# Will breeding efforts be counteracted by uncertain water supply due to prospective climate change?

Nikolai Svoboda *±1, Maximilian Strer <fnref n="1" /> & Johannes Hufnagel <fnref n="1" />

## 1 Introduction

Extreme yield drops in Europe in 2003 (loss of ~13 billion Euros) were partly associated with declines in precipitation of approx. 300 mm (Tubiello et al., 2007). Regional climate models (Meinke et al., 2013) show strong tendencies for decreasing rainfall in summer i.e. growing period for the North German Plain (-11%). Continuously increasing yields of the most field crops (Laidig et al., 2014) together with the knowledge of decreasing summer rainfall guided us to the question if crop yield will increase in future like it increased over the last 60 years, or if insufficient rainfall may cause a plateau or decrease in crop yield development?

## 2 Materials and Methods

In the present study Diepholz in the western part of the North German Plain was investigated. Diepholz has a long term mean temperature of 9.6°C and 719 mm of precipitation. In Diepholz, 2003 was the year with the lowest precipitation (523 mm) during the observation period (1981–2010). Regionalised observed weather data (STARS (Orlowsky et al., 2007), 1950 to 2010) and regionalized projected climate data (RCP 8.5, ECHAM6, MPI Hamburg, Germany, STARS, 2011 to 2070) was provided by the PIK on the basis of daily values. We analyzed two time slots with 20 years each. The first represents the status quo from 1981 to 2010 (slot1) and the second the future from 2051 to 2070 (slot2). Typical plant available soil water in the study region is 120 mm (pseudogleyic luvisol, mostly sand with small loam content and 60 cm rooting depth) and soil water storage is full in spring. Begin and end of crop transpiration during the growing period in the harvest year was determined on the basis of phenological data as provided by the German Weather Service (DWD). While transpiration in summer crops begins with the plant emergence (Roth et al., 2005) (silage maize 10.5.; potato 5.5.), winter wheat starts significant transpiration in the harvest year when mean daily temperature exceed 3°C in the phase of tillering (21.2. in the Diepholz region) (Roth et al., 2005). End of transpiration was assumed to match with the harvesting event (silage maize = 6.10.; potato = 19.7.; winter wheat = 6.8.). All future management dates were fixed on to the dates determined for the status quo. Yield data of silage maize, potato and winter wheat (nuts1; each for 1951 to 2010; n=60) was provided by the statistical office of Lower Saxony. For each crop we fitted a linear model to the yield (Rijk et al., 2013; Laidig et al., 2014). The slope of this function than, represented the annual yield increase of the past and served as the yield increase of the future. Water use efficiency of winter wheat was set to 349 l kg⁻<fnref n="1" /> while potato has 217 l kg⁻<fnref n="1" /> and silage maize has 191 l kg⁻<fnref n="1" /> (Roth et al., 2005).

## 3 Results

Mean annual precipitation does not change over time (709 and 708 in slot1 and slot2 respectively) but in previous studies we found a significant shift towards increased winter rainfall and consequently decreasing precipitation during summer (Svoboda et al., 2014). Rainfall during the growing period (begin of significant transpiration) in slot1 amounted in 318 mm for silage maize (10.5. to 6.10.), 160 mm for potato (5.5. to 19.7.) and 316 mm for winter wheat (21.2. to 6.8.). When summing up precipitation within the slot2 growing period we have 255 mm for silage maize (-63 mm), 139 mm for potato (-21) and for winter wheat 284 (-32) mm. Thus we have a decreased rainfall of 10 to 20 percent relative to the respective crop within the growing period due to the shift towards more winter precipitation. Taking this in mind, we calculated the yield in both time slots and, consequently, the water demand of the crops based on mean yield and water use efficiency. In slot1 winter wheat has a mean water demand of 249 mm (yield 7,13 t DM ha⁻<fnref n="1" />). For silage maize in slot1 we calculated a water demand of 262 mm (yield 12,06 t DM ha⁻<fnref n="1" />) and for potato 172 mm (yield 9,00 t DM ha⁻<fnref n="1" />). For the slot2 we found an increased water demand due to increased yield of 426 (+71) mm for winter wheat (yield 12,20 t DM ha⁻<fnref n="1" />), 304 (+42) mm (yield 15,92 t DM ha⁻<fnref n="1" />) for silage maize and 272 (+100) mm (yield 14,24 t DM ha⁻<fnref n="1" />) for potato. Thus we calculated an increased water demand of 16 to 71 percent related to the respective crop.

<footnote n="1">¹ Institute of Land Use Systems, Leibniz Centre for Agricultural Landscape Research</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: svoboda@zalf.de</footnote>
<table><caption>**Table 1.** slot1: Yield, relating water demand and possible water deficit of important crops (silage maize: SMA; potato: POT; winter wheat: WWE) today (slot1: 1991 – 2010) and in the future (slot2: 2051 – 2070).</caption><thead><tr><th>time slot</th><th>crop</th><th>plant<br>available soil<br>water<br>[mm]</th><th>growing<br>period<br>[date]</th><th>precipitation<br>during vegetation<br>period [mm]</th><th>total<br>available<br>water [mm]</th><th>yield<br>[t DM ha<sup>-1</sup>]</th><th>water<br>demand<br>[mm]</th><th>water deficit<br>[mm]</th></tr></thead><tbody><tr><td rowspan="3">slot1</td><td>SMA</td><td>120</td><td>10.5.-6.10.</td><td>318</td><td>438</td><td>12</td><td>262</td><td>176</td></tr><tr><td>POT</td><td>120</td><td>5.5.-19.7.</td><td>160</td><td>280</td><td>9</td><td>172</td><td>108</td></tr><tr><td>WWE</td><td>120</td><td>21.2.-6.8.</td><td>316</td><td>436</td><td>7</td><td>249</td><td>187</td></tr><tr><td rowspan="3">slot2</td><td>SMA</td><td>120</td><td>10.5.-6.10.</td><td>255</td><td>375</td><td>16</td><td>304</td><td>71</td></tr><tr><td>POT</td><td>120</td><td>5.5.-19.7.</td><td>139</td><td>259</td><td>14</td><td>272</td><td>-13</td></tr><tr><td>WWE</td><td>120</td><td>21.2.-6.8.</td><td>284</td><td>404</td><td>12</td><td>426</td><td>-22</td></tr></tbody></table>

Together with the plant available soil water at the beginning of the growing period (120 mm) we have a crop dependent water supply from 280 to 438 mm. During the slot1 there is sufficient water, as characterized by a positive water deficit, for all three crops but a small deficit becomes visible in the second slot. While for silage maize an oversupply of 71 mm is left, we could expect a water deficit of -13 for potato and of -22 mm for winter wheat (Table 1). Thus we could detect only little water deficit within the future time slot for potato and winter wheat while for silage maize no deficit becomes visible. For winter wheat the deficit becomes zero eight years earlier (2044 – 2063) with a yield of 11,6 t DM ha⁻¹ and for potato nine years earlier (2043 – 2062) with a yield of 13,5 t DM ha⁻¹.

## 4 Conclusions

In conclusion, we cannot expect any drought-related reductions in yield increase of silage maize. For potato as well as for winter wheat a plateau in yield increase is reached. For these crops management procedures has to be adjusted if yield gap should maintain on the present level. The intensification of irrigation is in this region a sustainable option, since the total precipitation is sufficient. Next steps will be identifying the count and length of drought periods in future scenarios. Drought periody may cause strong yield depression within the growing period due to drought stress. Calculating water demand of catch or intercrops, often grown before summer, may guide to increased water deficit and, consequently, makes adaption strategies more necessary.

## References

* Laidig, F.; Piepho, H.-P.; Drobek, T. & Meyer, U. (2014), 'Genetic and non-genetic long-term trends of 12 different crops in German official variety performance trials and on-farm yield trends', Theor Appl Genet.

* Meinke, I, Gerstner, E, von Storch, H, Marx, A, Schipper, H, Kottmeier, C, Treffeisen, R, Lemke, P. 2010. Regionaler Klimaatlas Deutschland der Helmholtz-Gemeinschaft. Mitteilungen DMG, 02/2010, pp. 5–7. http://www.norddeutscher-klimaatlas.de/klimaatlas/2071-2100/sommer/niederschlag/norddeutschland/mittlereanderung.html (in German) (last access: 28.03.2015).

* Orlowsky, B.; Gerstengarbe, F.-W. & Werner, P. C. (2008), 'A resampling scheme for regional climate simulations and its performance compared to a dynamical RCM', Theoretical and Applied Climatology 92, 209 – 223.

* Rijk, B.; van Ittersum, M. & Withagen, J. (2013), 'Genetic progress in Dutch crop yields', Field Crops Research.

* Roth, D.; Günther, R.; Knoblauch, S. & Michel, H. (2005), 'Wasserhaushaltsgrößen von Kulturpflanzen unter Feldbedingungen', Technical report, Thüringer Landesanstalt für Landwirtschaft.

* Svoboda, N.; Strer, M. & Hufnagel, J. (2014), 'Sustainable agriculture under the impact of climate change' 'Aspects of Applied Biology - Agronomic decision making in an uncertain climate'.

* Tubiello, F. N.; Soussana, J.-F. & Howden, S. M. (2007), 'Crop and pasture response to climate change', Proceedings of the National Academy of Sciences 104, 19686 – 19690.
# The contributions of recent climate change trends to maize phenology and yield in Northeast China

Zhijuan Liu<fnref n="1" /> & Xiaoguang Yang ±<fnref n="1" />

## 1 Introduction

Northeast China (NEC) is one of the major agricultural production areas in China, but it is also the most susceptible to climate variability. The total maize sown area accounts for 29% of the nation's totals and the production of maize in NEC accounted for more than 30% of the nation's totals (NBSC, 2008-2010). In the past five decades, NEC experienced a warming trend in surface average temperature equal to 0.38°C per decade (Liu et al., 2012). Therefore, it is important to investigate the contribution of climate change to maize yield to improve our understanding of how we can ensure increased yields in the future. It is difficult to separate the effects of different climatic variables to changes in crop production. Therefore, simulation models have been used extensively to estimate the impacts of climatic variables on crop productivity. The Agricultural Production Systems Simulator (APSIM) has proven to be an effective tool to investigate the potential impacts of climate variability on crop productivity (Asseng et al., 2004; Liu et al., 2010). Therefore we used the APSIM-Maize model to separate the impacts of changes in solar radiation (SR), maximum (Tmax) and minimum temperature (Tmin) on maize phenology and yields in NEC during the period from 1961 to 2010.

## 2 Materials and Methods

The study area is located in NEC, comprised of Heilongjiang (north), Jilin (center), and Liaoning (south) Provinces. In this study, 55 sites in NEC were selected from the weather stations operated by the National Meteorological Networks of China Meteorological Administration (CMA). Climate data includes daily mean, maximum, and minimum temperatures, daily sunshine hours, and daily precipitation from 1981 to 2007 at each station. Sunshine duration was converted into daily solar radiation using the Ångström formula (Black et al., 1954; Jones, 1992). APSIM-maize has been calibrated and used in NEC for simulating the growth and yield of maize (Liu et al., 2012 & 2013). Rely on these previous studies, APSIM-Maize was run with historical climate data (1961-2010) to quantify the impacts of changes in SR, Tmax, and Tmin on the duration of growing period and yields of maize in NEC. We designed three simulations to examine these effects, names as Run_SR, Run_Tmax, and Run_Tmin. Each simulation used the observed 50-year time series of one selected climatic variable, with the remaining variables held constant at 1961 values. Simulations assumed that water applications and nutrient inputs were taken as non-limiting in order to eliminate the effect of water and nutrient stresses on simulated maize yield, and other management details are kept constant throughout the simulated years 1961-2010.

## 3 Results – Discussion

In NEC, when the same hybrid was specified in APSIM for all years, a simulated increase of Tmax shortened the length of vegetative periods (LVP) and reproductive periods (LRP) at the rate of 0.1-0.9, and 0.1-1.2 days per decade depending on the locations, respectively. Moreover, the warming trend of Tmin also shortened the LVP and LRP but with a larger magnitude (0.2-2.0, and 0.1-2.3 days per decade). The simulation suggests that this negative impact was higher in the high latitude. The regional LVP and LRP with varying Tmax shortened at the rate of 0.26 and 0.50 days per decade, moreover, the regional LVP and LRP with varying Tmin shortened at the rate of 0.97 and 0.92 days per decade (p < 0.01) from 1961 to 2010 (Fig. 1).

Our results indicate that one climate variable at a time, whilst holding the other variables constant at 1961 values, changes in SR, Tmax, and Tmin would have led to a negative impact on maize yield, decrease it by 212.1, 72.4, and 72.2 kg ha⁻<fnref n="1" /> per decade (Fig. 2), caused a decrease in yield by 9.9%, 3.4%, and 3.4% during the past five decades, respectively (Table 1).

## 4 Conclusions

In this study, APSIM-Maize model was used to separate the impacts of changes in SR, Tmax, and Tmin on maize phenology and yields in NEC during the period from 1961 to 2010. Our results indicate that the warming trend significantly shortened the LVP and LRP, and climate trends (SR, Tmax, and Tmin) would have led to a negative impact on maize yield in NEC if there was no adaptation was taken.

<footnote n="1">¹Department of Agro-meteorology, College of Resources and Environmental Sciences, China Agricultural University</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: yangxg@cau.edu.cn</footnote>
<figure><img src="image_82.png" /><figcaption>Fig. 1. The relationships between changes in Tmax and Tmin and the length of vegetative (LVP, a) and reproductive period (LRP, b) of maize in each location and (c) entire NEC.</figcaption></figure>

<figure><img src="image_83.png" /><figcaption>Fig. 2. Simulated regional maize yield in 1961–2010, with varying (a) SR, (b) Tmax, and (c) Tmin, and annual changes of associated climatic variables. Other climate variables are kept at 1961 values.</figcaption></figure>

<table><caption>Table 1. Simulated changes in maize yield (%) under different climatic drivers (SR, Tmax, and Tmin) for different</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">Yield in 1961 (kg ha<sup>-1</sup>)</th><th colspan="3">Yield changes with varying climate variables in 50-year</th></tr><tr><th>SR</th><th>Tmax</th><th>Tmin</th></tr></thead><tbody><tr><td>Heilongjiang</td><td>8830</td><td>-5.8%</td><td>-4.4%</td><td>-1.4%</td></tr><tr><td>Jilin</td><td>10764</td><td>-13.1%</td><td>-3.2%</td><td>-8.4%</td></tr><tr><td>Liaoning</td><td>13007</td><td>-9.8%</td><td>-2.7%</td><td>-0.2%</td></tr><tr><td>Entire region</td><td>10727</td><td>-9.9%</td><td>-3.4%</td><td>-3.4%</td></tr></tbody></table>

*Acknowledgements*. This work was supported by National Natural Science Foundation of China (31471408 & 41401049).

References

Asseng, S., Jamieson, P.D., Kimball, B., Pinter, P., Sayre, K., Bowden, J.W. & Howden, S.M. (2004). Simulated wheat growth affected by rising temperature, increased water deficit and elevated atmospheric CO₂. *Field Crops Research*, *85*, 85–102.

Black, J.N., Bonython, C.W. & Prescott, J.A. (1954). Solar radiation and the duration of sunshine. *Quarterly Journal of the Royal Meteorological Society*, *80*, 231–235.

Jones, H.G. (1992). Plant and microclimate: a quantitative approach to environmental plant physiology. Cambridge University Press, Cambridge.

Liu, Y., Wang, E., Yang, X. & Wang, J. (2010). Contributions of climatic and crop varietal changes to crop production in the North China Plain, since 1980s. *Global Change Biology*, *16*, 2287–2299.

Liu, Z., Yang, X., Hubbard, K.G. & Lin, X. (2012). Maize potential yields and yield gaps in the changing climate of Northeast China. *Global Change Biology*, *18*, 3441–3454.

Liu, Z., Hubbard, K.G., Lin, X. & Yang, X. (2013). Negative effects of climate warming on maize yield are reversed by the changing of sowing date and cultivar selection in Northeast China. *Global Change Biology*, *19*, 3481–3492.

National Bureau of Statistics of China (NBSC). (2008–2010). China statistics yearbook. China Statistics Press, Beijing.
# Running NUANCES-FARMSIM to predict greenhouse gas emissions in mixed smallholder crop-livestock systems under different scenarios in Western Kenya

Daniel Ortiz Gonzalo<sup>±*<fnref n="1" /></sup>, Mariana Rufino<sup><fnref n="2" /></sup>, Todd Rosenstock<sup><fnref n="3" /></sup>, Philippe Vaast<sup>3,<fnref n="4" /></sup>, Myles Oelofse<sup><fnref n="1" /></sup>, Andreas de Neergaard<sup><fnref n="1" /></sup> & Alain Albrecht<sup><fnref n="5" /></sup>

## 1 Introduction

African smallholder farming systems are complex, dynamic systems with many interacting biophysical subcomponents (Van Wijk et al., 2009) that strongly influence livelihoods and the farm greenhouse gas (GHG) balance. A modelling tool, NUANCES-FARMSIM, brings together the different production components of African smallholder farm systems, however until now, the model has not included a component to analyze climate impacts. In this communication we test FARMSIM's outputs to predict GHG emissions under different scenarios.

## 2 Materials and Methods

We ran FARMSIM for a representative case study farm of the highlands of Western Kenya. Each of the components of the model (livestock, crop, soil and manure management) have already been tested (Rufino et al., 2007; Tittonell et al., 2008). We followed IPCC guidelines (IPCC 2006) to calculate emissions for four scenarios related to Kenyan policies and plans of agriculture intensification and climate change action (Table 1). We ran the model several times to average the interferences of stochastic values in the model outputs (e.g. sex of the new calves).

<table><caption>Table 1: Inputs, parameters and scenarios for FARMSIM</caption><thead><tr><th></th><th>CT<sup>1</sup></th><th>FERT</th><th>FEED</th><th>FERT+FEED</th></tr></thead><tbody><tr><td><strong>Cropping sub-system</strong> (0,8 ha of maize; 0,4 ha Napier grass)</td><td></td><td></td><td></td><td></td></tr><tr><td>NPK fertiliser to maize (kg ha<sup>-1</sup>)<sup>2</sup></td><td>0 0 0</td><td>72 34 84</td><td>0 0 0</td><td>72 34 84</td></tr><tr><td><strong>Livestock sub-system</strong> (Frisian cows (1 lactating cow, 1 calf); Calving interval=2 years (Deterministic))</td><td></td><td></td><td></td><td></td></tr><tr><td>Supplementation with concentrates: In all stages (kg month-1 per head)</td><td>0</td><td>0</td><td>60</td><td>60</td></tr></tbody></table>

## 3 Discussion

### Farm components and total farm GHG emissions (Fig. 1 & Fig. 2)

The fertilizer rise scenario (FERT) showed a 22% increase in total farm emissions from the control (CT), due to an increment of N-inputs in soils. With the rate of fertilizer applied (144 Kg N ha⁻<fnref n="1" /> year⁻<fnref n="1" />), the N₂O emissions of soils are almost doubled (97% increase). Improving the livestock feeding by adding supplementation in the form of concentrates (FEED, 60 Kg month⁻<fnref n="1" /> head⁻<fnref n="1" />) resulted in a 24% increase in total farm emissions, mainly because it also led to an increment of the dry matter intake of the cow (concentrates supplement the diet, they do not replace napier grass). We observed an increase in GHG emissions from the manure management system, due to an increase in the nitrogen content of cow excreta, which in turn results in manure with higher nitrogen content and a slight increase in soil emissions. Finally, the scenario which combines fertilizer rise and improved feeding (FERT+FEED), resulted in an increment of 52% of the farm emissions.

### Maize production and milk production (Fig 3 & Fig 4.)

Adding fertilizer (FERT) increased the production of maize by 6% on average (287 Kg ha⁻<fnref n="1" /> year⁻<fnref n="1" />). Improving the feeding (FEED) resulted in a 160% increase in milk production (869 L year⁻<fnref n="1" />). Fertilizer in the maize fields did not have a significant effect on milk production –in spite of the maize stover flow to the cows–, whilst improving the feeding resulted in a slight increment in maize production by 0,13%. The combination of both treatments (FERT+FEED) brought an increase both in maize and milk production. Note that the CT and FERT scenarios do not produce milk in the 4th year, while the scenarios with supplements (FEED and FEED+FERT) do produce milk.

### GHG emissions per product (Table 2).

Emissions per unit of output are reduced as the production increases in the case of the feeding improved scenario (FEED), but not for the fertilizer rise (FERT) or the combination of fertilizer rise and feeding improved (FERT+FEED).

