source of Learning and Development. Prentice-Hall, Englewood Cliffs.

* MeynardJ-M., Dedieu B., and Bos A.P. 2012. Chapter 18 Re-design and co-design of farming systems. An overview of methods and practicesFarming Systems Research into the 21st century: The new dynamic. Ika Darnhofer, David Gibon, Benoît Dedieu, Editors, 2012, Springer, 407-432

* Vall E. et Chia E., 2014. Coconstruire l'innovation : la recherche-action en partenariat. In : Agriculture familiales, et mondes à venir, JM Sourisseau (Ed.), Paris, France, Quae, 239-255.
# Integrating empirical and scientific knowledge to evaluate the transition to a once-a-day milking in dairy ewe farms

Amandine Lurette *±<fnref n="1" />, Catherine De Boissieu <fnref n="2" />, Emmanuel Morin <fnref n="2" />, Philippe Hassoun <fnref n="1" />, Francis Barillet <fnref n="3" /> & Charles-Henri Moulin <fnref n="4" />

## 1 Introduction

As for the whole French ovine sector, the Rayon de Roquefort sector is largely constrained by a conjoncture which combined an explosion of the production costs, a climatic variability which affects forage quality and an increase in the work load and drudgery. The once-a-day milking is an interesting technique in response to the breeders' expectations of finding livestock systems both economically and socially viable. It can lighten the routine work load in dairy flocks. The Lacaune ewe appears to be well adapted to the once-a-day milking situation (Vanbergue et al. 2013). However, the resort to this new technique in dairy ewe farms arises questions, both at the individual (balance between income and work load) and at the collective (collection and milking distribution) levels. In this study, we aim at presenting how an approach combining both empirical and scientific knowledges, and their consequential produced scenarios, are likely to stress the technical levers which could accompany the transition to the once-a-day milking in French dairy ewe flocks. The local stakeholders' (breeders, advisors and farms network) mobilisation was regularly held during the four years of the study to discuss on the modelling hypothesis, the scenarios to test and the validity of the results.

## 2 Materials and Methods

The model BOUSSOLE was developed to represent dairy ewe farms situated on the Rayon de Roquefort area. Its development is based on the use of virtual farm types. A virtual ideal farm type is the optimized economic and technical design of a farming system performed from a farm network. By providing a common language, the farm-types facilitate discussions between all the participants of the project. Seven virtual ideal farms were modelled to represent the diversity of production systems in the Rayon de Roquefort area. This diversity includes soil and climate conditions, farm dimensions, productions combinations in farms, and production periods. BOUSSOLE consists in three interactive modules representing: (1) the flock and its diet, (2) the forage system and (3) the farm's economy.

From the baseline scenario of the twice-a-day milking H0, BOUSSOLE renders it possible to design different configurations of the system. We modelled technical levers which aim at compensating the losses in both milk production and household income due to the transition of one milking per day. The selection of levers was based on (1) experimental results obtained through other parts of the Roquefort'In project in which this work is included; they take place in the Domaine de La Fage (Roquefort-sur-Soulzon, France) and in the agricultural secondary school of Saint-Affrique (Aveyron, France); (2) expertise of advisors in charge of technical support of dairy ewe farms in the Rayon de Roquefort area and (3) scientific experts from INRA and Livestock Institute. The management adaptations associated to these levers are simulated under several conditions of a once-a-day milking implementation: H1, where the transition to the once-a-day milking occurs at the lamb weaning (after about 30 days of lactation), at the first day of the milking period and H2, where the transition to the once-a-day milking occurs after the lamb weaning, and around the turnout date of the flock (around 8 and 10 weeks after the first day of the lactation period).

Finally, a 1250 farm sample under technical support is distributed into the seven farm-types according to their date of first day of milking. This distribution draws the 'virtual dairy area' from which the impact of collective scenarios of once-a-day milking implementation is explored.

## 3 Results – Discussion

For H1, the loss in milk production slightly varies according to the milking period duration of the farm-types analyzed (Table 1). Farm-types with short milking period are penalised far more than the others: e.g. 19 points of loss for ROQ05 (with 193 days of milking) and 17 points of loss for ROQ06 (with 273 days). A later transition to the once-a-day milking (H2) allows returning to the household income and milk production close to the baseline scenario values, but divides by two the gain in the routine work load obtained under the H1 scenario (Table 1).

<footnote n="1">¹ INRA, UMR 868 Selmet, 2 place Pierre Viala, 34060 Montpellier cedex</footnote>

<footnote n="2">² Institut de l'Elevage, BP 42118, 31321 Castanet-Tolosan cedex</footnote>

<footnote n="3">³ SupAgro, UMR 868 Selmet, 2 place Pierre Viala, 34060 Montpellier cedex</footnote>

<footnote n="4">⁴ INRA, UMR 1388 GenPhySE, 31326 Castanet-Tolosan, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: lurette@supagro.inra.fr</footnote>
<table><caption>**Table 1.** Impact of scenarios involving the transition to the once-a-day milking on the milk production of the flock (MP), Household Income (HI) and Routine Workload (RW) for the seven farm-types modelled (with an indice 100 = initial situation under twice-a-day milking H0).</caption><thead><tr><th rowspan="2"></th><th colspan="3">Scenarios H1 with management adaptations</th><th colspan="3">Scenarios H2 with management adaptations</th></tr><tr><th>MP</th><th>HI</th><th>RW</th><th>MP</th><th>HI</th><th>RW</th></tr></thead><tbody><tr><td>ROQ01</td><td>89</td><td>97</td><td>91</td><td>98</td><td>100</td><td>94</td></tr><tr><td>ROQ02</td><td>89</td><td>99</td><td>91</td><td>101</td><td>103</td><td>96</td></tr><tr><td>ROQ03</td><td>86</td><td>97</td><td>92</td><td>97</td><td>101</td><td>95</td></tr><tr><td>ROQ04</td><td>88</td><td>97</td><td>92</td><td>98</td><td>101</td><td>95</td></tr><tr><td>ROQ05</td><td>87</td><td>94</td><td>94</td><td>97</td><td>98</td><td>96</td></tr><tr><td>ROQ06</td><td>89</td><td>86</td><td>93</td><td>100</td><td>101</td><td>96</td></tr><tr><td>ROQ07</td><td>91</td><td>97</td><td>95</td><td>102</td><td>100</td><td>97</td></tr><tr><td><strong>Mean</strong></td><td><strong>88</strong></td><td><strong>95</strong></td><td><strong>92</strong></td><td><strong>99</strong></td><td><strong>101</strong></td><td><strong>95</strong></td></tr></tbody></table>

H0 : initial twice-a-day milking scenario / H1 : once-a-day milking at weaning / H2 : once-a-day milking around the turnout date

Assuming that the once-a-day milking would be accepted in the specification of the PDO Roquefort, the twelve breeders interviewed would applied it in their farm; certainly for nine on the twelve breeders of them, but under certain conditions for the others. Most of them considered that the once-a-day milking could occur in the middle of the milking period, around the turnout date. However one issue was noted. The transition to the once-a-day milking could present a social lock-in: «one is going to say that we will do nothing further» (On va s'entendre dire qu'on ne fait plus rien). Half of the breeders interviewed did not plan to associate any other technical levers with the implementation of the once-a-day milking (except the adjustment of the concentrates distributed). They also want to keep the flock size and to preserve some quiet time. For the other breeders, several ways of adaptations are possible such as to increase the duration of the milking period and/or a better use of pasture. The breeders questioned seem to be shifting towards a transition around the turnout date. For them, this option reduces the length of the working day and provides flexibility in the work day organisation during spring season.

If all farms use the once-a-day milking at weaning (H1), the amount of milk collected decreases of 18 points compared with the H0 situation (Table 2). The part of milk produced between January and April is slightly reduced. If all farms implemented the transition around the turnout date, the decrease in milk collected is lower (-8 points). With only 41% of the milk collected from the twice-a-day milking, mainly produced between December and February. At the dairy area level, the total amount of milk collected in H0 is almost reached the baseline scenario with the combination of 50% of farms transited around turnout date to a once-a-day milking production and 50% of farms remaining in twice-a-day milking situation. However, it does not allow to reach volumes of milk from twice-a-day milking system required for the production of Roquefort under the specifications of the PDO.

<table><caption>**Table 2.** Impact of the implementation of several scenarios of the implementation of the once-a-day milking in farms on the milk collection of the 'virtual dairy area'</caption><thead><tr><th rowspan="2">Scenarios</th><th rowspan="2">H0</th><th colspan="2">Basis scenarios</th><th>H2 with adaptations</th></tr><tr><th>H1</th><th>H2</th><th></th></tr></thead><tbody><tr><td>Total amount of milk (millions of liters)</td><td>170</td><td>139<br/>-18%</td><td>156<br/>-8%</td><td>169<br/>-1%</td></tr><tr><td>Total amount of milk from the twice-a-day milking (%)</td><td>100</td><td>0</td><td>41</td><td>69</td></tr></tbody></table>

H0 : initial twice-a-day milking scenario / H1 : once-a-day milking at weaning / H2 : once-a-day milking around the turnout date

In addition to the key assumptions associated to the system representation, the partners allow us to determine relevant indicators among the 72 output variables. The appropriate indicators facilitate the discussion within a large range of actors. Moreover, the actors embraced the notion of virtual dairy area. The outputs at both the farm and the dairy area collection levels elicited very relevant debates. As a consequence, discussion derived from the presentations of the simulated results to the actors (breeders, industrials...) allows us to complete the evaluation performed by modelling.

## 4 Conclusions

Based on current scientific knowledge on zootechnical response of Lacaune ewes and simulated results, an increase in the milking duration combined with a better use of pasture can limit losses in milk production and in household income due to the transition to the once-a-day milking. At the dairy area level, the scenarios tested show that the adoption of the once-a-day milking cannot be envisaged without modifications of the Roquefort PDO specifications.

*Acknowledgements.* The authors acknowledge all the partners of the Roquefort'In project.

## References

*Vanbergue E., Barillet F., Allain C., Autran P., Aurel M.R., Duvallon O., Portes D., Dessauge F., Boutinaud M., Dzidic A. & Marnet P.G.* (2013). Aptitude des brebis laitières de race Lacaune à la conduite en monotraite : Premiers éléments zootechniques et physiologiques. Renc. Rech. Rum., 20, 281-284.
# PATUCHEV and REDCap: two additional research and development schemes for high performance and sustainable goat farming

H. Caillat *<fnref n="1" /> & J. Jost <fnref n="2" />

## 1 Introduction

The western France concentrates over half of the country's capacity in terms of dairy goat production (64% of national collecte-Agreste, 2013). Between years 2000 and 2010, goat farms have gradually turned into intensive farming, thereby significantly increasing their need for purchased input. To increase productivity, the grazing has been stopped and the indoor breeding has been developed. Now, the consequences are that goat systems are self-sufficient only to 55% (Bossis et al., 2014) unlike dairy cow systems to 88% (Bunschwig et al., 2012). However, in a context of higher input costs, these systems become now unsustainable. Moreover, these intensive systems do not correspond to the social representation of goat breeding by the consumers. It is therefore important to find techniques of management which are efficient, which correspond to the product picture but also which preserve, the goat health and welfare, and maintain their performances, the income of farmers and the sustainability of farms. It is in this context that the French Institute for Agricultural Research (Inra), professionals of goat sector of Poitou-Charentes Region and structures of development have created two additional research and development schemes for high performances and sustainable goat farming.

## 2 PATUCHEV: an experimental device to assess high-performances and sustainable goat breeding systems

The experimental platform PATUCHEV is an experimental unit of the division "Animal physiology and livestock systems" of INRA. This device is located near Poitiers in the main area of dairy goat industry in France (Poitou-Charentes). In 2013, the French Institute for Agricultural Research has built especially an experimental goat shed with a solar-heated air hay dryer. This device is aimed at assessing and proposing innovative goat farming systems in order to lead to low input and sustainable goat farms and to answer research question: "what kind of systems would a better self-sufficiency in inputs under a production constraint?" This approach associates the research of economic, environment and social performances in trying to apply the principles of agroecology.

The experimental device is based on conception and long-term evaluation of three kind of systems with 60 goats each: two grazing herds, one kidding at the end of the winter and the other one in autumn, and the last one fed hay indoors all year round and kidding in autumn. 10 hectares divided between cultivated grassland and a cereal-protein crops mixture are allocated to each system (Fig. 1). To maximize the feed intake and to limit the use of concentrates, multi-specific cultivated grassland and solar-heated air dried hay are the key points of the forage system (Huyghe et al., 2008; Delaby et al., 2008). To limit the use of inputs, crops are fertilized with composted manure from the herd.

<figure><img src="image_187.png" /><figcaption>Fig. 1. Scheme of experimental device PATUCHEV</figcaption></figure>

Evaluation and comparison are based on multi-criteria approach with data collected throughout lactation, dairy goats' careers and crop rotations. The functional organization is based on participative approach. Patuchev project is managed

<footnote n="1">¹ INRA, UE1373 Fourrages Environnement Ruminants, F-86600 Lusignan, France</footnote>

<footnote n="2">² REDCap, Institut de l'Elevage-BRILAC, CS 45002, F-86550 Mignaloux-Beauvoir, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Hugues.Caillat@lusignan.inra.fr</footnote>
by a steering committee composed with scientists from each INRA division participating to the project, engineers from R&D structures, representants from goat regional professional sector and Région Poitou-Charentes. To improve the diffusion of knowledge, professional goat farming organizations have implemented a coordinated Research and Development scheme called REDCap.

**3 REDCap : an experimental and development network in dairy goat production**

REDCap is a network of goat breeders, engineers and technicians on the topics of self-sufficiency and grass-use in goat breeding. It was built in 2011 by the regional association of goat's milk producers and processors in Poitou-Charentes and Pays de Loire (West of France). This is a multi-partner network, composed of 15 technicians from 4 regional structures: Chambres d'Agriculture, CIVAM, Contrôle laitier and BTPL. The animation of the project is driven by an engineer of the French livestock Institute (Institut de l'Elevage), which also have to articulate this project with other one existing in France on the same topics. The REDCap is a major place to build references (on self-sufficiency and production costs), to experiment in farms to share knowledges between technician and breeders, and in the same row, to develop a bottom-up approach with researchers. The figure 2 shows the design of the REDCap project, and the articulation between the different partners.

<figure><img src="image_188.png" /><figcaption>Fig. 1. Design of the REDCap in Poitou-Charentes & Pays de la Loire.</figcaption></figure>

Different kinds of experiments are developed on the experimental device PATUCHEV and in farms (REDCap). The first associated experience between these two projects is a conception and evaluation of a multi-species grassland mixture adapted to specificities of goats, and sown on Patuchev platform and in 10 farms in autumn 2012. Dried hay in barn for goats is a second subject of study with the constitution of a farmers group to exchange and optimize this use.

**4 Conclusions**

This organization gives a wide place to exchange and thereby eases the emergence of innovative research issues. These two projects are conducted by 2 different organizations, but with a strong wish to work in interaction with each other, and all those involved. Therefore both manager of Patuchev and REDCap participate mutually to consultative and instance decision of each project. This overview shows that the context and tools to support the functioning of Research and Development in the goat sector are structured and operational. All partners have the wish to put these tools to service of goat sector to support its adaptation to the changing expectations and challenges.

*Acknowledgements.* The construction of experimental facilities (Patuchev) has received financial support Inra, Région Poitou-Charentes, European Union and Pôle d'Excellence Rural du Pays Mélusin. The REDCap network receives financial support of Région Poitou-Charentes and Région Pays de la Loire.

**References**

Agreste (2013) – Statistiques Agricole Annuelles

Bossis N., Legarto J., Guinamard C. (2014). Etat des lieux de l'autonomie alimentaire des élevages caprins français, Renc. Rech. Ruminants, **21**, p.127.

Delaby, L., Peccatte, J.R., 2008. Valeurs alimentaires de foins ventilés issus de prairies multi-spécifiques. Fourrages, **195**, 354-356

Devun J., Legarto J. (2011) : Fourrages conservés et modes de récolte : la situation selon les systèmes d'élevage en France. Fourrages, **206**, 91-105.

Huyghe, C., Litrico, I., 2008. Analyse de la relation entre la diversité spécifique des prairies et leur valeur agronomique (bibliographie), **194**, 147-160.
# Redesigning a dairy system based on agroecological principles using a collaborative method

Sandra Novak *±<fnref n="1" />, Rémy Delagarde <fnref n="2" />, Jean-Louis Fiorelli <fnref n="3" />, Jean-Claude Emile <fnref n="1" />, Anne Farruggia <fnref n="4" />, Laurence Guichard <fnref n="5" /> & Fabien Liagre <fnref n="6" />

## 1 Introduction

Dairy farming in Western Europe has to cope with increasing societal demands (i.e. not only milk production but also environment preservation, scarce resources savings, livestock welfare, maintenance of landscape...) in a context of growing uncertainty (climatic hazards, price volatility of farm products and inputs, epizootics....). To face these new challenges, the French National Institute of Agronomy (INRA) tasked three researchers with devising and building, together with experts and stakeholders, an innovative forage system breaking away from existing patterns.

This paper explains the methodology used as well as the main objectives and principles of the devised system.

## 2 Materials and Methods

In order to favour a real break from existing systems and to improve in deep the sustainability of the whole forage system, we decided to redesign it in a systemic way, as proposed by Hill & Mc Rae (1995). This approach allows widening the scope of possibilities and fits in with the *de novo* design as conceptualised by Meynard *et al.* (2012).

The method consisted first to cross-fertilise the ideas of a group of fifteen participants, gathering farmers, extension agents, technical institute engineers, a farm manager of an agricultural school, environmental association and agency representatives and researchers from various disciplines.

