# SCALING UP AGRICULTURAL CATCHMENT MANAGEMENT STRATEGIES FROM FIELD AND FARM SCALE TO LANDSCAPE SCALE

Chris Stoate<sup>*±1*</sup>, Jeremy Biggs<sup>2</sup>, Adrianna Hawczak<sup>2</sup>, John Szczur<sup>1</sup> & Sarah-Jane Osborne<sup>1</sup>

## 1 Introduction

Targets for water quality set by the Water Framework Directive (WFD) drive monitoring, research and agricultural landscape management across Europe. Despite this, there is limited understanding of the efficacy of mitigation measures developed at field and farm scale when applied together at the catchment scale, or of the implications for meeting local, national and global targets for food security and viable farm businesses. Since 1992, wide-ranging agri-environmental research has been carried out at the 333ha Allerton Project research and demonstration farm at Loddington (central England) to develop management practices that meet objectives for biodiversity conservation, water quality and other environmental objectives while maintaining profitable and productive farming (Stoate et al., 2012). Examples of individual management strategies include reduced tillage, contour cultivation, in-field barriers, cover crops, and constructed wetlands. Since 2010, a landscape scale experiment, ‘Water Friendly Farming’, has been established to test some of these measures within the wider farming community of the upper Welland river basin (Biggs et al., 2014). This work builds on previous catchment initiatives, including a social learning approach to community engagement (Stoate, 2010).

## 2 Materials and Methods

The Water Friendly Farming project adopts a rigorous BACI (Before, After, Control, Impact) experimental design and the study area comprises three headwaters catchments close to Loddington (Figure 1) covering a total area of approximately 3,000ha of pasture and arable land. Three years of baseline stream nutrient and sediment concentration data have been collected from the base of each catchment, and pesticide data have been collected in autumn/winter. Mitigation measures have been introduced and soil and nutrient maps and advisory visits provided to participating farmers from 2014. The focus is currently on identifying synergies between objectives for water quality improvement and improvements in crop production and soil and nutrient use efficiency. Remote sensing data have been used to identify areas of high runoff risk and poor crop establishment, and soil penetration resistance data have been collected across a sample of affected fields. Links between soil physical and biological properties associated with compaction are being investigated. Crop yields for Loddington have been recorded annually since 1993 and are available for other farms more recently. Research and management are adapted in response to feedback from participating farmers, enabling scientific and farmer knowledge to be combined. An Arable Business Group has been established for benchmarking economic performance of farm businesses, soil organic matter benchmarking has been requested by participating farmers, and a no-till drill is being made available for free trial.

<figure><img src="image_112.png" /><figcaption>**Fig. 1.** Diagramatic representation of upper Welland study areas</figcaption></figure>

## 3 Results – Discussion

Along with other farms across lowland England, wheat yields at Loddington have not increased in the past two decades (Figure 2) despite increased physiological potential through plant breeding. Yields were exceptionally low in 2012 when prolonged rain resulted in compacted waterlogged soil which also influenced yields in 2013. Suspended sediment concentrations varied considerably in response to rainfall. Using these data, estimates of soil loss from fields ranged from 0.3 to 0.6t/ha/yr (Figure 2). There was insufficient variation on soil and nutrient maps to stimulate change in farmers’ fertiliser application practice. Using remote sensing NDVI data, we recorded variable crop cover. This was

<footnote n="1"><sup>1</sup> GWCT Allerton Project, Loddington House, Loddington, Leics. LE7 9XE UK</footnote>

<footnote n="2"><sup>2</sup> Freshwater Habitats Trust,</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: cstoate@gwct.org.uk</footnote>
associated with soil compaction mainly on the field headlands (Figure 3). Increasing densities of the competitive grass weed blackgrass (*Alopecurus myosoides*) associated mainly with compacted and waterlogged soils have resulted in increased herbicide costs and to elevated herbicide concentrations in watercourese that are used for drinking water supply. Oilseed rape break crops enable blackgrass to be partially controlled but increase slug pressure on following crops, and the use of the molluscicide, Metaldehyde which exceeds statutory limits for drinking water supply. Farmers have identified a need to better understand soil and nutrient management in order to maintain the profitability of their businesses, enhance resilience to climate change, and reduce environmental impacts.

<figure><img src="image_113.png" /><figcaption>Fig. 2. Winter wheat yields at Loddington (left), estimated soil loss from ‘treatment’ catchments (right)</figcaption></figure>

<figure><img src="image_114.png" /><figcaption>**Figure 3.** NDVI image showing variable winter wheat establishment (left), compaction in field and headland (right)</figcaption></figure>

**4 Conclusions**

While WFD policy is concerned mainly with nutrient and pesticide concentrations in water and ecological impacts of sediment, issues that are of greater concern for farmers are soil function and crop performance, economically efficient use of nutrients and availability of a range of grass weed control methods including herbicides. While these farm-scale objectives are not economically compatible with national objectives for increased food production, we can identify synergies between WFD and farming objectives that can guide the adaptation of current farming systems to deliver public and private benefits.

*Acknowledgements*. Funding has been provided by the Environment Agency, Chemicals Regulation Directorate, Syngenta, and Catchment Sensitive Farming.

**References**

Stoate, C., Leake, A., Jarvis, P. & Szczur, J. (2012) *Fields for the future: The Allerton Project – a winning blueprint for farming, wildlife and the environment*. GWCT, Fordingbridge. 32pp.

Stoate, C. (2010) *Exploring a Productive Landscape – from long history to sustainable future in the Eye Brook Catchment*. The Game & Wildlife Conservation Trust/Eye Brook Community Project, Fordingbridge.

Biggs, J., Stoate, C., Williams, P., Brown, C., Casey, A., Davies, S., Diego, I., Hawczak, A., Kizuka, T., McGoff, E., & Szczur, J. (2014). *Water Friendly Farming: Results and practical implications of the first 3 years of the programme*. Freshwater Habitats Trust, Oxford, and Game & Wildlife Conservation Trust, Fordingbridge.
# Integrating agricultural and water policies for more sustainable crop production in arid Northwestern China

Til Feike *±1,2 & Martin Henseler 3,4

## 1 Introduction

Overexploitation of water resources constitutes a major threat to future global food security. In arid Northwestern China water consumption for agricultural production has increased tremendously over the last decades. This not only led to the degradation of natural ecosystems, but also increases competition among different anthropogenic water users severely endangering a sustainable development in ecological, economic and social terms. In the Aksu-Tarim Region water for crop production accounts for far more than 90% of total human water use (Feike et al., 2015). The projected increase in domestic and industrial water demand, the increasing importance of ecosystem restoration, as well as the increasing variability of fresh water supply due to climate change exert strong pressure on regional authorities to reduce agricultural water consumption and improve water use efficiency. Therefore it is of vital importance to provide scientific evidence to policy makers regarding suitability of regional policies aiming at water saving in crop production. Hence, the present study tests the effectiveness and efficiency of different agricultural water policies. As the implementation of such policies entails a certain risk of decreasing agricultural production and rural incomes, as observed for other regions in China by Chen et al. (2014), supporting agricultural policies are tested in combination, aiming at stable food and fiber provisioning as well as stable farmers' incomes.

## 2 Materials and Methods

Building on a detailed primary crop management data set of 256 farm households from selected regions along Aksu and Tarim River a positive mathematical programming (PMP) model was developed based on the approach presented by Röhm (2001) and Röhm and Dabbert (2003). The regional agro-economic supply model simulates crop production and respective water demand for irrigated crops in the study region. The model was calibrated for the 12 sub-regions located in the direct vicinity of Aksu and Tarim River. The model is based on the primary crop management data, supplemented by secondary data on production area and yields of all relevant crops (cotton, wheat, maize and melon). A system of non-linear gross margin functions optimizes resource allocation aiming at maximized profits (total gross margin). The non-linear supply model calibrated by PMP method ensures “smooth” model behavior and hence avoids over-specialization.

The empirically observed status quo conditions serve as model baseline, where different policy simulation scenarios are applied. The tested policies include three alternative policy instruments aiming at water saving, i) (increase of the existing) taxation of each unit of irrigated land (TAX), ii) restriction of the quantity of water to a defined share of the quantity available under baseline conditions (QUOTA), and iii) pricing of each unit volume of water demanded for irrigation (PRICE).

In a second step those water policies are tested in combination with agricultural policies, which aim at retaining food and fiber supply and avoid dramatic declines in farmers' income. Those agricultural policies include the subsidization of drip irrigation technology for cotton production (COTTON), as well as the subsidization of the production of wheat and maize (CEREAL). All five instruments were tested at different intensities to determine promising ranges and combinations of instruments. The present paper only presents the results of the entire study region, which are aggregated from the results of the 12 sub-regions.

## 3 Results and Discussion

The efficiencies of the three water saving policies are compared by their marginal cost curves (Fig. 1), which represent the marginal costs of the next realized unit of saved resource, i.e. water. Comparability of the three policy instruments is realized by displaying the quantity of saved water as units of conserved irrigated crop land. Fig. 1 shows that for intended water savings of up to 18% the instrument PRICE is most efficient, while QUOTA constitutes the least efficient. For water savings of more than 18% the instrument TAX features the lowest abatement costs. However, TAX does not make use of potential comparative advantages of water efficient vs. water inefficient crops, and is thus not considered in the multiple policy instrument analysis.

<footnote n="1">¹ Institute for Strategies and Technology Assessment, Julius Kühn-Institut (JKI) | Federal Research Centre for Cultivated Plants, Kleinmachnow, Germany</footnote>

<footnote n="2">² Institute of Farm Management (410c), University of Hohenheim, Stuttgart, Germany</footnote>

<footnote n="3">³ Thünen Institute of Rural Studies, German Federal Research Institute for Rural Areas, Forestry and Fisheries, Braunschweig, Germany</footnote>

<footnote n="4">⁴ EDEHN - Équipe d'Économie Le Havre Normandie, Université du Havre, Le Havre, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: til.feike@jki.bund.de</footnote>
<figure><img src="image_115.png" /><figcaption>Fig. 1. Marginal abatement cost curves for the three tested water policy instruments. Note: 1 mu irrigated area represents 546 m³ water; 1 mu = 0.067 ha; 1 RMB = 0.12 Euro</figcaption></figure>

The combined effects of the water policy instruments QUOTA and PRICE with the agricultural policy instruments COTTON and CEREAL at different levels are displayed in Table 1. The P 0.55 and Q 90% levels aim at reducing overall water consumption for crop production by 10%, while the P 0.85 and Q 80% aim for a reduction by 20%. In combination with the subsidies for cotton and cereal production a reduction in water consumption by 10% and 20% results in a decrease in cotton production by 6% and 15%, an increase in cereal production by 17% and 4%, and an agricultural income deficit by 6% and 13%, respectively.

<table><caption>Table 1. Effects of multiple instruments on the different policy objectives</caption><thead><tr><th>Water price / quota</th><th>RMB cmb<sup>-1</sup> / % of BL</th><th>P 0.55</th><th>Q 90%</th><th>P 0.85</th><th>Q 80%</th></tr><tr><th>Cotton drip subsidy</th><th>RMB cmb<sup>-1</sup></th><th>DS+250</th><th>DS+150</th><th>DS+200</th><th>DS+250</th></tr><tr><th>Cereal subsidy</th><th>RMB cmb<sup>-1</sup></th><th>CS+150</th><th>CS+150</th><th>CS+150</th><th>CS+300</th></tr></thead><tbody><tr><td>Cotton production</td><td>% of BL</td><td>94</td><td>93</td><td>86</td><td>84</td></tr><tr><td>Cereals production</td><td>% of BL</td><td>117</td><td>117</td><td>106</td><td>103</td></tr><tr><td>Monetary balance</td><td>% of TGM</td><td>-6</td><td>-6</td><td>-12</td><td>-14</td></tr></tbody></table>

## 4 Conclusions

For the Northwestern Chinese Aksu-Tarim region certain differences in effectiveness and efficiency of alternative water policy instruments were revealed, with water pricing constituting the most cost-effective tool. However, to reduce the negative impacts of water policies on other policy goals, i.e. food security and agricultural incomes, a targeted subsidization of cereal production and drip irrigation technology is needed. The applied modeling approach provides plausible results in line with other studies, and the model reactions can be partially validated by empirical data. Future modeling may expand to consider economy wide aspects as well as potential crop productivity increases. Ongoing genetic improvement of seed material and especially the potential improvement of farmers' skills by training may boost crop productivity and related water use efficiencies. Therefore the effectiveness of the investment in farmers' training on crop water saving should be investigated in future modeling studies. Finally, the successful implementation of integrated water and agricultural policy measures demand close cooperation between different regional authorities, namely the agricultural and water resource bureaus as well as other relevant stakeholders.

*Acknowledgements.* The present research was conducted in the frame of the "SuMaRiO – Sustainable Management of River Oases along the Tarim River, China" project funded by the German Federal Ministry for Education and Research (BMBF). We furthermore thank all SuMaRiO project partners, who contributed to this research.

## References

Chen, S., Wang, Y. & Zhu, T. (2014). Exploring China's Farmer-Level Water-Saving Mechanisms: Analysis of an Experiment Conducted in Taocheng District, Hebei Province. *Water* **6**, 547-563.

Feike, T., Mamitimin, Y., Li, L. & Doluschitz, R. (2015). Development of agricultural land and water use and its driving forces along the Aksu and Tarim River, P.R. China. *Environ Earth Sci* **73**, 517-531.

Röhm, O. (2001). Analyse der Produktions- und Einkommenseffekte von Agrarumweltprogrammen unter Verwendung einer weiterentwickelten Form der Positiven Quadratischen Programmierung, Berichte aus der Agrarwissenschaft. Dissertation, Uni Hohenheim, Shaker Verlag, Aachen.

Röhm, O. & Dabbert, S. (2003). Integrating agri-environmental programs into regional production models – an extension of positive mathematical programming. *American Journal of Agricultural Economics* **85**, 254-265.
# Farm type-specific adoption behaviour in sustainable soil nutrient management: the case of smallholder farms in Ioba province, Burkina Faso.