<footnote n="1">¹ PLEN (Department of Plant and Environmental Sciences), Faculty of Science, University of Copenhagen</footnote>

<footnote n="2">² CIFOR (Centre for International Forestry Research), CGIAR Consortium</footnote>

<footnote n="3">³ ICRAF (World Agroforestry Centre), CGIAR Consortium</footnote>

<footnote n="4">⁴ CIRAD (Centre de coopération International en Recherche Agronomique pour le Développement), UMR Eco&Sols</footnote>

<footnote n="5">⁵ IRD (Institut de Recherche pour le Développement), UMR Eco&Sols</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: gonzalo@plen.ku.dk</footnote>

<footnote n="1">¹Scenarios: CT=Control; FERT=Fertilizer Rise; FEED=Feeding improved through concentrates; FERT+FEED: Fertilizer rise and improved feeding</footnote>

<footnote n="2">²Fertilizers are added every 6 months (2 seasons, the annual added amount is twice the amount indicated in the table)</footnote>
<figure><img src="image_84.png" /><figcaption>Fig. 1 Total farm emissions during a period of 5 years for the 4 scenarios (CT, FERT, FEED, FERT+FEED)</figcaption></figure>

<figure><img src="image_85.png" /><figcaption>Fig. 3 Maize production for a period of 5 years</figcaption></figure>

<caption>Fig. 4 Milk production for a period of 5 years</caption>

<table><caption>Table 2. Emissions per product</caption><thead><tr><th rowspan="2">Year</th><th colspan="4">Kg CO2eq l-1 of milk</th><th colspan="4">Kg CO2 eqkg -1 of maize</th></tr><tr><th>CT</th><th>FERT</th><th>FEED</th><th>FERT+FEED</th><th>CT</th><th>FERT</th><th>FEED</th><th>FERT+FEED</th></tr></thead><tbody><tr><td>1</td><td>0,70</td><td>0,70</td><td>0,38</td><td>0,42</td><td>0,007</td><td>0,13</td><td>0,01</td><td>0,14</td></tr><tr><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0,10</td><td>0,18</td><td>0,10</td><td>0,17</td></tr><tr><td>3</td><td>0,73</td><td>0,73</td><td>0,48</td><td>0,56</td><td>0,26</td><td>0,38</td><td>0,28</td><td>0,38</td></tr><tr><td>4</td><td>0</td><td>0</td><td>1,09</td><td>1,24</td><td>0,10</td><td>0,18</td><td>0,11</td><td>0,18</td></tr><tr><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0,10</td><td>0,23</td><td>0,17</td><td>0,23</td></tr></tbody><tfoot><tr><th>Average</th><td>0,72</td><td>0,72</td><td>0,65</td><td>0,74</td><td>0,11</td><td>0,22</td><td>0,13</td><td>0,22</td></tr></tfoot></table>

## 4 Conclusions

FARMSIM can be used to predict GHG emissions and analyze trade-offs and synergies at farm scale. It provides the necessary outputs to study the interaction between the farm components and climate impacts. By calibration, a sub-model will be developed and loosely coupled to the relevant input and output parameters of the NUANCES-FARMSIM model, to examine productivity and climate system trade-offs to identify not only optimal farm management innovations but also constraints to their implementation.

## References

* Rufino, M.C., Tittonell, P., van Wijk, M.T., Castellanos-Navarrete, A., Delve, R.J., de Ridder, N., Giller, K.E., 2007. Manure as a key resource within smallholder farming systems: analysing farm-scale nutrient cycling efficiencies with the NUANCES framework. Livest. Sci. 112, 273-287

* Tittonell, P.A., 2008. Msimuwa Kupanda: Targeting Resources within Diverse, Heterogenous and Dynamic Farming Systems of East Africa. Wageningen University, Wageningen, p. 320.

* Van Wijk, M.T., Tittonell, P., Rufino, M.C., Herrero, M., Pacini, C., Ridder, N.D., Giller, K.E. 2009. Identifying key entry-points for strategic management of smallholder farming systems in sub-Saharan Africa using the dynamic farm-scale simulation model NUANCES-FARMSIM. Agric. Syst. 102, 89-101.

* IPCC 2006, Guidelines for National Greenhouse Gas Inventories. (Paris: OECD Press) (http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html, accessed: 15.02.15)
# T6. Designing sustainables agricultural systems with legumes

Chair: Marie-Hélène Jeuffroy, INRA

Co-chair: Snapp Sieglinde, Michigan State University
# Greener food production in Africa: intensification through multipurpose legumes

Sieglinde Snapp <fnref n="1" />, Regis Chikowo <fnref n="1" />,<fnref n="2" />, Mateete Bekunda <fnref n="3" />, Chiwimbo Gwenambira <fnref n="1" /> & Placid Mpeketula <fnref n="1" />,<fnref n="4" />

## 1 Introduction

The Green Revolution did not reach all parts of the world evenly. Many problems faced by farmers in sub-Saharan Africa are old and familiar, but preclude the large-scale approaches that drastically improved agricultural production in a few key crops in Asia. The unique climate and soil challenges of sub-Saharan Africa are now compounded by climate change and socio-economic instability. ‘Sustainable intensification’ is the research and development effort to find solutions that raise yields and incomes – and provide social and economic benefits – in a rapidly changing environment. Agrobiodiversity, soil health and multifuncionality are some key components of sustainable intensification. This talk will explore how multipurpose legumes that produce food, fodder and fuel while building soil resources are key to catalyzing SI in a maize-dominated farming system. Not all types of legume species are effective, nor are they all adoptable.

## 2 Materials and Methods

We initiated on-farm experimentation with over a thousand farmers to try out a basket of legume crop options grown with intensified planting arrangements, in sequence with maize and other staple crops. Four sites were chosen in central Malawi to represent an agricultural potential gradient from semi-arid, low production zone to a sub-humid high production zone. The mother and baby trial design and innovation platforms were used at these sites to foster participatory action research and extension. Food legumes such as modern varieties of common bean and groundnut were popular with farmers, but produced a modest number of benefits (cash and food). A ‘doubled up legume’ system that combined the complementary growth patterns of a food legume with a multipurpose legume (pigeon pea) was also popular and produced a wide range of benefits (cash, food, fuel, fodder, and soil fertility).

## 3 Results - Discussion

Farmer interest was evaluated by monitoring the number and size of field plots where farmers expanded production of new legume varieties. Use of inputs and spacing intensity provided further insights into farmer interest in sustainable intensification. Female farmers showed high interest in food, multipurpose and doubled up legume systems (Fig. 1).

<figure><img src="image_86.png" /><figcaption>**Fig. 1.** Count data on number of technologies farmers have expanded on their farms in central Malawi. F= Female farmers, M= Male farmers. Improved varieties of GN = Groundnut (peanut), SOY = Soybean, PP = Pigeonpea, CP=Cowpea, PP+GN = doubled up legume (intercropped legumes), MZ = maize</figcaption></figure>

<footnote n="1">¹ Plant Soil and Microbial Sciences Department, Michigan State University</footnote>

<footnote n="2">² University of Zimbabwe</footnote>

<footnote n="3">³ International Institute of Tropical Agriculture</footnote>

<footnote n="4">⁴ Department of Biology, Chancellor College, University of Malawi</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Snapp@msu.edu</footnote>
Crop model simulations were used to evaluate the performance of intercrop, rotational and sole crop variations of legume and maize systems. Soil moisture and nutrient dynamics associated with various maize-pigeonpea systems were explored for a range of climate scenarios. The simulations highlighted the buffering effects of intercrops for food security, particularly in terms of building soil fertility while ensuring maize yield every year when compared to a maize-pigeonpea rotation or the low yields of a sole-cropped maize system (Fig. 2). Soil types with higher initial fertility (soil type 1) supported higher maize yields from a sole-maize crop systems, but over time the pigeonpea diversified cropping systems built soil fertility and performed as well.

<figure><img src="image_87.png" /><figcaption>**Fig. 2.** Simulated maize yields using APSIM model, showing improvement in maize grain yield over time in maize-pigeonpea intercrop, maize grown after a pigeonpea rotation and continuous maize (fertilized at 20 kg N/ha). Error bars are standard errors of the mean generated through weather field variation. M. Ollenburger, 2012.</figcaption></figure>

<figure><img src="image_88.png" /><figcaption>**Fig. 3.** Trajectories of sustainable intensification based on household type.</figcaption></figure>

## 4 Conclusions

The combination of on-farm experimentation and crop simulation modelling has provided novel insights into the performance of multipurpose legume crops for a range of farmer socio-economic groups and environmental conditions. Female farmers in our central Malawi case study sites were particularly interested in food legumes, and in pigeonpea combinations such as doubled up legumes (pigeonpea grown as an intercrop with groundnut, and rotated with maize). Initially under higher soil fertility modelling results indicated that a maize-pigeonpea intercrop could reduce maize yields compared to sole crops, yet over time the intercrop produced two to three times as much maize yield and – importantly – produced a maize crop every year. On the other hand, the maize rotated system with multipurpose pigeonpea produced the highest yields and was the most stable cropping systems. This lead us to recommend that better off farmers use rotations with improved soybean and pigeonpea varieties, whereas doubled up legumes with pigeonpea are appropriate for female headed households and those with poor soils.

*Acknowledgements*. USAID Feed the Future Funding from Africa RISING.
A comparison of past and current cropping systems with designed innovative cropping
systems including legume crops, in three french regions.

Marion Soulié *±1,2, Laurence Guichard 1,2, Marie-Hélène Jeuffroy 1,2, Catherine Mignolet 3, Elise Pelzer ±1,2
& Céline Schott 3

**1 Introduction**

Since 20 years, French agriculture is facing a considerable decrease in legume areas, while numerous studies showed their contribution to a sustainable management of territories and resources (Voisin et al., 2014). Due to their numerous ecosystemic services, mostly due to their capacity for biological nitrogen fixation, legume crops, when introduced in cropping systems (CSs), may be able to cope with a diversity of challenges imposed to agriculture (e.g., climate change, resource scarcity). Our aim was to design innovative CSs including legumes and to compare their performances to those of past and current CSs, in three French regions (Bourgogne, Midi-Pyrénées and Pays de la Loire).

**2 Materials and Methods**

The first phase of our work aimed at rebuilding the main current CSs of the 3 regions. This was done with a database from the French Ministry of Agriculture, describing crop management and previous crops (5 previous years) of 7 crops in 2011: wheat, oilseed rape, corn, pea, sunflower, triticale and barley. First, we described patterns of crop successions. Next, thanks to multifactorial analysis (Multiple Correspondence Analysis and Factorial Analysis of Multiple Correspondences), we described each crop management and rebuilt main current cropping systems by combining crop successions and crop management.

In the second phase, we organized in each region a workshop with people concerned with local agricultural issues and constraints, including agricultural advisors, farmers and researchers (around 15 people per workshop), involved in conventional and/or organic farming. The goal was to design innovative CSs which included legumes as much as possible, as major sole crop, intercrop, living mulch of another cash crop, or as sole or mixed catch crop. It was recommended to design these systems in a hypothetical socio-economic context favourable to legumes (business opportunities, competitive sales prices compared to those of cereals...), in order to propose more innovative CSs.

The workshops were organized in 3 steps. During the first step, we shared knowledge with stakeholders about i) major past and current CSs in each region, ii) agricultural objectives and constraints for territories (e.g. nitrate vulnerable area, alfalfa dehydration project...) and for farmers (e.g. forage's quality requirements, lack of available legume cultivars...) and iii) ecosystem services/disservices provided by legumes. During the second step, we asked stakeholders to write around 5 sub-objectives and one solution per sub-objective, linked with the general objective of the workshop. This step aimed at ensuring a time for personal thoughts and expression. At the end of the second step, we summarized and organized collectively all the ideas and identified additional sub-objectives and constraints to design the CSs. The last step was a collective discussion: based on previous steps, stakeholders designed a batch of innovative CSs with legumes.

In the third phase, past and current CSs and innovative CSs will be assessed and compared on environmental, economic and social criteria with multicriteria assessment tools.

**3 Results – Discussion**

Only the results of Pays de la Loire's workshop will be presented in this paper. During the first phase, we showed that CSs in Pays de la Loire are varied: in fact, we highlighted more than 190 crop successions. Most of them are suitable for mix farming systems (crop and livestock): one third of CSs with at least 1 year of grassland and 40 % with at least 1 year of maize, mainly used for forage in this region. CSs including annual legumes represented less than 10 % of sample studied, with pea as the main annual legume species.

During the second step of the design workshop, numerous ideas (34 proposals) about reintroduction of legumes in CSs have been raised by stakeholders. For instance, i) sow a forage legume into grain legumes before harvest to control weeds at the end of crop cycle and before the sowing of the following crop; ii) intercrop wheat with pea to produce high protein content wheat with low nitrogen (N) fertilization. Six additional sub-objectives were identified from step 2 and used to guide step 3: i) reduce risk of N leaching, ii) optimize N fertilization, iii) optimize weeds control, iv) improve soil structure, v) secure forage production for breeders and vi) optimize quality of forage. Three innovative CSs including legumes were designed during the workshop: two are adapted for mix farming systems (main systems in Pays

<footnote n="1">¹ INRA, UMR 211 Agronomie, BP 01, F-78850 Thiverval-Grignon, France</footnote>

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

<footnote n="3">³ INRA, UR 055 ASTER-Mirecourt, 662 avenue Louis Buffet, F-88500 Mirecourt, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: marion.soulie@grignon.inra.fr elise.pelzer@grignon.inra.fr</footnote>
de la Loire) and one for cereals production systems. For each system designed, stakeholders described crop succession (including cover crops), crop management of each crop (soil tillage, quantity of N fertilizer...) as well as yield objectives. CS adapted for cereals production systems is described in figure 1. Compared to the common current CS managed in cereals production systems (winter oilseed rape-winter wheat-winter wheat), stakeholders hopped that the innovative CS will allow a reduction of N fertilization (thanks to numerous legumes in the rotation), a reduction of risk of nitrate leaching (thanks to cover crops in winter), a control of weeds (thanks to cover crops and introduction of new plant's family in the rotation) and an improvement of soil structure (thanks to the diversification of crops and their root system).

<figure><img src="image_89.png" /><figcaption>**Fig. 1.** Description of the innovative cropping system adapted for cereals production systems in Pays de la Loire</figcaption></figure>

At the end of the workshop, stakeholders wrote around five evaluation indicators they found essential to evaluate performances of CSs: some of those indicators will be used in the next phase of our work. Indeed, innovative CSs will be assessed on environmental, economic and social criteria with multicriteria assessment tools: CRITER (Fortino et Reau, 2010), Stephy (Attoumani-Ronceux et al., 2013) and PERSYST (Guichard et al., 2010). Performances of new CSs will be compared to those of current and past CSs managed.

On the final phase of the project, the local socio-economic partners and stakeholders will design legume development scenarios, including crop production and valorization, using the innovative CSs.

## 4 Conclusion

This approach is in progress and will be implemented also in the two other regions of our work (Midï-Pyrénées and Bourgogne), where agricultural systems are different. Design workshops fuel new exchanges between researchers (which have scientific knowledge) and local stakeholders (agricultural advisers and farmers, which are concerned by local agricultural issues and constraints). Confront this two kinds of knowledge allows to imagine even more innovative CSs while being suitable to local constraints and practices.

*Acknowledgements.* This work was supported by a grant overseen by the French National Research Agency (ANR) as part of the LEGITIMES project (ANR-13-AGRO-0004).

## References

Agriculture.gouv.fr (2015). La Statistique Agricole annuelle, presentation general. http://agreste.agriculture.gouv.fr/IMG/pdf_methosaa.pdf

Attoumani-Ronceux A. et al. (2013). Practical guide for the design of cropping systems less reliant on pesticides. Application in polyculture/mixed farming systems. French Ministries of Agriculture and Environment. RMT innovative cropping systems. English version, 2013.

Cnis.fr (2010). Enquête sur l'occupation et l'utilisation du territoire (Teruti-Lucas). http://www.cnis.fr/files/content/sites/Cnis/files/Fichiers/commissions/territoires/2010/documents_preparatoires/DPR_2010_COM_territoires_fiches_enquetes_opportunite.PDF

Fortino G. and Reau R. (2010). Two separate steps for cropping system assessment: characterization and final evaluation. In: Proceedings of Agro2010 the XIth ESA congress, Montpellier, France, 827-828.

Guichard, L., Bockstaller, C., Loyce, C. & Makowski, D. (2010). PERSYST, a cropping system model based on local expert knowledge. In: Proceedings of Agro2010 the XIth ESA congress, Montpellier, France, 827-828.

Voisin A-S., Guéguen J., Huyghe C., Jeuffroy M-H., Magrini M-B., Meynard J-M., Mougel C., Pellerin S., Pelzer E. (2014). Legumes for feed, food, biomaterials and bioenergy in Europe : a review. *Agronomy Sustainable Development* 34, 361-380.
# Design and ex ante assessment of cropping systems with legumes in four European countries

Céline Bourlet *±<fnref n="1" />*, Elise Pelzer <fnref n="1" />, Georg Carlsson <fnref n="2" />, Erik Steen Jensen <fnref n="2" />, Etienne-Pascal Journet <fnref n="3" />,<fnref n="4" />, Eric Justes <fnref n="3" />, Christophe Lecomte <fnref n="5" />, Rafael J. Lopez-Bellido <fnref n="6" />, Marek Seidenglanz ⁷, Anne-Sophie Voisin <fnref n="5" /> & Marie-Hélène Jeuffroy <fnref n="1" />

## 1 Introduction

Cropping systems (CSs) with grain legumes (GLs) provide many agronomical and environmental benefits. However, since the 90ies, the area dedicated to these crops has been strongly declining in Europe and currently represents no more than 1.8% of the arable land (FAOSTAT, 2014). Moreover, the contribution of CSs with GLs to sustainable development depends on their local adaptation and on their fit with most stakeholder priorities and management. The aim of this study was to design, together with local experts, adapted CSs with GLs in four European countries, and to assess their sustainability, accounting for the diversity of stakeholder points of view. The work was performed in Sweden, Spain, Czech Republic and France.

## 2 Materials and Methods

In order to assess the CS sustainability, we used two different tools: MASC<sup>©</sup> (Multi attribute Assessment of the Sustainability of CSs, to assess sustainable development at the CS scale; Sadok et al., 2009) and CRITER (calculating most indicators used as inputs in MASC<sup>©</sup>). These tools were adapted to take into account GL crops, and the socio-economic and pedo-climatic context of each country.

<table><caption>Table 1. Characteristics of the study regions</caption><thead><tr><td>Country</td><td>Region</td><td>Soil type</td><td>Climate</td><td>Reference CS</td></tr></thead><tbody><tr><td>Spain</td><td>Andalucia</td><td>Vertisol</td><td>Mediterranean</td><td>Faba bean-Wheat or Sunflower-Wheat (2 year rotations)</td></tr><tr><td>Sweden</td><td>Scania</td><td>Light clay</td><td>Temperate to</td><td>Wheat, Barley, Oilseed rape, Sugar beet (6 year rotation)</td></tr><tr><td>France</td><td>Parisian basin</td><td>Loamy clay</td><td>Temperate</td><td>Oilseed rape-Winter wheat-Spring barley (3 year rotation)</td></tr><tr><td>Czech Republic</td><td>Olomuc region</td><td>Silty sand</td><td>Continental</td><td>Oilseed rape-Winter wheat-Silage maize-Spring barley (4 year rotation)</td></tr></tbody></table>

The four countries represent various soil types and agro-ecosystems (Table 1). In a first step, a reference CS, typical for each region, was described and assessed with the CRITER-MASC<sup>©</sup> tools. In a second step, innovative CSs (nature of crops in the rotation, and their management plans) were collectively designed with researchers from each country together with a farmer (Spain) and a technical adviser (France), according to a defined set of objectives and constraints: introduce GLs in the CSs, improve yield stability, decrease fertilizer and pesticide use. All innovative CSs were assessed and then compared to the reference. Data for the description of CSs (soil, climate, crop management plans) were collected for the four countries from local farm managers and researchers or from previous experiments.

## 3 Results and Discussion

In all countries, designed innovative CSs involved at least two GL species, as sole crop or intercropped with cereals, as for the example of Sweden (Fig. 1). Innovative crop rotations were at least three years longer than reference ones, in order to diversify the crop sequence and respect delay between GL crops regarding diseases. Faba bean and pea were introduced as main crops in each country, as well as lupin in Sweden, chickpea in Spain and alfalfa in Czech Republic. Management of all crops was designed to decrease fertilizer and pesticide use. Cover crops, mostly based on forage legumes, were added to provide green manure. New management methods were applied to decrease agrochemical inputs, such as large rows to allow mechanical weeding, relay and companion crops or variety mixtures.

In France, the assessment showed that introducing GLs may increase the overall sustainability. It mainly improves the environmental component by decreasing negative impacts of fertilizers and pesticides. It did not systematically decrease the economic sustainability, mostly depending on the selling price of the grain legume

<footnote n="1">¹ UMR 211 Agronomie, INRA-AgroParisTech, France</footnote>

<footnote n="2">² Dep. Biosystems & Technology, Swedish University of Agricultural Sciences, Sweden</footnote>

<footnote n="3">³ UMR 1248 AGIR (Agroécologie-Innovations-Territoire), INRA-INPT, France</footnote>

<footnote n="4">⁴ UMR 2594 LIPM, CNRS, France</footnote>

<footnote n="5">⁵ UMR1347 Agroécologie, INRA, France</footnote>

<footnote n="6">⁶ Eco-efficient Cropping Systems, University of Cordoba, Spain, Agritec, Czech Republic</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: celine.bourlet@grignon.inra.fr</footnote>
introduced and differing between the innovative CSs: pea in FrI 1 and lentil in FrI 2 (Table 2). In Sweden, the reference CS was quite diversified (6 year rotation and three different crop families) and already had a good sustainability rate. However, introducing GLs in the crop rotation allowed improving air and soil quality as well as preservation of non-renewable resources. In the chosen region in Spain, the most important problem was soil erosion. The tool was modified to be adapted to those specific local conditions. The innovative CSs did not seem to improve the environmental dimension. This lack of changes may be explained by a low use of fertilizers on the reference CS and a choice to apply low tillage in the reference and innovative CSs, which did not allow decreasing the herbicide use.

In all countries, introducing GLs was usually linked with a lower input use (especially N fertilizer) and allowed diversifying the crop sequence. The use of less chemical inputs may therefore explain some of the better results for the environmental dimension. The social dimension is defined mainly as a balance between health risks for the farmer linked to pesticides and the CS complexity (number of crops and management). This explains that the social sustainability did not highly differ between innovative and reference CSs. The economic dimension also includes the long term production capacity which can explain why reference CS and innovative CSs may have the same sustainability, even if GLs are usually less profitable in the current economic context. It is important to keep in mind that the tools CRITER and MASC were not designed first to deal specifically with GLs (especially in intercrops or relay crop) and the calculation of some criteria still needs improvement based on additional scientific knowledge. The results of their assessments could evolve with improvement of the calculation of those criteria.