In 2012, three one-day workshops based on creativity, listening and kindness permitted to these participants from various disciplines and horizons to i/agree on the general objectives which have to be targeted by the new dairy system, and ii/ propose new ideas jointly deepened by the group to meet these goals. Thereafter the three appointed researchers render coherent these multiple proposals, by considering an agroecological approach at the system level (Novak *et al.*, 2013). From the end of 2012, a second group gathering seven experts in agronomy, animal production, forage systems, environmental assessment, and agroforestry already present in the first conception group, built the system operationally. Technicians devoted to the implementation of this new system were also involved in this work. The building of the system began by a clarification of its objectives and by a deepening of the first ideas using a similar methodology as that conceptualised by Hatchuel and Weil (2009) in terms of innovative design. During this work the reflection was broadened to include the livestock farming system which was designed to be consistent with the new devised forage system. The second group also clarified the research hypothesis tested in this project.

## 3 Results – Discussion

### The main objectives of the system

The participants in the design process decided that the main objective of the resulting system will be to permit farmers to live from milk production in a context of climatic hazards while saving scarce resources (water and fossil energy) and contributing to a sustainable agriculture. The sustainability of the system is appreciated regarding its ability to preserve the diverse compartments of environment and to contribute to the attenuation of climate change, while satisfying the demands of farmers and civil society, and improving the welfare of animals. We decided to call such systems aiming to use efficiently natural resources while respecting them and satisfying the expectation of its users as “bioclimatic systems” (in analogy with bioclimatic architecture).

This system aims thus to deliver a diversified panel of ecosystem services and especially, as classified by Zhang *et al.* (2007), provisioning services (milk, cash crops, timber production), supporting services (soil fertility, nutrient recycling, biodiversity) and regulating services (pollination, natural control of pests, water purification).

<footnote n="1">¹ INRA, UE Ferlus, Lusignan, France</footnote>

<footnote n="2">² INRA-Agrocampus Ouest, UMRPEGASE, Saint Gilles, France</footnote>

<footnote n="3">³ INRA, UR ASTER, Mirecourt, France</footnote>

<footnote n="4">⁴ INRA, UMRH, Saint Genes Chamanelle, France</footnote>

<footnote n="5">⁵ INRA, UMR Agronomie, Thiverval-Grignon, France</footnote>

<footnote n="6">⁶ AGROOF, Anduze, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: sandra.novak@lusignan.inra.fr</footnote>
**The main principles of the system**

The system relies on an agroecological approach at the farm level aiming to optimize the interactions between forage, crop and livestock systems, so as to save water and energy resources and to secure the forage production. It is based on the diversification of forage resources, the development of grazing, a larger use of legumes, the recycling of water and nutrients, and on a consistent strategy for the livestock system. The production of forage resources relies on three crop rotations (Novak et al., 2014a), one being totally grazed, with diversified multispecies grasslands and annual crops, associated with legumes, and also on the grazing of fodder trees and shrubs. The herd reproduction (Novak et al., 2014b) is based on two calving periods centered on spring and autumn, to ensure the coherence with the availability of grazed forage resources, and to overcome climatic hazards which could occurred at one period. The lactation length is extended to 16 months (calving interval of 18 months) for limiting the non-productive time during cow lifetime, and the negative environmental impacts associated. As this dairy system needs more robust cows, with good reproduction capacity, and well adapted to grazing and to forage resources of contrasting quality, we decided to use a three-way crossing of dairy breeds (Holstein, Scandinavian Red, Jersey).

The innovations relate thus mainly with:

1/ the **diversification of the elements of the system**, from the vegetal component (various crop species, cultivars and mixtures), to the animal component (crossing of three breeds, two calving periods), through the functions these elements may fullfill (e.g. fodder trees, dual purpose crops, weed control by grazing). These ideas agree with the current trend that diversity in farming systems could reduce their vulnerability to climate change (Mijatovic et al., 2013) while contributing to provide multiple ecosystem services (Kremen et al., 2012).

2/ a **valorisation of all the spatial dimensions** of the system, by introducing a vertical axis thanks to agroforestry which allows a more efficient use of solar radiation and nutrient and water resources. The **temporal dimension** of the system is also reconsidered either in the cropping system by limiting periods of bare soil in the crop rotation, or in the livestock system by taking into account the milk production at the cow lifetime instead of at one single lactation.

The main research hypothesis tested is that the increase of diversity in a dairy production system allows to conciliate good production levels and high environmental performance, and to improve the resilience of the whole system.

This new system began to be implemented in June 2013, at an INRA facility located in Lusignan (Poitou-Charentes, France) on 90 ha and with 72 dairy cows devoted to it. The first calvings resulting from the crossing of the initial Holstein herd with semens of Scandinavian Red and Jersey, as well as the first implementation of agroforestry plots occurred in 2014.

**4 Conclusion and perspectives**

This innovative dairy system brings together a new combination of elements, some of them being original and sometimes risky while being promising. Its implementation and long term study at the farm level opens the way to innovative approaches in terms of experimentation, indicators and partnership. Collaborations with scientists in agronomy, livestock research, animal and plant genetics and in environmental assessment will be developed in order to characterise this agroecological system and its evolution. This project also aims to give farmers the keys for a successful transition as well as operational results on these new diversified and saving off-farm inputs systems.

**References**

Hatchuel, A. & Weil, B. (2009). C-K design theory: An advanced formulation, *Research in Engineering Design*, **19**, 181-192.

Hill, S.B. &MacRae, R.J. (1995). Conceptual framework for the transition from conventional to sustainable agriculture. *Journal of sustainable agriculture*, **7**, 81-87.

Kremen, C., Iles, A.&Bacon, C.(2012).Diversified Farming Systems: An Agroecological, Systems-based Alternative to Modern Industrial Agriculture. *Ecology and Society*, **17**, 44.

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

Mijatović, D., Van Oudenhoven, F.,Eyzaguirre, P. & Hodgkin, T.(2012). The role of agricultural biodiversity in strengthening resilience to climate change: towards an analytical framework. *International Journal of Agricultural Sustainability*, **11**, 95-107.

Novak, S., Delagarde, R. &Fiorelli, J.L. (2013). Designing a bioclimatic forage system: the approach developed in Lusignan. Fourrages, **215**, 241-246.

Novak, S., Audebert, G., Delagarde, R., Emile, J.C., Farruggia, A., Fiorelli, J.L., Guichard, L. &Liagre, F. (2014a). Diversified grasslands for bioclimatic milk.AFPF congress, Versailles, pp. 144-145.

Novak, S., Chargelègue, F., Delagarde, R., Emile, J.C., Farruggia, A., Fiorelli, J.L., Guichard, L. &Liagre, F. (2014b). An innovative livestock management strategy for a bioclimatic dairy system.21èmes Rencontres Recherches Ruminants.French Livestock Institute, Paris, p. 357.

Zhang, W., Ricketts, T.H., Kremen, C., Carney, K. & Swinton, S.M. (2007). Ecosystem services and dis-services to agriculture. *Ecological Economics*, **64**, 253-260.
# CROP-LIVESTOCK INTEGRATION OF CEREAL-BASED MIXED FARMING SYSTEMS IN THE TERAI AND MID-HILLS IN NEPAL

Victoria Alomia ±<fnref n="1" />, Jeroen Groot<fnref n="1" />, Carlo Bettinelli<fnref n="1" />, Andrew Mc. Donald <fnref n="2" /> & Pablo Tittonell <fnref n="1" />

## 1 Introduction

In Nepal, the possibilities for expansion of agriculture are limited due to land scarcity. Therefore, intensification has been the strategy to increase production particularly in the lowland agro-ecosystems (Terai) (Dahal et al., 2007) due to their fertile soils, uniform climate and availability of improved communication systems that contribute to market access. The growing urbanization has increased the demand for livestock products in the area (Yadav and Devkota, 2005). In contrast, due to its topography the Mid-hills agro-ecosystems of Nepal face issues of remoteness, erosion and low availability of external inputs. Similar to almost all the farming systems in Nepal, households in the Mid-hills are based on cereal production (maize, wheat and rice) and livestock is a source of income and buffer against food shortages. The additional role of livestock in providing manure and draught power strengthens the integrated nature of farming systems of the Mid-hills (Kiff et al., 2000). Pilbeam et al. (2000) estimated that around 80% of N supplies to the soil are made via the manure pathway. However, the productivity of the crop-livestock systems is low. Farmers face trade-offs at farm and landscape level, especially crop-livestock intensification vs. labor availability, environmental impact and competing uses of natural resources. Although a small amount of fodder is obtained from on-farm trees and/or crop residues, tree leaves are generally gathered from communal forest areas (Kiff et al., 2000; Devendra and Thomas, 2002; Lawrence and Pearson, 2002; Thorne and Tanner, 2002). Yet, the forest resources have been reduced progressively in the whole country (FAO, 2011; Nepal Central Bureau of Statistics, 2012).

The mixed nature of the agro-ecosystems, the increasing demand for livestock products, and the continuous land fragmentation in both Terai and Mid-hills emphasizes the importance of better integrating crop and livestock subsystems to attain agricultural intensification. However, the livestock sector contributes notably to serious environmental issues with substantial impact on demand on land for pasture or feed crops, shortage and water pollution, and loss of biodiversity (Steinfeld et al., 2006). With this study we evaluate the degree of diversity and crop-livestock integration within diverse cereal-based farms in contrasting agro-ecosystems.

## 2 Materials and Methods

The study took place in two districts in the Mid-hills (Palpa, Dadheldura) and one district in the Terai (Nawalparasi). One hundred households were surveyed to obtain socio-economic and biophysical data. The Y-sampling frame (Tittonell, 2008) was used to select households. One farm was randomly selected, and nine farms were selected at 100, 300 and 900 meters distance in three directions from the first household. The farms were categorized based on resource endowment through hierarchical cluster analysis in R software. One typology was created for each district. From each farm type, ten representative farms were selected and surveyed in detail to calculate nitrogen (N) fluxes through ENA (Ecological Network Analysis) (Rufino et al., 2009). The Farm DESIGN model (Groot et al., 2012) was used to quantify the nitrogen flow matrix for ENA.

## 3 Results – Discussion

### 3.1. Socio-economic and structural characteristics of the farming systems

The average productive land size in the Mid-hills was 0.5ha and 1.2ha in Terai. Livestock densities were higher in Mid-hills (20 TLU/ha) than in Terai (9 TLU/ha). Dairy cows and bullocks were more common in the districts of the Mid-hills, and dairy buffalos in Terai. In both districts of the Mid-hills the average number of household members was slightly lower than in Nawalparasi (Terai), of these household members 50% in Mid-hills and 40% in Terai were involved in the farming activities. In all the three districts the farms relied mostly on family labour. The income derived from farming activities was higher in Teraithan in the Mid-hills, being almost 50% of the total income, while 30% in the Mid-hills.

### 3.2. Farm household typology

Farm households were grouped into four clusters based on labour, yearly income, farm size, food self-sufficiency and TLU considering main drivers of strategies represented by proxy indicators through PCA at each site independently. Clusters were interpreted as four farm types in each of the sites.

<footnote n="1">¹ Farming Systems Ecology, Plant Sciences, Wageningen University</footnote>

<footnote n="2">² International Maize and Wheat Improvement Centre</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: maria.alomiahinojosa@wur.nl</footnote>
## 3.3 Ecological Network Analysis

Farming systems differed strongly in N flows in the network. The number of animals and associated feed imports largely determined the size and activity of the networks. The T and TST (Throughput and Total System Throughflow), expressing respectively size and activity of the network, were both higher in Mid-hills than in Terai, increasing from the poor to the more wealthy types. As expected, imports of biomass were large but did not contribute in a large extent to internal nitrogen recycling, resulting Finn's cycling indexes (FCL) lower than 10% in all the systems. FCL were comparable to those found in the farms studied by Rufino et al. (2009b). Wealthier farm households recycled slightly more than poorer farm households. Imports per hectare were 3.5 times larger in Mid-hills than in the Terai, with great variability in each district.

Farms in the Mid-hills smaller than 0.1 hectare with a high livestock density relied mostly on imported feed to support the requirements of the livestock, thus these farms could be considered as landless livestock production systems. Large imports resulted in large losses. The total N losses per farm were on average 135 kg N and the farm with the highest losses had 315 kg N, in line or to some extent higher than the ones of several studies of nitrogen balances in the mid-hills of Nepal (Pilbeam et al. 2000, Bastakoti 2011, Giri and Katzensteiner 2013). The main N loss pathway was found in soil losses. Manure management was identified as one of the main constraints to N recycling. Poor farms had the lowest internal N recycling and highest dependency from external inputs, as well as lower labour and area productivity. Hr, expressing the diversity of N networks, was correlated to area and livestock. Organization of the N flows (AMI) was slightly higher in mid-hills, meaning that flows in the system were distributed more heterogeneously. Poor types farm households had in all the three districts the highest AMI/Hr (few flows connect few compartments), suggesting that these were less diversified systems.

## 4 Conclusions

The types identified in each of the agro-ecosystems differed in labour, income, land size, TLU and self-sufficiency. The N cycling indicators showed that in general all the farms recycled only a small portion of the nitrogen that flows in the network, therefore having high losses. Losses were higher in the Mid-hills mostly due to high imports and the deficient manure management. In the Terai region, recycling and nitrogen use efficiency at farm level were slightly greater. In all the farms with higher cycling indexes, farmers were growing fodder crops. However, farms across the districts were dependent on external inputs to support the internal N cycling in a similar degree. Diversity of N networks was correlated to land and livestock: farms with more resources were more diverse in N networks, while poor farms had the lowest internal N recycling and the highest dependency on external inputs.

The results of this study allow understanding the high diversity and degree of diversification and integration of distinct types of farms across contrasting agro-ecosystems, which has implications in the design of specific strategies to achieve sustainable intensification in each agro-ecosystem.

## References

* Dupont, D. (2012). The handbook of space invaders monitoring. Part II. Biological estimates. Big-Bang Academic Publisher Inc., Paris, Berlin, Stockholm.

* Bastakoti, N. (2011). Farm management strategies to enhance farm performance in Mid-hills of Nepal: in the context of climate change. Wageningen, Wageningen University

* Dahal, Bed M., Sitaula, Bishal K. and Roshan M. Bajracharya (2007) 'Sustainable Agricultural Intensification for Livelihood and Food Security in Nepal', Asian Journal of Water, Environment and Pollution 5(2): 1-12.

* Devendra, C. and Thomas, D. (2002) 'Crop-animal interactions in mixed farming systems in Asia', Agricultural Systems 71(1-2): 27-40. FAO (2011) Nepal and FAO Achievements and Success Stories: Electronic Publishing Policy and Support Branch FAO.

* Giri, A. and K. Katzensteiner (2013). 'Carbon and nitrogen flow in the traditional land use system of the Himalaya region, Nepal'. Mountain Research and Development 33(4): 381-390.

* Groot, J.C.J., Oomen, G.J.M., & Rossing, W.A.H. (2012). Multi-objective optimization and design of farming systems. Agricultural Systems 110, 63-77.

* Kiff, E., Turton, C., Tuladhar, J.K. and Baker, R. (1995) A review of literature relating to soil fertility in the hills of Nepal. in NRI/LARC project (ed.) Chatham Maritime. United Kingdom.

* Lawrence, P. R. and Pearson, R. A. (2002) 'Use of draught animal power on small mixed farms in Asia', Agricultural Systems 71(1-2): 99-110. Pilbeam, C. J., Tripathi, B. P., Sherchan, D. P., Gregory, P. J. and Gaunt, J. (2000) 'Nitrogen balances for households in the mid-hills of Nepal', Agriculture, Ecosystems & Environment 79(1): 61-72.

* Rufino, M. C., Hengsdijk, H. and Verhagen, A. (2009) 'Analysing integration and diversity in agro-ecosystems by using indicators of network analysis', 84(3): 229-247.

* Steinfeld, H., Gerber, P., Wassenaar, T., Castel, V., Rosales, Mauricio, and de Haan, C. (2006) Livestock's long shadow environmental issues and options, Rome: FAO.

* Thorne, P. J. and Tanner, J. C. (2002) 'Livestock and nutrient cycling in crop-animal systems in Asia', Agricultural Systems 71(1-2): 111-126. Tittonell, P.A. (2008). MisimuwaKupanda: targeting resources within diverse, heterogenous and dynamic farming systems of East Africa, ISBN: 9789085048077

* Yadav, J.L. and Devkota, N.R. (2005) 'Feeds and Feeding situation of livestock in the Terai region of Nepal', Animal Sciences University of Tribhuvan, Institute of Agriculture and Animal Science Rampur, Chitwan, Nepal. FAO Proceedings Nepal Chatper 13.
# Crop-Livestock Integration improves the Energy Use Efficiency of smallholder mixed farming systems - the case of western Burkina Faso

O.Ida Bénagabou*±1, 2, 3, Melanie Blanchard 1,2, Jonathan Vayssières 2, Mathieu Vigne 2, Eric Vall 2, Philippe Lecomte 2, Valérie Bougouma 3 & H.Bismark Nacro 3

## 1 Introduction

Increased food production to cover growing population needs, while limiting its impact on the environment, is a major challenge faced by the global agricultural sector. In sub-Saharan Africa mixed farming systems dominate. Crop Livestock Integration (CLI) is seen as a crucial pathway for supporting production and strengthening the resilience of family households facing economic and climate changes. Few quantitative studies demonstrate such potential. Indeed, based on three main biotechnical pillars, animal draft, manure production and crop residue storage, integration enhances the use of local renewable resources for production, including the cycling of co-products as resources for another activity within the system (Lhoste, 1987). The cycling of biomass and energy is regarded as an essential property for ensuring ecosystem sustainability (Allesina & Ulanowicz 2004). Based on a diversity of mixed farming systems, the study analyzes the quantitative links between diverse energy flows which are indicators for identifying biomass management practices that are alternatives to using external inputs.