Boundia Alexandre Thiombiano *±1, 2, 3 & Quang Bao Le 2, 4

## 1 Introduction

Sub-Saharan Africa has failed to ensure sufficient and sustainable food production (Food and Agriculture Organization [FAO] et al., 2013), mainly as a result of soil nutrient depletion (Pimentel and Burgess, 2013). The persisting food insecurity and poverty, despite proven sustainable soil nutrient management (SNM) practices (Vlek et al., 1997; Ingram et al., 2008), denote the failure of policy intervention to leverage farm incentives to adopt SNM practices (Anley et al., 2007). The main objective of this study was to analyse the farming system type-specific behaviour in the adoption and use of SNM practices in a semiarid region of Burkina Faso.

## 2 Materials and Methods

The study was conducted in Ioba Province, southwestern Burkina Faso. Based on soil degradation information, vegetation index (NDVI) and demographic data, three communes were selected. Six villages (i.e., two villages per commune) were chosen according to the two major soil types in each commune. We randomly sampled and surveyed 360 household-farms to obtain a multidimensional dataset, using the Sustainable Livelihood Framework (Sconnes, 1998) as a guide. Binary logistic regressions were used to analyse the determinants of mineral fertiliser use intensity, separate the adoption of mineral and organic fertilisers and combine mineral-organic fertiliser adoption at the plot level for different farming systems. Only maize plots were considered as it was the main food crop for which the farmers used most of the available fertilisers.

## 3 Results – Discussion

No multi-collinearity was found among explanatory variables (VIF < 0.5 and tolerance > 0.2). The Hosmer and Lemeshow test at 5% showed a good fit of the bi-logit models to the data. The calculated area under the receiver operating characteristic (ROC) curve was between 0.7 and 0.9, indicating that the models were good and excellent predictors of the outputs compared to chance.

<table><caption>Table 1. Adoption of mineral and organic fertilizers</caption><thead><tr><th rowspan="2">Explanatory Variable</th><th colspan="4">Adoption of mineral fertilizer</th><th colspan="4">Adoption of organic fertilizer</th></tr><tr><th>Whole population</th><th>Farm type I</th><th>Farm type II</th><th>Farm type III</th><th>Whole population</th><th>Farm type I</th><th>Farm type II</th><th>Farm type III</th></tr></thead><tbody><tr><td>Intercept</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Cash</td><td>1.5E-05***</td><td>3.5E-05**</td><td>2.8E-05**</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Remittance</td><td>n.s</td><td>9.9E-04**</td><td>n.s</td><td>n.s</td><td>1.3E-04*</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Cereals</td><td>n.s</td><td>6.4**</td><td>-12.0***</td><td>n.s</td><td>-2.9**</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Legum land</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Cotton land</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Irrigation</td><td>34**</td><td>73**</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Livestock</td><td>0.6**</td><td>n.s</td><td>2.0**</td><td>n.s</td><td>n.s</td><td>0.46*</td><td>n.s</td><td>n.s</td></tr><tr><td>Size</td><td>0.2**</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Age</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Education</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Training</td><td>n.s</td><td>n.s</td><td>1.3*</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Traction</td><td>2.5*</td><td>n.s</td><td>16.5*</td><td>n.s</td><td>n.s</td><td>n.s</td><td>8.99*</td><td>n.s</td></tr><tr><td>Road Access</td><td>0.1***</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>0.09*</td><td>n.s</td><td>n.s</td></tr><tr><td rowspan="3">Hosmer and Lemeshow test</td><td>χ<fnref n="2" /></td><td>13.2</td><td>18.2</td><td>4.7</td><td>4.4</td><td>6.2</td><td>4.42</td><td>4.49</td><td>4.5</td></tr><tr><td>df</td><td>8</td><td>8</td><td>8</td><td>8</td><td>8</td><td>8</td><td>8</td><td>8</td></tr><tr><td>p</td><td>0.12</td><td>0.02</td><td>0.786</td><td>0.8</td><td>0.6</td><td>0.82</td><td>0.8</td><td>0.8</td></tr><tr><td>% correct prediction</td><td colspan="2">73.2</td><td>82.2</td><td>76.70</td><td>79.4</td><td>68.4</td><td>72.6</td><td>72.6</td><td>66.7</td></tr><tr><td>Area under ROC</td><td colspan="2">0.8</td><td>0.9</td><td>0.9</td><td>0.9</td><td>0.7</td><td>0.8</td><td>0.7</td><td>0.8</td></tr></tbody></table>

Note: Symbols *, **, and *** indicate statistical significance at 10%, 5, and 1% respectively. n.s means not significant

The results (Table 1 and Fig. 1) showed that the variables identified as affecting factors in the whole population's

<footnote n="1">¹ West African Science Service Center on Climate Change and Adapted Land Use (WASCAL)</footnote>

<footnote n="2">² Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH Zurich)</footnote>

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

<footnote n="4">⁴ CRP-Dryland Systems, c/o International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 950764 Amman 11195, Jordan,</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: boundia@gmail.com</footnote>
adoption and use of fertilisers had different affecting patterns across farm types. Most of the variables were type-specific affecting factors, influencing only a particular farm type (e.g., livestock in Fig. 1a). A type-specific factor might also affect more than one farm type (but not all farm types) and have an aggregate effect on the whole population (e.g., training in Fig. 1a). A common affecting factor would influence the whole population, as well as all farm types. However, it might be an aggregated affecting factor and might have no significant effect on individual farm types (e.g., cash income and cereals in Fig. 1b). Furthermore, the amplitudes and signs (direction of the effect) of the variables' coefficients varied. These results revealed the existence of responsive heterogeneity of farms for the adoption and use of fertilisers. This difference in the farmers' adoption and use behaviour in sustainable SNM practices was driven by their livelihood characteristics.

<figure><img src="image_116.png" /><figcaption>**Fig 1.** Determinants of mineral fertilizer use and adoption</figcaption></figure>

## 4 Conclusions

The results generally match those of past studies on the determinants of fertiliser adoption and use (Anley et al., 2007; Martey et al., 2014). Our study additionally showed the existence of heterogeneity in the adoption behaviour and use of sustainable SNM practices. This implies that effective policy interventions promoting the adoption of SNM practices should be designed according to the farming system type for leveraging farm incentives to adopt SNM practices. Farm design studies also need to account for the farmers' behavioural heterogeneity. This study's results can be used for scaling-out research and serve as a framework for policy intervention and further studies in the region.

*Acknowledgements*. Authors are grateful to German Federal Ministry of Education and Research (BMBF) which fully funded this research through West African Science Service Center on Climate Change and Adapted Land use (WASCAL)

## References

Anley, Y., Bogale, A. & Haile-Gabriel, A. (2007). Adoption decision and use intensity of soil and water conservation measures by smallholder subsistence farmers in Dedo District, Western Ethiopia. Land Degradation & Development 18(3): 289-302.

FAO, IFAD & WFP (2013). The State of Food Insecurity in the World 2013. The multiple dimensions of food security. Rome, Italy, Food and Agriculture Organization of the United Nations.

Ingram, J. S. I., Gregory, P. J. & Izac, A. M. (2008). The role of agronomic research in climate change and food security policy. Agriculture, Ecosystems & Environment 126(1-2): 4-12.

Martey, E., Wiredu, A. N., Etwire, P. M., Fosu, M., Buah, S. S. J., Bidzakin, J., Ahiabor, B. D. K. & Kusi, F. (2014). Fertilizer Adoption and Use Intensity Among Smallholder Farmers in Northern Ghana: A Case Study of the AGRA Soil Health Project. Sustainable Agriculture Research 3(1): 36.

Pimentel, D. & Burgess, M. (2013). Soil Erosion Threatens Food Production. Agriculture 3(3): 443-463. Sconnes, I. (1998). Sustainable rural livelihoods. A framework for analysis. IDS Working Paper 72.

Vlek, P. L. G., Kühne, R. F. & Denich, M. (1997). Nutrient resources for crop production in the tropics. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 352(1356): 975-985.
# A MODELING FRAMEWORK FOR DESIGNING AND ASSESSING MULTI-FUNCTIONAL AGRICULTURAL LANDSCAPES WITH SCENARIO ANALYSIS

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

## 1 Introduction

In order to insure the provision of food and services by agriculture while limiting its negative impacts, innovative agricultural systems have to be designed. The design of such systems has shown some limits in addressing regional and global issues. For instance, at the field scale, some cropping systems may fail to reach the objectives defined at the regional scale due to the low scaling integration and the spatial heterogeneity in the region. Model-based prototyping of agricultural landscapes can allow the impact assessment at regional level of drivers that act at field, farm and regional levels. In order to ascertain whether a combination of levers can drive agriculture towards sustainability, we designed a modeling framework, to explore successive steps of scenario development and assessment with indicators. To this end we introduce an approach based on several types of scenarios.

## 2 Materials and Methods

The method is built to i) understand the potential levels of sustainability that can be reached by the cropping system mosaics and ii) gain knowledge on the potential levers of change of the cropping system mosaics (Fig. 1). The framework guides the assessment of the consequences of several types of scenario, with a regional bioeconomic model, on the organization of cropping systems. Indicators are used to assess the cropping systems externalities at regional scale. An iterative approach is presented to guide the use of the model with different phases (Fig. 1): scenarios are built in a pre-modeling phase; they are implemented in a modeling phase for visualizing their consequences on the cropping system mosaics; the post-modeling phase assesses the contribution of the cropping system mosaics to sustainable development.

<figure><img src="image_117.png" /><figcaption>Fig.1. The modelling framework for building sustainable agricultural landscapes</figcaption></figure>

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

<footnote n="2">² Montpellier Sup Agro UMR System 2 place Viala, 24060 Montpellier, France</footnote>

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

<footnote n="4">⁴ INRA, UMR 211 Agronomie, F-78850 Thiverval-Grignon, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: pierre.chopin@antilles.inra.fr</footnote>
We built our framework around the MOSAICA regional bioeconomic model (Chopin *et al.*, submitted). MOSAICA optimizes the overall farmer's utilities by allocating cropping systems to their spatially located plots. The allocation process of cropping systems to plots, recorded in a geographical database, is driven by several types of constraints or objectives implemented at different spatial scales among which biophysical, economic, structure constraints at field, farm, sub-regional or regional scales. The optimization of farmer's utilities or other variables of the system produce new agricultural landscapes that are assessed with a set of sustainability indicators.

In our framework, the MOSAICA model is used with four different types of scenario for prototyping agricultural landscapes for one specific goal (such as energy self-sufficiency). First, short-term optimized scenario provides a target value by optimizing one given sustainability indicator (e.g. the production of biomass for electricity). Secondly, an exploratory scenario combines several types of levers (economic, agronomic...) to reach the target value defined before (the overall farmer's revenue is maximized). Thirdly, normative scenario is introduced to assess whether or not the target can be reached with the levers from the exploratory scenario. Fourthly, a long-term optimized scenario is parameterized to assess the relevance of levers to improve the long-term potential of the system (by optimizing the production of biomass with the levers selected). The levers that improve the state of the system for each objective, (increase food production, employment...) are selected and combined in a last exploratory scenario called "Go sustainable" scenario to improve the overall response of agricultural landscapes to sustainable development.

## 3 Results – Discussion

<table><caption>Table 1. Results for the energy self-sufficiency objective with the different type of scenario</caption><thead><tr><th rowspan="2">Sustainability objective</th><th rowspan="2">Indicator</th><th rowspan="2">Initial</th><th colspan="4">Type of scenarios</th></tr><tr><th>Short-term optimized</th><th>Exploratory</th><th>Normative</th><th>Long-term optimized</th></tr></thead><tbody><tr><td>Energy self-sufficiency</td><td>Potential production of electricity (MW)</td><td>30</td><td>52</td><td>56</td><td>56</td><td>93</td></tr></tbody></table>

We here present some results from the use of the framework in Guadeloupe, a French archipelago in the Caribbean for the energy self-sufficiency objective (Chopin *et al.*, to be submitted). The levers tested are the decrease of subsidies for sugar, the setting up of a biomass industry for electricity production and the addition of a crop energy activity for farmers. The response to the sustainability objective increases with levers (Table 1) that help achieve the objective of producing 56MW.yr⁻¹ and also improve the potential of response of the landscape in long-term scenario to 93MW.yr⁻¹.

<figure><img src="image_118.png" /><figcaption>Fig. 2. Evolution of responses to sustainable issues compared to the initial situation</figcaption></figure>

The "Go sustainable scenario" which combines all the relevant levers selected for each of the five objectives improves the system by increasing its contribution to each objective selected (Fig. 2). For instance, the response of the food self-sufficiency objective increased by 150% and the overall agricultural value doubled compared to the initial landscape due to the development of crop-gardening, in the south of Guadeloupe, and energy crops within the entire region.

## 4 Conclusions

The modeling framework guides the use of the regional model could be used in other regions to help identify the most appropriate levers to increase the response of agriculture to sustainable development. This holistic approach provides analysis of changes that occur at the regional, the farm and the field scale, and can highlight the evolution of externalities of cropping system mosaics in a quantitative and spatially explicit way.

## References

* Chopin, P., Blazy, J.-M., Guindé, Loïc., Doré, T. MOSAICA: a multi-scale bioeconomic model for the design and ex ante assessment of cropping system mosaics. Submitted to Agricultural Systems.

* Chopin, P., Blazy, J.-M., Guindé, Loïc., Wery, J., Doré, T. Designing multi-functional landscapes in agricultural regions: Application to the Guadeloupe Island. To be submitted to Global Environmental Change.
# Crop-livestock farming systems in Australia: what levels of integration result in different benefits?

Andrew D. Moore*±<fnref n="1" />, Lindsay W. Bell<fnref n="1" />, Dean T. Thomas<fnref n="1" /> & Andrew P. Smith<fnref n="1" />

## 1 Introduction

In Australian broadacre mixed farms, cropping and livestock enterprises are integrated to varying extents. The integration can range from the operation of otherwise separate activities within a single business through to tightly integrated systems in which legume pastures supply nitrogen, and weed and disease breaks, for crops and crop biomass provides a substantial proportion of the forage consumed by animals (Bell & Moore, 2012). Farmers' choices about whether and how to integrate crops and livestock will reflect a range of interacting objectives and constraints, including (i) matching production to land types with comparative advantage; (ii) exploiting resources derived from one enterprise in the other enterprise (e.g. nitrogen fixed by legume pastures or crops for forage); (iii) opportunities to allocate resources tactically between enterprises; and (iv) the need to maintain the soil resource in each paddock in the long term (Bell & Moore, 2012). We sought to elucidate the contribution of progressively greater levels of crop-livestock integration to each of these objectives by means of whole-farm biophysical simulation analysis.