<figure><img src="image_90.png" /><figcaption>Fig. 1. Reference and designed cropping systems for Sweden</figcaption></figure>

<table><caption>Table 2. Sustainability assessment of cropping systems for three countries</caption><thead><tr><th></th><th colspan="3">Spain</th><th colspan="3">Sweden</th><th colspan="2">France</th></tr><tr><th>The higher mark the better</th><th>SpR 1</th><th>SpR 2</th><th>SpI 1</th><th>SwR</th><th>SwI 1</th><th>SwI 2</th><th>FrR</th><th>FrI 1</th><th>FrI 2</th></tr></thead><tbody><tr><th>Economic (between 5 and 1)</th><td>5</td><td>4</td><td>5</td><td>5</td><td>5</td><td>5</td><td>4</td><td>2</td><td>5</td></tr><tr><th>Social (between 5 and 1)</th><td>3</td><td>3</td><td>2</td><td>3</td><td>4</td><td>5</td><td>3</td><td>4</td><td>3</td></tr><tr><th>Envir. (between 5 and 1)</th><td>4</td><td>3</td><td>2</td><td>5</td><td>5</td><td>5</td><td>3</td><td>5</td><td>5</td></tr><tr><th>Overall sustainability (between 7 and 1)</th><td>6</td><td>4</td><td>4</td><td>6</td><td>7</td><td>7</td><td>4</td><td>5</td><td>6</td></tr></tbody></table>

As the characterization of sustainability can highly differ according to different stakeholders, the sustainability assessment of these CSs has to account for this diversity. Thus, in a next step, meetings with stakeholders will be organized in each country to catch their points of view on sustainability and integrate them to assess the sustainability of the designed CSs. Current and innovative performances will then be compared within each country. This meeting with stakeholders will also allow us to discuss the feasibility of innovative CSs and to identify the innovative ones accepted by most (or even all) stakeholders (Ravier et al., 2015).

## 4 Conclusions

This design and assessment work on CSs allowed us to compare different innovative CSs with GLs in different contexts. Even if results differ between situations and innovative CSs, the introduction of GLs brings some changes in the CS sustainability. It usually improves the environmental dimension while keeping a good economic sustainability. These assessments give to each country a more concrete frame to start working with farmers and stakeholders in order to improve CSs sustainability.

*Acknowledgements.* The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under the grant agreement n°FP7-613551, LEGATO project.

## References

* Food and Agriculture Organization of the United Nations (FAOSTAT), http://faostat3.fao.org/home/E. Accessed on September 2014.

* Ravier C., et al. (2015). Multi-criteria and multi-stakeholder assessment of cropping systems for a result-oriented water quality preservation action programme. *Land Use Policy* **42**, 131-140

* Sadok, W. Et al. (2009). MASC, a qualitative multi-attribute decision model for ex ante assessment of the sustainability of cropping systems. *Agronomy for Sustainable Development*, **29(3)**, 447-46.
# Designing legume-supported cropping systems

Moritz Reckling *±1,2, Göran Bergkvist 2, Frederick Stoddard 3, Christine A Watson 2,4, Aurelio Pristeri 5,
Ion Toncea 6, Robin Walker 4, Peter Zander 7, Jens-Martin Hecker 7 & Johann Bachinger 1

## 1 Introduction

Given the negative side-effects of agricultural practices, changes in land use, climate and international trade conditions, resource-efficient and climate change adapted production systems need to be developed. This is especially relevant for legumes since less than 30% of the plant-based protein feed is produced within Europe, so protein security is an issue, while rotations have become narrow and questions about their sustainability are often raised. Legume cultivation declined in Europe from 4.7% in 1961 to 1.7% in 2013 (FAOstat, 2014) due to agronomic and marketing constraints, increased specialisation and despite several agronomic, environmental and farm-economic benefits (Preissel et al., 2015; Reckling et al., 2014a). To design competitive cropping systems, we applied a framework developed in the Legume Futures project (Reckling et al., 2014b; Stoddard et al., 2014). Our objectives were to generate new legume-supported cropping systems as an alternative to current farming and to assess the impacts of legume-supported, legume-free and current cropping systems on economic agronomic and environmental parameters in five sites: Västra Götaland (SE), Eastern Scotland (UK), Brandenburg (DE), Sud-Muntenia (RO) and Calabria (IT).

## 2 Materials and Methods

The cropping system framework consists of a rule-based rotation generator and a set of algorithms to calculate impact indicators considering rotational effects of all crops (Reckling et al., 2014b). The framework follows three steps: i) generating rotations, ii) designing production activities and evaluating their impact using environmental, economic and agronomic indicators, and iii) assessing and designing cropping systems. In this paper we concentrate on arable cropping systems with grain legumes excluding forage crops. The following cropping systems were selected and compared: i) 'current farming without legumes' (based on regional statistics and knowledge from farm advisors), ii) 'economic best without legumes' (generated systems with highest gross margins), iii) 'economic best including legumes', iv) 'ecologic best without legumes' (generated systems with lowest N₂O-emissions) and v) 'ecologic best including legumes'. Impact indicators from all generated systems were normalized to allow a systematic comparison (0=the lowest value from the generated systems e.g. 100€ gross margin and 1=the highest value e.g. 600€ gross margin). A high value is considered positive for gross margin and N-efficiency (N output/external N input) and considered negative for N-fertilizer use, N₂O-emissions and N-leaching.

## 3 Results – Discussion

Adding legumes to cropping systems increased the options for crop rotation design in all regions. E.g. in RO where most farmers grow sunflower, maize and wheat (82% of the arable land), soy bean, pea and common bean are options to diversify cropping systems. The impacts of different cropping systems are shown in Fig. 1. In RO and UK, legumes were economically more attractive than legume-free alternatives and in three regions, legume-supported systems were more profitable than current farming. In IT cereals dominate current farming with high gross margins but a high risk of severe phytosanitary problems. Systems with legumes reduced N₂O-emissions because of a lower N-fertilizer use. With less N inputs, comparable or higher N outputs could be produced (higher N-efficiency). For 'economic best' systems, N-leaching was lower with legumes except in RO and IT. The systems with the lowest N₂O-emissions ('ecologic best') were less profitable and resulted in higher N-leaching in SE, UK and IT.

These findings indicate a site-specific potential for including more grain legumes into European cropping systems. However, constraints of legume cultivation that are not covered in the analysis i.e. low yield stability and few marketing options make the cultivation unattractive in many European regions. Legumes were found to be economically more

<footnote n="1">¹ Institute of Land Use Systems, Leibniz Centre for Agricultural Landscape Research (ZALF), Germany</footnote>

<footnote n="2">² Department of Crop Production Ecology, Swedish University of Agricultural Sciences (SLU), Sweden</footnote>

<footnote n="3">³ Department of Agricultural Sciences, University of Helsinki, Finland</footnote>

<footnote n="4">⁴ Crop and Soil Systems, Scotland's Rural College (SRUC), United Kingdom</footnote>

<footnote n="5">⁵ Department of Agricultural Science, Mediterranean University of Reggio Calabria, Italy</footnote>

<footnote n="6">⁶ National Agricultural Research and Development Institute Fundulea, Romania</footnote>

<footnote n="7">⁷ Institute of Socio-Economics, Leibniz Centre for Agricultural Landscape Research (ZALF), Germany</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: moritz.reckling@zalf.de</footnote>
attractive when assessed at the rotational level compared to assessments at the crop level (not shown here). This is supported by a recent meta-analysis on farm-economic values of grain legume pre-crop benefits (Preissel *et al.*, 2015).

<figure><img src="image_91.png" /><figcaption>**Fig. 1.** Assessment of different cropping systems with and without legumes in five sites across Europe.</figcaption></figure>

## 4 Conclusions

We conclude that legumes have an underutilized potential for European agriculture and that analyses with this framework support agronomists to design new cropping systems and to systematically assess their impacts.

*Acknowledgements.* The work was financed through the Federal Ministry of Food and Agriculture (BMBF), Germany; the Brandenburg Ministry of Sciences, Research and Cultural Affairs (MWFK), Germany; the EU FP7 project 'Legume Futures' (Grant 245216 CP-FP) and the BMBF funded FACCE-ERA-NET+ project Climate Change Adaptability of cropping and Farming systems for Europe (Climate-CAFÉ; Grant PTJ-031A544)

## References

* FAOstat, Production - Crops. Access: http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancorr [9.4.2014].

* Preissel, S., Reckling, M., Schläfke, N., & Zander, P. (2015). Magnitude and farm-economic value of grain legume pre-crop benefits in Europe: A review. *Field Crops Research, **175*, 64-79.

* Reckling, M., Preissel, S., Zander, P., Topp, K., Watson, C., Murphy-Bokern, D. & Stoddard, F. L. (2014a). Integrated analysis of effects of legumes within crop rotations. *Legume Futures Report 1.6*.

* Reckling, M., Hecker, J.-M., Schläfke, N., Zander, P., Bergkvist, G., Frankow-Lindberg, B., Båth, B., Pristeri, A., Monti, M., Toncea, I., Walker, R., Watson, C. A., Eory, V., Topp, C. F. E., Stoddard, F. L. & Bachinger, J. (2014b). The Legume Futures rotation generator I. Generation and evaluation of rotations. In: 13th ESA Congress 25-29 August 2014, Debrecen, Hungary. pp. 363-364.

* Stoddard, F. L., Reckling, M., Bachinger, J., Hecker, J.-M., Schläfke, N., Zander, P., Bergkvist, G., Pristeri, A., Toncea, I., Walker, R. L., Eory, V., Maire, J., Topp, C. F. E., Rees, R. M. & Watson, C. A. (2014). The Legume Futures rotation generator II. Agronomic analysis of rotations. In: 13th ESA Congress 25-29 August 2014, Debrecen, Hungary. pp. 361-362.
# Design of innovative legume-based systems: the case of soybean-based cropping systems in Oise, northern France

Elisa Marraccini *±1*, Alicia Ayerdi-Gotor <fnref n="2" />, Romain Armand <fnref n="3" />, Olivier Scheurer <fnref n="2" /> & Christine Leclercq <fnref n="2" />

## 1 Introduction

The diversification of crop rotations has been proposed as a solution to increase the sustainability of farming systems (Wezel *et al.*, 2013). In arable crop-oriented agricultural regions, the diversification of the crop rotations has often been based on the introduction of annual legume grain crops such as peas and faba-bean, which suffer from irregular yields (Jensen *et al.*, 2010). Farming systems of the Oise region (Northern France) are mainly crop-based, since 56% of farms are oriented to cereals and canola or industrial crops (Chambre d'Agriculture de Picardie, 2010). Local stakeholders are particularly interested in assessing the potential for the introduction of soybean in Oise, after earlier maturity cultivars have been developed (Fig. 1). In this context, we wonder which could be the potential for innovative and sustainable soybean-based rotations at the regional level to create a new supply chain.

## 2 Materials and Methods

Answer to this question required an integrated (technical, economic and social) and multi-scale (crop rotation, farming system and region) methodology. In a first step, agronomic yield potential of adapted soybean cultivars was assessed based on soil (11 soil types) and climatic (temperature, rainfall, ETP, solar radiation for 4 whether stations) assets of the region using the STICS model (Brisson *et al.*, 1998). Then, in the suitable climatic areas, crop rotations where located using the French Land Parcel Identification System (LPIS) on the last six years and soil type specified for each of them based on a combination of soil data base and soil map at 1/250 000 and agronomical rules. Subsequently, each crop rotation x soil type suitable for the introduction of soybean was identified and mapped according to the rules illustrated in Fig. 2. Furthermore, a comparison of gross margin (GM) between the current and innovative soybean-based cropping systems was provided for each combination of suitable crop rotation x soil type. In this model, crop yields per soil type were expert-driven and main costs and sales estimated from technical institutes data. Finally, we tested soybean introduction impact on labour flows on a sample of local farms representative of the 72% of the Usable Agricultural Area (UAA) of the regional farms more suitable for soybean introduction (Chambre d'Agriculture de Picardie, 2010) using the Mecagro calculator (Mousset, 1996) on actual and innovative farming systems introducing soybean.

<figure><img src="image_92.png" /><figcaption>**Fig. 1.** Location of the Oise region and climatic suitability for soybean.</figcaption></figure>

<figure><img src="image_93.png" /><figcaption>**Fig. 2.** Decision diagram rules about introducing soybean in regional crop rotations.</figcaption></figure>

<footnote n="1">¹ UP 2012-10-103 PICAR-T, Institut Polytechnique LaSalle Beauvais, France</footnote>

<footnote n="2">² Institut Polytechnique LaSalle Beauvais, France</footnote>

<footnote n="3">³ UP 2012-10-102 HYDRISE, Institut Polytechnique LaSalle Beauvais, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: elisa.marraccini@lasalle-beauvais.fr</footnote>
## 3 Results and Discussion

On the whole UAA censed in the LPIS of the Oise region (376,450 ha), the potential soybean surface was 9534 ha according to the climatic, sol and agronomic selected criteria. Of these surface, 38% derived from a faba-bean or pea-based crop rotation, 37% from canola-based one, 21% from a cereal after cereal and 4% from a cereal after two cereals one.

The modelled yields of soybean at the regional level under different climatic conditions and according to the main practices (e.g. rainfed) appeared heterogeneous, mainly depending on the spring drought affecting the germination and seedling growth (data not shown). These modelled yields are coherent with those measured in the field trials (Fig. 1). Nevertheless, in those years not suffering of spring droughts and on the more favourable soils (e.g. silt loam), modelled yields are comparable to those already obtained on irrigated soybean on later maturity cultivars (CETIOM, 2015).

At the crop rotation level, the difference in the GM appeared very variable even within the same decision rules on introducing soybean (in Fig. 2) and mainly depending on the initial crop rotation length. However, average and median values of the GM are proximal to zero (Fig. 3) for each considered crop rotation, indicating a very low difference of average annual GM in current and soybean-based cropping systems. In sub-samples of field blocks located in homogeneous soil types, this appeared less variable, especially in suitable soils, although no significant differences have been found in the average GM for different soil types.

<figure><img src="image_94.png" /><figcaption>**Fig. 3.** Examples of the variation of GM in different types of crop rotations before and after introducing soybean: left, case of one cereal after two (n=2270), right, case of canola and contract crops (n=1975). In both cases, the difference is assessed on different soil types: sandy loam (n= 41 and 466 respectively) and silt loam (n= 683 and 630 respectively). At the farm level, the introduction of soybean improved, or at least did not change, the labor flow in all the farm types (data not shown). The modeled labor variation seemed favorable both in terms of total worked hours and of management of the busy periods (early spring and late summer/early autumn).</figcaption></figure>

## 4 Conclusions

Our first results on the suitability of soybean introduction in the Oise region has shown that soybean potential (yield and surface) could sustain the creation of a new supply chain even though yield variability has to be expected with spring droughts. However, gross margin difference nor yield variability reduction do not seem to be sufficient for farmers to introduce soybean beside the positive impact on labor at the farm level.

*Acknowledgements.* We acknowledge the contribution of MSc students in "Agronomie et Territoire" of the Institut Polytechnique LaSalle Beauvais (promo 154) for their collective project; we thank Marie Dauplay for the database-building, Christian Dersigny for the meteorological data and Arnaud Van Booms for the field trial data.

## References

Brisson, N., Mary, B., Ripoche, D., Jeuffroy, et al. (1998). STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie 18:311-346.

CETIOM (2015). Guide de culture : soja 2015. Available online (retrived March 25th 2015): http://www.cetiom.fr/fileadmin/cetiom/kiosque/guide soja 2015/guide soja CETIOM2015.pdf

Chambre d'Agriculture de Picardie (2010). Présentation des systèmes d'exploitation agricole de Picardie. Available online (retrived March, 19th 2015): http://www.chambres-agriculture-picardie.fr/fileadmin/documents/publications/economie/Obs Durabilites/Brochure-typologie.pdf

Mousset, J. (1996). Mecagro, conseil en agroéquipement dans les exploitations de grandes cultures. Amiens, Le Biopole végétal, 388 pp.

Jensen, E. S., People, M. B., Hauggaard-Nielsen, H. (2010). Faba bean in cropping systems. *Field Crops Research, 115*, 203-216.

Wezel, A., Casagrande M., Celette, F., Vian, J.-F., et al. (2014). Agroecological practices for sustainable agriculture. A review. *Agronomy for Sustainable Development*, **34**, 1-20.
# Comparison of yields, protein contents and environmental impacts of grain legumes for designing legume-based cropping systems in Europe

Charles Cernay *±1*, Elise Pelzer 1,2, Jean-Marc Meynard 3,4, Tamara Ben-Ari 1,2 & David Makowski 1,2

## 1 Introduction

The European Parliament has recently emphasized the importance of increasing grain legume production in Europe (Häusling, 2011), to reduce soybean imports from the Americas, to diversify European cropping systems (Bues et al., 2013), and to reduce the negative environmental impacts of intensive cereal production. Numerous experiments were already carried out to evaluate performances of different grain legume species in local conditions but so far, no global quantitative synthesis of available data has been performed. Based on a comprehensive analysis of published experimental data and of FAO crop yield data, we estimated the agronomic and environmental performances for a large range of grain legume species across contrasted environments and crop management techniques.

## 2 Materials and Methods

The aim was to globally compare the yields, protein contents and environmental impacts derived from the nitrogen cycle of grain legume species that may be grown in Europe. First, we estimated inter-annual yield variability and risk of yield loss for major legume and non-legume crops using yield time series from the Food and Agriculture Organization statistics database (FAOSTAT, 2014) over the period 1961-2013. Normalized yield residuals were computed from the yield time series in Western, Eastern, Northern and Southern Europe, and North and South America. Measures of yield variability and risk of yield loss were then calculated from the distributions of normalized yield residuals. These risk measures were used to rank grain legume species in each region. Second, we carried out a meta-analysis of published experimental data in order to compare the yields, protein contents, and environmental impacts of several grain legume species grown in various conditions. The studies included in the meta-analysis were reporting data collected in experiments comparing at least two different grain legume species. Grain legume crops were compared using both direct and indirect comparisons. Variables related to climate conditions, soil types, and crop management techniques were used to explain variability in outcomes.

## 3 Results and Discussion

The measures of variability and risk estimated from the FAO yield data show that grain legume yields were significantly more variable than non-legume yields in Europe over 1961-2013 (Cernay et al., 2015). Levels of yield variability and risk of yield loss differ strongly across species and European regions. In general, yield variability for grain legumes is higher than wheat. Lupin is the species showing the highest level of variability in Northern, Eastern, and Western Europe (Fig. 1) and bean ranks first in Southern Europe. Among grain legumes, fababean is the least variable crop in Southern and Western Europe (Fig. 1), whereas soybean and pea are the least risky in Eastern and Northern Europe respectively. Expected yield losses can exceed 25% of expected yield values in some situations, especially for lupin and soybean in Western Europe. By contrast, differences between grain legumes and non-legumes are smaller in the Americas. Several factors may explain the contrasted species rankings, such as local agroclimatic conditions and crop management techniques.

<figure><img src="image_95.png" /><figcaption>**Fig. 1.** Standard deviation of normalized yield residuals for 10 crops in Western Europe over 1961-2013. Polynomial regression models are used to calculate normalized yield residuals. Horizontal lines correspond to 95% confidence intervals calculated by 10,000 bootstrap samples. Among the 10 crops, 5 are grain legume crops (bold names) and 5 are non-legume crops (non-bold). All crops are ranked according to standard deviation of normalized yield residuals</figcaption></figure>

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

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

<footnote n="3">³ INRA, UMR 1018 Sciences pour l'Action et le Développement: Activités, Produits, Territoires, F-78850, Thiverval-Grignon, France</footnote>

<footnote n="4">⁴ AgroParisTech, UMR 1018 Sciences pour l'Action et le Développement: Activités, Produits, Territoires, F-78850, Thiverval-Grignon, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Charles.Cernay@grignon.inra.fr</footnote>
The meta-analysis of published data allowed performances of 25 non-legume and 28 grain legume species to be compared. Due to the very high number of possible comparisons, a network representation was used to identify the pairs of species that were tested at the same experimental site(s) (Fig. 2). Results show that pea, chickpea, fababean, lentil and narrow leaf lupin were frequently tested together in Europe, North America and Oceania, whereas field experiments testing groundnut, cowpea, soybean, and pigeon pea were frequently conducted in Africa or Asia. The next step will consist in ranking all these species using direct and indirect comparison methods (Laurent et al., 2015). The rankings produced by the meta-analysis will then be compared with the rankings derived from the measures of variability and risk (Fig.1) in order to provide an overall assessment of grain legume species. Several factors characterizing experimental sites and cropping systems will be used to explain part of variability in outcomes.

<figure><img src="image_96.png" /></figure>

**Fig. 2.** Network of grain legume species (grey points) and non-legume species (empty points) reported in the meta-analysis. The links represent the pairs of species tested at the same experimental site(s), either as preceding or succeeding crops. The thickness of the links increases with the number of experimental sites at which the species are compared. The Latin names are abbreviated as follows: AH: *Arachis hypogaea*; AS: *Avena sativa*; BCa: *Brassica campestris*; BCh: *Brassica chinensis*; BJ: *Brassica juncea*; BN: *Brassica napus*; BR: *Brassica rapa*; CAb: *Crambe abyssinica*; CAr: *Cicer arietinum*; CC: *Cajanus cajan*; CTe: *Cyamopsis tetragonoloba*; CTi: *Carthamus tinctorius*; Fa: Fallow; FE: *Fagopyrum esculentum*; GM: *Glycine max*; HA: *Helianthus annuus*; HV: *Hordeum vulgare*; IB: *Ipomoea batatas*; LAI: *Lupinus albus*; LAN: *Lupinus angustifolius*; LAi: *Lupinus atlanticus*; LCi: *Lathyrus cicera*; LCu: *Lens culinaris*; LL: *Lupinus luteus*; LO: *Lathyrus ochrus*; LPI: *Lupinus pilosus*; LPu: *Lablab purpureus*; LS: *Lathyrus sativus*; LU: *Linum usitatissimum*; OS: *Oryza sativa*; PG: *Pennisetum glaucum*; PL: *Phaseolus lunatus*; PM: *Panicum miliaceum*; PS: *Pisum sativum*; PV: *Phaseolus vulgaris*; SB: *Sorghum bicolor*; SL: *Solanum lycopersicum*; TA: *Triticum aestivum*; TD: *Triticum durum*; TH: *Triticale hexaploide*; TS: *Triticum sativum*; TT: *Triticum turgidum*; VA: *Vigna aconitifolia*; VB: *Vicia benghalensis*; VE: *Vicia ervilia*; VF: *Vicia faba*; VM: *Vigna mungo*; VN: *Vicia narbonensis*; VR: *Vigna radiata*; VSa: *Vicia sativa*; VSu: *Vigna subterranea*; VU: *Vigna unguiculata*; ZM: *Zea mays*.