## 2 Material and Methods

The study was undertaken on eight mixed farms in Koumbia (western Burkina Faso). The panel covered the diversity of farms observed in the cotton zone, including 3 Crop Farmers (CF), 2 Crop-Livestock Farmers (CLF) and 3 Livestock Farmers (LF) (Vall et al., 2006). A conceptual model was designed to inventory the gross energy flows between the system and its environment (inflows, outflows) and the internal flows between compartments (humans, cattle, crops and manure, fodder and feed stocks; Fig. 1). An Ecological Network Analysis (ENA, Finn, 1980) was applied to the matrix of flows to describe the ecological functioning of these agro-ecosystems and calculate indicators describing the cycling (Cycling Index, CI) and autonomy (A) of the farms and the proportion of flows into the network caused by CLI practices (CLID). The gross energy efficiency (GEE) was also calculated, as well as other indicators describing integration practices, such as the amount of manure available per Tropical Livestock Unit (OM) and the amount of crop residues and fodder available per Tropical Livestock Unit (FOD).

<figure><img src="image_189.png" /><figcaption>**Fig. 1.** Conceptual model of gross energy flows for mixed farming systems</figcaption></figure>

## 3 Results and discussion

Applying an ENA to western Burkina mixed farming systems showed variable levels of cycling (0.03<CI<0.50) and autonomy (0.17<A<0.70) within and between farm types (Table 1). This variability resulted from a diversity of CLI farming practices (straw, forage crops, manure, compost, digester sludge). ENA applications to eastern Africa and Madagascar gave similar results for nitrogen cycling in mixed systems (Rufino et al., 2009; Alvarez et al., 2013).

<footnote n="1">¹ CIRDES -URPAN</footnote>

<footnote n="2">² CIRAD-UMR Selmet</footnote>

<footnote n="3">³ UPB, IDR</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: idadec@yahoo.fr</footnote>
Moreover, we found that the integration indicator (CLID) and the indicators describing practices (OMand FOD) were higher among crop and crop-livestock farmers, i.e. farms with a stocking rate below 2 TLU.ha⁻¹. Livestock farmers had low to medium levels of integration. Indeed, the large TLU number (> 8TLU.ha⁻¹) of these farms led them to drive their cattle to the surrounding rangelands to meet their forage needs. It decreased their autonomy for locally available common resources and reduced the potential for manure collection and cycling. Also, even though in absolute terms they often stored larger amounts of forage than the other two types compared to the size of the herd, this amounted to small quantities.

<table><caption>**Table 1.** Gross energy efficiency, cycling and crop-livestock integration indicators for western Burkina mixed farming systems</caption><thead><tr><th>Farm number</th><th>Livestock stocking rate (TLU.ha<sup>-1</sup>)</th><th>CI (Dmnl)</th><th>GEE (Dmnl)</th><th>Autonomy (Dmnl)</th><th>CLID= (Dmnl)</th><th>OM (kgDM. TLU.year<sup>-1</sup>)</th><th>FOD (kgDM.TLU<sup>-1</sup>.year<sup>-1</sup>)</th></tr></thead><tbody><tr><td>CF2</td><td>0.8</td><td>0,50</td><td>0,18</td><td>0,67</td><td>0,57</td><td>213</td><td>570</td></tr><tr><td>CF3</td><td>0.6</td><td>0,23</td><td>1,15</td><td>0,70</td><td>0,65</td><td>564</td><td>692</td></tr><tr><td>CF7</td><td>0.5</td><td>0,13</td><td>1,93</td><td>0,70</td><td>0,46</td><td>322</td><td>788</td></tr><tr><td>CLF5</td><td>1.7</td><td>0,37</td><td>0,27</td><td>0,59</td><td>0,55</td><td>752</td><td>367</td></tr><tr><td>CLF6</td><td>0.7</td><td>0,30</td><td>1,12</td><td>0,65</td><td>0,49</td><td>994</td><td>276</td></tr><tr><td>LF1</td><td>8.5</td><td>0,12</td><td>0,24</td><td>0,37</td><td>0,34</td><td>278</td><td>120</td></tr><tr><td>LF4</td><td>9.8</td><td>0,17</td><td>0,29</td><td>0,40</td><td>0,36</td><td>287</td><td>78</td></tr><tr><td>LF8</td><td>35.9</td><td>0,03</td><td>0,26</td><td>0,17</td><td>0,20</td><td>124</td><td>0</td></tr></tbody></table>

The cycling index (CI) was positively correlated with CLID for all farmers, i.e. CLI practices improved energy cycling on farms. The gross energy use efficiency (GEE) varied in turn from 0.18 to 1.93 and was positively correlated with autonomy (Fig. 2). Indeed, storage crop residues led to increased autonomy limiting imports and thus improved the gross energy use efficiency. Crop and crop-livestock farmers left a large amount of crop residues in the field that was subsequently consumed by other herds, thereby reducing energy cycling opportunities on the farm. It thus appeared that a stocking rate of 1.5 TLU.ha⁻¹ enabled a balance between needs and resources and provided favorable conditions for biomass cycling and optimum farm autonomy.

<figure><img src="image_190.png" /><figcaption>**Fig. 2.** Relations between autonomy (A) and gross energy efficiency (GEE)</figcaption></figure>

## 4 Conclusions

Using original indicators, this study confirmed that better crop-livestock integration increases the energy use efficiency and autonomy of farms. The variable levels of energy use efficiency, cycling and autonomy were the consequences of a wide diversity of CLI practices. It showed a lower degree of autonomy and cycling for livestock farmers than for crop and crop-livestock farmers. This diversity indicated that there is still plenty of leeway for improving integration and efficiency, as expected (Blanchard et al., 2013; Semporé et al., 2013).

## References

* Allesina, S. & Ulanowicz, R.E. (2004). Cycling in ecological networks: Finn's index revisited. *Computational Biology and Chemistry*, **28**, 227-233.

* Alvarez, S., Rufino, M.C., Vayssières, J., Salgado, P., Tittonell, P., Tillard, E. & Bocquier, F. (2013). Whole-farm nitrogen cycling and intensification of crop-livestock systems in the highlands of Madagascar: An application of network analysis. *Agricultural Systems*, **126**, 25-36.

* Blanchard M., Vayssieres J., Dugué P. & Vall E., 2013 Local Technical Knowledge and Efficiency of Organic Fertilizer Production in South Mali: Diversity of Practices, *Agroecology and Sustainable Food Systems*, **37**:6, 672-699.

* Finn, J.T. (1980). Flow analysis of models of the Hubbard brook ecosystem. *Ecology*, **61**, 562-571.

* Lhoste, P. (1987). L'association agriculture-élevage. Evolution du système agropastoral au Sine-Saloum Sénégal. Etudes etsynthèses de l'IEMVT 21. 314p.

* Rufino, M. C., Hengsdijk, H. & Verhagen, A. (2009). Analysing integration and diversity in agro-ecosystems by using indicators of network analysis. *Nutrient Cycling in Agroecosystems*, **84**, 229-247.

* Sempore, A.W., Andrieu, N., Le Gal, P.Y., Nacro, H.B. & Sedogo M. (2013). Towards a better crop-livestock integration in West African farms through a simulation-based approach with farmers. *Agroecology and Sustainable Food Systems* (submitted).

* Vall, E., Dugué, P. & Blanchard, M. (2006). Le tissage des relations agriculture-élevage au fil du coton. *Cahiers Agricultures* **15**, 72-79.
# French sheep meat sector and drivers of its evolution since 1970

Gabriel Teno *±1, Charles-Henri Moulin <fnref n="2" /> & Marie-Odile Nozieres <fnref n="3" />

## 1 Introduction

The modernization period of French and European agriculture was characterised by a process of intensification of production systems supported by very proactive agricultural policies, including guaranteed prices to producers. Today, the future of production systems is no longer measured certainty, but it is based on the ability of farmers to adapt to a changing and uncertain world (Dedieu *et al.*, 2008).

Indeed, advent of global warming, recurrent droughts and price volatility of raw materials are many uncertainties that farmers face. Furthermore, the antecedent crises such as BSE (Bovine spongiform encephalopathy), dioxin in chicken, milk melanised, and the upsurge of collective food intoxication, have driven significant changes in production systems by creating a large consumer confidence crisis. In addition, these changes on the structures and ways of production occur at the same time to the evolution of national and European regulations and generating more uncertainty with which farmers must contend (Tichit *et al.*, 2008). The French sheep production, particularly the sheep meat sector, knows a long crisis for over 30 years. Liberal trading and the European agricultural policies have contributed to jeopardize the future of French and European sheep flocking. Sheep farmers, as other producers, therefore need to adjust their ways of breeding sheep, and also to organize their marketing to sustain their incomes in the uncertainty environment.

In order to drive a comprehensive evolution of French sheep meat sector, it is useful to have a deep knowledge of its economic policy and environmental context. So, we study the French sheep meat sector from 1970 to 2012 in order to, i) understand the evolution of production and consumption, ii) draw up the evolution of agricultural policies and sheep meat market and, iii) analyse the drivers of these evolution.

## 2 Material and Methods

To analysis the evolution of sheep meat sector in France since 1970, we firstly proceed to collection and statistical analyses of secondary data. The results were then submitted to a critical analysis of a sheep sector expert, an economist from INRA and specialist of meat sector, especially the sheep meat sector (Boutonnet, 1998). This approach has already been mobilized in others studies on changes in agricultural sectors (Napoléone & Boutonnet, 2010; Labarthe, 2005). Data collected were used to construct graphs showing various evolutions of French sheep meat sector. We then analysis the results, by trying to identify successions of periods for the main element (increase, decrease or stability for quantitative variables; period with the same market rules and policy, different reforms, etc.) and trying to find the elements which could explain different evolutions. The role of sheep expert was, on the one hand, to give an appreciation on how we present the evolution of French sheep meat sector since 1970 and, on the other hand, to have from him more light on the drivers of this evolution.

<figure><img src="image_191.png" /></figure>

Source. Adapted from Rieucau (1964)

<figure><img src="image_192.png" /><figcaption>Fig.1. Evolution of French livestock</figcaption></figure>

Source. Own elaboration

<caption>Fig. 2. Evolution of total of French sheep livestock</caption>

## 3 Discussion

Since 1980, the French sheep livestock is declining gradually. This decline appears recurring and specific to sheep production. Indeed, the evolution of livestock production in France from 1840 to 1964 shows a drastic decline of sheep population compared to the stagnation of horse population and increase of cattle and pigs population (Fig. 1). The decrease of French sheep population in 19th century is due mainly to the reduction of use of sheep for soil fertilization

<footnote n="1">¹CIRAD, UMR SELMET, 2, place Viala, F-34060 Montpellier cedex 1, France</footnote>

<footnote n="2">²Montpellier SupAgro, UMR SELMET, 2, place Viala, F-34060 Montpellier cedex 1, France</footnote>

<footnote n="3">³INRA, UMR SELMET, 2, place Viala, F-34060 Montpellier cedex 1, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Gabriel.Teno@USherbrooke.ca</footnote>
with the development of mineral fertilisation and the crisis of the wool market with competition from new countries such as Australia and Argentina. The increase thereafter of French sheep population up to the 1970s (Fig. 2) would be the result of the Common Agricultural Policy (CAP) introduced in 1962.

This voluntary agricultural policy was strongly supported and aimed to ensure food self-sufficiency through the widespread intensification of agricultural productions. However, this growth of French sheep population, is also much related to the protectionist policy of French sheep meat market. Indeed, since the liberalisation of French sheep meat market to England in 1979, the sheep production in France knows enormous difficulties (Rieutort, 1995). The study of evolution of ewe's population in France, since 1970, shows a gradual decline. However, this reduction of ewe's population, more pronounced over the past 20 years, concerns only sheep meat sector because the numbers of dairy ewes are greatly increasing since 1970 (Fig. 3). Also, this drop was accompanied by a decline in productivity per ewe. Even if the approximate productivity per year per ewe has known a gradual increase between 1970 and 1980 from 18, 34 kg to 21, 65 kg, it decreases since this date to reach 18, 45 kg in 2010. Beside the decline of ewe's population, there has been a steady decline in the number of ewe's farms. As an illustration, the number of ewe's farms in France decreased from 168 390 in 1970 to 55 322 in 2010, a decrease of 67% in 40 years. Also, this decline, more pronounced over the past 20 years, concerns only the suckling ewe's farms because the number of dairy ewes farms has remained relatively stable (Fig. 4).

<figure><img src="image_193.png" /><figcaption>Source. Own elaboration
Fig. 3. Evolution in number of suckling and dairy ewes</figcaption></figure>

<figure><img src="image_194.png" /><figcaption>Source. Own elaboration
Fig. 3. Evolution in number of suckling and dairy ewes farms</figcaption></figure>

We note that the decrease in the number of suckling ewe's farms is much greater than the decrease of suckling ewe's population. Also, despite the considerable increase of dairy ewes population, the number of dairy ewe's farms has remained relatively stable (Laffont, 2012). Indeed, since 1980, we are witnessing a phenomenon of concentration of sheep farms in France with a gradually increase in the number of animals per farm (Rieutort, 1995). The farms with a small number, less than 100 head, decreases gradually in favour of farms with a big number of sheep. In 2009, 63% of meat ewes are kept in the farms of over 200 heads. This proportion reaches 84% for milk ewes. There would be a kind of concentration and progressive intensification of the activity of sheep production in France. This decline of national production (sheep population and sheep farms) could be explained by the decrease in the number of sheep farmers by stop of activity, and that is not compensated by the recovery or by the new entry in the activity. This decline in production is not also offset by the increase in the number of sheep per farm among sheep farmers who wishing to remain in the sector are forced to expand their flock for maintain their income.

## 4 Conclusion

This article presents the drivers of evolution of French sheep meat from 1970 to 2012. According to the results, the evolution of suckling ewes sector in France, since 1970, can be subdivided in three major periods.

The periods from 1970 to 1980, characterized by an increase in the overall level of consumption related to population growth, but mainly due to annual increase in level of consumption per capita. The period from 1980 to 1992, characterized by a decrease of level of national production with massive inflow of sheep meat in France, especially from United Kingdom and New Zealand, following the opening of French market. Finally, the period from 1992 to 2012 characterized by a significant decrease in the level of production, consumption and even imports, which results in significant decrease of self-sufficiency ranging from 80% in 1980 to 48% in 2010.

Hence, there is a real need for different stakeholders and promoters of sheep meat sector in France to more interact and think together for define an appropriate and suitable agricultural policy for sheep meat sector. This could help to address these constraints and provide sustainable solutions to French sheep meat production.

*Acknowledgements.* We thank the CIRAD and partners, through "MOUVE Project", for funding this study.

## References

* Boutonnet, J.P. (1998). Marchés des viandes : les clés de l'évolution. In Déméter 1999 Economie et stratégies agricoles, 59-119.

* Dedieu, B., Chia, E., Leclerc, B., Moulin, C.-H. & Tichit, M. (2008). L'élevage en mouvement. Flexibilité et adaptation des exploitations d'herbivores. Paris, France, Quae, 296 p.

* Labarthe, P. (2005). Trajectoires d'innovation des services et inertie institutionnelle : dynamique du conseil dans trois agricultures européennes. Lavoisier ; Géographie économique, société, 2005/3 - Vol. 7, p. 289-311.

* Laffont, M. (2012). Filière ovine en France. http://www.buvettedesalpages.be/crise_ovine/. Pastoralisme - Crise ovine - Conjoncture, Octobre 2012.

* Napoléone, M. & Boutonnet, J.P. (2010). Lecture diachronique de l'évolution des systèmes de production et des stratégies de firmes, en élevage caprin laitier - Quelle analyse de la durabilité ? Sous réseau FAO-CIHEAM Systèmes de production ovins et caprins - 7th séminaire « Economic, social and environmental sustainability in sheep and goat production systems» 10-12 novembre 2010 - Sarragosse- Espagne.
# Mongolian water quality problem and health of free-grazing sheep

Yu Yoshihara*±<fnref n="1" />, Chika Tada<fnref n="1" />, Moe Takada<fnref n="1" />, Nyam-Osor Purevdorj<fnref n="2" />, Khorolmaa Chimedtseren<fnref n="2" /> & Yutaka Nakai<fnref n="1" />

## 1 Introduction

Water is a major component of the animal body, where it is essential for the transport of nutrients, hormones, and waste products and the regulation of blood osmotic pressure, secretions such as saliva and milk, and body temperature. In grazing animals the amount of wet feces and urine per 1000 kg of live weight per day ranges from 79 to 112 kg; wastes from sheep and dairy cattle contain large amounts of N, P, K, and bacteria and pose a water pollution threat on grazing lands (Hubbard et al., 2004). Water consumption is depressed when manure-contaminated water is provided to cattle. As a result of depressed feed consumption, infection with pathogens and parasites, and less time spent grazing and more time resting, cattle gain less weight when drinking manure-contaminated dirty water than when drinking clean water (Willms et al., 2002; Lardner et al., 2005). After Mongolia's transition from a planned to a market economy, the total number of livestock there increased from 15 to 30 million between 1930 and 1990, and by 2014 it had reached about 50 million (National Statistical Office of Mongolia, 2013). Coincident with the resulting overgrazing, water pollution is becoming a problem. Concentrations of suspended particles and orthophosphates are increasing in Mongolia's stream systems, and phosphate levels have recently increased in Mongolian lakes (Shinnerman et al., 2009; Maasri & Gelhaus, 2011). Overgrazing is likely to be an important contributor to the eutrophication of Mongolia's water bodies and, through this, threatens animal health. Nevertheless, to our knowledge, no study has yet been published on the effect of livestock waste contamination of Mongolia's water on livestock performance and health. Here, we therefore examined the effect of water quality on the health and performance of Mongolian lambs.