## 2 Materials and Methods

Simulations of a representative mixed farm at Temora, Australia (Köppen climate Cfa) were constructed by linking the APSIM and GRAZPLAN biophysical models. The model configuration and modelled farm were similar to that in Moore (2014). Land resources were modelled as 3 soil types, one of which was non-arable (10% of farm area). Model runs were carried out with 6 different management systems that had successively greater levels of integration: (i) a cropping-only system involving rotations of wheat, canola and barley ("C"); (ii) a livestock-only system ("L") in which breeding ewes grazed a combination of perennial grass-legume, annual grass-legume and alfalfa pastures; (iii) a segregated mixed farming system ("S") in which the "C" and "L" management systems (54% and 46% of farm area) were allocated to the soil types better suited to each activity; (iv) a mixed farm with rotations of 3-5 year phases of legume-based pastures and grain cropson the better soil types ("R"); (v) a "strategically synchronised" mixed farm ("SS") in which the "R" management system was further integrated by allowing livestock to graze crop residues for a fixed period each summer and wheat crops during winter; and (vi) a "tactically synchronised" mixed farm ("ST") in which grazing of crop residues and dual-purpose crops (including canola) was both more frequent and more responsive to seasonal conditions. Stocking rates were adjusted so that the utilization rate of pastures was the same in all systems, but otherwise the enterprises in the modelled farms had similar levels of inputs. Farm gross margins were calculated using detrended historical price time series (updated from Bell & Moore 2011), and the downside financial risk of each farming system was measured using conditional value-at-risk based on the 20% of years with lowest gross margin. Simulated on-farm balances of greenhouse gases were calculated from the changes in soil C stocks, emissions of N₂O from soil and enteric methane using global warming potentials of 34 for methane and 298 for N₂O.

## 3 Results – Discussion

At Temora, a cropping-only farming system ("C") is more profitable on average than a livestock-only system ("L"), but the latter has lower risk (Bell & Moore 2011). The shift in expected farm gross margin as the system is changed from a naïvely allocated mixed farm to a "segregated" system ("M"→"S" in Figure 1a) shows that exploiting spatial variability in land resources is worth about $20 per farm hectare, and the segregated farming system has marginally less downside risk. The largest changes in profitability as the farm was increasingly integrated arise when crop-pasture rotations are introduced into the farming system (i.e. "S"→"R"); the rotational system captures more water (Figure 1b) and the yields of the wheat crops following alfalfa increase due to improved N supply. The value of production complementarities from integrated crop-livestock production in this region is well-recognised. However, the adoption of crop-pasture rotations also increases financial risk, owing to gaps in forage supply during transitions from crops to pastures and also due to occasional years where crop yields following alfalfa pastures are reduced due to lower water availability. Further integrating to a "synchronised" farming system ("SS") by allowing livestock to graze both crop residues in summer and growing crops in the winter, increases profit and reduces financial risk; however the main financial benefits arise from shifting from a "strategic" to a "tactical" approach to using crops as forage ("SS"→"ST"). As can be seen in Figure 2, the reason for this is that tactical grazing of cropping paddocks during February-April provides extra forage at the time of year when forageits supply is most limiting across the farm.

<footnote n="1">¹ Agriculture Flagship, CSIRO, Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Andrew.Moore@csiro.au</footnote>
<figure><img src="image_119.png" /><figcaption>**Fig. 1.** (a) Relationship between downside risk (measured using the conditional value-at-risk of gross margin) and long-term average gross margin (AU$), (b) farm-scale deep drainage, (c) farm-scale rate of change in soil C stocks and (d) within-paddock greenhouse gas balance for a simulated crop-livestock farms at Temora that operate with successively increasing levels of crop-livestock integration. Symbols for the farming systems are given in the text apart from “M”, which denotes a naïve mixture of the “L” and “C” systems with no differential allocation of land uses to soil types.</figcaption></figure>

<figure><img src="image_120.png" /><figcaption>**Fig. 2.** Changes in the sources of livestock forage as a mixed crop-livestock farm at Temora, NSW is managed with successively greater levels of integration of crop and livestock enterprises.</figcaption></figure>

The modelled livestock-only production system loses much less water as deep drainage than the cropping-only farming system (Figure 1b), and it retains more soil carbon even though its total net primary productivity is lower. Farming systems with crop-pasture rotations lose less water (Figure 1b) but lose more soil carbon (Figure 1c) than would be expected from the areas devoted to each enterprise: the deep-rooted alfalfa pasture phases capture water that is left behind by preceding annual crops, while the introduction of summer fallows between the pasture and cropping phases accelerates soil C decomposition. In-paddock net greenhouse gas emissions are higher for the livestock farming system due to a large contribution from enteric methane (Figure 1d). Each successive step toward crop-livestock integration increases in-paddock net greenhouse gas emissions, but for different reasons: while the GHG emissions increase from the separated to rotated farming system due to changes in the soil C balance, the further increases in in-paddock GHG emissions in the “SS” and then the “ST” systems are due to greater enteric methane from larger and more profitable flocks of sheep.

## 4 Conclusions

The functioning of integrated crop-livestock systems is notoriously difficult to understand owing to the number of biophysical interactions and the complexity of their management. The approach used here – analysing the differences between successive, smaller shifts in management – has given us insights into the advantages and disadvantages of crop-livestock integration of varying intensity that would not have been obtained had we simply compared single-enterprise and fully integrated farming systems. Environmental and resource management outcomes tended to differ among the levels of integration.

*Acknowledgements.* This work was co-funded by the Grains Research and Development Corporation (Australia), the Australian Government Department of Agriculture and CSIRO's Agriculture Flagship through the *Grain and Graze* program.

## References

Bell, L.W., Moore, A.D. (2011) Mixed crop-livestock businesses reduce price- and climate-induced variability in farm returns: a model-derived case study, World Congress on Conservation Agriculture 2011 Papers. ACIAR, Brisbane

Bell, L.W., Moore, A.D. (2012) Integrated crop-livestock systems in Australian broadacre agriculture: trends, drivers and implications. *Agricultural Systems* **111**, 1-12.

Moore, A.D. (2014) The case for and against perennial forages in the Australian sheep-wheat zone: modelling livestock production, business risk and environmental interactions. *Animal Production Science* **54**, 2029-2041.
# Co-Designing Organic Residue Recycling Chains in Off-Balance Regions

Tom Wassenaar ±<fnref n="1" /> & Jérôme Queste<fnref n="2" />

## 1 Introduction

An increasing number of (urbanizing) regions worldwide is increasingly out of balance, in terms of nutrient cycles and organic waste production and use. On the one hand, resulting land tenure and market mechanisms push farming systems of these regions to intensify. On the other hand, resulting social, regulatory and environmental constraints curb the potential for intensification through conventional practices. While crop farming systems of diminishing fertility remain largely dependent on fertilizer imports, organic waste flows out of animal farming systems – and other productive and consumptive systems – threaten ecosystems and socio-economic development. Synergetic connections between these systems, i.e. industrial symbiosis, carry the promise of unlocking development while increasing individual and overall resilience and sustainability.

The small and isolated territory of Réunion, with its fast growing population and standard of living, exemplifies such a region. The island imports large amounts of nutrients, be it to feed its population, its crops or its livestock. In the absence of significant nutrient exports the large structural excess keeps increasing. This hampers the island's development, while its agricultural sector remains largely dependent upon fertilizer imports. The agricultural recycling of raw livestock manure reaches the limit of its regulatory and practical capacity, while these unbalanced applications – with respect to crop needs – lead to very little fertilizer substitution. Beyond agriculture a range of other activities produce substantial amounts of organic waste, each with their own – and often contrasting – characteristics. Co-processing a propitious combination of waste sources may allow to elaborate organic or organo-mineral fertilizer and soil amendments with characteristics that meet both crop and farmer requirements. Within such an integrated waste management system cropping systems would thereby add a (waste) consumptive function to their productive functions. Far from a constraint, this carries the promise to increase their individual as well as the overall system's productive capacity. Designing such system changes calls for a science facilitated, both interdisciplinary and inter-sectorial, participatory process. We developed a method for co-designing realistic and accepted solutions and implemented it in Réunion in an attempt to kick-off an innovation process.

## 2 Method and Tools for the participatory design of recycling scenarios

The adoption of recycling “innovations” – even though the practice of Returning Organic Residues to Agricultural Land (RORAL) is as old as agriculture itself – by increasingly complex, integrated and globalized production systems is far from straightforward. Still more daunting is the definition of new practices adapted to the multiple (economic, social, societal and environmental) constraints of today’s world. Wassenaar et al. (2014) detail the epistemological basis of interdisciplinary research exploring the potential for favoring adoption of RORAL in off-balance regions. The proposed problem-solving approach is invariably built around very specific intervention possibilities that favor adaptation and uptake (Hagmann et al., 2002). Conventional technical science encompasses a systemic component in order to feed the co-design process with realistic, technically and agronomically valid ideas. Led by a „facilitator” social science group, the technical science group is then a constituent of the co-design process „following” its own technology, whereas other stakeholders’ motivation to participate is that „plausible promise” (initially) made by the R&D team to solve a real (farming) problem(Douthwaite et al., 2002). This implies the value-based choice by science of a technology to „follow”, i.e. to catalyze change. Accepting negotiation theory as a basis for organizing the participatory efforts, as argued by Leeuwis (2000), it becomes clear that the „facilitator” research group has a strong interest in having „involved actor” RORAL scientists associated with them: they provide the facilitator (who according to Leeuwis (2000) is not a neutral figure but in need of an active strategy, resources and a power-base to forge agreements) with credibility, insights and the capacity to fill knowledge gaps.

We developed and tested a method favoring collective learning through participatory design exercises. The crop farming systems’ demand driven, stepwise design process puts to work tools selected from among those developed for participatory research, as deemed appropriate to each individual step (e.g. problem trees, role playing games). The method comprises five steps: (1) establishing an initial, shared diagnostic of the current situation; (2) the exhaustive description of the characteristics of ideal fertilizer and soil amendment products for all potential uses followed by the analysis of their redundancy; (3) the formulation of hypothetical recycling chains manufacturing and distributing selected fertilizer/amendment products; (4) the combined and coupled representation of these production chains in development scenarios of the study region; (5) feasibility and sustainability assessments of the scenarios. Iterations among these latter steps will in many cases be required. This co-design process takes place at three distinct but

<footnote n="1">¹ UPR Recyclage et risque, CIRAD, Saint Denis, Réunion</footnote>

<footnote n="2">² UPR GREEN, CIRAD, Antananarivo, Madagascar</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: tom.wassenaar@cirad.fr</footnote>
interacting levels of participation: at a “technical” level ad hoc working groups of coopted members elaborate proposals which then constitute the basis for a consultation process at a “practical” level composed of selected representatives of all ground level stakeholder groups. Emerging orientations and problems are then discussed at an “institutional” level by representatives mandated by their institution or company.

In addition to the participatory research tools, in particular steps 4 and 5 of the co-design process call for systems modeling in order to conceptualize the system, to build a common understanding among stakeholders, to identify leverage points for interventions, to analyze different scenarios, to form the basis of decision support systems, to assist in stakeholder negotiations, to identify systems performance indicators and to facilitate impact assessments (Sayer and Campbell, 2002). Multi-agent modeling allows capturing farm/plant/site level knowledge elicited in the process, as well as representing and analyzing – at regional scale – prospective scenarios that constitute the outcome of that process. Coupled to biophysical models and global databases, such a model also allows simulating economic and environmental consequences, at local level and beyond, that contribute to the provision of relevant information for stakeholders feeding back into the process.

## 3 Application to Réunion

A project team including all major stakeholders (e.g. the main waste producers, collectors and processors; fertilizer industry; the farmer council and the federation of cooperatives) was built around a coordinating science core, legitimate to perform that function as an impartial stakeholder. All were convinced to participate through the proposed conflict- avoiding shift of paradigm from a waste disposal logic in favor of an agricultural demand focused approach. At the “practical” level, representatives of 12 target groups have been invited to a series of 5 workshops from mid-2011 to early 2014. A broad range of institutions was convened to steering committee meetings presided by a local and a national government official. The initial plausible promise – which put substantial accent on soil amendments, some little conventional processing techniques and the potential of some controversial waste sources – has been seriously altered during the co-design process. Largely as a result of local topographic, land tenure, agricultural, economic, regulatory and know-how constraints, but also factors like mistrust, envisaged new resulting recycling chains mainly focus on the production of fairly concentrated fertilizers. Soil amendments would continue to be provided to a structurally small market by improved existing recycling chains (green waste compost and sugar cane filter cake). Fertilizers would result from the co-composting of complementary organic wastes (layer manure with vinasse; poultry litter with pig slurry) using ground municipal green waste as a structuring agent. While vegetable gardening would consume one of these co-composts directly, the majority of the production would supply a fertilizer plant where they would be dried, supplemented and pelletized. Model simulations suggest that resulting fertilizers could potentially satisfy a large share of the major fertilizers demands: the sugar cane and vegetable gardening sectors. But their combined purchasing and application costs would be close to that of mineral fertilizer and the adaptation of policy instruments is required for these recycling chains to be viable.

## 4 Conclusions

RORAL research does not claim to come up with the solutions alone. It typically generates knowledge and tools to help generate and assess integrated solutions to complex problems. The Réunion proof of concept shows that it takes more expertise and parties to be able to identify effective and acceptable solutions favouring local recycling of organic residues in agriculture, representing a gain in sustainability for the region concerned without harming any of its constituent parts. Although successful, the proof of concept also highlights the importance of a careful planning of the participatory process" rhythm and time span. The consolidated definition of broadly accepted scenarios signaled the end of the participatory research process, but the innovation process continues. Qualitropic, a business and research cluster around the island's bioeconomy, currently seeks to develop and accompany industrial projects. Several factors hinder the implementation of such projects, among which the absence of technical references of the envisaged products" agronomic efficiency. Although on-going, establishing such references requires fairly long-term experiments.