Both methods provided interesting information on the performances of a large range of grain legume species. Based on historical time series, the first method is able to rank species according to their inter-annual yield variability and risk of yield loss at a large scale. Based on field trials, the second method evaluates the performances of grain legumes for various criteria and explains part of their variability in function of agroclimatic and technical factors.

## 4 Conclusions

Our results are expected to provide a solid basis to analyze the risks associated with the growth of grain legumes and to assess their values for improving the sustainability of European food systems. Comparing the yields, protein contents and environmental impacts for a diversity of grain legume species under contrasted environments and crop management techniques may help agronomists designing legume-based cropping systems in Europe.

*Acknowledgements.* This work was supported by the French National Research Agency (ANR) under the program Investing in the future Grant n°ANR-10-IDEX-0003-02 and is part of LabEx BASC (ANR-11-LABX-0034).

## References

Bues, A., Preißel, S., Reckling, M., Kuhlman, T., Topp, K., Wats, C., et al. (2013). The environmental role of protein crops in the new Common Agricultural Policy. European Parliament. Policy Department B: Structural and Cohesion Policies. http://www.legumefutures.de/legumefuturesnews/30052013brussels.html Accessed on March 2015.

Cernay, C., Ben-Ari, T., Pelzer, E., Meynard, J.-M., Makowski, D. (2015) Estimating variability in grain legume yields across Europe and the Americas. *Scientific Reports*, **5**, 111710.1038/srep11171.

Food and Agriculture Organization of the United Nations (FAOSTAT). http://faostat3.fao.org/home/E Accessed on September 2014.

Häusling, M. (2011). The EU protein deficit: what solution for a long-standing problem? (2010/2111(INI)). European Parliament. Committee on Agriculture and Rural Development. http://www.europarl.europa.eu/sides/getDoc.do?type=REPORT&reference=A7-2011-0026&language=EN
Accessed on March 2015.

Laurent, A., Pelzer, P., Loyce, C., Makowski, D. (2015). Ranking yields of energy crops: A meta-analysis using direct and indirect comparisons. *Renewable and Sustainable Energy Reviews*, **46**, 41–50.
# Pushing for intensified protein per capita production on smallholder farms in Malawi through 1000+ farmers experimenting with diverse grain legumes

Regis Chikowo *±1,2, Sieglinde Snapp <fnref n="1" />, Erin Anders <fnref n="1" />, Wezi Mhango <fnref n="3" /> & Mateete Bekunda <fnref n="4" />

## 1 Introduction

Low land productivity undermines potential food production, stifles income growth from the lack of surplus food and keeps many farming hosueholds impoverished and undernourished. Over reliance on cereal crop production for 'food security', with little investments in grain legumes is one of the major contributing factors to poor nutritional outcomes (Bezner-Kerr et al., 2006). For Malawi, where farm sizes are small ranging 0.5-1.5 ha, grain legume production suffers from low prioritization, as maize takes up the bulk of the land, yet for a farming system with an insignificant livestock component for meat products, intensified grain legume production would address part of the protein deficits in diets. Using simple but proven and effective agronomic practices, several initiatives on harnessing biological N₂-fixation in southern Africa (Mpepereki et al., 2000), have established that there is untapped potential in grain legume production. The purpose of this methodological paper is to present a co-learning approach in which researchers can work with hundreds of farmers who experiment with grain legume technologies at scale on their own farms, with deliberate emphasis on local grain legume processing to stimulate local consumption. We hypothesize that increased local consumption of grain legumes is a much stronger driver to grain legume production in an environment where extenal grain legume markets are volatile.

## 2 Materials and Methods

Between 2013 and 2015, we worked with smallholder farmers organized in 16 action groups in two districts of central Malawi (Dedza and Ntcheu) as part of the Africa RISING (Research in Sustainable Intensification for the Next Generation) project, to enhance farmer knowledge and support sustainable intensification pathways for productivity gains in maize-legume diversified systems. We employed approaches that would increase primary productivity of grain legumes, that would in turn enhance productivity of cereals grown in sequence. In a step-wise co-learning system, farmers in each of the action groups firstly worked with researchers to establish ‘mother trials’ that acted as platforms for initial learning (snapp et al., 2002). These trials had a strong bias on grain legumes, but also encompassed other technologies on soil fertility management. High yielding and ‘specific’ soyabean varieties were innoculated with *Badyrhizobium japonicum*, while groundnut, pigeonpea and cowpea varieties most suited to the different agroecologies were used. Based on earlier research insights, a ‘doubled-up legume technology’, in which pigeonpea is additively intercropped with another grain legume with a contrasting growth habit, was included in the experimental designs. Phosphorus was applied to all legumes at planting at a modest rate of 5 kg ha⁻<fnref n="1" /> P. After co-establishment of the mother trials, small packs of improved grain legume seeds were then distributed to farmers who went on to establish their own ‘baby trials’ composed of a subset of the treatments drawn from the mother trials. Farmers were encouraged to expand to beyond the initial 200 m<fnref n="2" /> that could be covered through the free seed, by use of other networks and own resources. In subsequent years, the strategy to increase planted area would be achieved through seed retention after harvest. A series of hands-on training workshops on local processing, especially for soyabean to remove antinutritive factors, were held during each postharvest season to produce tasty and nutritious food products.

## 3 Results - Discussion

During the first year of action research, farmers were not familiar with some of the technologies, notably the double-up legume technology. This was reflected in the small promotion of farmers of only 7% of the baby trials that included a doubled-up legume technology. As farmers interacted with the technologies over the next two seasons, there was evidence of increased uptake of the newly introduced technologies.

Across sites, sole cropped groundnut had yields ranging from 0.80–1.63 t ha⁻<fnref n="1" />, soyabean 0.64–2.64 t ha⁻<fnref n="1" /> and cowpea 0.70–1.41 t ha⁻<fnref n="1" /> (Fig. 1). Soyabean demonstrated the greatest elasticity, yielding poorly in a hot and low agroecology potential zone, and responding remarkably to both inoculation and P fertilization in cooler and high potential agroecologies. While farmers already had significant knowledge on local utilization of groundnut and cowpea, the greatest impact of our interventions on nutrition outcomes were related to the training of both men and women farmers

<footnote n="1">¹ Plant Soil and Microbial Sciences Department, Michigan State University</footnote>

<footnote n="2">² Crop Science Department, University of Zimbabwe</footnote>

<footnote n="3">³ Plant and Soil Sciences, Faculty of Agriculture, Lilongwe University of Agriculture and Natural Resources, Malawi</footnote>

<footnote n="4">⁴ The International Institute of Tropical Agriculture (IITA), AVRDC, Arusha, Tanzania</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: regischikowo@yahoo.co.uk</footnote>
on local processing of soyabean. With crude protein of 40%, and average productivity of nearly 2 t ha⁻¹ with inoculation and some P fertilizer application, famers could potentially generate 800 kg ha⁻¹ protein with a 4-month cropping season.

<figure><img src="image_97.png" /><figcaption>Fig. 1. Groundnut, soyabean and cowpea productivity across mother trial sites in central Malawi</figcaption></figure>

This approach, which couples ‘conventional research trials’ to ‘farmer experimentation’ organized in learning groups, accelerated learning and knowledge acquisition, among farmers, including the illiterate ones that often exclude themselves in knowledge intensive initiatives. Over the next cropping cycle, we will investigate the proportion of farmers who are now independently implementing variants of the grain legume technologies and assess any changes to farm decision making and trade-offs in the design of the cropping patterns at farm-scale.

## 4 Conclusions

Many potentially game-changing technologies have remained unsued by farmers due to various biophysical challenges, but we contend that poor packaging of the technologies as they are presented to farmers remains one of the critical handicaps. While still using conventional field experimentation methodologies, we have presented an action-oriented approach in which hands-on training of farmers from production to consumption shows great promise for revolutionlizing smallholder cropping patterns in central Malawi, creating a niche for increased farm protein production in the absence of a vibrant livestock component.

*Acknowledgements*. Funding of this work is through the USAID-funded Africa RISING program

## References

Bezner-Kerr, R., Snapp, S.S., Chirwa M., Shumba, L. & Msachi, R. (2006). Participatory research on legume diversification with Malawian smallholder farmers for improved human nutrition and soil fertility. *Experimental Agriculture*, **43**, 437-453.

Mpepereki, S., Javaheri, F., Davis, P. & Giller, K.E. (2000). Soyabeans and sustainable agriculture: Promiscuous soyabeans in southern Africa. *Field Crops Research* **65**, 137-149.

Snapp, S.S., Kanyama-Phiri, G., Kamanga, B., Gilbert, R. & Wellard K. (2002). Farmer and researcher partnerships in Malawi: developing soil fertility technologies for the near-term and far-term. *Experimental Agriculture*, **38**, 411-431.
# Below and aboveground pigeonpea productivity in a novel doubled-up legume cropping system across three agro-ecologies in central Malawi

Chiwimbo Gwenambira *±1*, Sieglende Snapp <fnref n="1" /> & Regis Chikowo <fnref n="1" />

## 1 Introduction

Agro-ecological intensification improves the performance of agriculture through integration of ecological principles into farm and system management. Enhanced use efficiency of limited nutrient and land resources is key to viable farming systems in densely populated African countries. Use of legumes that biologically fix nitrogen, provide high protein grain, and recycle nutrients is one approach that has shown promise in Malawi. However, empirical data on legume root and shoot biomass additions to the systems have largely remained scarce (Myaka et al., 2006).

## 2 Materials and Methods

The objectives of the study were to determine aboveground and belowground biomass of pigeonpea in doubled-up legume cropping systems involving groundnut and soyabean, and also in pigeonpea/maize intercrops. Field experiments were established across three agro-ecologies in central Malawi during the 2013/14 cropping season. Pigeonpea was planted as a sole crop or in an additive intercrop system with soyabean and groundnut—a novel system known as the doubled-up legume technology. Additionally, a pigeonpea/maize intercrop was included as a control. The success of the doubled-up legume systems hinge on the initially slow growth of pigeonpea, facilitating the growth of companion crops as if sole-cropped (Snapp et al., 2015). Six months after planting, pigeonpea plants across treatments were cut at ground level, and aboveground components were separated into stems, twigs, pods, and leaves. Senescent leaf litter was collected using traps. Roots of the same plants were excavated from the 0.20, 0.20–0.40, and 0.40–0.60 m layers, and soil samples were collected from each layer.

## 3 Results - Discussion

<figure><img src="image_98.png" /><figcaption>Fig. 1. Total shoot and root biomass of pigeonpea</figcaption></figure>

<footnote n="1">¹Plant, Soil and Microbial Sciences Department, Michigan State University</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: chiwimbopercy@gmail.com</footnote>
<figure><img src="image_99.png" /><figcaption>Fig. 2. Total senescent litter of pigeonpea.</figcaption></figure>

<figure><img src="image_100.png" /><figcaption>Fig. 3. Root biomass of pigeonpea in the 0–0.2m layer.</figcaption></figure>

<figure><img src="image_101.png" /><figcaption>Fig. 4. Root shoot ratios of pigeonpea in three agro-ecologies.</figcaption></figure>

Aboveground biomass of sole pigeonpea was 11.28 t ha⁻¹. The pigeonpea shoot biomass component of the pigeonpea/groundnut, pigeonpea/soyabean, and pigeonpea/maize intercrops was 6.73, 4.84 and 3.35 t ha⁻¹, respectively (Fig. 1). Senescent leaf litter was highest for sole pigeonpea and lowest for the pigeonpea/maize intercrop (Fig. 2). The root biomass was largely confined in the 0–0.20 m layer, with trends similar to that for shoot biomass (Fig. 3). Below the 0.20 m depth, fine pigeonpea roots were dominant. The root shoot ratios varied with agro-ecologies and cropping systems (Fig. 4)

4 Conclusions

Pigeonpea productivity in a pigeonpea/groundnut system is comparable to sole cropped pigeonpea, with additional grain benefits. However, intra-specific competition in a well fertilized pigeonpea and maize intercropping system is rather large. We conclude that both below-and aboveground biomass of pigeonpea can significantly increase nutrient and water use efficiency by fixing nitrogen, building soil carbon, and establishing a root system with the ability to capture and recycle nutrients.

*Acknowledgements*. Many thanks to Africa RISING, USAID and all of their partners

References

Myaka, F. M., Sakala, W. D., Adu-Gyamfi, J. J., Kamalongo, D., Ngwira, A., Odgaard, R., ... & Høgh-Jensen, H. (2006). Yields and accumulations of N and P in farmer-managed intercrops of maize-pigeonpea in semi-arid Africa. *Plant and soil*, *285*(1-2), 207-220

Snapp S., Chikowo R., Bekunda M., Gwenambira C., Mpeketula P. (2015) Greener food production in Africa: intensification through multipurpose legumes (Poster in the same FSD5 session).
# Combination of undersown catch crops and row-hoeing for optimizing nitrogen supply and weed control in organic spring barley

Thi Thuy Hong Phan *±1 & Kristian Thorup-Kristensen

## 1 Introduction

Current organic cereal production faces two main problems: nitrogen (N) deficiency and high weed pressure. The use of catch crops, including legumes is an important approach to improve soil fertility and also to reduce the environmental impact of agricultural activities. In Europe, catch crops are usually grown to take up N remaining in the soil after main crop harvest. However, in practice farmers prefer an early termination of catch crops or not establish them to allow intensive autumn soil tillage for weed control, especially perennial weeds, strongly reducing or withdrawing their desired effects. Therefore, an innovative weed control strategy is tested, which should reduce the need for tillage and allow the efficient use of catch crops as green manure and weed suppressor. In the current study, we aim to develop a row crop system for organic cereal production with an increased row-distance of 24 cm, the use of undersown winter hardy catch crops and inter row-hoeing. We test different catch crop mixtures, different sowing times and study the integrated effect of a combined undersown catch crop-row hoeing-row crop system on crop yield, crop and soil N dynamics, N effect (Neff) of catch crops for the succeeding crop and on weed control.

## 2 Materials and Methods

Two-year field experiments (2014 - 2015 and 2015 - 2016) are established in organic spring barley. The experimental factors include (1) row cropping system or conventional system (with 24 or 12 cm row-distances); (2) with or without catch crops; (3) different catch crop species mixtures and (4) three different catch crop sowing times.

Catch crops were sown at the same time as spring barley in April or delayed 3 and 6 weeks. Depending on the three sowing dates, row hoeing was made either 0, 1 or 2 times between the spring barley rows prior to sowing catch crops.

We used different legume-nonlegume mixtures to take advance the biological N fixation of legumes (i.e. white clover, red clover, Lucerne,...) and a more sufficient soil N depletion of non-legumes (i.e. rye grass, chicory, Dyer's woad,...).

All of them are winter-hardy species. After the spring barley harvest in August, the catch crops remain on the field during autumn and winter and are incorporated into the soil in March next year. Moreover, to mimic the common intensive soil tillage activities of farmers, the two fallow treatments of both two cropping systems were harrowed three times during September-October. Then, a pure-stand spring barley will be established in April of the second years.

We measure aboveground plant biomass of each plot at four dates: Early August before the barley harvest, November at the end of the growing season, early March before catch crop incorporation and early August before the new spring barley harvest. They are sorted into 4 groups of plant species: 1) legume, 2) non-legume, 3) thistle (Cirsium arvense) and 4) other weeds. Then we record their dry biomass and analyze the C:N ratio. We also measure the soil inorganic N content - N<sub>inorg</sub> (ammonium-N and nitrate-N) of three different soil layers (i.e. 0-50 cm, 50-100 cm and 100-150 cm) at two dates: Mid November and early May after the new spring barley establishment.

We also employ other assessment strategies for studying the effect of row hoeing and catch crops on the thistles: 1) direct counting the thistle population in the entire plots, according to their height categories and analyze their N content; 2) injecting <sup>15</sup>N in October, measuring the soil <sup>15</sup>N content up to a 100 cm depth in May and measuring the <sup>15</sup>N recovery in aboveground biomass of the new spring barley and thistles of the second year.

## 3 Results - Discussion

### Plant growth, yield effect and expected Neff

All of catch crop species had a limited growth as undersown, but a fast development after the barley harvest. Therefore, they did not affect the barley yield in the first year (data not shown). The same or higher total dry matter of white clover/rye grass mixture was obtained in the 24 cm row cropping system than in the 12 cm row system, even at later sowing times (Treatment 4 & 5 compared to Treatment 3, Fig. 1). This indicates that we can delay the sowing date of catch crops, allowing us to employ one or two times of the inter row-hoeing for weed control without reducing catch crop growths. Amongst the sowing dates, a short delay where catch crops are sown three weeks later than the main crop

<footnote n="1">¹ Department of Plant and Environmental Sciences, Crop Science Section, Faculty of Science, Copenhagen University</footnote>

<footnote>* Speaker</footnote>

<footnote>‡ Corresponding author: phan@plen.ku.dk</footnote>
after one row hoeing, the clover species produced twice as much biomass as if sown after six weeks and two row-hoeings. However, they suppressed the growth of their companion grass species. In contrast, the non-leguminous species expressed greater tolerance to later sowing. They obtained a similar dry biomass to in the first sowing date and much higher than if sown at the second sowing date. In terms of weed control, weed biomass were reduced more than 50% in all catch crop treatments compared to the fallow treatments. While two row hoeings and later sowing reduced legume growth, its effect on weeds was not clear. The effects of row cropping, hoeing and catch crops on total N content in the succeeding barley crop and the Neff of different catch crops will be measured. The Neff is expected to vary between catch crop mixtures and different sowing times, being high where a high catch crop N content was achieved.

<table><caption>**Table 1.** List of treatments presented in Figure 1</caption><thead><tr><td>Treatments</td><td>Catch crops</td><td>Row distance (cm)</td><td>Sowing times</td><td>Number of hoeing</td></tr></thead><tbody><tr><td>1</td><td>No catch crop</td><td>12</td><td>-</td><td>0</td></tr><tr><td>2</td><td>No catch crop</td><td>24</td><td>-</td><td>0</td></tr><tr><td>3</td><td>White clover/rye grass</td><td>12</td><td>1</td><td>0</td></tr><tr><td>4</td><td>White clover/rye grass</td><td>24</td><td>2</td><td>1</td></tr><tr><td>5</td><td>White clover/rye grass</td><td>24</td><td>3</td><td>2</td></tr><tr><td>6</td><td>Red clover/orchard grass</td><td>24</td><td>2</td><td>1</td></tr><tr><td>7</td><td>Red clover/orchard grass</td><td>24</td><td>3</td><td>2</td></tr></tbody></table>

<figure><img src="image_102.png" /><figcaption>**Fig. 1.** The mean of total aboveground dry matter of legume, non-legume, thistles and other weeds of different treatments in November 2014 of the 2014-2015 experiment, 3 replicates. Bars show standard errors.</figcaption></figure>

## Soil inorganic N and thistle effects

We expect the catch crops will effectively deplete the soil N in the autumn, and their mineralized $N_{inorg}$ will redistributed into the surface layer after their incorporation, leading to increased topsoil $N_{inorg}$ compared to un-covered treatments. Treatments with deeper-rooted species such as chicory, Dyer's woad and winter radish were included in the study, to evaluate their ability to transport N upwards from deep soil layers, thereby reducing N leaching losses (Thorup-Kristensen, 2001). In summary, the reduction of N in subsoil layers and the increase of $N_{inorg}$ in the topsoil in the next season by the catch crop treatments will produce an optimal condition for the N uptake of the shallow rooted crops like spring barley, compared to thistles with a high root density in the deeper soil layers. Therefore, in combination with row-hoeing, this continuous N competition from first the catch crops and then the succeeding barley crop is expected to weaken the thistles, leading to the decline of their growth and regeneration. To consolidate this hypothesis, we expect that the $^{15}$N study will show a lower $^{15}$N recovery in thistles after the catch crop treatments than after the treatments without catch crops.

## 4 Conclusions

We aim to contribute to the development of a row crop system which is more optimal for the development of catch crops than the conventional system while at the same time allowing the necessary control of thistles. The optimized row crop system with the employment of proper catch crop species, sowing time and row hoeing will be a promising system for developing higher yielding organic cereal crops and for more environmental friendly weed control.

*Acknowledgements.* We thank the Danish AgriFish Agency for sponsoring this research within the row crop project.

## References

* Thorup-Kristensen, K. (2001). Are differences in root growth of nitrogen catch crops important for their ability to reduce soil nitrate-N content, and how can this be measured? *Plant and Soil*, **230**, 185-195.
# A territorial design and assessment of legume-based cropping systems to gain ecosystem services

Marie-Hélène Jeuffroy *±<fnref n="1" />, Célia Cholez <fnref n="2" />, Guénaelle Corre-Hellou <fnref n="3" />, Marie-Benoit Magrini <fnref n="2" />, Martine Mauline <fnref n="3" />, Elise Pelzer <fnref n="1" />&Marion Soulié <fnref n="1" />

## 1 Introduction

The intensification of French agriculture, that began in the 1950s, and supported both by public policies and market dynamics, has led to an important simplification of cropping systems, which are nowadays mainly based on cereals and oilseed crops (Schott et al., 2010). These systems have proved their efficacy in producing large amounts of food and feed, but have led to large negative environmental impacts, in particular due to their high dependency on agrochemical inputs (MEA, 2005). Results from simulations, experiments and farmer's practices have shown that cropping systems based on legumes have agro-ecological advantages (e.g. Nemecek et al., 2015). However, the deployment of such cropping systems is limited in France by a technological lock-in involving most stakeholders and actors directly influencing agricultural practices (Meynard et al., 2013) such as collecting firms according to the expectations of manufacturing processors to respond to food demand. Yet, societal demand and public policies ask for the development of diversified agroecosystems that might allow an agroecological management on territories. We assume that the introduction of a higher proportion of legume species in the fields could help to reduce environmental impacts and the use of rare resources (such as fossil energy), to stabilize production and contribute to food challenge for the future. As technical means will not be sufficient to reach this target, we assume that the territory scale is consistent to mobilize organizational levers, linked with the stakeholders' activities. The aim of our study is to build, with the actors influencing the crop choice in three French regions, the conditions that could lock out the situation and support the development of grain legume crops.