## 2 Materials and Methods

We allocated 32 free-grazing lambs to four groups and provided each with water from a different source (upper stream, lower stream, well, and pond) for 49 days. We recorded the amount of water consumed by the lambs, as well as their body weight, behavior, white blood cell count, acute phase (haptoglobin) protein level, and fecal condition. We measured the chemical and biological qualities of the four types of water, and we detected enteropathogenic and enterohemorrhagic *Escherichia coli* in fecal samples by using a genetic approach.

## 3 Results – Discussion

Pond water contained high levels of nitrogen and minerals, and well water contained high levels of bacteria. The odor concentration index decreased in order from pond water to upper stream, lower stream, and well. On day 15 of the experiment, the following parameters were the highest in lambs drinking water from the following sources: water intake (pond or lower stream), body weight gain (pond), WBC count (lower stream), haptoglobin concentration (well), and enteropathogenic *E. coli* infection rate (lower stream). The total water intake per lamb group over a 14-day period was 16.36 L (upper stream), 21.69 L (lower stream), 12.32 L (well), and 20.74 L (pond) (Fig. 1). Water intake was lowest in the lambs given well water (d.f. = 3, F = 3.04, P = 0.04). Body weight gain per lamb over the 49-day study period was 4.67 kg (upper stream), 4.83 kg (lower stream), 5.17 kg (well), and 6.83 kg (pond water) (Fig. 2). Lambs that drank well water spent more time lying down and less time grazing than the others, and lambs that drank pond water spent more time standing and less time lying down. Lambs given upper or lower stream water exhibited more severe diarrhea on day 15 of the experiment than before the experiment.

<footnote n="1">¹ Graduate School of Agricultural Science, Tohoku University</footnote>

<footnote n="2">² School of Veterinary Medicine and Biotechnology, Mongolian State University of Agriculture</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: yoshiyu@bios.tohoku.ac.jp</footnote>
<figure><img src="image_195.png" /><figcaption>Fig. 1. Changes in total water intake by the 8 lambs in each water source group.</figcaption></figure>

<figure><img src="image_196.png" /><figcaption>Fig. 2. Changes in mean body weight of lambs (error bars, standard error of the mean) in each group during the water intake experiment.</figcaption></figure>

## 4 Conclusions

Mongolian sheep seemed to adapt to chemically contaminated water: their productivity benefited the most from pond water, likely owing to its rich mineral content. Lambs that drank lower stream water showed increases in enteropathogenic *E. coli* infection, clinical diarrhea, and WBC count. Lambs that drank well water, which was bacteriologically contaminated, had increased serum acute phase protein levels and poor physical condition; they were thus at increased risk of negative health and production effects.

## References

* Hubbard, R.K., Newton, G.L. & Hill, G.M. (2004). Water quality and the grazing animal. *Journal of Animal Science* **82**, 255-263. Kelderman, P. & Batima, P. (2006). Water quality assessment of rivers in Mongolia. *Water Science Technology* **53**, 111-119.

* Lardner, H.A., Kirychuk, B.D., Braul, L., Willms, W.D. & Yarotski, J. (2005). The effect of water quality on cattle performance on pasture. *Crop and Pasture Science* **56**, 97-104.

* Maasri, A. & Gelhaus, J. (2011). The new era of the livestock production in Mongolia: consequences on streams of the Great Lakes Depression. *Science of Total Environment* **409**, 4841-4846.

* National Statistical Office of Mongolia, (2013). Ulanbaatar.

* Raadsma, H.W., Kingsford, N.M., Spithill, T., Piedrafita, W.D. (2007). Host responses during experimental infection with *Fasciola gigantica* or *Fasciola hepatica* in Merino sheep: I. Comparative immunological and plasma biochemical changes during early infection. *Veterinary Parasitology* **143**, 275-286.

* Shinneman, A.L., Almendinger, J.E., Umbanhowar, C.E., Edlund, M.B. & Nergui, S. (2009). Paleolimnologic evidence for recent eutrophication in the Valley of the Great Lakes (Mongolia). *Ecosystems* **12**, 944-960.

* Willms, W.D., Kenzie, O.R., McAllister, T., Colwell, A., Veira, D.D., Wilmshurst, J.F., & Olson, M.E. (2002). Effects of water quality on cattle performance. *Journal of Range Management* **452-460**.
# W2. Annual crops based systems

Chair: Laure Hossard, INRA

Co-chair: Eric Scopel, CIRAD
# Cropping system intensification to increase food security and profitability among smallholder farmers in Zimbabwe

Siyabusa Mkuhlani *±1, Isaiah Nyagumbo 1, Walter Mupangwa 1, Neil MacLeod 2, Cam Mac Donald 2, Peter de Voil 3, Daniel Rodriguez 3

## 1 Introduction

Soil degradation, poor soil fertility, recurrent low crop and livestock productivity and food insecurity are the major problems bedevilling smallholder communities in Southern Africa. Most smallholders practise maize mono-cropping under conventional agriculture (CP) allocating minute portions of their fields to intercropping, rotations and sole legume cropping(CIMMYT, 1998). As a result, continuous maize production has led to chronic losses due to the low maize yields and returns in Zimbabwe. Legume production is however prospectively more promising due to the high producer prices (Akibode, 2011).

Sustainable cropping systems such as conservation agriculture (CA), intercropping, crop rotations and legume sole cropping are potential options for improving soil fertility, crop yields, household incomes and food security (Rusinamhodzi et al., 2012). Prior experience has highlighted great difficulties with implementing blanket interventions for smallholder communities thus necessitating the need for household type specific interventions(Vanlauwe et al., 2014). Few smallholder farmers however practice legume mono-cropping as they prioritize cultivation of food security crops first and then other crops(CIMMYT, 1998).

On-station research has shown potential long term increases in maize-legume system productivity under CA, intercropping, rotation and sole legume cropping systems. Limited research has, however, been undertaken to assess the productivity and socio-economic impacts of these systems at the farm household scale. Implementation of field experiments at such a scale may, however, prove to be complex, expensive and time consuming. The use of the APSIM and IAT simulation models provides a time and cost saving approach for undertaking this long term research.

## 2 Materials and Methods

The study was based on Murehwa district (17.7° S, 31.8° E and 1,365 metres above sea level) in Natural Region IIb of Zimbabwe. The area receives 750-1000 mm year⁻<fnref n="1" /> rainfall and is predominated by low fertile granitic sandy soils (Vincent and Thomas, 1961).

Household, socio-economic, cropping and livestock population dynamics data was collected through a questionnaire. Farmers were categorised into Resource endowed (RE) and Resource constrained (RC) classes. RE farmers are characterised by greater land ownership of at least 4ha arable land and food self-sufficiency. In addition they have greater livestock ownership with an average of 16 cattle, 20 goats, 22 pigs and chicken. RC farmers have land ownership of less than 0.5ha, nil or few livestock and suffer chronic food shortages.

The Agricultural Production Systems Simulator (APSIM) model (Keating et al., 2003) which simulates crop growth and development was calibrated based on the measured yields from CA, non-CA systems, intercropping, rotations and sole legume cropping systems from 2012/13-14/15 seasons in Murehwa. The Integrated Analysis Tool (IAT) which integrates crop productivity, livestock productivity and household socio-economic aspects to assess household productivity was also calibrated based on the household data for different farmer types. Maize and legume yields were simulated using APSIM for the period (1969-2000).

Each of the farmer types (Baseline scenario) were exposed to the following scenarios; adoption of (1) CA, (2) crop rotations, (3) intercropping, (4) sole legume cropping.

Farm household food security and enterprise profitability were then simulated using the IAT for the same period.

## 3 Results – Discussion

Adoption of CA systems by RC farmers has the potential to increase maize yields by at least 23% whereas RE farmers may experience at least a 6% increase relative to CP (Figure 1).

Maize yields are generally higher (p<0.05) under crop rotation as compared to sole cropping systems with Maize-cowpea rotation projected to have the highest (P<0.05) yields of at least 3000 kg ha⁻<fnref n="1" /> relative to Maize based Soybean, Groundnut and Mucuna rotation systems with projected yields of between 2000-2500 kg ha⁻<fnref n="1" /> (Fig. 1).

<footnote n="1">¹ CIMMYT, P.O Box MP163, Mt. Pleasant, Harare, Zimbabwe</footnote>

<footnote n="2">² CSIRO Agriculture Flagship, St Lucia, Queensland 4072, Australia</footnote>

<footnote n="3">³ QAAFI, Tor St., Toowoomba, Queensland 4350, Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>‡ Corresponding author: siyabusa@gmail.com</footnote>
<figure><img src="image_197.png" /><figcaption>**Fig. 1.** Mean simulated maize yields under intercropping, rotation, conservation agriculture and conventional agriculture cropping systems.</figcaption></figure>

NB: Means with the same letters denote no significant differences (p<0.05)

RC farmers under the baseline scenario face significant food shortages (P<0.05) of at least $15/month. However adoption of CA, intercropping and rotation systems will reduce (p<0.05) food shortages by at least 50%. Use of CA and rotation systems will increase food availability of RE farmers. Adoption of intercropping will reduce food availability by smaller (P>0.05) margins due to reduction in maize yields due to maize-legume intercrop competition. Sole legume cropping without maize may however, leave both farmers with the greatest (P<0.05) food insecurity (Fig. 2).

<figure><img src="image_198.png" /><figcaption>**Figure 2:** Value of maize deficit of RC and RE households under different cropping systems</figcaption></figure>

<figure><img src="image_199.png" /><figcaption>**Figure 3:** Household profitability of different farmer types under different cropping systems</figcaption></figure>

NB: Means with the same letters denote no significant differences at p<0.05.

Generally RC farmers achieve far lesser (P<0.05) returns than RE farmers regardless of the cropping system. Adoption of forage legumes by both farmer types however increases (P<0.05) farm profitability by at least 100%. Adoption of forage legumes is more profitable to RE farmers with land ownership of at least 2.5 ha as compared to the 0.7 ha available to RC farmers.

## 4 Conclusions

RC farmers generally face food shortages due to low maize yields as compared to RE farmers due to the low fertilizer use and seed rates attributed to poor resource endowment. Alternate cropping systems such as CA, intercropping and rotations increase maize and legume crop productivity thus increases profitability among the RC farmers. Adoption of forage legumes leaves both farmer types physically food insecure but financially food secure.

*Acknowledgements:* The work described here was funded by ACIAR under the ZIMCLIFS project CSE 2010/022.

## References

* Akibode, C.S., 2011. Trends in the Production, Trade, and Consumption of Food-Legume Crops in Sub-Saharan Africa.

* CIMMYT, 1998. Soil fertility research for maize-based farming systems in Malawi and Zimbabwe.

* Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J.P., Silburn, M., Wang, E., Brown, S., Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L., Freebairn, D.M., Smith, C.J., 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267–288.

* Rusinamhodzi, L., Corbeels, M., Nyamangara, J., Giller, K.E., 2012. Maize-grain legume intercropping is an attractive option for ecological intensification that reduces climatic risk for smallholder farmers in central Mozambique. Field Crops Research 136, 12–22. doi:10.1016/j.fcr.2012.07.014

* Vanlauwe, B., Asten, P. van, Blomme, G., 2014. Challenges and Opportunities for Agricultural Intensification of the Humid Highland Systems of Sub-Saharan Africa. Springer link.

* Vincent, V., Thomas, R.G., 1961. An Agroecological Survey of Southern Rhodesia: Agro-Ecological Survey. Government Printer, Rhodesia.
# Experimental assessment of winter malting barley genotypes in low-input system

Damien Beillouin<sup>±1,2,3</sup>, Jean-François Herbommez<sup>4</sup>, Claire Perrot<sup>1</sup>, Arnaud Gauffreteau<sup>2,3</sup>, Marie-Hélène Jeuffroy<sup>2,3</sup>

## 1 Introduction

Winter six-row malting barley is well adapted to the French agroclimatic conditions, allowing the country to be the first worldwide exporter of malt. Nowadays, government, consumers and brewers are calling for a reduction of agrochemical inputs use. Moreover, the brewing industry asks for specific grain quality that should be reached also in low-input (LI) conditions. Little is known about the impacts of this crop management type on grain yield and malting quality according to the cultivar, while specific genotype characteristics were identified for wheat in LI conditions to reach high agronomic performances (Loyce et al., 2008). This study aimed at assessing the agronomic performances of various barley genotypes in LI conditions in comparison with conventional crop management system.

## 2 Materials and Methods

A multi-environment trial (5 locations in Northern France) testing 20 genotypes (4 registered and 16 innovative) in 2 crop management systems (conventional and low input) was carried out in cropping season 2013-2014. The conventional system was based on regional recommended practices for farmers. The LI one consisted in a 30% reduction of N fertilizer rate and the suppression of growth regulators and fungicides. Three replicates were laid out in each site. Genotypes were randomized within the main treatment. Individual area plots ranged between 7.5 and 9.8 m<fnref n="2" />. Yield, grain protein content and grain size were measured and averaged for each genotype on the three mechanically harvested replicates in each of the 5 sites. As small grains (size < 2.5 mm) and grains with high (over 11.5 %) or low (under 9%) protein content are not suitable for malting, we determined these thresholds for considering grain convenient or not for malting industry. The calibrated yield was calculated as the grain yield with size over 2.5 mm.

## 3 Results – Discussion

In the conventional management system, almost 70% of Genotype X Site combinations would have been accepted for malting production (Fig. 1. a). Almost 2/3 of the grain N content measures were between 10 and 11%, with a mean grain protein content of 10.1 %. 31% of the Genotype X Site combinations did not reach the grain size threshold. In the whole, 12 genotypes out of 20 met both specifications of the malting industry in 3 sites out of 5, 7 matched the specifications for 4 sites and only one should have been considered as a malting genotype in all sites. The calibrated yield reached 84 q.ha⁻<fnref n="1" /> (with genotype mean ranging from 79 to 91 q.ha⁻<fnref n="1" />) for grains suitable for malting, and 74 q.ha⁻<fnref n="1" /> in the other cases.

<figure><img src="image_200.png" /><figcaption>**Fig. 1.** Grain protein content versus percent of grains over 2.5mm for Site X Genotype combinations in (a) conventional and (b) low input (LI) systems.</figcaption></figure>

<footnote n="1">¹ Florimond-Desprez, 3 Rue Florimond-Desprez, 59242 Cappelle-en-Pévèle, France</footnote>

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

<footnote n="3">³ AgroParisTech, UMR 211 Agronomie, Bâtiment EGER, 78850 Thiverval-Grignon, France</footnote>

<footnote n="4">⁴ Momont, 11 Rue de Martinval, 59246 Mons-en-Pévèle, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: d.beillouin@grignon.inra.fr</footnote>
The shift to a LI management system increased the number of situations (79% of the Genotype X Site combinations) where genotypes did not satisfy the specifications of the malting industry (Fig.1 b). The thresholds of grain protein content and grain size were not satisfied respectively in 38% and 47% of the situations. In only 6 % of the cases, both criteria were simultaneously not respected. On the whole, 4 genotypes would have always been rejected by malting industry, 12 met the specifications in only one site out of 5, three in 2 sites and only one in 3 sites (Fig. 2.). For both situations matching or not the specifications of malting industry, the calibrated yield was 14 q.ha⁻¹ lower in the LI than in the conventional system. When satisfying the malting conditions, LI genotypic yields ranged from 61 to 80 q.ha⁻¹.

<figure><img src="image_201.png" /><figcaption>**Fig. 2.** Number of sites (out of 5) for each genotype in the low-input system where the grain protein content and the grain size (black bars), the grain protein but not the grain size (light gray bars), the grain size but not the grain protein (dark gray bars) or none of these two variables (white bars) are suitable for malting.</figcaption></figure>

The mean protein content was 9.3%, but was particularly low in Prasville (8.9%) and Allemanche (8.5%) sites. Situations where grains did not satisfy the threshold of protein content could be explained by a dilution effect of N uptake by the grain yield. The mean grain N uptake were respectively 137 kg N ha⁻¹ and 161 kg N ha⁻¹ for situations under and over 9% Proteins in grains, for a same yield (not calibrated) of 71 q.ha⁻¹ in both cases. The grain yield was generated by a low number of grains per square meter (mean 16 130) combined with a high thousand kernel weight (mean 43.2) for the situations with less than 9% proteins in grains, and high number of grains per square meter (mean 19 860) combined with a low grain thousand kernel weight (mean 39.6) for situations with more than 9% proteins in grains. The proportion of grains which size was over 2.5 mm ranged from 50 to 100% with a large effect of the genotype. The variability of grain size was more than twice larger in LI than in conventional system. Situations where grains did not satisfy the threshold of grain size could be explained by a lower amount of N in grains, respectively of 133 kg and 174 kg N.ha⁻¹. For situations with more and less than 90% of grains over 2.5mm respectively, the mean numbers of grains per square meter were respectively 16 220 and 20 929, and the thousand kernel weights were respectively 43.9 and 37.6.

On the whole, two types of situations led to inappropriate grains characteristics. First, when the number of grains per square meter is low and the plant compensates with higher thousand kernel weight, affecting the grain protein content. Second, when the plant produces a very high number of grains per square meter, affecting the thousand kernel weight and compromising the grain size proportion.