*Acknowledgements.* This study was financially supported by the French Ministry of Food, Agriculture and Fisheries (without any involvement), special account for agricultural and rural development grant number 10004.

## References

Douthwaite, B., de Haan, N.C., Manyong, V. & Keatinge, D. (2002). Blending “hard” and “soft” science: the “follow-the-technology” approach to catalyzing and evaluating technology change. *Conservation Ecology*, **5**, 13.

Hagmann, J., Chuma, E., Murwira, K., Connolly, M. & Ficarelli, P. (2002). Success factors in integrated natural resource management R&D: Lessons from practice. *Conservation Ecology*, **5**, 29.

Leeuwis, C. (2000). Reconceptualizing participation for sustainable rural development: Towards a negotiation approach. *Development Change*, **31**, 931–959.

Sayer, J.A. & Campbell, B. (2002). Research to integrate productivity enhancement, environmental protection, and human development. *Conservation Ecology*, **5**, 32.

Wassenaar, T., Doelsch, E., Feder, F., Guerrin, F., Paillat, J., Thuriès, L. & Saint Macary, H. (2014). Returning Organic Residues to Agricultural Land (RORAL) – Fuelling the Follow-the-Technology approach. *Agricultural Systems*, **124**, 60–69.
# The farming system component of European agricultural landscapes

Erling Andersen ±<fnref n="1" />

## 1 Introduction

Agricultural landscapes are the outcome of combined natural and human factors over time. This paper explores the scope of perceiving agricultural landscapes as specific patterns of farming systems and landscape elements in specific biophysical and administrative endowments. The focus is on the farming systems component of the agricultural landscapes by applying a typology of farming systems to the sample farms of the Farm Accountancy Data Network and scaling up the results to the landscape level for the territory of the European Union (EU). The farming system approach emphasises that agricultural landscapes result from the social praxis of the farmers and takes into account the scale, intensity and specialisation of the agricultural production. From farming system design point of view, the approach can be used to set targets for explorative designs at the farm level. From a policy point of view, the approach offers handles to implement policies that design agricultural landscapes by targeting the farming system pattern.

## 2 Materials and Methods

For the purpose of this paper, an agricultural landscape is understood as a distinct pattern of farming systems and landscape elements in a specific biophysical and administrative endowment: An agricultural landscape = f(C(t), R(t), S(t), FS(t), STR(t)). Where C is climate, R is the administrative region, S the soils, FS the farming systems, STR the landscape elements and (t) the time. The data used as input for the analyses stems from the integrated project System for Environmental and Agricultural Modelling; Linking Science and Society (SEAMLESS). Firstly, the data offers a spatial framework developed to delineate spatial units with relatively homogeneous conditions for farming (Hazeu et al., 2010). Secondly, the data includes a typology of European farming systems including the three dimensions: a) the scale of production, b) the specialisation and land use and c) the intensity of farming (Andersen et al., 2007). Finally, the data links the spatial framework and the typology of farming systems based on a constrained optimisation model matching farm attributes and spatial characteristics subject to consistency constraints (Kempen et al., 2010). The integrated SEAMLESS database thus provides information on the share of the agricultural area of the spatial units managed by specific types of farming systems. The data can thus be used to map the farming component of the European agricultural landscapes, to describe the pattern of farming systems in each landscape and to calculate indicators of the spatial organisation of the farming systems. Spatial indicators are calculated for each agricultural landscape based on the habitat indicators from the BioBio project (biobio-indicator.org, 2012): The count of farming systems, the average size of land managed by the farming systems (farming systems patch size), the number of farming systems per 100 hectares (farming systems richness) and the evenness of the distribution of agricultural land between farming systems (farming systems diversity, Shannon index).

## 3 Results – Discussion

Tables 1 and 2 present selected results for four example landscapes from different parts of the European Union.

<table><caption>**Table 1.** Most important farming systems in four example landscapes and share of agricultural area (UAA) managed.</caption><thead><tr><th></th><th>Farming system</th><th>Share of UAA %</th></tr></thead><tbody><tr><td rowspan="3">AL1</td><td>Large scale, medium intensity, arable/cereal</td><td>14.6</td></tr><tr><td>mixed farms</td><td>14.3</td></tr><tr><td>Medium scale, medium intensity, arable/fallow</td><td>9.4</td></tr><tr><td rowspan="3">AL2</td><td>Large scale, medium intensity, arable/cereal</td><td>44.0</td></tr><tr><td>mixed farms</td><td>21.0</td></tr><tr><td>Large scale, high intensity, arable/cereal</td><td>5.7</td></tr><tr><td rowspan="3">AL3</td><td>Large scale, low intensity, sheep and goats</td><td>39.7</td></tr><tr><td>sheep and goats</td><td>27.9</td></tr><tr><td>Medium scale, low intensity, arable/cereal</td><td>5.6</td></tr><tr><td rowspan="3">AL4</td><td>Medium scale, medium intensity, dairy cattle/permanent grass</td><td>29.9</td></tr><tr><td>high intensity, dairy cattle/permanent grass</td><td>27.5</td></tr><tr><td>Medium scale, low intensity, beef and mixed cattle/permanent grass</td><td>10.9</td></tr></tbody></table>

AL1: South East France, AL2: Eastern Denmark. AL3: Eastern Scotland, AL4: Northern Austria

<footnote n="1">¹ Department of Geosciences and Natural Resource Management, University of Copenhagen</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: eran@ign.ku.dk</footnote>
<table><caption>**Table 2.** The diversity of European agricultural landscapes based on the farming system component: Four example landscapes and EU25 average.</caption><thead><tr><th></th><th>Farming systems count No.</th><th>Farming systems patch size hectare</th><th>Farming systems richness No. systems per 100ha</th><th>Farming systems diversity Shannon index</th></tr></thead><tbody><tr><td>AL1</td><td>38</td><td>2 440</td><td>0.041</td><td>2.8</td></tr><tr><td>AL2</td><td>36</td><td>12 991</td><td>0.008</td><td>2.0</td></tr><tr><td>AL3</td><td>32</td><td>19 405</td><td>0.005</td><td>1.9</td></tr><tr><td>AL4</td><td>17</td><td>549</td><td>0.180</td><td>1.9</td></tr><tr><td>EU25</td><td>27</td><td>1 297</td><td>0.161</td><td>2.2</td></tr></tbody></table>

The selected results give a first impression of the diversity of the agricultural landscapes of EU25 regarding farming systems present and the spatial organisation of the systems in the landscape. However, this is only a very small fraction of the information available. The farming system approach provides a link to the agricultural statistics in FADN with a large number of variables on farm structure, land management and economics of the farming systems. Furthermore, the spatial allocation to the landscape level enables linkages to information on soil and climate as well as on the socio-economic conditions for farming.

Rizzo et al., 2013 suggested improving the farming system design process by adding a landscape approach. The approach and results presented above provides a landscape approach to farming design in two ways: Firstly, in diverse landscapes the pattern of agricultural systems is equally important as the design of the individual systems. An additional landscape layer in farming system design focusing on the overall pattern of agricultural systems and the compatibility of different farming systems in the specific landscapes could provide a tool for top-down management of agricultural landscapes. For monitoring changes in agricultural landscapes, the farming system component is, together with the landscape elements, the most volatile factor. Using indicators about the farming systems thus enhance the options for detecting changes at an early stage. The detailed description of the farming system pattern also facilitates a discussion of farming systems contributing to the character of the agricultural landscape versus invasive farming systems. In a broader policy perspective, the understanding of the pattern of agricultural systems as a decisive factor in the making of agricultural landscapes enhances the possibilities to identify valuable agricultural landscapes. Furthermore, it provides a handle, i.e. farming systems, for targeting policies aimed at preserving and improving the agricultural landscapes as a public good. Relevant policy issues are for example High Nature Value farming and greening measures targeting the farming system level. Secondly, the detailed information on the pattern of farming systems at the landscape level provides additional input to the design process at the farm level. On the one hand, it will be possible to see the individual farming system in a broader context and to address the contribution of the re-designed system to improvement or maintenance of the agricultural landscape. On the other hand, it will be possible to systematically use the information on agricultural systems within the same agricultural landscape to optimise the environmental and economic performance of the farming system in the design process. Or it will be possible to use the information to set targets for explorative approaches that goes beyond the present farming system.

## 4 Conclusions

The pattern of farming systems is an important factor the constitution and functioning of agricultural landscapes. The method presented in the paper shows that the farming system component can be mapped and be used to describe the characteristics and the spatial organisation of the agricultural landscapes. From farming systems design point of view, the presented approach provides handles to set targets for explorative approaches in the design process at the farm level. Furthermore, it provides options to link farming system design and design of agricultural landscapes in policy making targeting patterns of farming systems at the landscape level.

*Acknowledgements.* The paper is based on data from the SEAMLESS Association integrated database and includes aggregated data from EU-FADN - DG AGRI L-3 and JRC/MARS Data Base - EC - JRC.

## References

* Andersen, E., Elbersen, B., Godeschalk, F. & Verhoog, D. (2007). Farm management indicators and farm typologies as a basis for assessments in a changing policy environment. *Journal of Environmental Management* **82**(3), 353-362.

* Hazeu, G., Elbersen, B., Andersen, E., Baruth, B., van Diepen, K. & Metzger, M. (2010). A biophysical typology for a spatially-explicit agri-environmental modelling framework. In F. Brouwer & M.K. Van Ittersum (Eds.), *Environmental and Agricultural Modelling: Integrated Approaches for Policy Impact Assessment* (pp. 159-188). Springer, Dordrecht, Heidelberg, London, New York.

* Kempen, M., Elbersen, B., Staristsky, I., Andersen, E. & Heckelei, T. (2010). Spatial allocation of farming systems and farming indicators in Europe. *Agriculture, Ecosystems and Environment* **142**(1-2), 51-62.

* Rizzo, D., Marraccini, E., Lardon, S., Rapey, H., Debolini, M., Benoît, M. & Thenail, C. (2013). Farming systems designing landscapes: land management units at the interface between agronomy and geography. *Danish Journal of Geography* **113**(2), 71-86.
# FARMING SYSTEM DYNAMICS AND THEIR DRIVERS IN MEDITERRANEAN LANDSCAPES: A CASE STUDY ON THE VAUCLUSE DEPARTMENT BASED ON AGRICULTURAL CENSUS DATA (PACA REGION, FRANCE)

Marta Debolini *±1 & Martine Guerif 1

## 1 Introduction

Mediterranean agricultural landscape have undergone strong changes on the last decades, such as agricultural intensification in the most fertile plain and coastal areas, extensification on the internal hilly and low-productive zone, until marginalization and abandonment on the more remote regions (Nainggolan et al., 2012; Cots-Folch et al., 2009). These dynamics caused in some cases environmental impact and they can affect ecosystem services fulfilment (Garcia-Ruiz & Lana-Renault, 2011; Hill et al., 2008). In most cases, these dynamics have been evaluated through land use change studies, using land cover as a proxy of agricultural systems.

In this work, we aim to implement a land system approach, combining land use and its management through various indicators in order to identify farming system trajectories occurring on the last 20 years and their drivers, to propose possible future evolution and alternative scenarios.

## 2 Materials and Methods

The applied methodology was structured on three main phases. The first one consisted on (1) the characterization of the existing farming systems on the study area through principal component analysis and cluster analysis, in order to create some farms' classes considering the main land use and management indicators. The exploited database is the national agricultural census at individual farm level, which is an exhaustive and temporally extended base on farm management, structure and practices. From this database, we extract the farms belonging to the Vaucluse department, obtaining a whole sampling of 7723 farms in 2000 (around 123000 ha) and 5850 farms in 2010 (around 113430 ha). The considered variables for the farming system classification were: percentage of each cropping system on the farm, percentage of irrigated crops, farm dimensions (cultivated area, UAA), presence of quality labels, livestock quantity, farm work units, farmers' age. The obtained classification was discussed with different stakeholders on the study area (i.e. producers associations, agricultural chamber) in order to validate it. Then, we carried out (2) an analysis of the recent trajectories on farming systems: the characterization of farming system has been realized for two time laps (2000 and 2010). This allowed tracing some farming system trajectories, identifying areas where different change processes took place, like intensification or specialization of agricultural system. Finally, (3) the detected trajectories will be related to a set of variables through spatial statistical models, going from agro-pedoclimatic indexes of feasibility for each crop, to the prices dynamics and the different agricultural and environmental policy applications. The objective is to identify possible driving factors of changes and to propose future evolutions of local farming systems and alternative scenarios. This last work phase is currently ongoing. The proposed drivers can be classified among five main categories: geographic (e.g. distance from routes or cities, distance to water sources), topographic (e.g. slope, exposition), agronomic (eco-climatic indices of feasibility for each crop), economic (e.g. cadastral value of the parcels, rentability of the crops), planning factors (e.g. existence of protected areas, building or non-building areas).

We applied the methodology on the Vaucluse department (PACA Region, France), which is a typical Mediterranean region characterized by a strong presence of perennial crops, such as vineyards and orchards (currently around 60% of the total UAA).

## 3 Results and discussion

From the cluster analysis, we obtained nine classes of farming systems in the study area: AOP vineyards, non AOP vineyards, aromatic crops farms, cereal farms, fodder crops and livestock farms, grapes associated with orchards or small percentage of vineyards, orchards, vegetables farms and nursery farms. Table 1 shows the average values of some relevant variables estimated for each class of farming system on 2010. The nine classes remain rather stable on the two observations (2000 and 2010), unless some tendency for concentration on the predominant crops for some farming systems.