## 2 Materials and Methods

We implemented the study in three areas in France: Bourgogne (East), Midi-Pyrénées (South-West) and Pays de la Loire (West).

We first analysed the factors that led to the huge decrease of legumes area in France. Interviews were realized with various stakeholders: agricultural and environmental institutions, economic organizations and farmers, in order to build a diagnosis of the factors that contributed to the lock-in of the agricultural system around the decrease of legumes.

Then, we explored combined dimensions that could help to a better acceptance of legumes by farmers, in particular: i) by analyzing with the stakeholders the impacts of past cropping systems evolution; ii) by sharing knowledge and references, built or assessed in local conditions, about ecosystem services provided by various legume crops according to their crop management; iii) by tracking innovative systems including legumes implemented by farmers with high performances; iv) by designing some innovative legume-based cropping systems, based on technical and economical local references from advisors and farmers; v) by proposing new organizations of supply chains between cooperatives, farmers and industry.

## 3 Results and Discussion

The factors explaining the decrease of legume areas are numerous and strongly linked to each other, highlighting coevolution dynamics that could result in a strong socio-technical lock-in (Kallis & Norgaard 2010; Geels 2011). The increasing intensification of French agricultural systems since the 1950s led to a high specialization of cropping systems, around a small number of crops (wheat, barley, oilseed rape, sunflower, maize according to the region). The dominant species were those with a higher short-term profitability. In return, the large investment dedicated to these crops led to an increase in their productivity, while mean yield of the minor crops stayed low. Moreover, the sensitivity of these crops (particularly pea) to climatic stress (heat and water stress during seed formation), frequently occurring in the recent years, explained the inter-year variability and the decreasing average yield, two causes leading most farmers to give up these crops. Finally, the occurrence of a disease, due to the soil-borne pathogen *Aphanomyces euteiches*, resulted in the impossibility of growing pea in the infested fields. Indeed, breeding and pesticide certification focused on species grown on large areas. At farm scale, the few information available on the legume pre-crop effect, on the adaptation of the management plan of the crop following a legume, and on the economic benefits estimated at the crop sequence scale (and not only at the crop scale) does not encourage farmers to grow these species. The collecting firms, the processing industries and the trade organizations aimed at cost saving, thus focusing their activity on dominant

<footnote n="1">¹ INRA, UMR Agronomie INRA-AgroParisTech, Batiment EGER, 78 850 Thiverval-Grignon, France</footnote>

<footnote n="2">² INRA, UMR1248 AGIR, INRA-INP-ENSAT, 31326 Castanet-Tolosan, France</footnote>

<footnote n="3">³ ESA, 55 rue Rabelais 49007 Angers cedex 01, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Jeuffroy@grignon.inra.fr</footnote>
species and imported species such as soybean, to minimize logistic costs. Finally, it appeared that most actors were not aware of the environmental benefits from legumes, while those are drivers for deep change in agriculture. Research also contributed to the lock-in, having more invested in the « genetic paradigm » than in the « agroecological paradigm », which led to the low valorization of the environmental benefits of legumes in the necessary change in agriculture (Vanloqueren & Baret, 2009).

The analysis of the cropping systems evolution from the 1970s showed a strong decrease of the legume species in the three territories, increasing the crop management dependency on synthetic inputs, and thus leading to increased environmental damages. Yet, legume crops can provide numerous ecosystemic services, which can be reinforced by the diversity of the species and of their crop management. They can be grown as sole crops in the crop sequence (pea, faba bean, lupin for example), or as intercrops (pea-wheat; lupin-triticale; lentil-durum wheat in the territories), or as cover crops between two main crops, or as relay intercrops, or as living mulch intercrop (such as frost-sensitive legumes intercropped with oilseed rape). The main services generally provided are (1) an increased yield of the following crop (or of the intercrop), (2) an improvement of the grain quality of the following cereal or cereal intercropped with the legume, (3) a reduction of N fertilization on the legume and on the following crop thus decreasing the associated impacts, (4) the decrease of N2O emissions compared with fertilised crops, (5) the decrease of fossil energy consumption at the crop and cropping system scales, (6) a reduction of weed infestation in intercrops compared with sole legumes or from relay cover crops, (7) a decrease of disease (and insects) infestation on both crops of intercrops, (8) the introduction of N in the system not dependant from N fertilizer, (9) a stable net margin in comparison with cropping systems without legumes, (10) a reduction of soil-disease pathogens infesting the dominant crops, and (11) the production of protein-rich seeds. On the contrary, the risk of nitrate leaching is generally increased after legumes, but this withdrawal can be overcome by growing catch crops after legumes.

While most cropping systems include sole crops in France, more and more farmers grow intercrops, due to their agronomic, environmental and economic interests. A survey of farmers growing intercrops showed a large diversity in the species associated, mainly linked with the targeted outlet and uses. The innovations were more diverse by cattle breeders than by cereal producers, due to the on-farm use of the harvested product.

In the three territories, innovative legume-based cropping systems were designed, based on technical and economical local references from advisors and farmers.

Finally, new outlets, mainly based on the use of plant proteins in the human diet, were explored as drivers for the development of legume areas. An increased need for plant proteins has been shown to be necessary to ensure food security at the global scale (Esnouf et al., 2011). Moreover, the second nutritional transition, consisting in the substitution of animal proteins with plant proteins, already occurred in several developed countries. Several innovations were already designed in the agro-food process: for example, technological progress has been made in processing pasta with both durum wheat and legume flours (Petitot et al., 2010), even if those new food products are not yet developed in european countries. In France, several cooperatives developed quality signs and labels of origin, thus improving the economic interest of farmers for growing these species.

**4 Conclusions**

Finally, we showed that grains legumes have mainly high perspectives in the transition to a sustainable agrifood system, combining a reduction in synthetic inputs use in the fields with the development of new food products based on plant proteins. Yet, this transition would require a strong coordination among supply chain actors, involving farmers, cooperatives, breeders, processing industries, advisors, public policies, and consumers.

*Acknowledgements.* This work was supported by a grant overseen by the French National Research Agency (ANR) as part of the LEGITIMES project (ANR-13-AGRO-0004).

**References**

Geels F., 2011. The multi-level perspective on sustainability transitions: Responses to seven criticisms, Environmental Innovation and Societal Transitions, 1(1), 24-40.

Kallis, G., & Norgaard, R. B. (2010). Coevolutionary ecological economics. *Ecological Economics*, *69*(4), 690-699

Meynard, J.M., Messéan, A., Charlier, A., Charrier, F., Fares, M., Le Bail, M., Magrini, M.B. & Savini, I.(2013). Freins et leviers à la diversification des cultures. Etude au niveau des exploitations agricoles et des filières . Synthèse du rapport d'étude, INRA, 52 p.

Millennium Ecosystem Assessment (2005). Ecosystems and Human Well-being: General Synthesis. World Resources Institute, Island Press,Washington DC.

Nemecek, T., Hayer, F., Bonnin, E., Carrouée, B., Schneider, A. & Vivier C.(2015). Designing eco-efficient crop rotations using life cycle assessment of crop combinations. *European Journal of Agronomy*, *65*, 40-51.

Petitot, M., Boyer, L., Minier, C. @ Micard, V. (2010). Fortification of pasta with split pea and faba bean flours: pasta processing and quality evaluation. *Food Research International*, *43*, 634-641.

Schott, C. Mignolet, C. & Meynard, J.M.(2010). Les oléoprotéagineux dans les systèmes de culture : évolution des assolements et des successions culturales depuis les années 1970 dans le bassin de la Seine. *Oléagineux, Corps gras, Liquides*, *17*,276-291.

Vanloqueren, G.& Baret, P. V., 2009. How agricultural research systems shape a technological regime that develops genetic engineering but locks out agroecological innovations. *Research policy*, *38*, 971-983.
# Legume-grass mixtures grown on arable land under the conditions of water deficits

Jaroslav Lang*±1 & Daniela Knotova<fnref n="1" />

## 1 Introduction

Legume-grass mixtures grown on arable land are an important part of crop rotations. Forage produced from these mixtures is an essential part of feeding rations. Clovers and grasses are tolerant to colder climate and require supply of water. In lowlands with precipitation deficits. Alfalfa-grass mixtures are a popular forage crop. In these mixtures, grass assures high yields in the spring and at the beginning of summer. Later on the intensity of grass growth is usually decreasing due to lower precipitations and alfalfa begins to dominate as the main yield component. At present, some regions of the Czech Republic suffer from severe periods of drought, which result in a minimal growth intensity of grasses. For that reason our trials were focused on those grass species and varieties, which are tolerant to acid conditions.

## 2 Material and Methods

In 2011, several experiments were established in the locality Troubsko (South Moravia, Czech Republic) with the following legume-grass mixtures: Mixture 1 – alfalfa (Medicago sativa, cv. 'Zuzana') and orchard grass (Dactylis glomerata, - result of a new breeding denominated as VV 115-132/10) in the seed ratio 80:20 %; and Mixture 2 – alfalfa (cv. 'Zuzana') and orchard grass (a new breeding denominated as VV 115-132/10) in the seed ratio 50:50 %. Mixtures 3 and 4 with the same seed ratios were used as controls; In these control mixtures, however, the orchard grass was replaced by an interspecific hybrid 'Felina' (hybrid of rye grass and tall fescue) This hybrid is commonly used under arid conditons of growing (dlf, 2013 [on-line]). Evaluated were results obtained in years 2012 and 2013. In each experimental year, altogether three weighed harvests of crops cut in the stage of an optimum maturity were obtained. No fertilisation was used in this experiment. Prior to each harvest, samples from one square meter of experimental polts were collected to determine percentages of both mixture components.

<table><caption>**Tab. 1.** Monthly sums of average precipitations and temperatures as recorded on the experimental site in the course of the growing season (Kožnarová & Klabzuba, 2002)</caption><thead><tr><th rowspan="3">Month</th><th colspan="4">Year 2012</th><th colspan="4">Year 2013</th></tr><tr><th colspan="2">Temperatures °C]</th><th colspan="2">Precipitations [mm]</th><th colspan="2">Temperatures [°C]</th><th colspan="2">Precipitations [mm]</th></tr><tr><th>Average</th><th>Evaluation</th><th>Total</th><th>Evaluation</th><th>Average</th><th>Evaluation</th><th>Total</th><th>Evaluation</th></tr></thead><tbody><tr><td>March</td><td>6.2</td><td>1</td><td>1.8</td><td>-3</td><td>1.0</td><td>-1</td><td>42.1</td><td>1</td></tr><tr><td>April</td><td>9.9</td><td>0</td><td>12.1</td><td>-2</td><td>9.9</td><td>0</td><td>18</td><td>-1</td></tr><tr><td>May</td><td>16.0</td><td>1</td><td>25.4</td><td>-2</td><td>13.7</td><td>0</td><td>105.6</td><td>1</td></tr><tr><td>July</td><td>18.8</td><td>1</td><td>60.6</td><td>0</td><td>17.4</td><td>0</td><td>116.2</td><td>1</td></tr><tr><td>June</td><td>20.4</td><td>2</td><td>60.0</td><td>0</td><td>21.5</td><td>3</td><td>4.8</td><td>-3</td></tr><tr><td>August</td><td>19.7</td><td>2</td><td>72.4</td><td>0</td><td>19.9</td><td>2</td><td>68.8</td><td>0</td></tr></tbody></table>

Evaluation scales of monthly temperatures and precipitaions:

3 – extremely cold; 2 – very col; 1 – cold; 0 – normal; 1 – warm; 2 – very warm; 3 – extremely warm.

3 – extremely dry; 2 – very dry; 1 – dry; 0 – normal;; 1 – wet; 2 – very wet; 3 – extremely wet

## 3 Results – Discussion

### Percentages of individual components in mixtures with alfalfa

In the first experimental year (2012), the shares of orchard grass in individual mixtures were as follows: Mixture 1 – 12.5 %; Mixture 2 – 17.7 %; Mixture 3 – 22.3 % and Mixture 4 – 19.7 %. In the course of the growing season, this year was extremely dry and there were practically no differences in percentages of grass in individual mixtures. The obtained results indicate that, under arid conditions, the competitiveness of orchard grass was similar to that of the hybrid 'Felina'. In 2013, however, supranoamal precipitations caused an increase in percentages of orchard grass in individual mixtures (Mixture 1 – 41.6 %, Mixture 2 – 34.1 %); In harvest years 2012 and 2013, the differences in percentages of the hybrid 'Felina' were not significant. (Mixture 3 – 26.3 % and Mixture 4 – 40.4 %, respectively). In 2013, however, a higher percentage of orchard grass in Mixture 4 indicated a better competitiveness of orchard grass under conditions of

<footnote n="1">¹ Agricultural Research, Ltd. Troubsko</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: lang@vupt.cz</footnote>
sufficient humidity. Vorlíček *et al.* (2009) recommended that for production of quality forage the percentage of the grass component in the first cutting should not be higher than 25 %. This means that under arid conditions the share of grass component in mixture would be as much as 50 %. In localities with abundant precipitations, the share of orchard grass should be reduced to less than 20 % while that of the hybrid 'Felina' can be 20 %.

## Yields

DM yields were influence above all by the weather. In 2012, higher yields of alfalfa-orchard grass mixtures were statistically significant. In 2013, the yield of Mixture 4 was significantly lower.

<table><thead><tr><th rowspan="2">No.</th><th rowspan="2">Mixture</th><th colspan="4">Yield of DM in 2012 [t/ha]</th><th colspan="4">Yield of DM in 2013 [t/ha]</th></tr><tr><th>1.harvest</th><th>2.harvest</th><th>3.harvest</th><th>total</th><th>1.harvest</th><th>2.harvest</th><th>3.harvest</th><th>total</th></tr></thead><tbody><tr><td>1</td><td>alfalfa + orchard grass</td><td>2.03</td><td>3.59</td><td>2.33</td><td>7.95</td><td>5.58</td><td>4.22</td><td>5.22</td><td>15.02</td></tr><tr><td>2</td><td>alfalfa + orchard grass</td><td>1.63</td><td>3.54</td><td>2.46</td><td>7.64</td><td>5.65</td><td>4.17</td><td>5.83</td><td>15.65</td></tr><tr><td>3</td><td>alfalfa + Felina</td><td>1.67</td><td>2.80</td><td>2.31</td><td>6.79</td><td>5.16</td><td>4.43</td><td>5.40</td><td>14.98</td></tr><tr><td>4</td><td>alfalfa + Felina</td><td>1.89</td><td>3.29</td><td>2.26</td><td>7.44</td><td>4.93</td><td>4.19</td><td>4.69</td><td>13.81</td></tr></tbody></table>

## 4 Conclusions

It can be concluded that dry matter yields are naturally influenced by the weather course during the year. Under conditions of an optimum humidity, the tested orchard grass cultivar 'Zuzana' was a stronger competitor than the hybrid 'Felina' and for that reason it is recommended to use less than 20 % of its seed material. As far as the hybrid 'Felina' was concerned, the optimum share of its seed material was 20 %. Under arid conditions, the competitiveness of orchard grass was comparable with that of the hybrid 'Felina' and the share of its seed in mixtures should be increased to as much as 50 %. From the viewpoint of yields, the late orchard grass cultivar VV 115-132/10 represented a suitable component of mixtures sown in both dry localities and those with normal precipitations. The late cultivar VV 115-132/10 seemed to be a suitable alternative (substitute) of currently used festucoid hybrids especially in legume-grass mixtures grown on arable land even in dry localities.

*Acknowledgement.* These results were obtained thanks to a (partial) institutional support enabling a long-term conceptual development of the research organisation and from research project NAZV QJ1310100.

## References

* DLF Hladké Životice: Naše odrůdy [on-line] 2013. [cit. 2013-09-05]. Dostupné z: http://www.dlf.cz/upload/fl - felina.pdf

* Kožnarová, V., Klabzuba, J.: Doporučení WMO pro popis meteorologických, resp. klimatologických podmínek definovaného období. Rostlinná výroba 2002: roč. 48, č. 4, s. 190 – 192.

* Vorlíček, Z., Hanuš, O., Šindelková, I.: Zvýšení podílu energie v objemných krmivech ekologických farem pěstováním vhodných travních a jetelovinotravních směsí. Certifikovaná metodika, Troubsko 2009, 16 s. ISBN 978-80-86908-09-0
# T7. Scaling up from farm to landscape and multiscale scenario analysis of agricultural systems

Chair: Martin Van Ittersum, Wageningen UR

Co-chairs: Bernard Hubert, Agropolis International
Marc Benoit, INRA
# INTEGRATING FIELD, FARM AND TERRITORY SCALES IN THE DESIGN OF AGRICULTURAL SYSTEMS

Jean-Marc Blazy *±<fnref n="1" />, Pierre Chopin <fnref n="1" />, Thierry Doré <fnref n="2" />,<fnref n="3" /> & Jacques Wery <fnref n="4" />

## 1 Introduction

In order to cope with several actual and future major issues like climate change, natural resources depletion, protection of biodiversity, ensuring food security, innovative agricultural systems have to be developed. These systems have to satisfy a set of objectives at the field, the farm and the territory scales and their design has to take into account explicitly the interactions between these scales (Veldkamp & Lambin, 2001). We propose a simple framework to this end and apply it to two questions in Guadeloupe: 1) the *ex ante* assessment of low-input innovative cropping system and their potential of adoption by heterogeneous farmers and 2) the design of new mosaics of cropping systems satisfying several sustainability goals at the territorial level and taking into account farms' constraints.

## 2 Materials and Methods

The framework presented in Fig. 1 shows how the nature of the innovations that have to be implemented into current cropping systems (field scale) can be orientated by the farm and the territory scales. Firstly the innovations at field scale can perform differently in the territory, because farms are heterogeneous in terms of biophysical situation and socio-economic resources. Secondly the effective implementation of innovations at field level is conditional upon farmers' decision of adoption which can be influenced by the three levels. Finally reaching sustainability goals at territorial scale requires an optimal combination of innovative cropping systems at field scale while taking into account farm level constraints.

<figure><img src="image_103.png" /><figcaption>Fig. 1. Framework of interrelationships between field, farm and territory scales in the design of agricultural systems</figcaption></figure>

From a practical point of view, the framework is aimed at building an architecture of models to design and assess new agricultural systems emerging from innovations at several spatial scales. It could also serve to assess trade-offs between sustainability goals and across scales. The use of a combination of tools is required to implement the framework. We tested three types of tools combinations: 1) quantifying the impacts of innovation on cropping system performances at field scale with a biophysical model parameterized with a regional typology of farms, 2) modelling farmers' decision of adoption of new cropping systems as a function of cropping system performance, farmers individual characteristics and economic incentives and 3) integrating information from field and farm scales into a bio-economic multi-criteria regional model to prototype mosaic of cropping systems satisfying several sustainability goals.

<footnote n="1">¹ INRA, UR1321 ASTRO Agrosystèmes Tropicaux, F-97170 Petit-Bourg (Guadeloupe), France</footnote>

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

<footnote n="3">³ INRA, UMR 211 Agronomie, F-78850 Thiverval-Grignon, France</footnote>

<footnote n="4">⁴ Montpellier Sup Agro, UMR System, 2 place Viala, F-34060 Montpellier, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: jean-marc.blazy@antilles.inra.fr</footnote>
## 3 Results - Discussion

First we present in Table 1 the *ex ante* assessment of the introduction of new intercropping techniques in banana cropping systems in Guadeloupe and Martinique (Blazy *et al.*, 2010). Our results show that performance of the innovative cropping systems are clearly influenced by the farm type. Then the willingness to adopt is also different according to the farm type and depends upon the performances of the system (size of workforce seems to be crucial for adoption) and the territorial context (compare Guadeloupe and Martinique islands). This verify the hypotheses that effective increase of the sustainability at the territorial scale requires accounting for adoption constraints and heterogeneity at the field and farm scale.

<table><caption>**Table 1.** Ex ante assessment of two innovative banana cropping systems based on intercropping for two farm types.</caption><thead><tr><th rowspan="2">Farm type</th><th colspan="2">Highlands smallholders</th><th colspan="2">Flatlands industrial farms</th></tr><tr><th>Canavalia ensiformis</th><th>Impatiens sp</th><th>Canavalia ensiformis</th><th>Impatiens sp</th></tr></thead><tbody><tr><td>Innovations (service crop intercropping)</td><td></td><td></td><td></td><td></td></tr><tr><td>Banana yield (t ha<sup>-1</sup> yr<sup>-1</sup>)</td><td>+15.9</td><td>0.0</td><td>+4.4</td><td>+0.4</td></tr><tr><td>Work (days ha<sup>-1</sup> yr<sup>-1</sup>)</td><td>+75.4</td><td>+10.4</td><td>+42.0</td><td>+16.8</td></tr><tr><td>Net income (€ ha<sup>-1</sup> yr<sup>-1</sup>)</td><td>+3472.1</td><td>-426.4</td><td>-830.9</td><td>-1390.5</td></tr><tr><td>Willingness to adopt in Guadeloupe (%)</td><td>58%</td><td>81%</td><td>46%</td><td>54%</td></tr><tr><td>Willingness to adopt in Martinique (%)</td><td>16%</td><td>47%</td><td>35%</td><td>54%</td></tr></tbody></table>

Then we present the results of the design and assessment of a multi-objective mosaic of cropping systems satisfying the principles of climate-smart agriculture (Lipper *et al.*, 2014). To this end, the bioeconomic model MOSAICA (Chopin *et al.*, in press) was used by i) optimizing the satisfaction of the objectives of “an adaptation scenario” made of innovations at the farm and policy levels (introducing energy crop, organic fertilization and environmental incentives) and ii) taking into account spatial heterogeneity, resources availability and risk attitudes of the diverse farm types of the island. The results showed that the landscape evolves greatly under the “adaptation scenario” (Fig. 2) and that the objective of reducing by 10% all GHG emissions of Guadeloupe could be achieved while also increasing other sustainability indicators (Table 2).