## 4 Conclusions

The malting barley production was more difficult in the experimented LI system than in the conventional system: no genotype fulfilled malting conditions in the 5 sites for the LI system. The challenge for LI production will be to reach a sufficient high level of protein content without compromising grain size. Optimized N fertilizer management strategies could limit the first problem. Genotype choice could increase the proportion of large grain size. A combined design of crop and genotype ideotypes seems necessary to succeed in low-input malting barley production.

*Acknowledgements.* This research was funded by the French Ministry of Agriculture and Fisheries under the CASDAR project "ECO2MALT". We would also to thank the Florimond-Desprez, Momont, and INRA UMR Agronomie technical teams for trial management and technical assistance.

## Reference

Loyce, C., Meynard, J.M., Bouchard, C., Rolland, B., Lonnet, P., Bataillon, P., Bernicot, M.H., Bonnefoy, M., Charrier, X., Debote, B., Demarquet, T., Duperrier, B., Félix, I., Heddadj, D., Leblanc, O., Leleu, M., Mangin, P., Méausoone, M., Doussinault, G., 2008. Interaction between cultivar and crop management effects on winter wheat diseases, lodging, and yield. Crop Prot. 27, 1131-1142.
# Potential yield and yield gap at farm level are different from the field level: A case study on a large Dutch potato farm

Pytrik Reidsma ±<fnref n="1" />, Jarno Rietema<fnref n="1" />, Yulin Yan<fnref n="1" />, Joop Kroes<fnref n="2" /> & João Vasco Silva<fnref n="1" />

## 1 Introduction

Yield gap analysis is receiving increasing interest, as yield increases are needed to feed a growing human population (Van Ittersum et al., 2013). For sustainable intensification, yield increases need to go hand in hand with increasing resource use efficiency. Yield gaps and resource use (in-) efficiencies are typically assessed at field level. In the real world, they are explained by a range of factors that intersect and are integrated at farm level. Agro-ecological conditions may vary among and within fields, and crop management may be constrained. Therefore, it is relevant to assess the potential farm level yield and to understand crop yield gaps taking into account farm level information.

## 2 Materials and Methods

Our case study was performed at Van den Borne Aardappelen, a 500 ha potato farm located on sandy soils in North Brabant (The Netherlands), cultivating around 140 fields per year. Fields are in rotation with dairy farmers, from whom fields are rented. Precision farming techniques and decision support systems are widely used in this farm, resulting in nearly 'optimal' management, relatively high resource use efficiency and high yields. In 2013 and 2014 data have been collected on crop management and yields per field, and measurements were taken throughout the season.
We use the coupled hydrology-crop model SWAP-WOFOST (www.swap.alterra.nl) to estimate the potential and water- and oxygen-limited yields. The potential yield was calibrated on the highest yielding fields in 2014 and validated on the highest yielding fields in 2013. Using soil maps with soil moisture data, and precipitation data from 10 local weather stations, water- and oxygen-limited yields were estimated for a range of fields.

Frontier analysis and statistical methods were used to explain the potato yield gap for the ±140 fields (Fig. 1). Explaining factors were grouped according to Genotype (G) x Environment (E) x Management (M).

<figure><img src="image_202.png" /><figcaption>**Fig. 1.** Using frontier analysis for explaining potato yield gaps in fields within a farm. Each dot represents an individual field. Y<sub>HF</sub> indicates the yield in the highest yielding field. All fields on the frontier are so-called technical efficient considering the specific input. In the figure, field 1 is efficient regarding K and inefficient regarding N. K is thus the limiting factor. When a field is below the frontier for all inputs, and thus non of the input factors are limiting, inefficiency can be explained by inappropriate management referring to time, space and form of the inputs applied.</figcaption></figure>

## 3 Results – Discussion

Variability in potato yields among and within fields is high. Yields per field vary between 20 to 100 t ha⁻<fnref n="1" />, with an average around 55 t ha⁻<fnref n="1" /> in 2013 and 65 t ha⁻<fnref n="1" /> in 2014 (Fig. 2).

<footnote n="1">1 Plant Production Systems Group, Wageningen University, the Netherlands</footnote>

<footnote n="2">2 Alterra, Wageningen UR, the Netherlands.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: pytrik.reidsma@wur.nl</footnote>
<figure><img src="image_203.png" /><figcaption>**Fig. 2.** Amount of fields plotted against obtained tuber yields (in t ha⁻¹) for cultivation years 2013 and 2014 on the farm of Van den Borne Aardappelen.</figcaption></figure>

Considering a planting and harvesting window of one month, and a maximum irrigation capacity for half of the fields, at farm level it is impossible to reach the potential yield in all fields. Yields of some fields are limited by water stress, others by oxygen stress. In addition, due to land pressure, the farmer is forced to rent fields with poor soil quality in combination with fields with good soil quality. Nematode pressure can be very high and difficult to control in some of these fields. Further, nutrient, crop protection and weeding application is not always optimal due to the size and shape of the fields, timing of operations, and tree shading. Also the source of the seed potatoes influence obtained potato yields, and the farmer does not have full control about this.

Fig. 3 shows how planting date influences the maximum possible tuber yield. Both in 2013 and 2014 fields that are planted early and late have lower yields compared to an optimum in the middle. It is however not possible for the farmer to plant all the fields at the optimum date. When combining different inputs in a frontier analysis, limiting factors can be determined per field, and potential yields can be determined per field and at farm level.

<figure><img src="image_204.png" /><figcaption>**Fig. 3.** Obtained yields per field (in t ha⁻¹) plotted against planting date. Frontiers are included to showing the highest yield possible at a certain planting date, according to observations.</figcaption></figure>

## 4 Conclusions

It is generally acknowledged that potential yields cannot be achieved at farm level, and 80% of the potential yield is considered exploitable. A variety of reasons have been given, including weather variability and economic optimization (e.g. Lobellet al. 2009). In this study we make farm level constraints to achieve potential yields in each field specific, and show that although potential yields may be achieved in some fields, at farm level the potential yield is lower.

## References

* Lobell, D., Cassman, K.G. & Field, C. (2009). Crop Yield Gaps: TheirImportance, Magnitudes,and Causes. *Annual Review of Environment and Resources*, **34**, 179-204.

* Van Ittersum, M.K., Cassman, K.G., Grassini, P., Wolf, J., Tittonell, P. & Hochman, Z. (2013). Yield gap analysis with local to global relevance - A review. *Field Crops Research*, **143**, 4-17.
# Combining systems analysis tools for the integrated assessment of scenarios in rice production systems at different scales.

Jean-Marc Barbier*±<fnref n="1" />, Stefano Bocchi<fnref n="2" />, Sylvestre Delmotte<fnref n="1" />, Andrea Porro<fnref n="2" />, Francesca Orlando<fnref n="2" />, Mirco Boschetti<fnref n="3" />, Pietro Alessandro Brivio<fnref n="3" />, Giacinto Manfron<fnref n="2" />,<fnref n="3" />, Simone Bregaglio<fnref n="2" />, Giovanni Capelli<fnref n="2" />, Roberto Confalonieri<fnref n="2" />, Françoise Ruget<fnref n="4" />, Vincent Courderc<fnref n="1" />, Laure Hossard<fnref n="1" />, Jean-Claude Mouret<fnref n="1" /> & Santiago Lopez-Ridaura<fnref n="1" />,<fnref n="5" />

## 1 Introduction

The integrated assessment of farming systems and the ex-ante analysis of future agricultural scenarios commonly make use of simulation models reproducing the sub-domains of the agro-ecosystems (Delmotte, Lopez-Ridaura et al. 2013). In the ScenaRice project we propose to integrate the information produced by stand-alone tools and models (e.g., field data, farmers' interviews, remote sensing analysis, crop model results, expert-based rules, farming system typologies, optimization models) to assess at different levels of complexity and scales the consequences of possible evolutions of rice farming systems. Central to our approach is the development of future scenarios considering plausible changes in agricultural policy, technological development and climatic conditions. Then, the different tools and models provide information at different scales to assess these scenarios: the field, the farm and the region. We applied this combination of data source in case studies in the main Italian and French rice areas and to some extent, to case studies in Madagascar and Sierra Leone. In this paper, we present the articulation of the different sources of information for the assessment of scenarios related to the evolution of the rice farming systems in Camargue, South of France.

## 2 Materials and Methods

The Camargue is a deltaic region in the South of France, characterized by low lands almost at sea level, and a mosaic landscape composed of natural and cropped area. Rice and wheat are the most important crops and their future in term of production level and presence in the area is threatened by multiple drivers, including climate change, economic and regulatory conditions. Four scenarios were developed through three workshops with representative of the main stakeholders of the region. These scenarios were then assessed using the different tools and data source above mentioned and detailed below. Remote sensing analysis of time series of MODIS satellite images allowed to estimate dates for the main agricultural management practices and phenological stages of rice and wheat based cropping systems (Boschetti, Stroppiana et al. 2009). This information has been used as inputs for the STICS (Coucheny, Buis et al. 2015) and WARM (Confalonieri, Rosenmund et al. 2009) crop models parameterized to reproduce the development and growth of the most cultivated rice varieties. The simulation outputs allowed assessing quantitative (e.g., aboveground biomass and yield) and qualitative aspects (e.g., head rice yield, protein content) of rice productions and of the other main crops (notably wheat and alfalfa) at field scale. The outputs of crop models, combined with other existing databases and farmers interviews, served to (i) define agricultural activities being currently done in Camargue and that could be possible in the future (considering the scenarios developed), and (ii) to assess the potential performances of these activities (and their variability) under different climate change scenarios. Remote sensing also provided information about the land use (e.g. cultivated surface of winter and summer crops) at farm level for 13 consecutive years, to identify main current trajectories of change. We conducted multivariate analysis of databases (including the farm trajectories identified using remote sensing) to build farm typologies. Finally, a multiple goal linear programming model has been developed to assess the scenarios in term of trade-offs and combinations of resources allocation, regarding a set of indicators at farm and regional level including socio-economic and environmental indicators (e.g. pesticide use and green-house gases emissions) (Lopez Ridaura, Delmotte et al. 2014). The integration of all the data allowed the integrated assessment of future farming systems in the context of the four scenarios built together with local stakeholders.

## 3 Results – Discussion

The four scenarios developed with the main stakeholders of the region are related to the economic and regulatory conditions for the rice production and to the impacts of climate change, including also drivers such as the price of energy and inputs, regulations related to pesticide use and greenhouse gas emissions, and the development of organic

<footnote n="1">1 INRA, UMR Innovation 951, Montpellier, France</footnote>

<footnote n="2">2 University of Milano, Milano, Italy</footnote>

<footnote n="3">3 CNR-IREA, Milano, Italy</footnote>

<footnote n="4">4 INRA, UR Emmah, Avignon, France</footnote>

<footnote n="5">5 CIMMYT, Texcoco, Mexico</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: jean-marc.barbier@supagro.inra.fr</footnote>
farming. In the communication, we will show the results obtained with the different tools and models at the field, farm and regional levels with examples taken from the different scenarios identified. Fig. 1 A presents the validation of the remote sensing analysis of wheat sowing dates, over 3 farms and 3 years. This analysis was extended to the whole region and for the 2001-2010 period, allowing us to analyze and understand the variability of wheat sowing date, notably in relation to the distribution of raining events in fall. From this analysis, we derived rules to determine potential wheat sowing dates to be used for simulations of climate change scenarios with the STICS and WARM crop models. Fig. 1 B shows the validation of the STICS crop model for the simulation of rice in Camargue, by comparing for multiple years observed and simulated yield variability. Both the STICS and WARM were used to simulate rice activities in Camargue, while STICS was used to simulate the other crops. Finally, fig. 1 C shows the impacts of the application of a given scenario on multiple indicators of alternative farming systems for a single farm, obtained from both an expert prototype and a bio-economic model. The bio-economic model was used to simulate the consequences of the four scenarios on the different farm types of Camargue (identified in the farm typology) and to upscale the consequences at the regional level, notably including indicators related to the supply-chain, the water quality, the greenhouse gas emissions or the feeding potential of agriculture in the region. These results will be presented in more details in the communication.

<figure><img src="image_205.png" /><figcaption>**Fig. 1.** A. Comparison of observed and simulated (estimated by remote sensing data analysis) sowing dates of wheat for three farms over three years in Camargue. B. Comparison of the variability of observed and simulated rice yield in Camargue with the STICS crop model for 11 different years. C. Comparison of three farming systems for one of the scenarios: the current situation, a prototype developed by experts and a farming system designed by the bio-economic model.</figcaption></figure>

## 4 Conclusions

The integrated assessment of the evolution of farming systems in the future requires the mobilization of multiple sources of information, given the great uncertainty associated with changes in agricultural policy, technology and climatic conditions. In the communication, we will report in more details and on the basis of real cases the results of the combination of the different tools and approaches above mentioned, and notably the original combined use of remote sensing, crop models and bio-economic model, and highlight the added value for the integrated assessment of farming systems under scenarios notably related to climate change.

*Acknowledgements.* This research is being conducted within the Scenarice project (n°1201-008) funded by the Agropolis and Cariplo foundations.

## References

Boschetti, M., D. Stroppiana, et al. (2009). "Multi-year monitoring of rice crop phenology through time series analysis of MODIS images." International Journal of Remote Sensing 30(18): 4643-4662.

Confalonieri, R., A. S. Rosenmund, et al. (2009). "An improved model to simulate rice yield." Agron. Sustain. Dev. 29(3): 463-474. Coucheney, E., S. Buis, et al. (2015). "Accuracy, robustness and behavior of the STICS soil-crop model for plant, water and nitrogen outputs:

Evaluation over a wide range of agro-environmental conditions in France." Environmental Modelling & Software 64(0): 177-190.

Delmotte, S., S. Lopez-Ridaura, et al. (2013). "Prospective and participatory integrated assessment of agricultural systems from farm to regional scales: Comparison of three modeling approaches." Journal of Environmental Management 129(0): 493-502.

Lopez Ridaura, S., S. Delmotte, et al. (2014). Multi-criteria and multi-scales evaluation of different organic farming extension scenario. Organic Farming, prototype for sustainable agriculture. S. Bellon and S. Penvern. Dordrecht, NLD: Springer: 453-478.
# Trajectories of farming systems and land use changes in Southern Ethiopia

Yodit Kebede ±<fnref n="1" />, Frédéric Baudron <fnref n="2" />, Felix Bianchi <fnref n="1" />, Kristin Abraham <fnref n="3" />, Kassahun Lemi Woyessa <fnref n="1" />, Pablo Tittonell <fnref n="1" /> & Lammert Kooistra <fnref n="3" />

## 1 Introduction

Maize is a crucial staple crop for Ethiopia and plays a major role in providing food security. While the region of Hawassa in Southern Ethiopia is one of the main maize production areas of the country, the productivity remains low. Several factors can expel, in this low productivity. In order to understand these factors, and their change over time we undertook this diagnostic study on past farming systems dynamic and their impact on actual farming systems diversity. Farming orientation and practices of individual farmers is constrained by their socio-economic situation, by biophysical conditions and other external drivers such as political context. This produces the diversity observable at farm scale, which in turn shapes the landscape structure. The diversity at farm scale changes over time producing different landscapes structures. The general objective of this study is to describe the land use/land cover changes, the changes in farming systems (in terms of farm typologies) and their drivers. In particular, we aim to answer to the following questions: (i) what is the farm size, production orientation and crop diversity of current farming systems? (ii) How do these characteristics change over time? (iii) How do these changes influence landscape composition? To answer these questions, we used triangulation of information collected via participatory methods, secondary data and satellite images analysis (Fig. 1).

## 2 Materials and Methods

### 2.1 Participatory methods

Three districts with contrasting farming systems were selected in the Hawassa area in Southern Ethiopia as study sites (Hawassa Zuria, Tula and Wondo Genet). Primary data were collected via (i) informal discussion, (ii) focus group discussion (FGD), and (iii) life history interviews. The focus group discussion was conducted with key informants in each district to understand their perceptions of the changes in farming systems and how these changes are reflected in the landscape via simple sketches of their district. The life history interviews were conducted on a stratified random sample of 40 households selected from a survey of 177 farmers conducted in 2013. The stratification was based on participatory farm typology data

<figure><img src="image_206.png" /><figcaption>Fig. 1. Methodology of the study</figcaption></figure>

<figure><img src="image_207.png" /><figcaption>Fig. 2. Sketch of Wondo Genet (top) and Hawassa Zuria (down) in 1991 (left) and in 2014 (right)</figcaption></figure>

### 2.2 Land cover change analysis

Three Landsat images of the study area for the years 2014, 1998, 1984 were chosen to conduct an object-based image analysis (OBIA). The selected dates allow the comparison of the periods prior and after 1991, the year that the communistic Derg regime ended. The analysis focussed on grassland, annual crops, perennial crops and urban areas.

## 3 Results and Discussion

3.1 In informal discussions elderly people indicated three main periods with a pronounced impact on their farming systems and livelihoods (prior-1974, the period 1975-1991 and post-1991), which coincide with regime changes in Ethiopia. Farmers' sketches of their district in 1991 and 2014 revealed that Before (Fig. 2). before 1991 Wondo Genet was dominated by natural forest and pasture land, coffee, enset and maize crops (Fig. 2). After 1991, the natural forest had disappeared and that the area under that production had expanded. Similarly, in

<footnote n="1">¹ Wageningen University,- Farming Systems Ecology group, Wageningen, The Netherlands</footnote>

<footnote n="2">² CIMMYT Ethiopia, Global Conservation Agriculture Program, Addis Ababa, Ethiopia</footnote>

<footnote n="3">³ Wageningen University,- Laboratory of Geo-information Science and Remote Sensing, Wageningen, The Netherlands</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: yodit.kebede@wur.nl</footnote>
Hawassa Zuria the natural forest was removed in 1991 and replaced by enset and maize. Along Lake Hawassa vegetables were introduced. The analysis of life histories of 40 farmers of the three districts shows a decrease of land holding and tropical livestock unit (TLU) density (Fig. 3). Changes in area of maize and perennial crops (khat and enset) were district specific. The area of maize was rather constant in Wondo Genet and Tula, but in Hawassa Zuria more dynamic can be observed. In some farms maize area increased, while others remained at the same size. Enset production, an important crop for food security increased significantly in Hawassa Zuria. This increase has been promoted by development projects.