Concerning the farming system changes, Fig.1a shows the amount of surfaces for each farming system on 2000 and 2010. The results show a relevant increase on AOP vineyards and the decrease of the less specialized and rentable farming systems, such as cereal cultivation and table grapes usually cultivated on the mountain areas, which

<footnote n="1">¹ INRA UMR EMMAH, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, Domaine St. Paul 84914 Avignon Cedex 9</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: marta.debolini@paca.inra.fr</footnote>
progressively reduce their surface. A specific dynamics observed on the study area was the increase of the number and associated surface of the fodder and livestock farms. This corresponds to the new horse farms set up on the last few years in the region, associated to agritourism. In order to understand the farming system trajectories, a circular plot was produced starting from the contingency matrix for 2000 and 2010. The circular plot obtained is showed on Fig.1b. The observed trajectories have been classified in three main groups: (1) from non AOP vineyards to AOP vineyards, which can be considered as a specialization of the farming system; (2) from cereals, orchards or mixed farming systems to AOP vineyards, which in some cases can be considered as an intensification of the farming system; and (3) from cereal and orchards to fodder crops and livestock, which is a specific type of specialization for the study area, as previously specified.

<table><caption>**Table 1.** Average values of the variables estimated through the cluster analysis for each class of farming system.</caption><thead><tr><th>Class</th><th>UA<br>A (ha)</th><th>Work<br>units</th><th>Livestock</th><th>Cereals (%)</th><th>Aromatic crops (%)</th><th>Fodder crops (%)</th><th>Vegetables (%)</th><th>AOP Vineyards (%)</th><th>Non AOP Vineyards (%)</th><th>Grapes (%)</th><th>Orchards (%)</th><th>Nursery (%)</th><th>Irrigation (%)</th></tr></thead><tbody><tr><td>1</td><td>18</td><td>2226</td><td>0</td><td>4</td><td>0</td><td>1</td><td>1</td><td>79</td><td>2</td><td>2</td><td>3</td><td>1</td><td>4</td></tr><tr><td>2</td><td>9</td><td>1040</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>19</td><td>66</td><td>5</td><td>4</td><td>0</td><td>4</td></tr><tr><td>3</td><td>44</td><td>1435</td><td>3</td><td>10</td><td>59</td><td>8</td><td>2</td><td>8</td><td>0</td><td>0</td><td>4</td><td>0</td><td>5</td></tr><tr><td>4</td><td>36</td><td>1528</td><td>1</td><td>81</td><td>0</td><td>2</td><td>7</td><td>2</td><td>0</td><td>0</td><td>3</td><td>1</td><td>12</td></tr><tr><td>5</td><td>41</td><td>1320</td><td>93</td><td>10</td><td>0</td><td>80</td><td>2</td><td>2</td><td>0</td><td>0</td><td>4</td><td>0</td><td>18</td></tr><tr><td>6</td><td>11</td><td>2055</td><td>0</td><td>1</td><td>0</td><td>0</td><td>3</td><td>18</td><td>1</td><td>53</td><td>16</td><td>0</td><td>36</td></tr><tr><td>7</td><td>11</td><td>2489</td><td>0</td><td>1</td><td>0</td><td>0</td><td>2</td><td>6</td><td>0</td><td>1</td><td>83</td><td>0</td><td>40</td></tr><tr><td>8</td><td>7</td><td>2623</td><td>0</td><td>3</td><td>0</td><td>0</td><td>75</td><td>1</td><td>0</td><td>0</td><td>3</td><td>0</td><td>72</td></tr><tr><td>9</td><td>4</td><td>3492</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>2</td><td>0</td><td>0</td><td>1</td><td>93</td><td>81</td></tr></tbody></table>

<figure><img src="image_121.png" /><figcaption>**Fig. 1.** (a) Changes in terms of surfaces from 2000 to 2010 for each farming system class (all farms) and (b) Circular plot of the farm trajectories on the study area (2500 farms).</figcaption></figure>

## 4 Conclusions

The methodology applied in this study shows a good capacity to characterize the existing types of farming systems in the study area, and understand the dynamics currently acting. Next development of the study (currently ongoing) will be the test of some possible drivers in order to explain the detected trajectories. Moreover, the data processing will be extended to the 1988 census in order to have a greater time laps and some long terms trajectories. This will be the basis for a modeling process aimed to propose future possible scenarios for agricultural land use changes.

*Acknowledgements.* For the data access, the authors benefit from the services of the “Centre d’accès sécurisé distant” (CASD) dedicated to authorized researchers further to the permission from the “Comité français du Secret Statistique” (CSS).

## References

Cots-Folch R., Martinez-Casasnovas J.A. & Ramos M.C. (2009). Agricultural trajectories in a Mediterranean mountain region (Priorat, E Spain) as a consequence of vineyard conversion plans. *Land Degradation & Development*, **20**, 1-13.

Garcia-Ruiz J.M. & Lana-Renault N. (2011). Hydrological and erosive consequences of farmland abandonment in Europe, with special reference to the Mediterranean region – A review. *Agriculture, ecosystems and Environment*, **140**, 317-338.

Hill J., Stellmes M., Udelhoven Th., Roder A. & Sommer S. (2008). Mediterranean desertification and land degradation. Mapping related land use change syndromes based on satellite observations. *Global and Planetary Change*, **64**, 146-157.

Nainggolan D., de Vente J., Boix-Fayos C., Termansen M., Hubacek K. & Reed M.S. (2012). Afforestation, agricultural abandonment and intensification: Competing trajectories in semi-arid Mediterranean agro-ecosystems. *Agriculture, ecosystems and Environment*, **159**, 90-104.
# Meta-analysis of yield gap explaining factors and opportunities for alternative data collection approaches

Eskender Beza ±1,2, João Vasco Silva 2, Lammert Kooistra 1 & Pytrik Reidsma 2

## 1 Introduction

The yield gap concept is based on production ecological principles and can be estimated as the difference between a benchmark potential yield and the actual yield level, which indicates the potential to improve agricultural production in a specific location (van Ittersum et al., 2013).

Many studies have examined yield gaps at the scale of the region or agro-climatic zone, using aggregated data (e.g. Mueller et al., 2012; Neumann et al., 2010) and these are helpful in quantifying the scope for yield improvements in a broad sense (Oliver & Robertson, 2013). Estimating the yield gap is the first step, but especially relevant is to reveal the underlying explanatory factors contributing to the yield gap. As decisions are made by farmers, farm level yield gap analysis specifically contributes to better understanding, and provides entry points to increase production levels in a specific farming system. However, explaining the yield gap at the farm or field level is often constrained by the lack of necessary data; obtaining information about farm management, crop management, farm and farmers characteristics for a large number of farms is costly and time-consuming. The main objective of this research was to assess data availability for yield gap analysis, and to identify the most often considered and explaining crop yield gap factors. In addition, opportunities for bottom-up data collection approaches like crowdsourcing have been assessed for collecting the identified most often explaining factors of yield gaps.

## 2 Materials and Methods

As part of this research, a meta-analysis of different yield gap studies (50 scientific articles) has been performed. A detailed literature search of yield gap studies was conducted and factors considered and explained the yield gap in each of the 50 selected studies were identified. The identified factors were compiled and included into a database under four main categories (management, edaphic, farm characteristics and socio-economic). In total 270 records with unique identifiers (IDs) were included into the data base and used for subsequent analysis. One record is a unique combination of location, crop, year, benchmark yield estimation method and yield gap explanatory method. Prior to the meta-analysis, factors in the main categories were further grouped into their respective sub-groups (e.g., for the management category resulting in 7 sub-groups like fertilization, planting, etc.). By using the compiled information, the most often considered and explaining factors of the yield gap were identified based on the percentage of unique records in the database. Besides a global comparison of yield gap explaining factors, differences between continents and crops were also analysed.

## 3 Results – Discussion

Most yield gap studies are performed in Africa and Asia, and focus on rice, wheat and maize (Table 1). In general, management and edaphic factors are more often considered to explain the yield gap compared to farm characteristics and socio-economic factors (Fig. 1). However, when considered, both farm characteristics and socio-economic factors often explain the yield gap. Fertilization and soil fertility factors are the most often considered management and edaphic factors. In the fertilization group, factors related to quantity (e.g., N fertilizer quantity) are more often considered compared to factors related to timing (e.g., N fertilizer timing). However, when considered, timing explains the yield gap more often. At the global level, in general explaining percentage for edaphic factors is lower compared to other categories. For specific continents this may be different; in Africa for example the relevance of the edaphic category to explain yield gaps is larger.

Explaining factors clearly vary between continents and crops. For example, while soil fertility is considered relatively often both in Africa and Asia, it is often explaining in Africa, but not in Asia. Looking at crop specific yield gap explaining factors; fertilization, land preparation and crop protection factors often explain the rice yield gap in Africa, whereas crop characteristics and planting factors explain the rice yield gap in Europe. Fertilization, crop characteristics and planting explain maize yield gap in Africa, whereas fertilization, crop characteristics and irrigation often explain maize yield gap in Asia.

Although the data included in yield gap analysis depend on the objective, knowledge of explaining factors, and method data availability is a major limiting factor. Alternative bottom-up data collection approaches (e.g. crowdsourcing) can be used to overcome this and improve yield gap analysis. Farmers communities could be motivated

<footnote n="1">¹ Laboratory of Geo-Information Science and Remote Sensing, Wageningen University</footnote>

<footnote n="2">² Plant Production Systems group, Wageningen University</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: eskender.beza@wur.nl</footnote>
to provide data on relevant factors including the timing aspect while receiving advice for improvement of crop management in return.

<table><caption>**Table 1:** Summary of the studies included into the database</caption><thead><tr><th rowspan="2">Type of crop</th><th rowspan="2">Studies included</th><th colspan="8">No. of studies per continent</th></tr><tr><th>Africa</th><th>Asia</th><th>Europe</th><th>Global</th><th>N. America</th><th>S. America</th><th>Australia</th></tr></thead><tbody><tr><td>Rice</td><td>18</td><td>7</td><td>7</td><td>2</td><td>2</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Wheat</td><td>14</td><td>-</td><td>4</td><td>3</td><td>2</td><td>2</td><td>1</td><td>2</td></tr><tr><td>Maize</td><td>17</td><td>4</td><td>6</td><td>1</td><td>2</td><td>3</td><td>1</td><td>-</td></tr><tr><td>Soybean</td><td>4</td><td>-</td><td>2</td><td>-</td><td>1</td><td>-</td><td>1</td><td>-</td></tr><tr><td>Cassava</td><td>2</td><td>1</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Banana</td><td>2</td><td>2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Others¹</td><td>8</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

1: Millet, Tomato, Peanut, Quinoa, Sunflower, Sugarcane, Mango and Potato

<figure><img src="image_122.png" /><figcaption>**Fig.1.** Percentage of considered and explaining factors across all studies for management (a), edaphic (b), farm characteristics (c) and socio-economic (d) sequenced based on explaining percentage</figcaption></figure>

## 4 Conclusions

* Most yield gap analysis studies concentrate on considering factors from one or two categories (Fig.1). The reason for the less consideration of a specific factor for explaining the yield gap might be lack of availability of data. This information gap could be filled by using **bottom-up data collection approaches like crowdsourcing** which might help to collect more of the explanatory factors at the farm level.

* Information related to quantity (e.g. N fertilizer quantity, irrigation amount and no. of weeding operations) is more often collected than timing (e.g. N fertilizer timing, irrigation timing and timing of weeding). However, it would be important that data on timing is also collected. Crowdsourcing based methods (e.g. farmers send timing information via SMS) could be a good way to acquire real-time information about timing of management activities.

* The results of this study show that biophysical factors were most often considered compared to farm characteristics and socio-economic factors. However, when considered both farm characteristics and socio-economic factors often explain the yield gap; thus future yield gap studies might need to collect and consider farm characteristics and socio-economic factors to explain the yield gap as well.

* Explaining factors are clearly spatial explicit and thus region specific approaches for data acquisition could be considered by making a selection of relevant factors region or crop specific.

## References

* Mueller, N.D., Gerber, J.S., Johnston, M., Ray, D.K., Ramankutty, N. &Foley, J.A. (2012). Closing yield gaps through nutrient and water management. *Nature*, **490**, 254-257.

* Neumann, K., Verburg, P.H., Stehfest, E. &Müller, C. (2010). The yield gap of global grain production: A spatial analysis. *Agricultural Systems*, **103**, 316-326.

* Oliver, Y.M. &Robertson, M.J. (2013). Quantifying the spatial pattern of the yield gap within a farm in a low rainfall Mediterranean climate. *Field Crops Research*, **150**, 29-41.

* van Ittersum, M.K., Cassman, K.G., Grassini, P., Wolf, J., Tittonell, P. & Hochman, Z. (2013). Yield gap analysis with local to global relevance—A review. *Field Crops Research*, **143**, 4-17.
# Sustainable extensification—breathing new life into Africa's sleeping giant

Mary Ollenburger *±1*, Katrien Descheemaeker <fnref n="1" />, Todd Crane <fnref n="2" /> & Ken Giller<fnref n="1" />

## 1 Introduction

The World Bank's 2009 study "Awakening Africa's Sleeping Giant" highlights the potential of the West-African Guinea Savannah zone for improved agricultural production due to its favorable agroecology and land availability. In the Guinea Savannah areas of Southern Mali, while research has focused on intensifying agricultural production, yields for major crops have stagnated over the past 20 years (Foltz et al. 2012). We used a simple farm model to analyze intensification scenarios and their effects on livelihood indicators to understand what incentives farmers have for intensifying or expanding crop production.

Most modeling studies use a few case study or representative farms to explore detailed scenarios of presumed future conditions. The complex and data-demanding nature of such models necessarily reduces the number of farms and scenarios considered. We instead limited the scenario complexity and information required for each farm in order to consider whole farm populations and wide ranging scenarios, of which one is presented here. This allows us to define a solution space: the range of plausible outcomes under a set of explicit assumptions.

## 2 Materials and Methods

The study area consists of three villages (Sibirila, Dieba, and Flola) in the district of Bougouni, region of Sikasso, in the southern part of Mali. Populations range from 575 to 1159 per village. These villages are in the Guinea Savannah zone, with an average annual rainfall of about 1200 mm falling between the months of May and October. This area forms part of the action zone of the Compagnie Malienne pour le Développement du Textile (CMDT), which provides inputs for cotton and maize on credit.