<figure><img src="image_104.png" /><figcaption>**Fig. 2.** Prototyping climate-smart mosaic of cropping systems.</figcaption></figure>

<table><thead><tr><th>Indicators</th><th>Units</th><th>Initial situation</th><th>Adaptation Scenario</th></tr></thead><tbody><tr><td>Mean farmers' income</td><td>€ ha<sup>-1</sup> yr<sup>-1</sup></td><td>3510</td><td>4940</td></tr><tr><td>Energy self-sufficiency from energy crop</td><td>%</td><td>3%</td><td>13%</td></tr><tr><td>Food self-sufficiency</td><td>%</td><td>15%</td><td>17%</td></tr><tr><td>Proportion of rivers potentially polluted</td><td>%</td><td>39%</td><td>8%</td></tr><tr><td>Total amount of subsidies</td><td>M€ yr<sup>-1</sup></td><td>75</td><td>61</td></tr></tbody></table>

## 4 Conclusions

Our framework provides a conceptual representation of the interrelationships between the field, the farm and the territory scales. The application of the framework in Guadeloupe through two different combination of models confirmed that taking into account the complexity of these interrelationships is crucial for the design of new agricultural systems. Innovations at field scale have to target heterogeneous farmers’ objectives and constraints for adoption. Policy can be an efficient way to impulse and promote the adoption of these innovations. Bio-economic models are necessary for building new optimal agricultural landscape because of the complexity in hierarchical organization. The results that agronomist can obtain with such approaches offer promising perspective in designing scenarios of transition of agricultural systems toward a higher sustainability level, which is of interest for helping decision making of policy makers.

## References

* Blazy, JM., Txier, P., Thomas, A., Ozier-Lafontaine, H., Salmon, F., Wery, J. (2010). BANAD: A farm model for ex ante assessment of agro-ecological innovations and its application to banana farms in Guadeloupe. *Agricultural Systems*, **103**, 221-232.

* Chopin, P., Blazy, JM., Guindé, L., Doré, T. (in press). A spatially explicit multi-scale bioeconomic model to design and assess agricultural landscapes. Proceedings of the *Farming System Design 2015* congress, Montpellier 7-10 September 2015.

* Lipper, L. & al. (2014). Climate-smart agriculture for food security. *Nature Climate Change*, **4**, 1068-1072.

* Veldkamp, A., Lambin, E.F., 2001. Predicting land-use change. *Agriculture, Ecosystems and Environment*, **85**, 1-6.
# PARTICIPATORY SCENARIOS' DEVELOPMENT AND ASSESSMENT FOR SUSTAINABLE FARMING SYSTEMS IN CAMARGUE, SOUTH OF FRANCE.

V. Couderc<fnref n="1" />, L. Hossard ±<fnref n="1" />, J.M. Barbier<fnref n="1" />, J.C. Mouret<fnref n="1" />, S.L. Ridaura <fnref n="1" />,<fnref n="2" /> & S. Delmotte <fnref n="1" />

## 1 Introduction

In Camargue, South of France, rice cultivation plays a crucial role in desalinating the soils by being cultivated in flooded conditions and provisioning fresh water to the natural wet areas. Rice cultivation also generates employments in the rice chain and plays a crucial role in tourism sector by maintaining traditional landscapes. However, rice fields are a major source of pesticide losses (Comoretto et al., 2008) and most probably of greenhouse gas (GHG) emissions (Linquist et al., 2012). Alternative farming systems, such as organic or low input systems, are expected to improve the sustainability of agriculture in the region (Lopez Ridaura et al., 2014). However, to engage actions to facilitate the development of such alternatives, policy makers and stakeholders need information about their sustainability in various possible future contexts (e.g. changes in policy, price of energy or inputs, etc.). Participatory scenarios development and their integrated assessment aim at providing information to the stakeholders about future sustainable farming systems (Delmotte et al., 2013). In this paper, we present the application of such an approach in Camargue.

## 2 Materials and Methods

To generate such knowledge, we first developed four explorative and narrative scenarios for the future of agriculture in Camargue with key stakeholders from the regional natural park (defending stakes of nature preservation), the French Centre for rice development (promoting new rice cultivation practices) and an association of livestock breeders (defending traditional bull rearing activities). Three successive focus group sessions were organized, in order to: (i) identify the main current and future drivers of change on farming systems in Camargue, (ii) design and discuss the four scenarios and (iii) define the possible impacts of the scenarios on farming systems and the subsequent possible adaptation strategies for farmers and stakeholders.

Secondly, we assessed these scenarios using a multi-criteria methodology at farm and territorial scale (i.e. considering the whole cultivated areas of Camargue). We used a bio-economic optimization model, which identifies optimal crops' allocations under sets of objectives and constraints (Delmotte et al., 2013). In this study, each of the four scenarios led to a specific set of objectives and constraints for the model, which were explored with a group of simulations, in order to assess trade-offs among different objectives and indicators: for example gross margin and labour at the farm level, and food production, GHG emissions, and pesticide use at the regional level. For each scenario, the parameterization step consisted in (i) considering only the drivers of changes that could be included in the model (e.g. not the livestock, and agrotourism activities), and (ii) quantifying remaining drivers, using existing databases, expert knowledge and bibliography.

We present below the results of one scenario designed by the stakeholders, compared with a baseline scenario (current situation) in term of crops allocations and performances.

## 3 Results and Discussion

The main drivers identified are related to economic, environmental and regulatory features (public subsidies, commodity prices, environmental regulations, climate change and water availability; Table 1).

<table><caption>**Table 1.** Characteristics of the Scenario 1: "Camargue is classified as a region with specific handicaps"</caption><thead><tr><td>Main drivers of changes</td><td>Scenario 1: "Camargue is classified as a region with specific handicaps"</td></tr></thead><tbody><tr><td>Public subsidies</td><td><ul><li>Modulated SFP (projection PAC reform) : 326€/ha to 387€/ha</li><li>Aid for the maintenance of organic farming 100€/ha</li><li>Agroenvironmental measures for : i/ploughing the rice straws : 74€/ha;<br/>ii/ sowing the rice in dry conditions : 66€/ha iii/including leguminous in rotations : 60€/ha</li><li>Compensatory allowance for permanent natural handicaps (due to salt pressure) : 150€/ha</li></ul></td></tr><tr><td>Prices of commodities and market</td><td><ul><li>Current prices</li><li>Organic market : saturated when 20% of the Camargue is converted to organic farming</li></ul></td></tr><tr><td>Climate Change</td><td><ul><li>Higher temperatures during rice cultivation : increase rice yields by 10%</li></ul></td></tr><tr><td>Energy and inputs prices</td><td><ul><li>Prices of all inputs increased by 30% (water, energy, fertilisers, pesticides, seeds)</li></ul></td></tr></tbody></table>

<footnote n="1">¹ Innovation et Développement dans l'Agriculture et l'Agro-alimentaire (Innovation) - Institut national de la recherche agronomique (INRA) : UMR951</footnote>

<footnote n="2">² place Viala, 34060 Montpellier cedex 1, France</footnote>

<footnote n="2">² Climate Change and Food Security (CCAFS) - Global Conservation Agriculture Program - CIMMYT (Mexico)</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: laure.hossard@supagro.inra.fr</footnote>
Under the scenario "Camargue is classified as a region with specific handicaps", conversion to organic agriculture and legumes incorporation in rotations seems to respond to the increase of inputs and energy prices and Single Farm Paiement (SFP) decrease (Fig.1 A&B). Legumes cropping is promoted by the creation of a specific agro-environmental measure (AEM), at the expense of rice and cereals. Although efficient regarding to gross margin and to reduce fertilizers inputs, rice straw incorporation and rice dry sowing and corresponding AEM are not chosen by the model as best options for such context of changes (Fig.1 B). In this scenario, the alternative agricultural activities lead to a reduction on GHG and particulate emissions, and a reduction in energy and water consumption (Fig.1 C).

Under this scenario, rice and cereals production volumes decrease, (Fig.1 C) which may weaken the supply chains and local processing industries. The large increase in fodder production volumes may have a negative impact on fodder prices, except if, according to stakeholders, farmers shift to export their fodder. To reduce the risk of a strong fodder price decrease, decisions makers can seek on support the livestock sector, to ensure a larger local market for this production. Better crop-livestock integration systems (such as pastured cover-crops) should also be supported in this case by public policies. Such policies can be justified by the positive impact of these fodder-based systems on environmental externalities. The adaptations to this scenario of changes also lead to a decrease in energy value of the production (Fig.1 C), i.e. fewer people can be fed by the Camargue crop productions. However, the energy efficiency remains constant due to less energy consumption. Others simulations show that intermediate adaptations, with less fodder acreages and more rice and cereal acreages, can generate different trade-offs between crop and livestock systems, and between socio-economic performances (such as labour and energy value of the production) and environmental performances. These result, as well as results at farm scale will be exposed in the final presentation.

<figure><img src="image_105.png" /><figcaption>**Fig. 1.** Crops allocations at regional scale for baseline situation (A) and under scenario 1 (B1), and Comparison of performances for the baseline and the simulated scenario at regional scale (C)</figcaption></figure>

## 4 Conclusions

The next step of this work, which will be exposed in the final presentation, is to compare the crops allocations and performances under the four scenarios of changes. These four scenarios are sufficiently contrasted to highlight robust adaptations under different contexts of changes. The participatory development and integrated assessment of scenarios is expected to (i) help the identification of sustainable adaptations in farming systems at both farm and territory scale, (ii) favor cooperation and negotiation by collectively stimulating knowledge, sharing opinions and exploring trade-offs between several objectives, and (iii) highlight territorial specificities to help local adaptation of public policies.

*Acknowledgements.* This research was conducted within the Scenarice project (n°1201-008) funded by the Agropolis and Cariplo foundations; and FranceAgrimer (n°2013-1290). We thank local stakeholders for their participation in this study.

## References

Comoretto, L., Arfib, B., Talva, R., Chauvelon, P., Pichaud, M., Chiron, S., & Höhener, P. (2008). Runoff of pesticides from rice fields in the Ile de Camargue (Rhône river delta, France): field study and modeling. *Environmental Pollution (Barking, Essex: 1987)*, *151*(3), 486-93.
doi:10.1016/j.envpol.2007.04.021

Delmotte, S., Lopez-Ridaura, S., Barbier, J.-M., & Wery, J. (2013). Prospective and participatory integrated assessment of agricultural systems from farm to regional scales: Comparison of three modeling approaches. *Journal of Environmental Management*, *129*, 493-502.
doi:10.1016/j.jenvman.2013.08.001

Linquist, B., Groenigen, K., Adviento-Borbe, M. A., Pittelkow, C., & Kessel, C. (2012). An agronomic assessment of greenhouse gas emissions from major cereal crops. *Global Change Biology*, *18*(1), 194-209. doi:10.1111/j.1365-2486.2011.02502.x

Lopez Ridaura, S., Delmotte, S., Le Page, C., Le Quere, L., Goulevant, G., Chauvelon, P., Sandoz, A., Mouret, J.-C. (2014). Multi-Scale Integrated Assessment of Regional Conversion to Organic Farming (OF). In S. Bellon & S. Penvern (Eds.), *Organic farming, prototype for sustainable agricultures* (Springer., pp. 453-478). Dordrecht, NLD.
# The farm and landscape gap - partial versus emerging global efficiencies in agricultural systems

Santiago Lopez-Ridaura *±1 & Pablo A. Tittonell 2

## 1 Introduction

Resource Use Efficiency (RUE) has historically attracted extensive attention and debate in relation to agricultural production (De Wit, 1992, Lopez-Ridaura et al., 2007). Trenbath (1986) distinguishes the efficiency of resource capture and the efficiency of resource conversion to describe RUE, stating that increased RUE can be attained by either increasing capture efficiency, conversion efficiency or both. RUE in agricultural research has largely referred to the interactions between the resources most often limiting crop yield under field conditions (i.e. water and/or nutrients) or feed conversion rates in animal production.

One important feature of these theories is that they describe RUE in general terms and therefore can, in principle, be applied to different resources at different scales. The same laws can be used arguably for resources such as labour, land and capital invested by the farm household and their efficiency in the generation of wealth or food security. Also, at regional scale, these analyses can relate resources such as infrastructure, markets and public/private investment to the value of agricultural production. Efficiency in agricultural systems is not scale-agnostic and it often depends on cross-scale interactions, such that apparent inefficiencies at one scale may represent net efficiencies at another one. Likewise, high efficiencies observed at small scales may not necessarily translate into high efficiencies when scaled up to whole farming systems or landscapes.

## 2 Case studies

This can be illustrated with the following example on fertilizer applications to grasslands in the highly productive dairy systems of Western Europe, using data from the literature (Table 1). When measured in controlled experiments grass productivity (Lolium perenne L.) responds positively to increasing N application rates of up to 600 kg ha⁻<fnref n="1" />, leading to high productivity levels while achieving N use efficiencies > 20 kg grass DM per kg N applied. Back in the 1990s, this was used as an argument to plea for greater rates of fertilizer N applications than the current limit of 250 kg ha⁻<fnref n="1" />. It was argued that the most efficient system is the most productive one (de Wit, 1992). Yet when measured in real farm conditions grass productivity and its N use efficiency did not improve at application rates beyond 250 kg N ha⁻<fnref n="1" />, due to losses associated with direct grazing (trampling, selectivity, harvesting efficiency) and/or silage. This points out to the need of taking whole-farm system perspectives in designing efficient N management strategies at the field scale in dairy farming.

<table><caption>**Table 1.** Grassland production and N use efficiency (NUE) in response to N additions under controlled conditions and in real dairy farms in The Netherlands (data source: Lantinga *et al.*, 1999)</caption><thead><tr><th rowspan="2">N application rate</th><th colspan="2">Biomass production in controlled experiment (clippings)</th><th colspan="2">Production in actual dairy farms, subject to losses and trampling (direct grazing, silage)</th></tr><tr><th>Yield (t dm ha<sup>-1</sup>)</th><th>NUE* (kg kg<sup>-1</sup>)</th><th>Yield (t dm ha<sup>-1</sup>)</th><th>NUE* (kg kg<sup>-1</sup>)</th></tr></thead><tbody><tr><td>0</td><td>4.4</td><td>-</td><td>4.0</td><td>-</td></tr><tr><td>250</td><td>10.5</td><td>24.4</td><td>9.4</td><td>21.6</td></tr><tr><td>400</td><td>13.2</td><td>22.0</td><td>10.5</td><td>16.3</td></tr><tr><td>550</td><td>15.8</td><td>20.7</td><td>9.7</td><td>10.4</td></tr><tr><td>750</td><td>15.1</td><td>14.3</td><td>10.2</td><td>8.3</td></tr></tbody><tfoot><tr><td colspan="5">*In this example, NUE is calculated as the partial factor productivity of N: NUE = (Yield<sub>N</sub> - Yield<sub>0</sub>)/N applied</td></tr></tfoot></table>

In smallholder contexts, high farm-scale resource use efficiencies are not necessarily achieved on farms that can afford their access to external inputs, but rather in farms that achieve greater internal recycling. An example from ecological network analysis of N flows in smallholder crop-livestock systems of sub-Saharan Africa illustrates this (Fig. 1). Wealthier farms that can afford greater N throughputs were less efficient at converting N into biomass than farms that exhibited greater N recycling. In the latter, modest N inputs to the farm will meet stronger responses in terms of

<footnote n="1">¹ International Maize and Wheat Improvement Center (CIMMYT) – Climate Change and Food Security (CCAFS), Texcoco, Mexico</footnote>

<footnote n="2">² Farming Systems Ecology (FSE)- Wageningen University and Research Centre (WUR), Wageningen, The Netherlands</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: s.l.ridaura@cgiar.org</footnote>
biomass productivity. And, since recycling is tighter, such inputs of N may come into the system through different entry points (e.g. as animal feed) allowing for more flexibility.

<figure><img src="image_106.png" /><figcaption>**Fig. 1.** Calculated farm N use efficiency (with respect to total plant and animal biomass production) as a function of (A) the total farm N throughput (sum of all inflows and flows within compartments) and (B) the Finn's cycling index, as calculated for smallholder farms in Ethiopia, Kenya, Zimbabwe and Madagascar (adapted from: Rufino *et al.*, 2009 and Alvarez *et al.*, 2014)</figcaption></figure>

**3 Discussion and Conclusion**

Farms and landscapes represent examples of systems with complex feedbacks across scales and where social and ecological dimensions interact. A farm or an agricultural landscape can be seen as a system in which fields and farms (and other entities) represent nested system components, or as a mosaic of ecological units that exhibit lateral interactions. These two alternative visions of landscapes have shaped the way in which we analyze their functions and design management interventions. In farming systems design, understanding the relationship and tradeoffs between partial efficiencies at different scales is essential to "target" the systems levels and components at which increased efficiency is sought. A form of farm and landscape agronomy is needed that goes beyond pursuing partial results such as closing crop or animal yield gaps, to propend to resource use efficiencies that emerge at scale (farms, landscapes) as a result of interactions between system components and spatial units at lower integration levels. Our hypothesis is that such 'emerging global efficiencies' might be better targeted for increased sustainability, as efficiency at higher levels is more than just the sum of efficiencies at lower levels.

**References**

Alvarez, S. ; Rufino, M.C. ; Vayssières, J. ; Salgado, P. ; Tittonell, P. ; Tillard, E. ; Bocquier, F. 2014. Whole-farm nitrogen cycling and intensification of crop-livestock systems in the highlands of Madagascar: An application of network analysis. Agricultural Systems, 126: 25-37.

De Wit, C.T. (1992) Resource use efficiency in agriculture. Agricultural Systems 40, 125-151.

Lantinga, E.A., P.J.A.G. Deenen and H. Van Keulen. 1999. Herbage and animal production responses to fertilizer nitrogen in perennial ryegrass swards. II. Rotational grazing and cutting. Netherlands Journal of Agricultural Science, 47, 243-261

López-Ridaura, S., van Keulen, H. and Giller, K.E. 2007. Designing and evaluating alternatives for more sustainable natural resource management in less favoured areas. In: R. Ruben and J. Pender (eds) Sustainable Poverty Reduction in Less-Favoured Areas. CABI Publishing, Wallingford, U.K., pp. 65-90

Rufino M.C., Tittonell P., Reidsma P., Lopez Ridaura S., Hengsdijk H., Giller K.E., Verhagen A. (2009). Network analysis of N flows and food self-sufficiency: a comparative study of crop-livestock systems of the highlands of East and southern Africa. Nutrient Cycling in Agroecosystems, 85 (2): 169-186.

Trenbath, B.R. (1986) Resource use by intercrops. In: Francis, C.A. (ed.) Multiple Cropping Systems. Macmillan, New York, pp. 57-79.
# Coll-ICLS: a participatory tool to support crop-livestock integration among farms

G. Martin *$\pm$1, M. Moraine$^1$, J. Ryschawy$^{1,2}$, M.A. Magne$^{1,3}$, J.P. Sarthou$^{1,2}$, M. Duru$^1$ & O. Therond$^1$

## 1 Introduction

Farm-level crop-livestock integration is a theoretical ideal to improve the sustainability of agriculture. It promotes ecological interactions over space and time between system components (i.e. crops, grasslands and animals) and create opportunities for synergistic resource transfers between them (Hendrickson *et al.*, 2008). They offer opportunities to at least partially substitute external inputs used in intensive and specialized systems with ecosystem services, such as symbiotic nitrogen fixation or biological regulation of pests and diseases. Nevertheless, integrated crop-livestock farms have declined in number in Europe, and the trend towards specialization continues. The drivers of specialization (supply chains, technical consulting services, education of farmers) reduce the chance of re-diversifying farms. Moreover, the necessary skills and knowledge to manage integrated crop-livestock farms have often been lost after specialization.

Several authors (Lemaire *et al.*, 2014; Russelle *et al.*, 2007) now suggest that crop-livestock integration can be organized beyond the farm level through local groups of specialized farmers negotiating land-use allocation patterns and exchanging materials (manure, straw, etc.). In regions with high animal density, straw-manure exchange among farms is a common example of crop-livestock integration. The levels of spatial, temporal and social coordination among farms defines the nature, area and spatial configuration of the crops, grasslands and animals in the different farms and landscapes concerned, which in turn influences the provision of ecosystem services like soil fertility, erosion control and field-level biological regulation services. At this stage, the question for research is that of the participatory design methods to be developed to support candidate farmers in designing coordination among farms.

In this article, we present Coll-ICLS (Collective Integrated Crop-Livestock Systems), a simulation tool aimed at supporting crop-livestock integration among farms.

## 2 Coll-ICLS to support the design of crop-livestock integration among farms

Participatory design of collective integrated crop-livestock systems with Coll-ICLS is implemented through three iterative steps (Fig. 1a): (i) problem specification (current farm functioning and associated multi-domain issues); (ii) design of (spatial, temporal and social) coordinations among farms and farmers and subsequently of new farming systems; and (iii) assessment across space and time of their potential effects and of trade-offs between the individual and collective levels. It is also expected that Coll-ICLS will structure the negotiation process between farmers to identify consensual solutions through the three iterative steps and achieve legitimacy at both the individual and collective levels. Coll-ICLS relies on the conceptual framework that supports adaptation of agricultural systems proposed by Martin (2015). It has already been applied to several agricultural design support systems like Forage Rummy. This framework builds on collective workshops involving researchers, agricultural consultants and farmers who collectively manipulate boundary objects (e.g. cards and computer models).

Problem specification builds upon a diagnosis of each individual farm prior to collective workshops in order to assess sustainability issues within each farm. For instance, where grain and forage price is concerned, self-sufficiency for animal feeding is measured within each livestock farm. Similarly, where maintenance of soil fertility is at stake, consistency of tillage and fertilization practices are regarded with respect to land use. The scope for coordination among farms permitted by their resources (land, animals, human workforce, machinery, buildings), their current and potential productions (products: grain, forage, meat, milk; and co-products: straw, manure) and their needs (fertilizers, animal feed) is also evaluated. Individual farmers' desires with respect to coordination among farms are also gathered at this stage and confronted with opportunities at the collective level.