<figure><img src="image_208.png" /><figcaption>**Fig. 3.** Changes in land holding, TLU maize area and perennial crop area (khat and enset) between 1991 and 2014 in Hawassa Zuria, Tula and Wondo Genet</figcaption></figure>

<figure><img src="image_209.png" /><figcaption>**Fig. 4.** Area of major land cover types in 1984, 1998 and 2014 in the Hawassa area</figcaption></figure>

3.2 Landsat image analysis indicated that most changes in land use occurred after 1998 (Fig. 4). The increase of perennial crops enset and khat between 1998 and 2014 is confirmed by the FGD data indicating that the area of enset increased in Hawassa area and the area of khat increased the most in Wondo Genet. At the same time, the area of annual crops areas decreased because of a shrinking area of maize in Wondo Genet and Tula, and the expansion of Hawassa city and the emergence of three towns in Wondo Genet. Grassland has virtually disappeared in the Hawassa area.

## 4 Conclusions

This study shows that farming systems and landscape composition in the Hawassa area are highly dynamic. This conclusion is supported from independent analyses of participatory data and land cover change. Across the three study sites natural forest has disappeared, and the per capita land holding and TLU have decreased. In two out of the three districts the area of cash crop (khat) has increased. This suggests that the land holding is no longer sufficient to sustain the livelihood of farmers triggering a shift to cash crop production.

## References

* Abebe T., Wiersum T., Bongers F. 2009, Spatial and temporal variation in crop diversity in agroforestry homegardens of southern Ethiopia, Published online: 31 July 2009

* Abate T. 2008. Maize stalk Borers of Ethiopia: Quantitative Data on Ecology and Management in Dawit, A., Mwangi W., Nigussie, M., Spielman D., 2008. The maize seed system in Ethiopia: challenges and opportunities in drought prone areas, African Journal of Agricultural Research Vol. 3 (4), pp. 305-314

* Lambin E.F., Geist H. J., and Lepers E. 2003, Dynamics of Land use and Land Cover Change in Tropical Regions, Annu. Rev. Environ. Resour., 2003.28:205-41

* Pacini G.C., Colucci D., Baudron F., Righi E., Corbeels M., Tittonell P., Stefanini F.M., 2014. Combining Multi- dimensional scaling and cluster analysis to describe the diversity of rural households Experimental agriculture, 50 (3): 376-397.

* Tittonell, P., Vanlauwe, B., Leffelaar, P.A., Rowe, E.C., Giller, K.E., 2005. Exploring diversity in soil fertility management of smallholder farms in western Kenya: I. Heterogeneity at region and farm scale. Agriculture, Ecosystems & Environment 110, 149-165
# Sweet sorghum: methodological exploration of a multifunctionality to innovate in Haitian agriculture

Annaïg Levesque *±1*, Ludovic Temple 2, Serge Braconnier 3 & Bénédique Paul 4

## 1 Introduction

Sorghum is a hardy tropical plant, less demanding in water and nutrients, that can grow in poor soils. This cereal is used for human and animal consumption. While it is more used for animal consumption in developed countries, in Southern countries, it can represent an important part of the human diet, even if it is less and less popular.

Recently the international scientific community has been more and more interested by this plant particularly the sweet varieties (Damasceno et al., 2014), which are able to combine a grain production with a sugar accumulation in their stalks giving them a multipurpose characteristic (Braconnier et al., 2014).

In Haiti, a research and development project (S3F for Haiti) was implemented by CHIBAS (a Haitian foundation for agricultural research), Université Quisqueya (a Haitian university), and CIRAD (a French institution leading research in tropical agriculture), from 2010 to 2015, to introduce and develop sweet sorghum in Haiti. This multipurpose cereal, producing grains and sweet juice extracted from its stalks, can be used to produce bio-ethanol or alcohol while biomass (leaves and bagasse) left after juice extraction, represents a good source of forage or animal feed (Leclerc et al., 2014). The development of these uses can provide an answer to Haiti's needs for food, feed and fuel.

The main question discussed in the project is to know how to assess in itinere the conditions to develop a cross-sectoral innovation, the sweet sorghum in Haiti. The hypothesis is that these conditions are revealed by: i) the analysis of existing and potential impacts on different sectors which are involved (animal feed, alcohol, human food), ii) the identification of the main bottlenecks, iii) the synergies between stakeholders (producers, industrials, State agents and research) around productions of information, knowledge, technologies and political orientations.

## 2 Materials and Methods

We mobilize the sectoral innovation system (Malerba, 2002; Touzard et al, 2015) as analytical framework. This reference frame characterizes stakeholders system which is mobilized by the innovation process related to the introduction of a new cereal variety, and the conditions of its use in different potential value chains.

The analysis is structured by the sectoral dimension according to two macro-value chains related to food supply (human and animal) and energy supply. It provides a representative frame of the meso-economic relations in terms of vertical relationships between the different stakeholders (Temple et al., 2011) involved in (1) production, (2) processing (grain, sugar and biomass) into intermediate and final products and (3) commercialization for the different uses (granule as cattle food, alcohol as "kleren" which is a local rum, syrup, bran, bagasse silage). It allows us to characterize the appropriate technological, economic and social conditions for sweet sorghum introduction.

An important set of secondary and primary data was collected throughout the project. Three surveys were achieved successively (Charles et al, 2012; Lamour, 2013; Levesque, 2014) on a global sample of 70 resource persons from institutions and companies, that structure the Haitian system of research and innovation.

Direct face to face surveys were also conducted successively in 2012, 2013 and 2014 with samples of producers and industrials (small, medium and big processors) in various sectors.

During this study, the sweet sorghum implementation in different value chains was analyzed in three situations: in an industrial sugar cane area where we could find big "kleren"-processing plants, in a craft and hilly area full of small "kleren"-processing plants, and in the poultry industry area where sorghum can be used as animal feed.

## 3 Discussion

The sweet sorghum innovation can have different impacts on the development of Haiti. It can create jobs through the emergence of a new industry related to "kleren" production that would also stimulate the national sugarcane industry and sorghum grain production. The quantification of this job creation was appreciated by experimental observations realized in the different analysed situations. Thus, in the studied industrial area, from 61 to 131 man-months would be required, while in the hilly craft area the demand would be 60 man-months concerning industrial sectors. Moreover, sweet sorghum can maintain industries and agricultural activities based on sugarcane industry. For food producers, collecting grains in a production area requires people to organize the collection, and to load/unload grains.

The sweet sorghum innovation can also impacts positively on households' food safety through (i) an increased and

<footnote n="1">¹ Institut des Régions Caudes - Montpellier Supagro</footnote>

<footnote n="2">² Centre de cooperation International en Recherche Agronomique pour le Développement (CIRAD)</footnote>

<footnote n="3">³ Centre de cooperation International en Recherche Agronomique pour le Développement (CIRAD)</footnote>

<footnote n="4">⁴ Centre de Recherche en Gestion et en Economie du Développement (CREGED), Université Quisqueya (UniQ)</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: annaig.levesque@gmail.com</footnote>
diversified income for producers giving them a better access to food and (ii) a higher grain yield from the sweet varieties.
This innovation presents potentialities to use co-products in animal feed and could positively impact on the trade balance
of Haiti through a decrease of corn and meat imports. The mobilized methodological framework permits to highlight the
macroeconomic conditions to achieve these impacts from observations made on users' experimental devices.

Two types of factors limit the innovation processes and thus the achievement of the potentials impacts: technological and socioeconomic factors.

Considering the technological factors, the conditions of sweet sorghum use are heterogeneous in production areas,
according to the priority given to potential uses of the stalks co-products (“kleren”, animal feed...). It is necessary to
adapt sweet sorghum varieties to these different uses. The use of sweet sorghum to produce “kleren” requires to reduce
as much as possible the time to harvest and transport stalks process units for preventing Brix decrease. To challenge
these limits, technological, organizational and institutional (Paul, 2012) innovations in the supply chain are needed
(moving mills, varieties, contracts).

About the socioeconomic factors, the use of sweet sorghum in industrial sectors (alcohol, animal feed) implies to consider
the determinants of their competitiveness, partly linked to the orientations taken by commercial politics and public
supports. The use of sweet sorghum grains as feed in the industrial poultry industry depends mainly on the price difference
between sorghum grains and the competitor products, particularly imported maize. The use of bagasse as fuel for alcohol
production implies to take into account the potentially negative social externality as this prevent other uses as feed for
cattle, which play a saving role, or as soil fertilizer (organic matter and nutrients recycling).

Facing these bottlenecks, the adoption of this innovation requires to reinforce the technology as well as the organization
of the activities to improve economic profitability of sweet sorghum use by the different stakeholders. Capacity to pool
the supply logistics of producers and industrials as well as favourable orientation of public policies are two important
axes to develop the market.

## 4 Conclusions

The innovation, carrying potential positive impacts on the development and the households' economic situation, could be
implemented and diffused only if the identified blocking factors are unlocked by adapted policies in research and
innovation. A main issue is the development of interactions between stakeholders involved in the technological and
organizational aspects that will determine future orientation of innovation processes. Nevertheless, this innovation may
result in important upheavals through a reorganization of the livestock and a decrease in soil fertility, if no alternative is
proposed to producers for replacing sorghum stalks left on the fields which pay play a role in the production system.

Furthermore, this study strengthens methodological frameworks of in itinere assessment of the conditions for
implementing an innovation in a context of Southern country agriculture.

*Acknowledgements.* Thanks to all who grant some time to help the project. Thanks to Gaël Pressoir, Elise Leclerc, Daphnée Charles, Adnet Lamour who were helpful and who made a great job about sweet sorghum. Thanks to the ANR which has funded the project S3F.

## References

Braconnier, S., Amaducci, S., Basavaraj, G., Borges Damasceno, C., Buschmann, R., Clement-Vidal, A., Cornelius, C., Fracasso, A., Gutjahr, S., Janssen, R., Khawaja, C., Köppen, S., Luquet, D., Monti, A., Parrella, R., Parthasarathy, R., Reddy Belum, V.S., Rettenmaier N., Reinhardt G., Rutz, D., Schaffert, R., Shargie, N., Snijman, W., Srinivasa Rao, P., Terblanche, H., Trouche, G., Zacharias, A., Zavala-Garcia, F. & Zegada-Lizarazu, W. (2014). Sweet sorghum as an alternative energy crop: results from 5 years of research (SWEETFUEL Project). *22nd European Biomass Conference and Exhibition, Hamburg, Germany, June 23-26*.

Charles, D., Pressoir, G., Temple, L. & Leclerc, E. (2012). Conditions économiques du développement d'une innovation variétale : le sorgho sucré multi-usage en Haïti. *Rapport de projet "Agence Nationale de la Recherche Flash Haïti"*, uniQ, Cirad & Chibas, 95 p.

Damasceno, R., Schaffert, E. & Dweikat, I. (2014). Mining Genetic Diversity of Sorghum as a Bioenergy Feedstock. *Plants and Bioenergy, Advances in Plant Biology* 4, 81-102.

Lamour, A. (2013). Perspectives d'innovation pour développer l'usage du sorgho sucré dans l'alimentation animale en Haïti. *Mémoire de fin d'étude Montpellier Supagro IRC, Cirad & Chibas*, 100 p.

Leclerc, E., Pressoir, G. & Braconnier, S. (2014) L'avenir prometteur du sorgho sucré. *Field Actions Science Reports*, **9**, 9 p.

Levesque, A. (2014). Le sorgho sucré, une plante aux multiples valorisations sources d'innovation en Haïti. *Mémoire de fin d'étude Montpellier Supagro IRC, Cirad, Chibas & uniQ*, 148 p.

Malerba, F. (2002). Sectoral Systems of Innovation and Production. *Research Policy*, **31**, 247-264.

Paul, B. (2012). Institutional Change in Haiti, the real challenges. *Recherche, Etude et développement*, **5**, 27-33.

Temple, L., Kwa, M., Tetang, J., Bikoï, A. (2011). Organizational determinant of technological innovation in food agriculture and impacts on sustainable development. *Agronomy for sustainable development*, **31**, 745-755.

Touzard, J-M., Temple, L., Faure, G., Triomphe, B. (2015). Innovation systems and knowledge communities in the agriculture and agrifood sector. *Journal of Innovation Economics management* (in press).
W3. Cropping systems design: what can we do with field experiments and expert knowledge?

Chair: Jean-Marc Meynard, INRA
Co-chair: Raphaël Metral, Montpellier SupAgro
# System experiments: methodological progress

Jean-Marc Meynard *±1

1 INRA, UMR 1048 SAD-APT INRA AgroParisTech, 78850 Thiverval-Grignon

## 1 Introduction

Unlike traditional analytical experiments, where elementary techniques are tested to analyze their main effects and their interactions, system experiments consist of implementing cropping systems, livestock systems or farming systems, i.e. consistent combinations of technical choices. System experiments aim at testing in the field the capacity of innovative cropping, livestock or farming systems to reach the objectives for which they were designed (Meynard et al., 2012). They are a methodological resource in the design process, making a full-scale assessment of the performances of systems designed by expert prototyping or model-based design, putting their construction principles to the test and helping to improve them. There is an increased interest in system experiments today because of the challenges of redesigning farming systems, mainly associated with the negative externalities of dominant high-input agriculture. These experiments can be carried out in experimental or real farms. System experiments on cropping systems have usually been carried out at field scale, on annual or multiannual time steps. Experiments on livestock systems are often carried out at whole-farm level on multiannual time steps. This paper will give an overview of methodological progresses concerning this experimental paradigm, taking into account both animal and crop productions.

## 2 Management of experiments with decision rules

The first system experiments (1990s and early 2000s) were carried out on the basis of general directives given to the experimenters (for example « *When possible, meadows were cut early and conserved in wrapped round bales. The nitrogen supply was taken as based on the legumes for the pastures and the protein-rich plants for crops to replace purchased nitrogen fertiliser* », Benoit et al., 2009), or pre-established schedules of operations (for example, Clements et al., 1995). The proposal of Reau et al. (1996) to base technical choices on decision rules made it possible to reconcile the flexible adaptation of techniques to diverse farming situations with a formalization enabling all the experimenters in a network (or the same experimenter over several successive years) to make coherent decisions. Usually a rule is made up of (i) a function, which links the decision to the objectives of the system, (ii) a solution, which displays the possible actions according to the context in conditional form ("If . . . then . . . ; else..."), and (iii) an evaluation criterion to check whether the objectives were reached or not. Decision rules are applied on the basis of indicators related to the soil, the plants, the herd or the climate, which are clearly formalised and accessible to the decision-maker (Debaeke et al., 2008). The evaluation of the cropping or livestock system becomes the evaluation of the set of decision rules. Explicitly formulated, these rules make it possible to reproduce the system in other times or other conditions: after assessment, they become the basis for diffusing, to the farmers, the achievements of the system experiment (Dejoux et al., 2003).

## 3 The assessment of the tested systems, to prepare their scaling out

The multicriteria assessment of the systems tested consists of checking that they achieve their economic, environmental or social objectives. Classically, a list of assessment criterions is drawn up at the beginning of the experiment and indicators or measured variables are identified for each criterion. For example, Benoit et al. (2009) assessed two ovine systems differing by the degree of self-sufficiency and by the ewes' reproduction rhythm (one lambing per ewe per year vs. 3 lambings over 2 years) on the following criterions: "*ewes' reproductive performance, lamb growth rate, carcass characteristics and quality, animal health, forage and feed self-sufficiency, soil mineral balance and gross margin*". Loyce et al., (2012) proposed to analyse the sensitivity of economic criterions to the price of grains or of inputs. Setting up multilocal networks of system experiments, in farmers' fields, makes it possible to identify the conditions under which a cropping or livestock system gives satisfactory results; but such networks are still rare, because of the cost and unwieldy nature of these experiments, which sometimes require large areas of land to be realistic (Dejoux et al., 2003). However, Meynard et al. (1996) stressed that this global assessment should be completed by an agronomic evaluation, which consists of checking that the principles on which the system was built are validated. Loyce et al. (2008) checked the basic principles of low input wheat management: by reducing crop density and early nitrogen input, and by choosing resistant cultivars, fungal diseases and lodging are controlled just as much as with more classical high input management (using fungicides and growth regulators). To check such principles, additional experimental treatments (e.g. low input system with high yielding disease susceptible cultivar) are sometimes necessary. An agronomic diagnosis, carried out on the systems tested, makes it possible to determine the causes of a yield gap, a health problem or an environmental pollution. An erroneous decision rule can then be corrected, and the scaling out of the tested systems or of their construction principles can be prepared. Dejoux et al. (2003) give an example, showing that rapeseed management based on early sowing (1 month earlier than normal sowing) in general gives economically and environmentally satisfactory results, except on shallow soils with low available nitrogen. As shown by these examples,

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Meynard@grignon.inra.fr</footnote>
the systems assessments are usually multidisciplinary : the disciplines of plant and animal health are called upon, as well as agronomics and environmental sciences. In long-term experiments, the cumulative effects created by the systems can even become the heart of investigations by specialists in ecology or soil physics (Henneron et al, 2015).