For the purpose of this study, a household was defined as "a group of people who manage land together." Because Malian farm families are often multi-generational and polygamous, these households range greatly in size, from three to 86 in this population. Farm characterization was based on simple farm characteristics including family size and land allocations by crop. This information was collected for all 109 farms in the study villages in 2013 with assistance from the CMDT. Scenarios were developed using yield information from two sources: first, the Africa RISING Mali Baseline Survey (ARBES), conducted in 2014 in 8 villages in the district of Bougouni including the study villages, and second, a household characterization survey covering two of the three study villages. Data from this second survey was also used to calculate input use and associated costs, crop price information, and proportions of each crop used or sold. Using this data we performed a scenario analysis of yield gap narrowing for each farm. We first characterized the current baseline using data on crop area allocations for the population of households, multiplied by median yields. Then we examined a scenario using the same crop area allocations, but increasing yields of three major crops—cotton, maize, and groundnut—to their 90th percentile values. For each scenario we calculated total farm production, food self-sufficiency in terms of calories produced per adult equivalent, and gross margins, also per adult equivalent. To calculate gross margins we used prices at harvest for grain produced and costs for purchased inputs including fertilizers and pesticides. We performed these calculations first based on total production and then based on the reported proportions sold. Food self-sufficiency scores were based on the median proportion of each crop consumed.

## 3 Results - Discussion

Crop production in the study area is diverse, as seen in Figure 1. Cotton and groundnut are the key crops for household income, while maize is a staple food crop. The relative area contributions of these crops vary with farm size: the contribution of groundnut is relatively more important for very small and very large farms than for mid-sized ones. Larger farms tend to grow a greater variety of crops, while small farms tend to devote their limited available land to cotton, maize, and groundnut. Those farmers who do not grow cotton tend to grow more sorghum and less maize, as sorghum, while its yields are lower, is less dependent on external inputs than maize.

Of the 109 households in our dataset, 35% (38 households) did not satisfy their calorie requirements from the median consumed fraction of farm production, while 83% satisfied at least 80% of their requirements. Varying the percentage of crops sold only changed the food self-sufficiency status of 3 households. While the majority of food-deficient households are small families with small amounts of land, there are also a few farms with large areas and very large families, resulting in small land area per capita. Gross margins for crop production are generally low. Based on the

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

<footnote n="2">² International Livestock Research Institute</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: mollenburger@gmail.com</footnote>
median percentages sold, and with median yields, the total annual income from crop production averages only 79 USD per active household member per year (ranging from USD -35 to USD 460). Hence, for all but a few households, income is well below the extreme poverty threshold of USD 1.25/day. When food consumed by the household is included, annual incomes range from 31 to 537 USD per active member, with a mean of 203 USD.

The near-term best-case scenario of increasing productivity of the three key crops from median to 90th percentile yields entails a productivity improvement from 486 to 1020 kg ha⁻¹ for groundnut, from 1600 to 2530 kg ha⁻¹ for maize and from 930 to 1530 kg ha⁻¹ for cotton. In this case, 19 households show a substantial improvement in food self-sufficiency, moving from less than 90% food self-sufficient to over 100%. Only 9 households remain deficient in calories with current consumption patterns. Income from crops ranges from USD -19 to USD 941 (mean USD 186) when considering only the sold fraction, and USD 38 to 1102 (mean 358) when including all production (Figure 2).

<figure><img src="image_123.png" /><figcaption>**Fig. 1.** Crop area allocations for 109 farms in three villages in Bougouni district in southern Mali. Other crops include sorghum, millet, fonio, and cowpea</figcaption></figure>

<figure><img src="image_124.png" /><figcaption>**Fig. 2.** Food self-sufficiency (a) and gross margin from crops when including all production (b) for 109 farms in three villages in Bougouni district in southern Mali. Dark bars are at 50th percentile yields for all crops, and light bars at 90th percentile yields for cotton, maize, and groundnut. In (a) the horizontal line represents full food self-sufficiency, while in (b) it represents the USD 1.25/day limit for extreme poverty. Farms are ranked from smallest to largest as in Figure 1.</figcaption></figure>

## 4 Conclusions

From this analysis we can see that there are limited incentives for farmers to intensify within their current crop production systems. For some farmers, increasing productivity has a significant impact on their food self-sufficiency status, and thus may be attractive. However, for those who are already food self-sufficient, even if they achieve near-highest farmer yields for the area they are unlikely to move out of extreme poverty by relying on income from crop production. Larger changes such as dramatic yield jumps, changes in price and cost structure, or reducing labor constraints so as to allow for expansion of crop production area will be needed in order to substantially improve farmer livelihoods through crop production.

*Acknowledgements.* The CMDT office in Bougouni assisted in collecting crop area allocation data. Household characterization survey data collected by Wageningen University as part of the AfricaRISING project, funded by USAID.

## References

* Morris, M., H.P. Binswanger-Mkhize, and D. Byerlee. (2009) Awakening Africa's Sleeping Giant. World Bank. 218pp

* Foltz, J.D., U.T. Aldana, and P. Laris. (2012). The Sahel's Silent Maize Revolution: Analyzing Maize Productivity in Mali at the Farm-level. NBER Working Paper No. 17801 26pp.

* Azzarri, Carlo; Haile, Beliyou; Howard, Patrice; Roberts, Cleo, 2014, "Mali Africa RISING Baseline Evaluation Survey", International Food Policy Research Institute
# Faming systems analysis and land policy: The case of the *Office du Niger* area in Mali

Laurence Roudart *±1* & Benoît Dave 1

## 1 Introduction

Since the 2000s, the Government of Mali has allocated large tracts of land in the *Office du Niger* area to new investors (Cotula *et al.*, 2009; Hertzog *et al.*, 2012). Up to now, the irrigated area there has been cropped by family farmers almost exclusively (Bélières *et al.*, 2011). Part of the family farmers asked to be recognized as investors by paying an annual contribution to an investment fund aimed at financing new irrigation facilities for family farmers (SEXAGON, 2010). This paper uses the concept of farming system to analyze (i) the conditions under which such a proposal could be feasible from an economic viewpoint and (ii) various scenarios regarding such an alternative land policy combined with a credit policy.

## 2 Materials and Methods

Drawing on Chombart de Lauwe and Poitevin (1957), we conceived a farming system as the combination of production factors and production activities on a farm. We used this definition in a field survey carried out in 2011 with 380 family farm managers living in 19 villages spread over 5 sectors of the *Office du Niger* area. This survey relied mainly on a questionnaire including both closed-ended and open-ended questions, and aiming to analyse the agricultural activities carried out for the whole family under the responsibility of the family head, thus excluding agricultural activities whose incomes accrue to sub-groups or individuals within the family (Ancey, 1975; Gafsi *et al.*, 2007).

Each of the 380 interviews produced detailed technical and economic information on farm land, labour, equipment, buildings, crops management, livestock management, as well as utilizations of the farm produce (own-consumption, sale, seed reserve...). Some of the data thus collected are numerical and constitute a base of approximately 140,000 data. Others are qualitative and relate primarily to the reasons that farmers allege to explain their practices.

Twelve categories of farming system were distinguished according to the combination of equipment type (3 types: manual, animal-drawn, motorized) and cropping activities type (4 types: wet-season rice only, wet-season rice and dry-season rice, wet-season rice and dry-season vegetables, wet-season rice and dry-season rice and dry-season vegetables).

Peasant families are complex entities in this area: they can include between 1 and 10 households, each having between 1 and 4 maternal cells: we call maternal cell the group formed by a mother and her children. The collected data led to distinguish eight family types according to the number of maternal cells: from 1 to 8. For each farming system category cum family type, we developed an empirical descriptive model representing the variation of the net added-value according to the area cultivated by the family, for three added-value levels: low (20th percentile of our sample), median and high (80th percentile of our sample) (Roudart, 2001) (Fig. 1).

<figure><img src="image_125.png" /><figcaption>Consumption needs 20th percentile added-value median added-value 80th percentile added-value</figcaption></figure>

<caption>**Fig. 1.** Family Farm Net Added-Value and Family Consumption Needs Depending on the Cropped Area</caption>

<footnote n="1">¹ Center for the Study of International Cooperation and Development, Faculty of Social and Political Sciences, UniversitéLibre de Bruxelles</footnote>

<footnote>* Speaker</footnote>

<footnote>‡ Corresponding author: lroudart@ulb.ac.be</footnote>
## 3 Discussion

### i) Conditions of economic feasibility of the land policy proposal from the SEXAGON

The analysis of the models shows that family farms can contribute to land investments under several conditions: 1) have an animal-drawn equipment at least (which excludes farms with manual tools only); 2) perform at least one dry-season crop in addition to wet-season rice (which requires access to sufficient irrigation water in the dry season); 3) have irrigated areas close to the maximum cultivable by the family labor. Under these conditions, the land contribution can range from 50,000 to 75,000 CFA Francs/ha/year, while leaving room for other investments (Dave et al., 2012).

### ii) Various scenarios of land and credit policies

Based on previous analyses, one can test several scenarios of irrigated land allocation policies. The scenario 0 corresponds to the situation in 2010. All other scenarios assume that the proportions of family types and of farming system categories remain the same as in 2010, and that the dry-season irrigated area remains the same as in 2010: indeed, during this season, present water withdrawals for irrigation of the Office du Niger area already barely enable to maintain a proper flow for downstream populations in years of average rainfall (Schüttrumpf & Bokkers, 2008). Scenarios 1, 2 and 3 rely on the assumption that the irrigated area is extended to nearly 134,000 ha in the wet season (against 86,000 ha in 2010), which is the maximum area considering the current carrying capacity of primary irrigation canals (Sangaré, 2010). The scenarios 1a, 2a and 3a add to scenarios 1, 2 and 3 the hypothesis that a credit policy to purchase inputs allows all farmers to achieve wet-season rice and dry-season rice yields equal to the 80th percentile yields achieved in 2010. Finally, the scenarios 4 and 4a assume that 100% of family farms in 2010 reach their maximum area given their family labor. This implies an enlargement of primary irrigation canals but it remains consistent with several estimates of the irrigable area in the wet season (Couture et al., 2002).

<table><caption>**Table 1.** Impacts of various land and credit policies scenarios in the Office du Niger area</caption><thead><tr><th></th><th>Scenario 0</th><th>Scenario 1</th><th>Scenario 1a</th><th>Scenario 2</th><th>Scenario 2a</th><th>Scenario 3</th><th>Scenario 3a</th><th>Scenario 4</th><th>Scenario 4a</th></tr><tr><th></th><th>Baseline situation in 2010</th><th colspan="2">Farm areas as in 2010</th><th colspan="2">Maximum areas for part of the farms, given family labor in 2010</th><th colspan="2">Maximum areas for part of the farms, assuming that family labor is maximum</th><th colspan="2">Maximum areas for 100% of farms, given family labor in 2010</th></tr></thead><tbody><tr><th>Total cropped area (ha)</th><td>86,000</td><td>134,000</td><td>134,000</td><td>132,532</td><td>132,532</td><td>134,785</td><td>134,785</td><td>211,263</td><td>211,263</td></tr><tr><th>Number of family farms</th><td>22,500</td><td>35,000</td><td>35,000</td><td>22,500</td><td>22,500</td><td>22,500</td><td>22,500</td><td>22,500</td><td>22,500</td></tr><tr><th>Number of people living in farms</th><td>363,870</td><td>566,021</td><td>566,021</td><td>363,870</td><td>363,870</td><td>363,870</td><td>363,870</td><td>363,870</td><td>363,870</td></tr><tr><th>Rice production (t)</th><td>332,288</td><td>480,005</td><td>612,208</td><td>503,200</td><td>631,045</td><td>511,173</td><td>642,372</td><td>773,461</td><td>961,370</td></tr><tr><th>Rice own-consumption+ seeds (t)</th><td>101,884</td><td>158,486</td><td>158,486</td><td>101,884</td><td>101,884</td><td>101,884</td><td>101,884</td><td>101,884</td><td>101,884</td></tr><tr><th>Rice tradable surplus (t)</th><td>230,405</td><td>321,520</td><td>453,722</td><td>401,316</td><td>529,161</td><td>409,290</td><td>540,488</td><td>671,577</td><td>859,486</td></tr></tbody></table>

Scenario 1 maximizes rice own-consumption. With almost the same total area, the same farming systems and the same yields, scenario 3 maximizes rice production and tradable surplus.

## 4 Conclusions

A policy of investment in irrigation facilities and of allocation of irrigated land to family farmers, combined with a credit policy for the purchase of inputs, could lead Mali to a situation close to grain self-sufficiency, and even to a grain surplus.

## References

* Ancey, G. (1975). *Niveaux de décision et fonctions objectif en milieu rural africain* (No. 3). Paris: AMIRA.

* Bélières, J.-F., Hilhorst, T., Kébé, D., Keïta, M. S., & Sanogo, O. (2011). Irrigation et pauvreté: le cas de l'Office du Niger au Mali. *Cahiers Agricultures*, 20(1), 144-149.

* Chombart de Lauwe, J., & Poitevin, J. (1957). *La gestion des exploitations agricoles*. Paris: Dunod.

* Cotula, L., Vermeulen, S., Leonard, R., & Keeley, J. (2009). *Land grab or development opportunity? Agricultural investment and international land deals in Africa*. London, Rome: IIED, FAO, IFAD.

* Couture, J.-L., Lavigne Delville, P., Spinat, J.-B., & EDR Cluster. (2002). *Institutional innovations and water management in Office du Niger (1910-1999). The long failure and new success of a big irrigation scheme*. (No. 29). Paris: GRET.

* Dave, B., Mazoyer, M., & Roudart, L. (2012). *Paysans investisseurs. Etude de la faisabilité économique de la proposition du SEXAGON*. Bruxelles: Université Libre de Bruxelles.

* Gafsi, M., Dugué, P., Jamin, J.-Y., & Brossier, J. (Ed.). (2007). *Exploitations agricoles familiales en Afrique de l'Ouest et du Centre: enjeux caractéristiques et éléments de gestion*. Versailles: Quae.

* Hertzog, T., Adamczewski, A., Molle, F., Poussin, J.-C., & Jamin, J.-Y. (2012). Ostrich-Like Strategies in Sahelian Sands? Land and Water Grabbing in the Office du Niger, Mali. *Water Alternatives*, 5(2), 304-321.