Following Martin (2015), manipulated objects during collective workshops are of two types: (i) material objects (e.g. cards) enabling modeling (representation) of the current situation and design of possible solutions to the problem, and (ii) computer objects (e.g. computer models) enabling simulation, i.e. assessment of these solutions. Material objects represent physical and functional entities managed by farmers, e.g. fields, cropping systems, animal groups and crop products. They are intended to create a connection between workshop participants (Klerkx *et al.*, 2012). Computer objects provide instantaneous integrated evaluation (in the form of maps, graphs and indicators) of candidate solutions designed by workshop participants to stimulate their reflections and negotiations. Such collective workshops are also expected to foster hybridization of scientific (to ensure credibility) and empirical knowledge (to ensure saliency and legitimacy).

During the workshops, farmers use first, as material objects, a map of their fields and current land use. They also use a table dedicated to describe the intended coordination with other farms (type, amount and timing of product and co-

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

<footnote n="2">2 ENSAT, UMR 1248 AGIR, F-31326 Castanet Tolosan, France</footnote>

<footnote n="3">3 ENFA, UMR 1248 AGIR, F-31326 Castanet Tolosan, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: guillaume.martin@toulouse.inra.fr</footnote>
product exchanges and consequent changes in land use and herd management). Changes in cropping systems and allocation of animals at grazing (e.g. introduce cover crops to be exported to livestock farms as fodder) are reported on the map of their fields. Throughout this process, discussions among farmers enable them to integrate individual projects into a collective solution to be simulated. The last kind of material object is a sheet synthetizing the outputs of successive simulated scenarios.

The computer model of Coll-ICLS (Fig. 1b) incorporates (i) biophysical models simulating the different investigated activity-location combinations (i.e. cropping and grassland systems), animal reproduction, feeding and production, etc.; (ii) decision models representing farmers’ management of the farm components (crops, grasslands, animals, feedstuffs, etc.) and coordination among farmers; (iii) logistics models representing raw material fluxes between farms and associated resource use (workforce, machinery, etc.). This computer model allows simulating impacts of weather variations within and between years on crop, forage and animal production, resulting exchanges among farms and ecosystem services promoted. Therefore, it is possible to assess the variability of individual and collective performances and to identify acceptable trade-offs between individual and collective performances under constraints such as suitability of land, labor availability, etc.

<figure><img src="image_107.png" /><figcaption>**Fig. 1.** Overview of Coll-ICLS: overall approach (left: 1a) and simplified view of the conceptual model of an example of collective integrated crop-livestock system (right: 1b)</figcaption></figure>

## 3 Conclusions

Coll-ICLS is currently being tested in the framework of a research and development program with organic crop and livestock farmers in southwestern France. Its potentialitiesto supportcrop-livestock integration among farms might be challenged by current projects being set up to organize crop-livestock integration at the regional level through e.g. alfalfa supply chains involving crop farmers as alfalfa growers and livestock farmers as alfalfa consumers.

*Acknowledgements.* This work was funded by the French ANR Agrobiosphère program as part of the TATABOX project (ANR-13-AGRO-0006)and by the EA division of INRA. It also benefited from outcomes of the European project CANTOGETHER (FP7, Grant agreement No. 289328)

## References

Hendrickson, J.R., Hanson, J.D., Tanaka, D.L., Sassenrath, G.(2008) Principles of integrated agricultural systems: Introduction to processes and definition. *Renew. Agric. Food Syst.* **23**, 265-271.

Klerkx, L., van Bommel, S., Bos, B., Holster, H., Zwartkruis, J. V., Aarts, N.(2012) Design process outputs as boundary objects in agricultural innovation projects: Functions and limitations. *Agr. Syst.* **113**, 39-49.

Lemaire, G., Franzluebbers, A., Carvalho, P.C. de F., Dedieu, B. (2014) Integrated crop-livestock systems: Strategies to achieve synergy between agricultural production and environmental quality. *Agric. Ecosyst. Environ.* **190**, 4-8.

Martin, G. (2015) A conceptual framework to support adaptation of farming systems - Development and application with Forage Rummy. *Agric. Syst.*, **132**, 52-61.

Russelle, M.P., Entz, M.H., Franzluebbers, A.J. (2007) Reconsidering Integrated Crop-Livestock Systems in North America. *Agron. J.* **99**, 325.
# Does the recoupling of crop and livestock production at the district level lead to environmental benefits? A case-study approach in Europe.

John Regan ±<fnref n="1" />,<fnref n="2" />, Silvia Marton<fnref n="3" />, Olivia Barrantes<fnref n="4" />, Eimear Ruane<fnref n="5" />, Marjoleine Hanegraaf<fnref n="6" />, Jérémy Berland<fnref n="7" />, Ramon Reine<fnref n="4" />, Sylvain Pellerin<fnref n="1" />,<fnref n="2" /> & Thomas Nesme<fnref n="1" />,<fnref n="2" />

## 1 Introduction

As the resources required for agricultural production become scarcer, and environmental regulations designed to reduce agriculture's impact on the environment become more restrictive, farmers will have to reorientate their production systems to better utilise the natural resource base. This can be done by taking advantage of synergies between farm enterprises through complementarity between crop and livestock production (Villano et al., 2010). Integration can occur at the farm-level when livestock are incorporated into farm operations specifically to capture positive synergies among enterprises (Clark, 2004). Integration may also occur at district-level where spatially separated crop and livestock farms cooperate for mutual benefit via contracts and partnerships, through material exchange for instance. Despite the potential of such arrangements to improve nutrient recycling, few research studies have assessed their efficacy (Asai et al., 2014). Questions remain as to whether collaboration among-farms might achieve the same range of synergies as within-farm integration (Russelle et al., 2007). Therefore, our objective is to develop an empirical assessment of the ecological benefits and drawbacks of crop-livestock integration strategies currently employed at the district scale in Europe.

## 2 Materials and Methods

We assessed four district-level crop-livestock integration strategies already employed in different biogeographical regions of Europe: (1) Local exchange of manure for straw among farms in the Ebro Basin, Aragon, Spain; (2) Provision of high quality forages for dairy cows through a forage dehydration facility in Domagné, Ille et Villaine, France; (3) Land sharing between dairy and arable farms in Winterswijk, Netherlands; and (4) Animal exchanges between lowland and highland regions in Switzerland. For each strategy, a farming system approach was employed to compare the characteristics and performances of three existing farming systems: non-cooperating specialised (i.e., no integration between crop and livestock), mixed farms (i.e. farm-scale integration) and cooperating specialised farms (i.e. district-scale integration through among-farm mixing). Within each group, farms were selected to be as representative as possible of their group. For each farming system, data were collected for ca 5 farms and then compared in terms of farming practices, input use, feeding strategies, fertilising strategies, land use, nutrient recycling, and economic performance. The empirical farm data used in this study were collected for 2012/2013 via farmer interviews performed in 2014.

## 3 Results – Discussion

Hereafter, we present only the results related to local exchange of manure for straw among farms in the Ebro River, Spain (Table 1). The results showed that N surplus expressed on a per hectare basis was higher on cooperating dairy than on specialised dairy farms and that mineral fertiliser use on cooperating arable farms was higher than on specialised arable farms. Such results were due to higher stocking rate on cooperating dairy farms and to intensive arable farming on cooperating arable farms, as illustrated by intensive soil tillage and irrigation. Cooperating dairy farms had lower district N autonomy than specialised dairy farms, but cooperating arable farms had slightly higher district N autonomy than their specialised counterparts. Specialised dairy farms outperformed cooperating dairy farms when assessed in terms of crop diversity (as measured using Shannon's Diversity Index (Eiden et al., 2000)) and percentage of land area alternating spring and winter crops. A higher level of plant diversity and a greater land area alternating spring and winter crops, as observed on specialised dairy farms, is evidence of greater potential for natural pest control and may thus reduce the frequency of pesticide use.
There was no evidence of improved efficiency on cooperating arable farms, as they had similar efficiency values to specialised arable farms. Cooperating dairy farms had a slightly lower N surplus per ton of milk produced than specialised dairy farms but had a lower N efficiency in terms of kg of N in sold products per kg of N input.

<footnote n="1">¹ INRA, UMR 1391 ISPA, CS 20032, 33882 Villenaved'Ornon, France</footnote>

<footnote n="2">² Bordeaux Science Agro (Univ. of Bordeaux), CS 40201, 33175 Gradignan Cedex, France</footnote>

<footnote n="3">³ Institute for Sustainability Sciences, Agroscope, Reckenholzstrasse 191, CH-8046 Zurich, Switzerland</footnote>

<footnote n="4">⁴ Department of Agriculture and Environmental Sciences, University of Zaragoza, Spain</footnote>

<footnote n="5">⁵ Livestock Systems Department, Animal and Grassland Research and Innovation Centre, Teagasc, Ireland</footnote>

<footnote n="6">⁶ Nutrient Management Institute, P.O. Box 250, NL-6700 AG Wageningen, The Netherlands</footnote>

<footnote n="7">⁷ Coopédom, SARL Déshyouest, 11 rue Louis Raison, 35113 Domagné, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: john.regan@bordeaux.inra.fr</footnote>
<table><caption>Table 1. Farm properties and performance for the Ebro Basin, Spain</caption><thead><tr><th rowspan="2">Indicator type</th><th rowspan="2">Farm properties and indicators of performance</th><th colspan="3">Baseline or reference farm groups</th><th colspan="2">Cooperating farm groups</th></tr><tr><th>Specialised Dairy (n=4)</th><th>Specialised Arable (n=5)</th><th>Mixed Dairy (n=4)</th><th>Cooperating Dairy (n=5)</th><th>Cooperating Arable (n=4)</th></tr></thead><tbody><tr><td rowspan="3">Farm properties</td><td>Agricultural Area (ha)</td><td>35</td><td>195</td><td>306</td><td>30</td><td>159</td></tr><tr><td>Stocking rate (LSU ha<sup>-1</sup>)</td><td>3.0</td><td>-</td><td>0.8</td><td>6.6</td><td>-</td></tr><tr><td>Milk production (Lcow<sup>-1</sup>)</td><td>10510</td><td>-</td><td>10508</td><td>10405</td><td>-</td></tr><tr><td rowspan="2">N balance</td><td>Mineral N fertiliser use (kg N ha<sup>-1</sup>)</td><td>2</td><td>66</td><td>125</td><td>72</td><td>163</td></tr><tr><td>Slurry exported (e) or imported (i) (kg N ha<sup>-1</sup>)</td><td>7.3(e)</td><td>0</td><td>0</td><td>366 (e)</td><td>30 (i)</td></tr><tr><td rowspan="2">Intensity</td><td>N surplus (kg N ha<sup>-1</sup>yr<sup>-1</sup>)</td><td>345</td><td>23</td><td>124</td><td>496</td><td>80</td></tr><tr><td>Conventional tillage area (% of UAA)</td><td>73</td><td>6</td><td>70</td><td>90</td><td>97</td></tr><tr><td rowspan="3">Resistance and resilience</td><td>Irrigated area (% of UAA)</td><td>100</td><td>26</td><td>97</td><td>82</td><td>85</td></tr><tr><td>Shannon's Diversity Index</td><td>1.15</td><td>1.18</td><td>1.60</td><td>0.83</td><td>1.21</td></tr><tr><td>Alternating spring and winter crops (% of UAA)</td><td>54</td><td>60</td><td>53</td><td>42</td><td>25</td></tr><tr><td rowspan="4">Efficiency</td><td>District N autonomy* (%)</td><td>24</td><td>38</td><td>32</td><td>16</td><td>41</td></tr><tr><td>N surplus (kg N ton-milk<sup>-1</sup>)</td><td>13.9</td><td>-</td><td>24.8</td><td>12.9</td><td>-</td></tr><tr><td>N efficiency (kg N sold kg<sup>-1</sup> N<sup>-1</sup>input)</td><td>0.31</td><td>0.70</td><td>0.60</td><td>0.25</td><td>0.71</td></tr><tr><td>N surplus per output (kg N kg<sup>-1</sup> N<sup>-1</sup> sold)</td><td>2.20</td><td>0.49</td><td>0.78</td><td>2.16</td><td>0.48</td></tr></tbody></table>

Similar results were observed in the other biogeographical regions of Europe: in Ille et Villaine, cooperating dairy farms had a higher no. of milking cows per hectare and milk yield per cow compared to specialised non-cooperating farms. In Switzerland, cooperation via animal exchanges (i.e. sending of lowland purebred calves to mountain heifer rearing farms before they return as pregnant heifers) was expected to afford lowland dairy farmers the land and time required to diversify their system and engage in cropping activities. However, lowland farmers chose to intensify their operations and increase milk production (dairy farming is more profitable on a per hectare basis) as opposed to diversifying them by growing more cereals (cropping is more profitable on a per hour worked basis) for livestock feeding. Farmers chose to intensify dairy operations because land availability is more limiting than labour availability in the region.

Overall, these results show that crop and livestock integration at the district scale does not necessarily lead to environmental benefits. Instead, in most cases, cooperation had the counterintuitive effect of increasing input use: cooperating farms had more intensive farming systems than non-cooperating specialised or mixed farms (eg, in terms of stocking rate or milking cow per ha, input use and N balance). Therefore, the benefits of integration were restricted by farmers choosing to use cooperation as a means to overcome environmental regulations and intensify their operations (since it provides a way to handle the large N excess that accompanies intensification) as opposed to diversifying them.

## 4 Conclusions

Our study provides first empirical evidence that cooperation among farms at the district scale does not necessarily lead to environmental benefits. Instead, in most cases, cooperation had the counterintuitive effect of increasing input use. However, it is not clear yet if cooperation helped farmers to intensify their system, or if cooperation is required to sustain already intensive systems. These results provide a platform to discuss about integration strategies between crop and livestock and to design resource efficient farming systems at different spatial scales.

*Acknowledgements.* This work has been funded under the EU seventh Framework Programme by the CANTOGETHER project N°289328: Crops and ANimals TOGETHER. The views expressed in this work are the sole responsibility of the authors and do not necessary reflect the views of the European Commission.

## References

Asai, M., Langer, V., Frederiksen, P. & Jacobsen, B.H. (2014). Livestock farmer perceptions of successful collaborative arrangements for manure exchange: a study in Denmark. *Agricultural Systems*, **128**, 55-65.

Clark, E.A. (2004). Benefits of re-integrating livestock and forages in cropproduction systems. p. 405–436. In Clements, D., Shrestha, A. (eds.), *New Dimensions in Agroecology*. Food Products Press: Binghamton, NY.

Eiden, G., Kayadjanian, M. & Vidal, C. (2000). Capturing landscape structures: Tools, http://ec.europa.eu/agriculture/publi/landscape/ch1.htm (accessed on 25 March 2015).

Russelle, M.P., Entz, M.H. & Franzluebbers, A.J. (2007). Reconsidering Integrated Crop-Livestock Systems in North America. *Agronomy Journal*, **99**, 325-334.

Villano, R., Fleming, E. & Fleming, P. (2010). Evidence of farm-level synergies in mixed-farming systems in the Australian wheat-sheep zone. *Agricultural Systems*, **103**, 146-152.

<footnote>*District N autonomy: N input (via material exchange of straw or manure, biological fixation and deposition)/total N input.</footnote>
# A system analysis of crop rotations with sugar beet and/or silage maize for biogas production - ecological and economical approaches

Anna Jacobs ±<fnref n="1" />, Sebastian Auburger <fnref n="2" />, Enno Bahrs <fnref n="2" />, Wiebke Brauer-Siebrecht <fnref n="1" />, Olaf Christen <fnref n="3" />, Philipp Götze <fnref n="3" />, Heinz-Josef Koch <fnref n="1" />, Oliver Mußhoff <fnref n="4" />, Niels Pelka <fnref n="4" />, Jan Rücknagel <fnref n="3" /> & Bernward Märländer <fnref n="1" />

## 1 Introduction

Like all arable systems, the production of biogas from biomass crops needs to reach economic, ecologic, and social standards in order to contribute to a sustainable development of energy production (European Commission, 2010; Tilman et al., 2002). This target applies for the entire production chain: For the biogas production itself, for the farm cultivating the biomass crops, and for the field with its cultivation system. Nowadays, silage maize is the most important biomass crop in Germany and the high concentration of its cultivation provokes social and ecological problems (Zegada-Lizarazu & Monti, 2011). Our joint project (five subprojects) questions if and where sugar beet as a biomass crop offers an ecological and economical efficient alternative. Within this, the aim of the study was to implement a systematic methodological approach linking reliable data from field trials with different modeling approaches which focus on region-specific yield levels, farms, and biogas plants in Germany (Jacobs et al., 2014).

## 2 Materials and Methods

Field trials were located on highly productive sites in Bavaria, Lower Saxony, and Saxony Anhalt, Germany, (2010-2014; Jacobs et al., 2014). The biomass crops, silage maize and sugar beet, were grown in continuous cultivation and in crop rotations (one year of biomass crop followed by twofold winter wheat). In practice, sugar beet are not-self compatible and its continuous cultivation was part of the methodological approach. (i) We assessed the potential methane yield (Weißbach, 2008; Weißbach, 2009) in the field trials (2011-2014). (ii) Further, the economic preference of silage maize and sugar beet for biogas production (costs per Nm<fnref n="3" /> methane) was evaluated in a nation-wide but district-specific modeling for Germany (own model including storage losses and field trial results). (iii) The contribution of sugar beet for biogas production to the stabilization of the profit margin of farms with different socio-economic structures (e.g. size, equipment, risk-acceptance by farm manager) was modeled (own model after Lehmann et al., 2013 based on field trials). We further estimated the ecological impacts and risks of crop cultivation in the field trials on (iv) soil health, indicated by balance of humus-C (soil organic matter; REPRO-model; Hülssbergen, 2003; 2011-2014) and by soil compaction risk in the top 20 cm (REPRO-model; Rücknagel et al., 2015; 2010-2012), on (v) water bodies (N-balance; Sieling & Kage, 2006; 2011-2014), and on (vi) air (greenhouse gas emissions; De Klein et al., 2006; IFEU, 2013; 2011-2014). The ecological assessments were done for entire crop rotations in order to avoid allocation mistakes.

## 3 Results – Discussion

(i) In the field trials, we found silage maize's methane hectare yield, as the mean of 2011-2014 and of continuous cultivation and crop rotations, of 8,365-8,782 Nm<fnref n="3" /> ha⁻<fnref n="1" /> (Bavaria), 6,837-7,069 Nm<fnref n="3" /> ha⁻<fnref n="1" /> (Lower Saxony), and 7,135 Nm<fnref n="3" /> ha⁻<fnref n="1" /> (Saxony Anhalt). Methane hectare yield of sugar beet taproots was lower (Bavaria: 7,652-7,861 Nm<fnref n="3" /> ha⁻<fnref n="1" />; Lower Saxony: 5,152-6,244 Nm<fnref n="3" /> ha⁻<fnref n="1" />; Saxony Anhalt: 3,206 Nm<fnref n="3" /> ha⁻<fnref n="1" />) due to generally lower dry matter yield. However, differences in dry matter yield were negligible when entire crop rotations (triennial sum of yield) were assessed (not shown). (ii) The nation-wide modeling pointed out those German districts (13%) where the costs per Nm<fnref n="3" /> methane produced out of sugar beet were lower than for silage maize. This economic preference of sugar beet was shown in regions where either sugar beet yield is relatively high (along the Rhine and the Danube) or where silage maize yield is relatively low (North of Germany, around Berlin). Further, 20% of the German districts showed a slight economic preference (max. 5% higher) of silage maize compared to sugar beet as crops for biogas production. (iii) Moreover, when sugar beet for biogas production was introduced into the cropping system, the socio-economic farm model predicted a reduction of fluctuations in the profit margin. However, for the experimental sites in Lower Saxony and Saxony Anhalt, this economic risk reduction was achieved only for low and very low risk-acceptance by farm managers and decreased in the mean profit margin by 1% and by 3%, respectively. For the site in Bavaria, the cropping system including sugar beet for biogas production was associated with an increase in the profit margin by 6%

<footnote n="1">¹ Department of Agronomy, Institute of Sugar Beet Research, Göttingen</footnote>

<footnote n="2">² Institute of Farm Management, Faculty of Agricultural Sciences, University of Hohenheim</footnote>

<footnote n="3">³ Department of Agronomy and Organic Farming, Faculty of Natural Sciences III, University of Halle-Wittenberg</footnote>

<footnote n="4">⁴ Department of Agricultural Economics and Rural Development, Faculty of Agricultural Sciences, University of Göttingen</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: jacobs@ifz-goettingen.de</footnote>
and the economic risk reduction was achieved even for a risk-neutral farm manager. In terms of ecological impacts and risks, (iv) humus-C balance showed similar values for silage maize and sugar beet and was reported here as the mean of both biomass crops. It was clear that the crop rotations better-preserved a sustainable humus-C level in the soil by matching the threshold regulated in Germany of (-75 to 125 kg C ha⁻¹; Anon., 2004) (crop rotations: Bavaria: -10 kg C ha⁻¹ a⁻¹, Lower Saxony: -47 kg C ha⁻¹ a⁻¹; continuous cultivation: Lower Saxony: -614 kg C ha⁻¹ a⁻¹; Saxony Anhalt: -869 kg C ha⁻¹ a⁻¹). The soil compaction risk index was more inhomogeneous and showed low to very high risks (Rücknagel, 2007) for single years of the crop rotations. However, in average, soil compaction risk was lower in crop rotations than in continuous cultivation where high to extremely high risks were indicated for three out of four cases (more often for sugar beet than for silage maize). (v) The N-balance ranged between -111 and 33 kg N ha⁻¹ in average among sites, crop rotations, and continuous cultivation (lower values for silage maize than for sugar beet) and never exceeded the threshold for farms regulated in Germany (60 kg N ha⁻¹; Anon., 2007). (vi) The triennial sum of greenhouse gas emissions of crop cultivation were higher for crop rotations (Bavaria: 11.1-11.9 t CO₂eq ha⁻¹ 3a⁻¹, Lower Saxony: 11.6-11.7 t CO₂eq ha⁻¹ 3a⁻¹) than for continuous cultivation (Lower Saxony: 8.0-8.6 t CO₂eq ha⁻¹ 3a⁻¹, Saxony Anhalt: 7.6-8.3 t CO₂eq ha⁻¹ 3a⁻¹). Again, values of both biomass crops were similar but, in general, the emissions were lower from sugar beet cultivation. However, the ecological parameters need to be related to the methane yield in order to evaluate the intensity of the respective impacts and risks. Moreover, the biogas production itself is to be included in the methodological approach which will be presented and discussed during the conference.