## 4 Learning in a system experiment; step by step design

Sometimes, in a multiannual experiment, the annual assessment of a system leads to the conclusion that the formulation of a decision rule is inconsistent with the other rules or with the system objectives. What is the best? Leaving the rule unchanged, so that it remains the same set of rules which governs the experiment throughout its duration, or changing it, to improve the system in real time (Debaeke et al., 2008)? This second option, based on the fact that the experimenter learns from the assessment, as a farmer would do, is at the heart of “step-by-step design process”: Coquil et al. (2009) and Meynard et al. (2012) thus propose the organization of learning loops within the system experiment, which allows developing gradually a cropping or livestock system to make it more and more successful in achieving its objectives. The function of the system experiment changes from the assessment of a designed system towards the progressive design of the system. This step-by-step design is based on a spiral of continuous improvement: real time assessment indicates the criterions that are not met satisfactorily; an agronomic diagnosis makes it possible to identify the agronomic and ecosystem functions which are in question and technical actions which should be changed; on the basis of this diagnosis, evolutions of the farming systems are proposed and implemented. Then a new assessment is made. new evolutions of the systems follow, etc. According to such an approach, Gouttenoire et al. (2010), analyzing the causes of a drop in the fertility of dairy cows in an organic low-input grazing system, suggested modifying the dates of the breeding or lengthening the lactation periods (up to 2 years) to improve reproductive performance.

Step-by-step design also makes it possible to confront situations where, for lack of sufficient knowledge, it is not possible to write some decision rules at the start of the experiment: so, decisions for system management are made on the basis of the expertise of the experimenter; the actions implemented are then evaluated, which leads to learnings and to the progressive formalization of the decision rules. In an experimental farm, learning is collective: while the experimenter learns about decision rules, the tractor drivers or herdsmen also learn how to implement practices; they all change their way of observing crops and animals, in relation with system assessment criterions (Fiorelli et al., 2014). The results of such experiments are not reduced to the design and assessment of innovative systems: they also produce tools to encourage learning, which can then be passed on to the farmers (Coquil et al., 2009). More than the systems themselves, scaling out concerns the step-by-step approach to design, and the tools (indicators, diagnostic tools, library of innovations) which underlie it (Meynard et al., 2012). Examples of step-by-step system experiments, in mixed farming and market-gardening, are given in other papers during this conference (Durant 2015, Lefèvre et al., 2015).

## 5 Conclusions

Ultimately, from being a resource for assessing systems designed in silico or in design workshops, the system experiment also becomes a resource for organizing the mixing between expert knowledge and scientific knowledge in designing new systems, and a resource for collective learnings in the teams of experimenters.

## References

* Benoit, M., Tournadre H., Dulphy, J.P., Laignel, G., Prache S.& Cabaret J. (2009) Is intensification of reproduction rhythm sustainable in an organic sheep production system? A 4-year interdisciplinary study Animal, 3:753-763.

* Clements D.R., Weise S.F., Brown R., Stonehouse D.P., Hume D.J., & Swanton C.J.. (1995) Energy analysis of tillage and herbicide inputs in alternative weed management systems: Agriculture, Ecosystems & Environment 52 119-128.

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

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

* Dejoux J.F.; Meynard J.M.; Reau R.; Roche R.; Saulas P.; 2003. Evaluation of environment friendly crop management systems for winter rapeseed based on very early sowing dates. Agronomie. 23, 725-736.

* Durant, D. (2015) An example of agroecological transition on the Saint-Laurent-de-la-Prée Research farm: method and first results; his symposium

* Fiorelli C., Auricoste C., Meynard J.M. 2014 Concevoir des systèmes de production agroécologiques dans les stations expérimentales de l'INRA: des changements importants de référentiel professionnel pour les agents et les collectifs de recherche. Courrier de l'Environnement de l'INRA (64) 57-68

* Gouttenoire, L., Fiorelli, J.L., Trommenschlager, J.M., Coquil, X., Cournut, S., 2010. Understanding the reproductive performance of a dairy cattle herd by using both analytical and systemic approaches: a case study based on a system experiment. Animal 4: 827-841.

* Henneron L., Bernard L., Hedde M, Pelosi C., Villenave C., Chenu C., Bertrand M., Girardin C., Blanchart E. (2015) Fourteen years of evidence for positive effects of conservation agriculture and organic farming on soil life. Agron. Sustain. Dev. 35:169-181.

* Loyce C., Meynard J.M., (...), Doussinault G. (2008). Interaction between cultivar and crop management effects on winter wheat diseases, lodging, and yield. Crop Protection 27: 1131-1142.

* Loyce C., Meynard J.M., (...), Doussinault G. (2012). Growing winter wheat cultivars under different management intensities in France: A multicriteria assessment based on economic, energetic and environmental indicators. Field Crops Research 125:167-178

* Lefèvre A., Salembier C., Perrin B., Lesur-Dumoulin C. & Meynard J.M. (2015) Design, experimentation and assessment of four protected vegetable cropping systems adapted to different food systems. 5th international symposium for farming system design, Montpellier

* Meynard, J.M., Dedieu, B., & Bos, A. P. (2012). Re-design and co-design of farming systems. An overview of methods and practices. In Farming Systems Research Into the 21st Century: The New Dynamic. Springer.

* Meynard J.M.; Reau R.; Robert D.; Saulas P.; 1996. Evaluation expérimentale des itinéraires techniques. In « Expérimenter sur les conduites de culture : un nouveau savoir-faire au service d'une agriculture en mutation ». DERF-ACTA, Paris, pp : 63-72.

* Reau R.; Meynard J.M.; Robert D.; Gitton C.; 1996. Des essais factoriels aux essais « conduite de culture ». In « Expérimenter sur les conduites de culture : un nouveau savoir-faire au service d'une agriculture en mutation ». DERF-ACTA, Paris, pp ; 52-62.
SYPPRE :A project to promote innovations in arable crop production mobilizing farmers and stakeholders and including co-design, ex-ante evaluation and experimentation of multi- service farming systems matching with regional challenges.

Clotilde Toqué *±<fnref n="1" />*, Stéphane Cadoux <fnref n="2" />, Pascaline Pierson <fnref n="1" />, Rémy Duval <fnref n="3" />, Anne-Laure Toupet <fnref n="1" />, Francis Flenet <fnref n="2" />, Benoît Carroué <fnref n="4" />, Frédérique Angevin <fnref n="5" />& Philippe Gate <fnref n="1" />

**1 Introduction**

Agriculture must succeed in reconciling food production, energy production and conservation of the environment. This requires a shift towards new production systems, based on ecological intensification, adapted to local conditions, and manageable by farmers. To address this issue, the French technical institutes Arvalis-Institut du végétal, Terres Inovia and ITB, launched the collaborative project SYPPRE (SYstèmes de Production Performants et Respectueux de l'Environnement). It is based on three work packages: observatory of current agriculture practices (WP1), four platforms to co-design and experiment innovative cropping systems adapted to local conditions (WP2), and facilitation of farmers groups in the re-design of their farming systems (WP3). In this paper, we highlight the method used in WP2 to co-design and select multi-services cropping systems for long term experiments.

**2 Materials and Methods**

The national framework for the SYPPRE project has been specified from the share of a prospective view for crop productions over the fifteen next years. To stay close to farmers issues and to save links with territorial specificities, the project has been then declined in four locations representative of main arable crop productions: PIC (Picardie, northern France, deep loamy soils, industrial productions, irrigated; LAU (Lauragais region, southern France, clay-limestone, arable crops, rainfed); CHAM (Champagne, chalky soils, industrial productions, rainfed); BER (Berry, shallow clay-limestone, arable crops, rainfed). The locations would help to explore a range of soil, climate and production system conditions, and thus a diversity of solutions to achieve the common stakes.

For each location we applied the methodology of 'de novo' co-design of cropping systems (Meynard et al., 2012) to reconcile global issues and local constraints. Working groups have been set up with farmers, local advisors, researchers, crop specialists, and also grain collectors to keep a view on new production opportunities. Each person was chosen to balance the profiles in the groups, including experts in local issues and local knowledge, experts bringing good exploration knowledge and skills, and changes leaders (Reau et al., 2012).

We followed five steps with each regional group to design innovative farming systems: i) identifying local issues based on a prospective study and defining local framework, ii) defining the most representative cropping system in the region, and its limitations, based on regional statistics and local expertise, iii) identifying candidate crops and suitable agronomic strategies, based on general knowledge and local expertise, iv) designing innovative cropping systems, v) making *ex ante* assessment of each system. The evaluation was performed with SYSTERRE® (Berrodier & Jouy, 2013), Odera-Systèmes (Pernel et al., 2011), AMG (Andriulo et al., 1999) and MASC (Sadok et al., 2009). A loop of improvement was carried out before selecting the most promising prototype to be experimented in each platform.

**3 Results – Discussion**

The prospective study led to focus SYPPRE on three main stakes. Indicators were proposed for their assessment: i) High productivity, to achieve higher needs for biomass to face an increasing demand for food, energy and proteins (Gross product and gross energy of whole plants), ii) High profitability for farmer (semi-direct margin), iii) Low inputs and environmental impacts (Treatment Frequency Index - TFI; amount of fertilizer-N; Green House Gas emissions - GHG, energy consumption, C and N stocks).

During the first meeting the designing groups completed the national framework of SYPPRE with local goals and constraints. They also agreed on the most representative cropping system to improve (Tab. 1). Situations are contrasted, from very simplified cropping systems (LAU, BER), to input dependent systems for CHAM and PIC mainly due to industrial crop production and a stronger pressure from pests and disease. The four groups have in common to search for an improvement of soil fertility as a solution to increase plant productivity and/or decrease dependency towards mineral fertilizers.

<footnote n="1">¹ ARVALIS Institut du Végétal, F-91720 Boigneville</footnote>

<footnote n="2">² TERRES Inovia, F-78850 Thiverval-Grignon</footnote>

<footnote n="3">³ ITB, F-75008 Paris</footnote>

<footnote n="4">⁴ UNIP,F-75008 Paris</footnote>

<footnote n="5">⁵ INRA, UAR 1240 Eco-Innov, F-78850 Thiverval-Grigon</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: c.toque@arvalisinstitutduvegetal.fr</footnote>
<caption>**Table 1.** Local stakes and representative systems for each location, and innovations proposed in the workshops</caption>

WW : Winter Wheat ; WOSR : Winter Oil Seed Rape S Beet : Sugar Beet ; S Barley : Spring Barley ; W Barley : Winter Barley

<table><thead><tr><th></th><th>Representative system</th><th>Local stakes</th><th>Main ideas (crops and agronomic strategies)</th></tr></thead><tbody><tr><td>PIC</td><td>SBeet / WW / Potatoes / WW / WOSR/ WW<br><i>Ploughing</i></td><td>√productivity ; √mineral nitrogen use; √soil fertility (less compaction)</td><td>Vegetables; legumes (intercrop only); double-cropping; WORS in association</td></tr><tr><td>CHAM</td><td>WOSR / S Barley / S Beet / WW<br><i>Ploughing</i></td><td>√quality of the products ; √soil fertility ; √GHG</td><td>√Crop rotation (10yrs); legumes; low input needed crops; varieties tolerant to nitrogen stress; double-cropping; soil cover; alternatings in soil tillage; biocontrol</td></tr><tr><td>LAU</td><td>Sun Flowers / Durum WW<br><i>Ploughing</i></td><td>√quality of the products ; √soil fertility (less erosion)</td><td>√Crop rotation (8yrs); legumes; permanent soil cover; Minimum tillage; double-cropping (biomasse production); decision rules and tools to √ nitrogen and water efficiency</td></tr><tr><td>BER</td><td>WOSR / WW / W Barley<br><i>Reduced tillage</i></td><td>√economic robustness ; √use of inputs ; √soil fertility; weed control</td><td>√Crop rotation (9yrs); legumes; new productions (soja, lentils, ...); permanent soil cover; Direct Seeding; robots, drones; catching plant on field margin</td></tr></tbody></table>

„Library of ideas" were complied during the second meetings of the groups (see extract in Table 1). Each practice was described with its agronomic function and expected benefits for the systems. Practices like introduction of legume or permanent cover and reduced tillage have been mentioned in the four groups. Scarcely developed but interesting, double cropping or crops in association (even with cereals) were also proposed to improve soil productivity. Uses of robots, biocontrol solutions or stress tolerant varieties have been considered to reduce inputs dependency. Five to nine systems have been co-designed in each group from the libraries of ideas before selecting the most promising prototype.

<caption>**Fig. 1.** Candidate prototype in Berry and first results of ex-ante assessment with Systerre, Odera and AMG-Simeos</caption>

WW : Winter Wheat ; WOSR : Winter Oil Seed Rape S Beet : Sugar Beet ; S Barley : Spring Barley ; W Barley : Winter Barley

<figure><img src="image_210.png" /></figure>

<table><thead><tr><th>Criteria</th><th>Indicator</th><th>Objectives</th><th>Results</th></tr></thead><tbody><tr><td rowspan="2">Productivity</td><td>Gross product</td><td>√=</td><td>= to √</td></tr><tr><td><strong>Gross energy</strong></td><td>√=</td><td>√</td></tr><tr><td rowspan="5">Economy</td><td>Semi-direct margin</td><td>√=</td><td>√</td></tr><tr><td>GHG emission</td><td>√</td><td>√</td></tr><tr><td>Energy consumption</td><td>√</td><td>√</td></tr><tr><td>TFI</td><td>√</td><td>√</td></tr><tr><td>Amount of fertilizer</td><td>√</td><td>√</td></tr><tr><td rowspan="2">Environment</td><td><strong>C &amp; N stocks</strong></td><td>√</td><td>=</td></tr><tr><td>Weeds density</td><td>√</td><td>√</td></tr></tbody></table>

In the example (Fig. 1), the candidate prototype for BER is an eight-year rotation system. New productions have been introduced to help weeds control and to reduce sensitivity to climatic and market hazards, which is confirmed by the ex-ante assessment. Introduction of legumes in the crop sequence even in the intercrop seems to be efficient enough to reduce nitrogen use and energy consumption and GHG emission at the same time. Minimum tillage and permanent cover would help reducing erosion. Impact on organic matter is below the objective but satisfying for the location. Gross product increases. Gross energy production is not satisfying at the moment but calculations have to be fine-tuned.

## 4 Conclusions

The five-step method we followed was efficient: it led to define the systems to set up in the four plateforms. We made assumptions with experts to carry out ex-ante assessments. It led to the identification of lacks of knowledge on the real effect of innovative practices. This would imply the implementation of factorial trials beside the systems, to make these platforms privileged places for knowledge's production and dissemination.

*Acknowledgements.* GIS HPEE, A. Schaub, R.Reau, A. Van Bocsom, G.Sauzé, J-L Verdier, P Amette, participants to the co-design in workshops

## References

Andriulo A., B. Mary et J. Guérif. (1999). Modelling soil carbon dynamics with various cropping sequences on therolling pampas. Agronomie. 19. 365-377.

Berrodier M., JOUY L. (2013). SYSTERRE : Méthode de calcul des indicateurs. Arvalis institut du Végétal. Tutoriel.

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

Pernel J; Munier-Jolain N. (2011). OdERA-Systèmes, Outil d'Évaluation du Risque en Adventices dans les Systèmes de culture. Colloque SCI. Poster.

Reau R., Monnot L-A., Schaub A., Munier-Jolain N., Pambou I., Bockstaller C., Cariolle M., Chabert A., and Dumans P. (2012). Les ateliers de conception de systèmes de culture pour construire, évaluer et identifier des prototypes prometteurs. Innovations agronomiques 20, 5-33.

Sadok, W., Angevin, F., Bergez, J.-E., Bockstaller, C., Colomb, B., Guichard, L., Reau, R., Messéan, A., Doré, T. (2009). MASC, a qualitative multi-attribute decision model for ex ante assessment of the sustainability of cropping systems. Agronomy for Sustainable Development 29, 447-461.
# Describing cropping system tested in an experimental network: contribution to analysis of results and sustainability performances and to inspiration of farmers, trainers and R&D

Marie-Sophie Petit<sup>±1</sup>, Violaine Deytieux<sup>2</sup>, Anne Schaub<sup>3</sup>, Camille Fonteny<sup>3</sup>, Clotilde Toque<sup>4</sup>, Sébastien Minette<sup>5</sup>, Stéphane Cadoux<sup>6</sup>, Anne-Laure Toupet<sup>4</sup>, Michaël Geloen<sup>7</sup>, Christophe Vivier<sup>8</sup>, Eric Bizot<sup>8</sup> & Raymond Reau<sup>9</sup>

## 1 Introduction

To face the renewed issues of agriculture, research, development and training actors have to join their efforts in order to (i) re-design and develop high performant arable and mixed farming cropping systems (CS), and (ii) inspire (future) farmers (Reau and Doré, 2008). In this way, the Joint Technology Network (JNT) for "innovative cropping systems", created in 2007, coordinates in France a network of cropping system experimentations (Deytieux et al., 2010). Its goals are (i) to develop a knowledge network including different expertises, disciplines and approaches, (ii) to suggest operational approaches to design and evaluate innovative cropping systems in order to identify the most promising ones, (iii) to test in a network of field experiments and farm monitoring the performances and feasibility of new cropping systems in order to share and transfer them to farmers, research, development and training actors.

In this paper, we present a method of description of cropping system which contributes to the analysis of each cropping system and of several cropping systems in multi site analysis by benefiting from the dialogue and reflexivity in the JNT.