* Roudart, L. (2001). Morcellement et modernité de l'agriculture égyptienne. In A.-M. Jouve (Ed.), *Le morcellement des terres en Méditerranée*. Paris: Karthala.

* Sangaré, A. (2010). *Etude relative à l'établissement d'un bilan des ressources en eau au droit de la zone Office du Niger. Rapport final*. Groupe de la Banque Africaine de Développement.

* Schüttrumpf, R., & Bökkers, T. (2008). *Analyse du potentiel d'irrigation lors de la saison sèche dans la zone Office du Niger*. Ségou (Mali): Office du Niger.

* SEXAGON (Syndicat des exploitants agricoles de l'Office du Niger). (2010). *Paysans investisseurs à l'Office du Niger*. Niono (Mali): SEXAGON.
# Land pressure and agrarian mutation, spatial modelling of farming systems evolution from plot to regional scale in West Burkina Faso

Camille Jahel *±<fnref n="1" />, Christian Baron<fnref n="1" />, Eric Vall<fnref n="2" />, Agnes Bégué<fnref n="1" />, Kalifa Coulibaly<fnref n="3" />, Medina Karambiri<fnref n="3" />, Mathieu Castets<fnref n="1" />, Stéphane Dupuy<fnref n="1" /> & Danny Lo Seen<fnref n="1" />

## 1 Introduction

The cotton region of West Burkina Faso has seen notable transformations these last two decades. In the 70's, the arrival and rapid extension of a cash crop, cotton, has led to a new agrarian organization, with new cropping practices and social patterns. At the same time, the development of the plough has permitted to increase the cultivated surfaces. Those mutations have been enhanced by the huge population increase since the 90's, principal driver of the cultivated area expansion at the expense of forest. The last 20 years have seen the progressive disappearance of fallows, the increase of livestock and the lack of fodder for all animals. The low lands, that were used to water the herds, have been colonized by crops, causing conflicts and bringing about the departure of many breeders. A good understanding of these mutations is required to guide the establishment of adapted public policies. However, the analysis of agricultural mutations and production trends faces important scale issues. Agrarian changes are the result of interacting processes occurring at different scales, which raises the issue of documenting the main trends without distorting the information when trying to upscale or downscale it. The dynamics at plot level are linked to cropping practices, whereas farmer strategy is made at the farm scale. Public policies, the evolution of food prices, and farmer organization will have an influence at the regional scale. Thus a multi-scalar approach is necessary to understand and monitor those dynamics, which takes into account the determinant processes (Veldkamp & Lambin, 2001; Verburg et al., 2013). In this study we experiment an approach integrating a crop model (Sarra-H: Baron et al., 2005) into a spatial dynamics modelling environment (Ocelet: Degenne et al., 2010) to analyse the main processes occurring in an area representative of the cotton region of West Burkina Faso during these last 15 years.

## 2 Materials and Methods

The study area covers about 6500 km<fnref n="2" /> and is located in the Tuy province in West Burkina Faso (Fig. 1). About 150 plots pertaining to five villages (Koumbia, Gombeledougou, Boni, Dimikuy, Founzan) were followed during the 2014 growing season. Data collected on the plots were used to calibrate the crop model and to evaluate yield spatial variability. At farm scale, based on existing studies we identified three farmer group types (farmers, farmer-breeders, and breeders) and their corresponding strategies. Spatial analyses and expert knowledge have also emphasized essential elements as village organization, migration dynamics, impacting policies and prices variation. Remote sensing archive images (Landsat and Spot) gave information about the landscape and plot structure in the 2000's whereas a time series analysis revealed important land cover changes in the study area.

The model has been built by integrating the crop model Sarra-H into a spatial model, using the Ocelet domain specific language and model building environment. Sarra-H simulates the growth and potential yield of dry cereals (millet, sorghum and maize) in the Tropics. Three processes are simulated with a daily time step: water balance, carbon balance and phenology. Sarra-H uses three types of input parameters: soil, agricultural practices and meteorological (including rainfall) data. Ocelet is a simulation tool for landscape dynamics, based on interaction graphs. Graphs consist of entities, characterized by a set of properties, which are linked together by different kinds of relations (spatial, functional and hierarchical). The scenario contains a series of instructions which, when executed during a simulation, make the entities evolve according to their relations.

The spatialized Sarra-H model was built upon three types of spatial entities: Farm, Plot and Climate. Farm entities were defined according to the typology described in Marre-Cast and Vall (2013). Plot entities were defined in their area, crop and soil types. Climate entities were defined as areas with the same meteorological (temperature, relative humidity, global radiation) and rainfall conditions. Plots are linked to the Farm entities to which they belong, where characteristics like surface area, maximum inter-plot distance and distance to the village are considered. Each Plot entity is also linked to a Climate entity of the same area. Each plot therefore has one crop variety (that changes every year) and belongs to one farm that, in turn, has a practice strategy and a Climate entity that defines the meteorological conditions. Moreover, soil characteristics are properties of plots. In this way, Sarra-H can simulate, for several years with a daily step, yield, LAI, biomass, etc. for each crop in each plot.

<footnote n="1">¹ UMR TETIS, CIRAD</footnote>

<footnote n="2">² UMR SELMET, CIRAD</footnote>

<footnote n="3">³ UR PAN, CIRDES</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Camille.jahel@cirad.fr</footnote>
This model has been applied to the Tuy province in Burkina Faso, by simulating the annual crop production and the expansion of cultivated areas at the expense of forests, between the years 2000 and 2014. Three processes were taken into account when simulating the evolution of the cultivated area: farm expansion, farm division and migrant installation. Each of these three processes had an impact on forests. Clearing is modeled by ranking fallow lands according to soil quality, slope, distance to a road, and whether they belong to protected areas. For example, an expanding farm would acquire new plots one by one, the best available lands first, until reaching a certain surface. The attainable surface area can be parameterized according to farm type (farmers, farmer-breeders, and breeders) and strategies.

## 3 Results - Discussion

The simulation results are able to highlight both inter- and intra-annual changes like:

- The expansion of cultivated lands in the different sub-zones. For example, the model reproduces the rapid clearing of the forest in the West of the village of Koumbia, where many migrants settled these last years. In contrast, the area East of the Kapo Forest has seen a slower expansion of cultivated lands, as the zone was reserved for pastures and the lands were poorer (Fig.1).

- The difference in crop development between plots, according to their corresponding agro-ecological conditions. The total production of the zone is estimated, taking into account the new crops installed each year.

<figure><img src="image_126.png" /><figcaption>**Fig. 1.** Localization of the study area in West Burkina Faso, West Africa and results of the simulation of agrarian dynamics in the area of Koumbia.</figcaption></figure>

## 4 Conclusions

Modelling with interaction graphs allowed us to link information at different scales, and to integrate and spatialize the Sarra-H crop model. The model could be forced with coarser scale processes (migration, farm life cycle) to simulate the annual expansion of cultivated areas, at the expense of forests, and also finer scale information (farm strategy, local agricultural practices) to simulate crop production every year during the last fifteen years. The model developed takes into account farmer strategies, demographic dynamics and spatial heterogeneities. It was able to reproduce the expansion of cultivated lands in the different sub-zones, although the expansion rate was slightly under-estimated. Work is ongoing to use expert knowledge to better calibrate the model in order to obtain a rate of expansion that is confirmed by observations. The next step is now to extend the model to cotton, a major crop in the zone, to estimate the yearly total production.

*Acknowledgements.* This study was supported by the SIGMA European Collaborative Project (FP7-ENV-2013 SIGMA -Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM- project no. 603719).

## References

- Baron C., Sultan B., Balme M., Sarr B., Traoré SB., Lebel T., Janicot S. & Dingkuhn M. (2005). From GCM grid cell to agricultural plot: Scale issues affecting modelling of climate impact. *Philosophical transactions of the Royal Society of London. Biological sciences*, **360**, 2095-2108.

- Degenne P., Ait Lahcen A., Curé O., Forax R., Parigot D. & Lo Seen D. (2010). Modelling with behavioural graphs. Do you speak Ocelet? International Congress on Environmental Modelling and Software, July 5-8, Ottawa, Ontario, Canada.

- Marre-Cast L. & Vall E. (2013). Strategies et trajectoires des exploitations de polyculture-élevage de l'Ouest du Burkina-Faso. Communication pour le séminaire ASAP, Plateformes d'innovation et intensification écologique, Bobo-Dioulasso, Burkina Faso.

- Veldkamp A. & Lambin E.F. (2001). Predicting land-use change. *Agric Ecosyst Environ*, **85**:1-6.

- Verburg P.H., Mertz O., Erb K.H., Haberl H. & Wu W. (2013). Land system change and food security: towards multi-scale land system solutions. *Current Opinion in Environmental Sustainability*, **5**, 494-502.
CHARACTERIZATION OF CROP ROTATIONS VARIABILITY BY COMBINING
MODELLING AND LOCAL FARM INTERVIEWS

Florent Levavasseur <fnref n="1" />, Clémence Bouty <fnref n="2" />, Aude Barbottin <fnref n="1" />, Valentin Verret <fnref n="3" /> & Philippe Martin <fnref n="2" />

**1 Introduction**

The characterization of actual crop rotations on a large territory is a major challenge for local stakeholders in order to understand agricultural impacts on natural resources, e.g., nitrate leaching (Beaudouin et al., 2005). Although various mathematical descriptions and simulations of crop rotations have been proposed (e.g., Castellazi et al., 2010), few models propose to reconstruct actual crop sequences and to simplify their diversity in a reasonable number of crop rotations. The aim of this work was thus to combine modelling and farm interviews to characterize crop rotations which were representative of different soil characteristics and farming systems over a whole agricultural area of 70,000 ha.

**2 Materials and Methods**

Our study area was the Niort Plain in western France (about 71,000 ha) over the 2007-2012 period. 699 farms with at least one parcel in the study area were identified. This area is characterized by a diversity of farming systems, i.e., specialized farms in cereals or livestock and mixed crop-livestock farms, and a diversity of soils (deep alluvial soils, shallow calcareous soils, etc.).

We developed the RPG Explorer software to facilitate the analysis of the spatially-explicit data from the French Land Parcel Identification System (LPIS), called RPG. The main tasks performed by the software are: (i) classifying farms in two groups according to the proportions of temporary and permanent grasslands in their cropping plans (ii) intersecting yearly LPIS GIS data in order to reconstruct crop sequences over the period 2007-2012, and (iii) modelling the main crop rotations representative of the observed crop sequences, depending on the farming systems and/or soil units. The crop rotation model implemented in RPG Explorer is derived from the linear optimization model CropRota (Schönhart et al., 2011). It uses as input data the agronomic value of all pre-crop – following crop sequences (2-year sequences), the maximal frequencies of each crop in crop rotations, and the observed proportion of each crop, 2-year sequence and 3-year sequence reconstructed by RPG Explorer. The model derives the proportion of each potential rotation so that the agronomic value over all rotations is maximized. The proportions of each rotation are further constrained so that the modelled proportions of each crop, 2-year sequence and 3-year sequence match their observed proportions. In comparison to CropRota, RPG Explorer considers the proportions of observed 2 and 3-year sequences in optimization, which allows to model rotations that better match observed crop sequences, whatever their agronomic value.

To discuss the model outputs, 85 farm interviews were conducted on the study area: 40 cereal farms and 45 mixed crop-livestock farms. The main rotations were requested for each farm, as well as the reasons of their choice and their location (only for 31 interviews).

**3 Results – Discussion**

RPG Explorer identified 292 cereals farms and 407 mixed crop livestock farms. 5890 6-year sequences were reconstructed for cereals farms and 12381 for mixed crop-livestock farms, including 2112 and 4231 different 6-year sequences over the period 2007-2012. On the basis of the yearly cropping plan and of the 2 and 3-year sequences included in these 6-year sequences, RPG explorer modelled 160 rotations for cereals farms and 230 rotations for mixed crop-livestock farms. The 15 most frequent rotations represented 58 % and 60 % of the area for the cereals farms and the mixed crop livestock farms respectively (Table 1). Rapeseed-winter wheat-sunflower-winter wheat, sunflower-winter wheat and maize monoculture were the three main crop rotations modelled (excluding grasslands and set-aside), and were the main rotations according to the interviews too (28/85). Except rotation 12, all modelled rotations were identified in the surveys. Nevertheless, some farmers declared rotations that were not modelled by RPG Explorer, especially rotations of more than 6 years which were not modelled due to computation limit. For example, the 8-year rotation tG-tG-tG-C-RS-W-S-W was surveyed and partially corresponded to the modelled rotation 12.

Some differences were observed between cereal farms and mixed crop-livestock farms (Table 1). Because of the role of grasslands in cattle feeding, the proportions of rotations with temporary grassland was higher for mixed crop-livestock farms (e.g., rotation 12). Conversely, cereals farms integrated more oil-seed crops and protein crops in their rotations (rotations 11, 14 and 15), which highlighted a higher diversification of cash crops in their cropping plans.