## 4 Conclusions

Overall, we concluded that sugar beet for biogas production is economically not in general preferable opposing silage maize due to higher cultivation costs and lower methane hectare yield. However, in 13% of German districts, the opposite was shown. Especially when production costs can be reduced, sugar beet offers an economically good choice as a crop for biogas production and was further concluded as a mean to stabilize the profit margin of a farm's cultivation program. Regarding the absolute results (not related to methane hectare yield) about ecological impacts and risks, we considered that neither silage maize nor sugar beet was outstanding from the other and values were even more similar when the biomass crops were cultivated in crop rotations. Consequently, we suggested that the entire crop rotations or cropping systems are generally to be evaluated for holistic conclusions. Overall, sugar beet can contribute to a sustainable development of biomass crop production especially when it is supposed to reduce silage maize cultivation, to increase crop rotation variability, or to reduce the economic risk for the farm.

*Acknowledgements.* This study was done with support from Federal Ministry of Food and Agriculture by decision of the German Bundestag and via the Fachagentur Nachwachsende Rohstoffe e. V. within the joint project "The sugar beet as an energy crop in crop rotations on highly productive sites - an agronomic/economic system analysis".

## References

* Anonymous (2004). Verordnung über die Grundsätze der Erhaltung landwirtschaftlicher Flächen in einem guten landwirtschaftlichen und ökologischen Zustand (Direktzahl-Verpflichtungenenverordnung - DirektZahlVerpfV). *Bundesgesetzblatt* **58**, 2778-2784.

* Anonymous (2007). Verordnung über die Anwendung von Düngemitteln, Bodenhilfsstoffen, Kultursubstraten und Pflanzenhilfsmitteln nach den Grundsätzen der guten fachlichen Praxis beim Düngen (Düngeverordnung – DüV). *Bundesgesetzblatt* **7**, 221-240.

* De Klein, C., Novoa, R.S.A., Ogle, S., Smith, K.A., Rochette, P., Wirth, T.C., McConkey, B.G., Mosier, A. & Rypdal, K. (2006). N₂O emissions from managed soils and CO₂ emissions from lime and urea application. in: 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme. Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K. (eds). *IGES, Japan*, 11.2-11.54.

* European Commission (2010). Report from the Commission to the Council and the European Parliament on sustainability requirements for the use of solid and gaseous biomass sources in electricity, heating and cooling. http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52010DC0011&from=EN, Dec 2nd, 2010.

* Hülsbergen, K.-J. (2003): Entwicklung und Anwendung eines Bilanzierungsmodells zur Bewertung der Nachhaltigkeit landwirtschaftlicher Systeme. Shaker, Aachen.

* IFEU (2013). ENZO₂-Treibhausgasrechner für Biokraftstoffe und flüssige Bioenergieträger. Version 1.3. http://www.ifeu.de/index.php?bereich=nac&seite=ENZO₂, March 3rd, 2015.

* Jacobs, A., Auburger, S., Bahrs, E., Brauer-Siebrecht, W., Buchholz, M., Christen, O., Götze, P., Koch, H.-J., Mußhoff, O., Pelka, N., Rücknagel, J. & Märländer, B. (2014). Die Zuckerrübe als Energiepflanze in Fruchtfolgen auf hoch produktiven Standorten - eine pflanzenbaulich/ökonomische Systemanalyse. *Sugar Industry* **139**, 117-127.

* Lehmann, N., Briner, S. & Finger, R. (2013). The impact of climate and price risks on agricultural land use and crop management decisions. *Land Use Policy* **35**, 119-130.

* Rücknagel, J. (2007): Entwicklung eines Modells zur Analyse und Bewertung der Schadverdichtungsgefährdung von Ackerstandorten. *Schriftenreihe der Pflanzenbauwissenschaften des Institutes für Agrar- und Ernährungswissenschaften, Martin-Luther-Universität Halle-Wittenberg, Bd. 1*, Der Andere Verlag, Tönning.

* Rücknagel, J., Hofmann, B., Deumelandt, P., Reinicke, F., Bauhardt, J., Hülsbergen, K.-J. & Christen, O. (2015). Indicator based assessment of the soil compaction risk at arable sites using the model REPRO. *Ecological Indicators*, **52**, 341-352.

* Sieling, K. & Kage, H. (2006). N balance as an indicator of N leaching in an oilseed rape - winter wheat - winter barley rotation. *Agriculture, Ecosystems and Environment* **115**, 261-269.

* Tilman, D., Cassmann, K.G., Matson, P.A., Naylor, R. & Polasky, S. (2002). Agricultural sustainability and intensive production practices. *Nature* **418**, 671-677.

* Weißbach, F., 2008. On Assessing the Gas Production Potential of Renewable Primary Products. *Landtechnik* **63**, 356-358. Weißbach, F., 2009. Gas production potential of fresh and ensiled sugar beets in biogas production. *Landtechnik* **64**, 394-397. Zegada-Lizarazu, W. & Monti, A. (2011). Energy crops in rotation. A review. *Biomass and Bioenergy* **35**, 12-25.
# Design of cropping systems at territorial scale: methodological lessons from case studies

Rémy Ballot<fnref n="1" />, Emilia Chantre<fnref n="2" />, Laurence Guichard<fnref n="1" />, Florence Jacquet<fnref n="3" />, Laurette Paravano<fnref n="4" />, Lorène Prost⁵, Clémence Ravier<fnref n="1" />, Raymond Reau<fnref n="1" /> & Marie-Hélène Jeuffroy<fnref n="4" />⁻<fnref n="1" />

## 1 Introduction

Water quality in France is highly degraded, and must be improved according to the European Water Framework Directive. As agriculture has been recognized liable for diffuse water pollution, measures have to be taken in water catchment areas, often including a redesign of cropping systems to efficiently limit nitrate and pesticides transfer to water while keeping an economically powerful agriculture. Such a redesign at a territorial scale raises methodological issues: who should be involved on the design process and how, to allow a sustainable and long-term change of local practices? Which knowledge should be used, in order to take into account the characteristics of the local situations? As the change is thought to last several years, which process should we start to favor actors' learning? From two methods developed and implemented in six case studies, we analyzed the main characteristics and specificities of a process of design at territorial scale in order to be efficient.

## 2 Presentation of the two methods of design at territorial scale

Two methods aiming at collectively designing cropping systems at territorial scale were developed: Co-click'eau, based on the adaptation to a water catchment area of the method proposed by Jacquet *et al.* (2011) and Brienon, named after the place the approach was built (Burgundy, France) (partly described in Ravier *et al.*, 2015).

The Co-click'eau method includes 6 steps (Gisclard *et al.*, 2014): (1) the choice of indicators for the assessment of cropping systems and territory scenarios, (2) a synthetically characterization of the present situation of the area (water quality issues and current cropping practices), (3) the building of a grid describing crop*soil*current and alternative practices combinations, with their main performance (yield, pesticide and N fertilizer use or gazeous losses emissions, work load, gross or direct margin, ...), (4) the collective identification of the main territory objectives and constraints (e.g. maximum profitability, minimizing pesticide use, a maximum area in organic agriculture), (5) the design of scenarios with a mathematical programming model satisfying the aims, and the assessment of the various scenarios (described as percentages of the cultivated area corresponding to each combination of the grid), and (6) a discussion on the results to build the action plan (if the results were not satisfactory, a new loop with steps 4+5+6 began). Steps 1, 4, 5 and 6 were realized by a steering committee, involving diverse stakeholders, including consumers, while steps 2 and 3 were implemented by a technical committee (farmers, advisors knowing the area).

The Brienon method includes 8 steps: (1) the analysis of the requirements built from interviews with the various stakeholders in the catchment area, (2) a collective identification of the targeted aim for the water quality, with the steering committee, (3) a diagnosis on the cropping systems in the area, (4) the design of new cropping systems, realized by local farmers (5) the assessment of the new cropping systems regarding the targeted aims, with adapted tools, (6) the building of an action plan, after a discussion of the possible cropping systems to be implemented with the local farmers, (7) the proposal and discussion of the action plan with the steering committee, (8) proposal for the monitoring of the action plan.

## 3 Results and Discussion

Since 2011-2012, these two methods have been tested in several water catchment areas located in the Northern part of France, each measuring between 17 and 47 km<fnref n="2" />, and used predominantly for field crop production, winter wheat and rapeseed in particular (see Chantre *et al.*, 2014). Action plans were validated in each of these areas, sometimes following the actions plans built from our methods, sometimes not. Where the action plans were in line with the one built thanks to one of the two methods, change of practices has begun to be implemented. In Brienon for instance, the dynamics is very promising and the monitoring shows that the actions and results are in line with what was expected.

From these different experiences, their successes and their failures, we propose to discuss five characteristics of both

<footnote n="1">¹ INRA, UMR 211 Agronomie, BP 01, F-78850 Thiverval-Grignon, France</footnote>

<footnote n="2">² MAAF/DGPAAT/SDBE 3, rue Barbet de Jouy 75 349 PARIS 07, France</footnote>

<footnote n="3">³ INRA, UMR Moisa, 2 place Viala, 34000 Montpellier, France</footnote>

<footnote n="4">⁴ Chambre d'agriculture de l'Yonne, 14 bis rue Guynemer, F-89005 Auxerre cedex, France 5 : INRA, UMR 1326 LISIS, F 77454 Marne-La-Vallée, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Jeuffroy@grignon.inra.fr</footnote>
methods that seem important to design cropping systems at territorial scale. First, both methods involve the various actors concerned by the agriculture in the territory, including citizens, with their specific aims and values. Rather than building a common aim to reach, we propose various means to explore and take into account the diversity of the actors' targets, agree on their assessment criteria, and identify the agricultural solutions which respect as much as possible the diverse aims (Ravier *et al.*, 2015). Keeping the objectives of each stakeholder helps to alleviate tensions between differing opinions, thus facilitating the dialogue between them, particularly between farmers, the suppliers and water consumers, asking for a service. Considering together water quality and economic, social and environmental performance of agricultural practices in the targets helps at involving the various stakeholders in the approach.

Second, both methods show that describing and characterizing the current cropping systems, and their estimated impact on water quality, is a crucial step before designing new cropping systems and territory scenarios. This step allows the actors to build a shared vision of this territory and issues, by identifying and sharing the main practices that prevent to reach the targeted aims, justifying the need to design new cropping systems and territory scenario.

Third, both methods show that a step in sharing scientific and expert knowledge among actors about local agricultural conditions, and about innovative practices, together with their performance, is powerful. It also appears important that the results of the *ex ante* assessment of the innovative cropping systems and/or scenarios, designed for the territory, be collectively discussed in order that every participant learns from the links between these results and the characteristics of the combinations practice*soil type in the area.

Fourth, exploring possible solutions, even if obstacles to their implementation exist (lack of market opportunities, economic performance...) and assessing their impacts at the territorial scale helps at defining the final scenarios in the action plan. Creativity can be enhanced by the exploration of breakthrough scenarios. This step is more powerful when assessment or optimization tools are used.

Fifth, choosing together the timescale of the action plan (what is possible to do and when), and the intermediary indicators that monitor the actions implemented and results obtained, contributes to the motivation of the participants. The monitoring of the implemented process allows the participants to assess the efficiency of the solutions proposed or to collectively imagine other solutions if the results are not as good as expected.

**4 Conclusions**

Finally, it appeared, from these case studies, that the implication of the actors all along the process, together with regular discussions on the intermediate results of implemented innovative practices, is required for a full and continuous implication of stakeholders. Sharing the information, diagnosis, knowledge, objectives at each step of the approach allows a learning loop for the participants, about the challenge, the methods, the organization, innovative technical practices, but also about the diversity of points of view in the catchment area (Chantre *et al.*, 2014). Several tools are needed for this approach that should be adapted to the activity and learning of the stakeholders: tool for representing the diversity of practices, tool for identifying optimal scenarios, tool for assessing solutions at the territorial scale, tool for monitoring the action...

The success of the process greatly depends on the management of the stakeholders' positions and motivations: taking into account the diversity of the opinions, working with an open mind in order to find innovative solutions, accepting that the first solutions proposed do not allow to reach the targets and motivate the farmers to be more innovative.

*Acknowledgements*. This study was partly funded by POPSY-ANR Systerra2008-12 and by ECOPHYTO plan - ONEMA.

**References**

Jacquet, F., Butault, J.P., &Guichard L.(2011). An economic analysis of the possibility of reducing pesticides in French field crops, *Ecological Economics* Vol. 70 (9): 1638-1648

Gisclard, M., Chantre, E., Cerf, M., Guichard, L.(2015). Co-click'eau : une démarche d'intermédiation pour la construction d'une action collective locale ? *Natures Sciences et Sociétés*. sur www.nss-journal.org

Chantre, E., Prost, L., Guichard, L., &Reau, R. (2014). Re-thinking agricultural practices to improve water quality: twoparticipatory methodologies for collaborative learning. 11th European IFSA Symposium. 1-4 April 2014, Berlin, Germany

Ravier, C, Prost, L, Jeuffroy, MH, Wezel, A, Paravano, L, &Reau, R. (2015). Multi-criteria and multi-stakeholder assessment of cropping systems for result-oriented water quality action program. Land Use Policy. 42:131-140. http://dx.doi.org/10.1016/j.landusepol.2014.07.006
# Indicators for the evaluation of nitrogen efficiency and nitrogen balance in agriculture for 27 Member States of the European Union

Olivier Godinot ±1,2,3, Philippe Leterme 1,2,3, Françoise Vertès 2,1 & Matthieu Carof 1,2,3

## 1 Introduction

In the European Union, only 31% of nitrogen (N) inputs in agriculture are recovered in intended products (Leip *et al.*, 2011). This low N use efficiency results in major N losses, which have serious impacts on human health and the environment. Improving N efficiency is one of the key options to reduce this environmental impact while ensuring the needs of a growing population (Sutton *et al.*, 2011). Indicators are necessary to assess N efficiency. The most used N efficiency indicator is derived from farm-gate balance (FGB; Kaffka *et al.*, 1989) and is called nitrogen use efficiency (NUE; Halberg, 1999). However, both FGB and NUE suffer from several limitations (Godinot *et al.*, 2014).

Novel indicators called system N efficiency (SyNE) and system N balance (SyNB) solve some limitations of NUE and FGB (Godinot *et al.*, 2014). However, as animal production is less efficient than crop production, those indicators cannot help indentifying possible improvement margins for mixed farming systems. A third indicator, called relative N efficiency (RNE), deals with these biological differences by expressing efficiency relative to the maximum attainable efficiency of each product (Godinot *et al.*, 2015).

These three indicators (SyNE, SyNB and RNE) have been applied and validated at farm scale (Godinot *et al.*, 2014, Godinot *et al.*, 2015). The goal of this work is their application on 27 Member States of the European Union, in order to test their interest in describing N management and N efficiency progress margins at the country scale.

## 2 Materials and Methods

Indicator calculation includes indirect N losses due to the production of inputs and soil N change. Net N fluxes are calculated when used inputs are similar to produced outputs. Potential efficiency is defined as the highest efficiency found in the literature for each net output. This leads to the following formulas:

$$ \text{SyNE} = \text{net outputs} / (\text{net inputs} + \text{indirect N losses} - \text{soil N variation}) $$

$$ \text{SyNB} = \text{net inputs} + \text{indirect N losses} - \text{soil N variation} - \text{net outputs} $$

$$ \text{RNE} = \text{SyNE} / \text{potential efficiency} $$

Data used for calculation were collected for each of the 27 EU Member States from years 2000 to 2008. Land use, crop and animal production and import data were provided by the statistical service of the United Nations Food and Agriculture Organization. Inorganic and organic fertilizer use, biological N fixation by legumes crops and energy consumption for the agricultural sector were provided by Eurostat. Atmospheric N deposition was calculated from the European Monitoring and Evaluation Program. N losses related to the production and transport of crop, fertilizer and energy inputs were estimated from life cycle inventory data (Godinot *et al.*, 2014). The annual change in soil organic N content was estimated for permanent crops and grasslands (+43 kg N ha⁻<fnref n="1" />) and annual crops (-70 kg N ha⁻<fnref n="1" />) based on average organic carbon change at the European scale (Vleeshouwers and Verhagen, 2002).

## 3 Results – Discussion

SyNE was first compared to the usual indicator NUE. SyNE varies from 0.16 to 0.42 with an average of 0.23; NUE varies between 0.20 and 0.49 with an average of 0.35 (Fig. 1). The 12 points difference illustrates the lack of consideration of some fluxes in NUE. Including indirect N losses leads to a low decrease (-1 point on average). However, considering them allows comparing small input importers such as Eastern European countries (Slovakia, Latvia, Lithuania, Romania) with large importers (Malta, the Netherlands and Belgium). Net fluxes calculation results in a 5 points decrease on average. It mostly penalizes countries that import feed and grow similar crops (Malta, Belgium, Portugal, Greece). Considering soil N changes results in a 7% decrease on average (Fig. 1), especially in countries with more annual crops (Romania, Hungary, Bulgaria) while it increases the efficiency of countries with more permanent crops and grasslands (Ireland, Greece, United-Kingdom). Compared to NUE, SyNE expresses the global efficiency of agriculture from cradle to the gate of the “territory-farm”. This perimeter is similar to the N footprint of agricultural production for a given country.

RNE completes SyNE for comparison between countries with different production by expressing the efficiency of the agricultural sector of a country according to its potential, which depends on the nature of agricultural productions. RNE

<footnote n="1">¹ AGROCAMPUS OUEST, UMR1069 SAS, F-35042 Rennes, France</footnote>

<footnote n="2">² INRA, UMR1069 SAS, F-35042 Rennes, France</footnote>

<footnote n="3">³ Université européenne de Bretagne, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: olivier.godinot@agrocampus-ouest.fr</footnote>
varies between 0.28 and 0.78 with an average of 0.43 and is correlated to SyNE (r = 0.75; p < 0.001) but an individual comparison of countries shows remarkable differences in rankings for some countries (Fig. 2). For instance, Ireland has the same system N efficiency as Czech Republic (0.20) but a better relative efficiency (0.56 against 0.32) due to its higher share of animal production. RNE is an interesting tool to estimate the room for improvement for N efficiency given each country's production mix. Using this indicator, Czech Republic (RNE = 0.32) seems to have more progress margin compared to Malta (RNE = 0.44), and even more than Ireland (RNE = 0.56), although those three countries have a similar SyNE of 0.20.

<figure><img src="image_108.png" /><figcaption>Fig. 1. Differences between SyNE and NUE for EU27</figcaption></figure>

<figure><img src="image_109.png" /><figcaption>Fig. 2. Comparison of SyNE and RNE for EU27</figcaption></figure>

The average national System N Balance is 113 kg N.ha⁻¹ Agricultural Area for the 27 Member States and varies from 31 to 432 kg N.ha⁻¹ AA. SyNB shows a strong correlation with net animal production (r=0.96; p < 0.001) and net feed input (r=0.94; p < 0.001). The combined use of RNE and SyNB gives more insights into N management priorities : countries with high SyNB should reduce their balance first, while countries with low RNE and system N balance could work on improving their relative efficiency.

<figure><img src="image_110.png" /><figcaption>Fig. 3. Interest of combining RNE and SyNB for country analysis</figcaption></figure>

## 4 Conclusions

This work demonstrates the feasibility and the interest of calculating system N efficiency, relative N efficiency and system N balance at the national scale. Although the proposed indicators present higher uncertainty (mostly due to soil N change) they legitimate comparisons between countries. Their reliability will increase with the progress of knowledge on soil N dynamics. Their combined use gives new insights into N management at country scale: system N efficiency and balance allow evaluating the use of N resources in agriculture as well as the resulting environmental pressure, while relative N efficiency allows assessing the margin of progress of each country given its productions.

## References

* Godinot, O., Carof, M., Vertès, F., Leterme, P., 2014. SyNE: An improved indicator to assess nitrogen efficiency of farming systems. *Agricultural Systems*, **127**, 41–52.

* Godinot, O., Leterme, P., Vertès, F., Faverdin, P., Carof, M., 2015. Relative nitrogen efficiency: a tool to compare diversified farming systems. *Agronomy for Sustainable Development*. doi: 10.1007/s13593-015-0281-6

* Halberg, N., 1999. Indicators of resource use and environmental impact for use in decision aid for Danish livestock farmers. *Agriculture Ecosystems and Environment*, **76**, 17–30

* Kaffka, S. ; Koepf, H. H., 1989. A case study on the nutrient regime in sustainable farming. *Biological Agriculture and Horticulture*, **6(2)**, 89–106.
* Leipzig, A., Achermann, B., Billen, G., Bleeker, A., Bouwman, A.F., de Vries, W., Dragosits, U., Döring, U., Fernall, D., Geupel, M., & others, 2011.
* Integrating nitrogen fluxes at the European scale. The European Nitrogen Assessment. Cambridge University Press, Cambridge (UK). 345–376.

* Sutton, M.A., Howard, C.M., Erisman, J.W., Billen, G., Bleeker, A., Grennfelt, P., van Grinsven, H., Grizzetti, B., 2011. The European nitrogen assessment: sources, effects, and policy perspectives. Cambridge University Press, Cambridge (UK).

* Vleeshouwers, L.M., Verhagen, A., 2002. Carbon emission and sequestration by agricultural land use: a model study for Europe. *Global Change Biology*, **8**, 519–530.