## 2 Materials and Methods

In 2015, the network of cropping system experimentations includes seventy cropping systems, which are experimented in forty-five experimental sites. The network represents a wide range of pedo-climatic conditions, even though most of the sites are located in Northern France (Deytieux et al., 2010; Deytieux et al., 2012). Two complementary experimental approaches are used in the network: experimentations in experimental stations which allow to be more prospective and to avoid some technical or economical constraints; experimentations in farmers "fields" which are more relevant to test the global feasibility of the cropping system by including some economical and social constraints. All tested cropping systems are adapted and designed to meet local issues and contexts.

In the network, three main theme groups are identified (Deytieux et al., 2010): arable cropping systems mainly based on Integrated Crop Management (ICM) principles (Cash crops ICM), mixed farming cropping systems based on ICM principles (Mixed crops ICM), arable cropping systems focused on high energetic and/or greenhouse gases performances research (Energy/Gas) which have a double aim focused on both ICM and energy-gas issues.

To capitalise on the cropping systems tested in multi annual experiment, the animation team of the network developed and formalized a method to describe, understand, analyse and discuss about the tested cropping system. This formalisation consists of describing : (1) the origins of the design and implementation of the tested cropping system, (2) the context (localisation, climate, biotic pressure, socio-economic context...), (3) the experimental plan, (4) the expectation of the territory "stakeholders", (5) the expectations of the crops "manager" (5) the decisional-model for each agrosystem services (Reau et al., submitted), (6) the practiced system, which is the synthesis of the interventions occured in the fields during several years of test, (7) the agronomic results (crops and soil states, ...), (8) the technical results such as yield and quality, (9) the sustainability performances obtained which are analysed in regards to those expected by the designers of the cropping system, (10) the performances of sustainability analysed with MASC 2.0© (Craheix et al., 2012), (11) the keys of a successful management of the cropping system and new knowledge, (12) the main features of the cropping system.

<footnote n="1">¹ Chambre d'agriculture de Bourgogne, 3 rue du golf, F-21800 Quétigny, France</footnote>

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

<footnote n="3">³ Association pour la Relance Agronomique en Alsace, BP 30022 Schiltigheim, F-67013 Strasbourg, France</footnote>

<footnote n="4">⁴ Arvalis - Institut du végétal, F-91720 Boigneville, France</footnote>

<footnote n="5">5 Chambre d'agriculture de Poitou-Charentes, INRA Les Verrines, F-86600 Lusignan, France</footnote>

<footnote n="6">⁶ CETIOM, Avenue Lucien BRETIGNIERES, F-78850 Thiverval-Grignon, France</footnote>

<footnote n="7">⁷ Chambre d'agriculture de la Nièvre, 25, boulevard Léon Blum BP 80 CS 40080, F-58028 Nevers cedex, France</footnote>

<footnote n="8">⁸ Chambre d'agriculture de l'Yonne, 14 bis, rue Guynemer, F-89015 Auxerre cedex, France</footnote>

<footnote n="9">⁹ INRA, UMR 211 Agronomie, F-78850, Thiverval-Grignon, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: marie-sophie.petit@bourgogne.chambagri.fr</footnote>
## 3 Results – Discussion

In the cropping system experimental network, the first step has consisted of describing and analysing more than 33 systems since the second semester of 2013. For example, the Courgenay CS tested in Burgundy, by the Chambers of agriculture, in a clay and limestone soil, in an oceanic climate, was described by a small working group and presented to the experimentaters of the network in December 2014. The aims of this CS are to manage weeds while reducing pesticide use if possible (ryegrass < 1plant.m², control of bromine infestation, circles of thistles ≤ 2 m²), to maintain the system gross margin to 1200 €.ha⁻¹ (with a winter wheat price at 200 €.t⁻¹) with a maximal accepted loss of yield of -5 q.ha⁻¹ of winter wheat. The decisional-model for weed management (Fig.1) explains the combination of solutions (techniques, decisional rules) implemented to achieve these aims (Reau *et al.*, submitted). The CS, tested since 2005 has succeed in managing weeds except the population of thistles which increased. Its performances of sustainability evaluated in multicriteria analysis with MASC 2.0 were quite high with a very good economic and environmental sustainability (Fig. 2).

<figure><img src="image_211.png" /><figcaption>**Fig.1.** The decisional-model of weed management of the CS tested in Courgenay (Burgundy, France)</figcaption></figure>

<figure><img src="image_212.png" /><figcaption>**Fig.2.** The performances of sustainability of the CS tested in Courgenay (Burgundy, France). The assessment was performed with the MASC 2.0© method.</figcaption></figure>

Once described, the Courgenay CS was presented, in a workshop with 2 other cropping systems identified as quite similar in terms of aims and crop management strategies (Courgenay, Epoisses tested by INRA Dijon in Burgundy, Lusignan tested by INRA and Chamber of Agriculture of Poitou-Charentes). This workshop aimed at describing, understanding, discussing and analysing the 3 CS with the crop managers, the experimentaters involved and other experimentaters of the JNT. This collective workshop allowed to exchange about weed management techniques, to explain and precise some of the strategies and results. It also allowed a dialogue between the crop managers and the experimentaters, helping them to step back from their experimental activities. By the questions and answers with the network members, the understanding of the cropping system was improved thanks a „reflexive“ analysis and allowed to discuss the strength of the weed management strategies and their potential extrapolation in other contexts. This exercise needed to well describe the cropping system which took some time.

## 4 Conclusions

The method to describe tested cropping systems has proved to be a useful tool in the network. By confronting their experience on tested cropping systems, crop managers and experimentaters have the ability to take part and improve cropping system analysis, to capitalize on the global and specific crop management strategies they learned to put in practice. The next steps will be (i) to extend this activity of CS description to all the cropping systems of the network, (ii) to organize worshop to discuss and share on the tested CS, their results and performances. (iii) to study in what extend such CS description is a useful resources for action, advice and training and think about other ways to make useful ressources from knowledge on cropping systems performances produced in the network iv) to communicate.

*Acknowledgements.* This work has been funded under the Joint technology network for „innovative cropping system” by the Ministry of Agriculture, Food and Forest with the CASDAR funds.. We thank all the exprimentaters, crops”managers and participants of the experimental cropping system network of the Joint technology network for „innovative cropping system”.

## References

Craheix D., Angevin F., Bergez J.-E., Bockstaller C., Colomb B., Guichard L., Reau R., Doré T., 2012, MASC 2.0, un outil d'évaluation multicritère pour estimer la contribution des systèmes de culture au développement durable. Innovations Agronomiques 20, 35-48

Deytieux V., Vivier C., Minette S., Nolot J-M., Piaud S., Schaub A., Lande N., Petit M-S., Reau R., Fourrié L., Fontaine L., 2012. Expérimentation de systèmes de culture innovants : avancées méthodologiques et mise en réseau opérationnelle, Innovations agronomiques n°20 p. 49-78, Colloque RMT Systèmes de culture innovants du 21 octobre 2011.

V. Deytieux, P. Dumans, O. Guérin, N. Landé, S. Minette, J.M. Nolot, M.S. Petit, R. Reau, A. Tordeur, C. Vivier, 2010, A multi-actors experimental network to assess innovative arable cropping systems in: J. Wery, I. Shili-Touzi, A. Perin (Eds.) Agro2010 theXIth ESA Congress, Agropolis International, Montpellier, France, 2010, pp. 995-996

Reau R., Cellier V., Deytieux V., Petit M-S, Schaub A., Cotinet P., Giteau J-L., submitted, Describing decisional-model of agrosystem services for analyzing and scaling out innovative cropping systems, 5th International Symposium for Farming Systems Design 7-10 September 2015, Montpellier, France (submitted).

Reau R., Doré T., 2008, Systèmes de culture innovants et durables : Quelles méthodes pour les mettre au point et les évaluer ?. Dijon, France : Editions Educagri, 2008. 175 p
# Design and multicriteria assessment of low-input cropping systems prototypes based on agroecological principles in southwestern France

Eric Justes ±<fnref n="1" />, Daniel Plaza-Bonilla <fnref n="1" />, Grégory Véricel <fnref n="1" />, Yolaine Hily <fnref n="1" />, Didier Raffaillac <fnref n="1" />, André Gavaland <fnref n="1" />, & Jean-Marie Nolot <fnref n="1" />

## 1 Introduction

The challenges of agriculture in the current context of global change and the increasing social awareness of the impacts of agricultural activity on the environment and human health has led to the need of designing more sustainable cropping systems. However, to be viable, those innovative cropping systems must be assessed on a multicriteria basis to determine their long-term performance in agronomical, socioeconomic and environmental terms (Sadok *et al.*, 2009).

## 2 Materials and Methods

A cropping system experiment was initiated in 2003 at INRA in Auzeville (SW France). Prototypes of low-input alternatives to the traditional durum wheat (*Triticum turgidum* L.) – sunflower (*Helianthus annuus* L.) rainfed rotation were designed using the prototyping methodology mobilizing scientific expert knowledge in co-design workshops (e.g. Vereijken, 1997). In a first step (2004-2009) the prototyping of innovative cropping systems was based on the following goals: (i) reduce the amount of inputs (i.e. N fertilizer, pesticides), (ii) better couple cash crop N needs and N cycling and (iii) reduce N losses as leaching. To reach those goals six rotations of 3-yearwith different levels of inclusion of grain legumes (GL0, GL1 and GL2, no, one and two grain legumes included in the rotation, respectively), with (CC) or without (BF, bare fallow) catch crops were established and tested:(Fig. 1).

<figure><img src="image_213.png" /><figcaption>Fig. 1. Conceptual diagram of the 3-year rotations tested in the experiment between 2004 and 2009. Green arrows indicate the cover crops of the CC treatment for the 3 rotation prototypes including these species.</figcaption></figure>

In 2010, a second step aimed at re-designing the prototypes based on integrated pest management technics by going further in the mobilization of agroecological principles in terms of (i) building entire rotations based on intercrops to better exploit the potential of resource use complementarity, (ii) establishing a baseline of a 50% reduction in the use of phytosanitary products according to the Ecophyto 2018 plan of the French government and (iii) increasing the diversity of species within the rotation (cash and cover crops)in order to have a biodiversified-based cropping system (Fig. 2).

<figure><img src="image_214.png" /><figcaption>Fig. 2. Conceptual diagram of the 3-year rotations tested in the experiment after 2010. Green arrows indicate the cover crops of the CC treatment for the 3 rotation prototypes including these species.</figcaption></figure>

The evaluation of the cropping systems was carried out by complementary experimental and modelling approaches in order to assess their agronomic and economic feasibility and evaluate their environmental impacts in terms of

<footnote n="1">¹ UMR 1248 AGIR, INRA-Toulouse, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Eric.Justes@toulouse.inra.fr</footnote>
greenhouse gases emissions and pesticides transfer and N leaching to ground waters. Quantitative and qualitative methods and the multicriteria MASC 2.0 analysis were used for identifying strengths and weaknesses of the prototypes.

## 3 Results - Discussion

During the 2004-2009 period the incorporation of one (GL1) and two (GL2) grain legumes in the 3-year rotations led to a diminution of the soil organic carbon (SOC) stock (0-30 cm soil depth) of 542 and 490 kg ha⁻¹yr⁻¹, respectively, as an average of the CC and BF treatments. Contrarily, the GL0 rotation maintained the amount of SOC. The use of cover crops mitigated by a 13 and a 67% the loss of C in the GL1-CC and GL2-CC rotations, respectively.

The results of the multicriteria assessment of the first three-year rotation (2011-2013) are shown in Table 1. To carry out the analysis some key indicators were selected, according to the objectives of the designers.

<table><caption>**Table 1.**First multicriteria approach: key indicators about production and environmental aspects (average 2011-2013).</caption><thead><tr><th>Cropping system</th><th>SNM* (€ha<sup>-1</sup>)</th><th>% diff. reference</th><th>Wheat yield (t ha<sup>-1</sup>)</th><th>% diff. reference</th><th>Sunflower yield (tha<sup>-1</sup>)</th><th>% diff. reference</th><th>N fertilizer (kg N ha<sup>-1</sup>)</th><th>% diff. reference</th><th>TFI**</th><th>% diff. reference</th></tr></thead><tbody><tr><td><strong>Reference</strong></td><td><strong>899</strong></td><td></td><td><strong>6.0</strong></td><td></td><td><strong>2.6</strong></td><td></td><td><strong>119</strong></td><td></td><td><strong>2.5</strong></td><td></td></tr><tr><td>Low inputs (LI)</td><td>952</td><td>+6%</td><td>5.6</td><td>-8%</td><td>3.0</td><td>+15%</td><td>96</td><td>-19%</td><td>0.7</td><td>-73%</td></tr><tr><td>LI-CC</td><td>934</td><td>+4%</td><td>6.1</td><td>-</td><td>3.2</td><td>+21%</td><td>96</td><td>-19%</td><td>0.9</td><td>-66%</td></tr><tr><td>Very low inputs (VLI)</td><td>844</td><td>-6%</td><td>6.1</td><td>-</td><td>3.2</td><td>+22%</td><td>43</td><td>-63%</td><td>1.4</td><td>-43%</td></tr><tr><td>VLI-CC</td><td>795</td><td>-12%</td><td>5.8</td><td>-4%</td><td>3.8</td><td>+44%</td><td>51</td><td>-57%</td><td>1.1</td><td>-55%</td></tr><tr><td>VLI-IC</td><td>682</td><td>-24%</td><td>3.9 (1.6)***</td><td></td><td>1.5 (1.1)</td><td></td><td>40</td><td>-66%</td><td>0.9</td><td>-62%</td></tr><tr><td>VLI-IC-CC</td><td>559</td><td>-38%</td><td>3.57 (1.4)</td><td></td><td>1.4 (1.1)</td><td></td><td>45</td><td>-62%</td><td>0.8</td><td>-70%</td></tr></tbody></table>

The last results show that the objectives of reducing the use of pesticides (i.e. TFI reduction) and N inputs while maintaining yields were reached, except for the most innovative systems based on intercropping (i.e. VLI-IC and VLI-IC-CC). However, all the Very low input systems (VLI) showed a significant decrease in their economic performance.

The use of the model MASC 2.0 allowed getting a global view of the sustainability of the prototypes and pointing out eventual side effects not taken into account during the design process. This model based on qualitative attributes allowed taking into account social, economic and environmental issues all together (Table 2).

<table><caption>**Table 2.**Multicriteria assessment with MASC 2.0: summary of results for the different prototypes (2011-2013 period).</caption><thead><tr><th>Cropping system</th><th>Economic pillar</th><th>Social pillar</th><th>Environmental pillar</th><th>Global sustainability</th></tr></thead><tbody><tr><td><strong>Reference</strong></td><td><strong>5*</strong></td><td><strong>5</strong></td><td><strong>3</strong></td><td><strong>6</strong></td></tr><tr><td>Low inputs (LI)</td><td>5</td><td>5</td><td>4</td><td>7</td></tr><tr><td>LI-CC</td><td>5</td><td>5</td><td>4</td><td>7</td></tr><tr><td>Very low inputs (VLI)</td><td>5</td><td>5</td><td>3</td><td>6</td></tr><tr><td>VLI-CC</td><td>4</td><td>5</td><td>3</td><td>6</td></tr><tr><td>VLI-IC</td><td>4</td><td>5</td><td>3</td><td>6</td></tr><tr><td>VLI-IC-CC</td><td>4</td><td>3</td><td>2</td><td>3</td></tr></tbody></table>

Globally, most of the innovative cropping systems showed a good level of global sustainability, equivalent to the reference and for the case of LI and LI-CC even better. Some attributes belonging to the social pillar were degraded due to a higher complexity of the prototypes (data not shown). Besides, the reduction of the use of pesticides in the VLI cropping systems did not allow improving systematically the environmental pillar since other indicators counterbalanced the results. Finally, these evaluations were a part of an iterative design process and have been integrated through the redesign of the prototypes in 2014.

## 4 Conclusions

The cropping system prototypes demonstrate that some innovative practices (e.g. use of cover crops, legumes introduction in intercropping) allow reducing the reliance to the use of inputs such as nitrogen fertilizers and pesticides while satisfying economic performances. The break crop positive effect of grain legumes was also pointed out as ever demonstrated by Kirkegaard *et al.* (2008). Nevertheless, some innovative based on intercropping are still limited by technical difficulties (e.g. crop establishment, harvest, grain sorting) and lack of economic performance. Further monitoring of prototypes should allow quantifying better their long term impacts on the different sustainability pillars.

**Acknowledgements.** We acknowledge the assistance of Didier Chesneau, Michel Labarrère, Eric Bazerthe, Patrick Bruno for the maintenance and management of the field experiments. This research was supported by EU (GLIP FP6 EU project) and French ANR (MicMacproject).

## References

* Kirkegaard, J., Christen, O., Krupinsky, J. &Layzell, D. (2008). Break crops in temperate wheat production. *Field Crops Research* **107**, 185-195.

* Sadok, W., Angevin, F., Bergez, J.E., Bockstaller, C., Colomb, B., Guichard, L., Reau, R., Messean, A. &Doré T. (2009). Masc, a qualitative multi-attribute decision model for ex ante assessment of the sustainability of cropping systems. *Agronomy for Sustainable Development* **29**, 447-461.

* Vereijken, P., 1997. A methodical way of prototyping integrated and ecological arable farming systems (I/EAFS) in interaction with pilots farms. European journal of agronomy **7**, 235-250.

<footnote>*Semi net margin; **Treatment frequency index; ***Values inside () correspond to the yield of the accompanying crop of the mixture.</footnote>

<footnote>*The higher, the better</footnote>