<footnote n="1">¹ INRA, UMR INRA-AgroParisTech 1048 SAD-APT, Bâtiment EGER, BP 01, 78850 Thiverval-Grignon, France</footnote>

<footnote n="2">² AgroParisTech, UMR INRA-AgroParisTech 1048 SAD-APT, Bâtiment EGER, BP 01, 78850 Thiverval-Grignon, France</footnote>

<footnote n="3">³ INRA, UMR INRA-AgroParisTech 0211 Agronomie, Bâtiment EGER, BP 01, 78850 Thiverval-Grignon, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: pmartin@agroparistech.fr</footnote>
<table><caption>Table 1. Proportions of modelled crop rotations and their occurrence in interviews</caption><thead><tr><th rowspan="2">Rotation number</th><th rowspan="2">Rotation description *</th><th colspan="2">All systems</th><th colspan="2">Cereals farms</th><th colspan="2">Mixed crop-livestock and livestock farms</th></tr><tr><th>Area (%)</th><th>Interviews</th><th>Area (%)</th><th>Interviews</th><th>Area (%)</th><th>Interviews</th></tr></thead><tbody><tr><td>1</td><td>RS-W-S-W</td><td>17,89%</td><td>28 / 85</td><td>17,33%</td><td>13 / 40</td><td>15,76%</td><td>15 / 45</td></tr><tr><td>2</td><td>pG</td><td>10,71%</td><td>Undefined**</td><td>0,64%</td><td>Undefined**</td><td>15,44%</td><td>Undefined**</td></tr><tr><td>3</td><td>M</td><td>7,11%</td><td>23 / 85</td><td>7,04%</td><td>15 / 40</td><td>6,17%</td><td>8 / 45</td></tr><tr><td>4</td><td>S-W</td><td>6,73%</td><td>10 / 85</td><td>9,41%</td><td>8 / 40</td><td>4,15%</td><td>2 / 45</td></tr><tr><td>5</td><td>RS-W</td><td>3,98%</td><td>1 / 85</td><td>6,60%</td><td>1 / 40</td><td>1,81%</td><td>0 / 45</td></tr><tr><td>6</td><td>tG</td><td>3,75%</td><td>Undefined**</td><td>0,25%</td><td>Undefined**</td><td>5,38%</td><td>Undefined**</td></tr><tr><td>7</td><td>RS-W-B</td><td>2,96%</td><td>3 / 85</td><td>3,51%</td><td>3 / 40</td><td>2,21%</td><td>0 / 45</td></tr><tr><td>8</td><td>S-W-B</td><td>2,52%</td><td>6 / 85</td><td>3,14%</td><td>4 / 40</td><td>1,79%</td><td>2 / 45</td></tr><tr><td>9</td><td>SA</td><td>2,10%</td><td>Undefined**</td><td>3,25%</td><td>Undefined**</td><td>1,10%</td><td>Undefined**</td></tr><tr><td>10</td><td>M-W</td><td>2,07%</td><td>3 / 85</td><td>1,74%</td><td>0 / 40</td><td>2,00%</td><td>3 / 45</td></tr><tr><td>11</td><td>RS-W-P-W</td><td>1,20%</td><td>6 / 85</td><td>2,23%</td><td>3 / 40</td><td>0,40%</td><td>3 / 45</td></tr><tr><td>12</td><td>tG-tG-tG-C-RS-W</td><td>1,15%</td><td>3 / 85</td><td>0,00%</td><td>0 / 40</td><td>1,70%</td><td>3 / 45</td></tr><tr><td>13</td><td>RS-W-M-W</td><td>1,09%</td><td>7 / 85</td><td>0,00%</td><td>2 / 40</td><td>1,62%</td><td>5 / 45</td></tr><tr><td>14</td><td>RS-W-B-P-W-B</td><td>0,82%</td><td>3 / 85</td><td>1,24%</td><td>3 / 40</td><td>0,45%</td><td>0 / 45</td></tr><tr><td>15</td><td>RS-W-O-W</td><td>0,76%</td><td>1 / 85</td><td>1,27%</td><td>0 / 40</td><td>0,35%</td><td>1 / 45</td></tr></tbody></table>

There was a spatial structuration of modelled rotations according to soil units (Fig. 1). For example, maize monoculture (rotation 3) was dominant in the valleys while the rotation 1 was dominant on the plateaus. This location of maize monoculture in the valleys was confirmed by 9 out of 15 interviews in which its location was specified.

<figure><img src="image_127.png" /><figcaption>Fig. 1. Maps of the modelled proportions of two rotations</figcaption></figure>

Soil types were not the only drivers of crop rotations. Farm interviews highlighted that availability of irrigation was a major driver of some atypical rotations, e.g., rotations with field vegetables or maize monoculture on the shallow soils of the plateaus. Agro-environmental schemes also explained some rotations: integration in rotations of temporary grasslands (alfalfa) for biodiversity preservation or of protein crops in order to reduce nitrogen inputs.

Farm interviews allowed us to specify the exact crops included in rotations, e.g., durum wheat, triticale, sorghum or oat, silage maize or grain maize, alfalfa or ray-grass, whereas LPIS data only specified respectively “other cereals”, “maize” and “temporary grasslands”. This additional knowledge is required to assess the environmental impacts of crop rotations, which can be very different according to the considered crops, e.g., nitrate leaching for alfalfa and ray-grass. In the future, a more quantitative process should be proposed to validate the modelled rotations but raises the issue of the availability of exhaustive and spatialized data at field scale about crop rotations. Other approaches for simplifying the diversity of crop sequences could also be of interest, e.g. the classification proposed by Leenhardt *et al.*, 2012.

## 4 Conclusions

Our results showed that combining modelling and local interviews can help to define and spatialize the main rotations over an agricultural area. While modelling is needed to define the proportions of rotations that match the observed crop sequences, farms interviews are still mandatory to understand the drivers of rotations and refine their description.

*Acknowledgements.* This work was partly supported by ONEMA and ADEME. The authors also thank the local stakeholders (SEV) for providing a part of interview data.

## References

Beaudouin, N., Saad, J.K., Van Laethem, C., Machet, J.M., Maucorps, J. & Mary, B. (2005). Nitrate leaching in intensive agriculture in Northern France: Effect of farming practices, soils and crop rotations. *Agriculture, Ecosystems and Environment*, **111**, 292-310.

Castellazi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown, I., Conrad, K.F., Perry, J.N. (2010). Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale. *Environmental Modelling & Software*, **25**, 1881-1889.

Leenhardt, D., Théron, O. & Mignolet, C. (2012). Quelle représentation des systèmes de culture pour la gestion de l'eau sur un grand territoire ? *Agronomie, Environnement & Sociétés*, **2**, 77-89.

Schönhart, M., Schmid, E. & Schneider U.A. (2011). CropRota – A crop rotation model to support integrated land use assessments. *European Journal of Agronomy*, **34**, 263-277.

<footnote>*B: spring or winter barley, C: other cereals, pG/tG: permanent/temporary grassland, M: maize, O: other oil seeds, P: protein crops, RS: rape seed, S: sunflower, SA: set-aside, W: winter wheat
** Not systematically asked in the interviews</footnote>
# Use of aerial photogrammetry to minimize erosion threats from broad scale resource developments on farmlands.

Perry L. Poulton *$\pm$1, Neil I. Huth 1, Peter Caccetta 2, Brett Cocks 1, Jeremy Wallace 2 & Xiaoliang Wu 2

## 1 Introduction

Broad scale coal seam gas (CSG) development is driving significant landscape change in the Darling Downs and Maranoa agricultural regions of Southern Queensland, Australia. Approved tenements cover some 24,000 km<fnref n="2" /> and have identified reserves of 28613 PJ of gas (Huth *et al.*, 2014). While regional scale economic benefits of CSG development are acknowledged, few studies have evaluated the impact on farmers that must now coexist in a “shared space” with large-scale resource extraction enterprises (Huth *et al.*, 2014). Landholders face the prospect of having to negotiate directly with large corporations seeking to superimpose a CSG footprint, driving significant landscape changes in the form of access roads, culverts, pipeline corridors, well pads and water storage reservoirs on the existing farming enterprise. To date, ‘ad-hoc’ negotiations on infrastructure development with individual landholders has been implemented on a farm-by-farm basis. For this landscape, change, brings with it a high risk of hydrological impact and increased erosion due to extensive addition of roads and infrastructure. Uncoordinated development has the potential to impact stakeholders from farmers (silting of farm dams, loss or diversion of overland flow from catchment areas), local councils (flooded roads, ineffective drainage lines), state government (water quality of streams, river systems) to CSG enterprises (flooding of existing infrastructure). This research evaluates the effectiveness of a high resolution surface water flow model for identifying and monitoring changes to surface water flow or soil elevation which may indicate diversion of water flows, soil loss or build up of sediment, in the future.

## 2 Materials and Methods

High performance computing based aerial digital photogrammetry was employed in generating image mosaics as demonstrated by the example in Fig. 1a and in the creation of a digital surface model (DSM) at 20 cm resolution (Fig. 1b) for a 1200 km<fnref n="2" /> focal region currently undergoing CSG development on the eastern edge of the Surat basin (26.839 S, 150.333 E). Empirical radiometric calibration (to ground reflectance) of each digital aerial frame was carried out in accordance with methods described in detail by Collings *et al.*, 2011). The reflectance-calibrated data allows generalizable and repeatable image processing techniques to be applied to the data as a whole to derive spatially and temporally consistent information. Surface infrastructure and vegetation is then removed to generate a ground elevation model (GEM) (Fig. 1c). Model validation found errors of ~5.0 cm for GEM surface elevations for individual 20 cm pixels compared with 2.0 cm obtained from Real Time Kinematic (RTK) GNSS measurements and is approaching the 2.0 cm precision level expected of DGPS systems currently employed in high-resolution surveys. Flow paths were predicted using the GEM and the terrain analysis as described by Caccetta *et al.* 2010. The prediction is based on estimates of upslope area, and the maps produced are often referred to water accumulation maps. Surface flowpaths for both 100 cm and 20 cm horizontal resolution were compared with ground observations using a DGPS of ±30 cm horizontal accuracy. The water accumulation map (100 cm resolution) represents the above slope catchment area associated with each 20 cm pixel and is colour classified on a log scale between low accumulation (green) and potential for high accumulation (larger upstream catchment) indicated in red (Fig. 1d).

## 3 Discussion

While application of satellite acquired remote sensing data for landscape inventory and monitoring is seen as cost effective and has generated reliable information at large spatial scales their value in fine-scale management has been limited by image resolution (Tuominen and Pekkarinen, 2004). Digital acquisition of aerial stereo photography is of suitable resolution for fine-scale terrain modelling and contoured topographic mapping but requires radiometric correction to reduce interpretation error from bidirectional reflectance on spectral intensities. Fig. 1 demonstrates the capacity to generate high-resolution contoured surfaces and water flow maps capable of identifying fine scale erosion rills and depressions in fields, effectiveness of erosion management structures, and changes in surface water flows caused by farm tracks. Surface flow models based on a fine scale digital ground elevation model at the catchment scale are an effective tool for monitoring impact of the wider CSG footprint on surface hydrology and in identifying potential problems during early negotiation and decision planning of infrastructure at the farm, shire and regional level. Exposure of water accumulation maps in discussions with CSG farm managers and agricultural contractors have confirmed that information on location and catchment area of water flows will help inform land holders and CSG staff during planning for CSG infrastructure placement. Repeated surveys can be cost effective in the longer term by highlighting changes in

<footnote n="1">¹ CSIRO Agriculture Flagship, Toowoomba, Queensland, Australia</footnote>

<footnote n="2">² CSIRO Digital Productivity, Floreat, Western Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: perry.poulton@csiro.au</footnote>
water flow or soil surface elevation that may indicate diversion of water flows, soil loss or build up of sediment within the survey area. Sources of any sediment build-up can be easily identified by following the predicted water flow paths to that location. Concerns by landholders regarding surface water flows can be better communicated through the use of water accumulation maps.

a)

<figure><img src="image_128.png" /></figure>

b)

<figure><img src="image_129.png" /></figure>

c)

<figure><img src="image_130.png" /></figure>

d)

<figure><img src="image_131.png" /><figcaption>**Fig. 1.** (a) Aerial photogrammetry derived RGB image. (b) Digital surface model (DSM at 20 cm resolution highlighting treelines and surface infrastructure. (c) Generated ground elevation model (GEM) after removal of above ground structures. (d) Modelled accumulated flow paths indicating low (green) to high (red) accumulation overlaid on RGB image.</figcaption></figure>

## 4 Conclusions

Results demonstrate that use of high performance computing based digital photogrammetry predicts high-resolution surface elevations, enabling generation of landscape scale surface flow maps suitable for assessing impact at the sub-meter level on surface hydrology. These water accumulation or flow maps have the potential to inform discussion between farmers and the CSG industry and allow for better CSG-farm designs now and ongoing monitoring of changes in water flow or soil surface elevation which may indicate diversion of water flows, soil loss or build up of sediment, in the future. Surface resolution of satellite derived data will only continue to improve in the future and is anticipated to provide a cost effective method, compared with photogrammetry over large areas, for temporal monitoring of surface hydrology and erosion risk in dynamically changing agricultural landscapes. Future research will focus on extending surface flow modelling to predict and map erosion risk at the farm, catchment and regional scale.

*Acknowledgements.* This research was supported by the Gas Industry Social and Environmental Research Alliance (GISERA), a collaborative vehicle established by CSIRO, Australia Pacific LNG and Queensland Gas Company to undertake publicly-reported research addressing the socio-economic and environmental impacts of Australia's natural gas industries. This work was also supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia.

## References

Caccetta, P.A., R. Dunne, R. George, and D. McFarlane. 2010. A Methodology to Estimate the Future Extent of Dryland Salinity in the Southwest of Western Australia. *J Environ Qual* 39:26-34

Collings, S., P. Caccetta, and N. Campbell. 2011. Empirical Models for Radiometric Calibration of Digital Aerial Frame Mosaics. *IEEE Trans. Geosci. Remote Sens.* 49(7): 2573-2588

Huth N.I.; Cocks B., Dalgliesh N., Poulton, P., Marinoni O., Navarro J. 2014. Farmers' perceptions of coexistence between agriculture and large scale coal seam gas development: working paper, June 2014, CSIRO, Australia. ISBN (online): 978-1-4863-0362-5

Tuominen, S. and Pekkarinen, A. 2004. Local radiometric correction of digital aerial photographs for multi source forest inventory. *Remote Sensing of Environment* 89: 72-82.
# Use of crop sequences for data-mining of remotely sensed time series across multiplescales: opportunities for scaling up research on agricultural dynamics

Davide Rizzo ±<fnref n="1" />, El Ghali Lazrak<fnref n="2" />, Jean-François Mari <fnref n="2" />,<fnref n="3" />, Laurence Hubert-Moy <fnref n="4" /> & Marc Benoît <fnref n="2" />

## 1 Introduction

Farming activities are rapidly evolving thanks to technological improvements, even though global statistics indicate a stagnation or a collapse in total yields for major crops. Further improvements require therefore agronomists to enhance the ways they address the farming use of land and water (Acevedo 2011). Considering the limitedness of these two resources farming would definitely benefit from smarter spatial design of cropping and mixed crop-livestock systems (Dury et al. 2013; Moraine et al. 2014; Murgue et al. 2015). Moreover, researchers both from agronomy (e.g., Boiffin et al. 2014; Leenhardt et al. 2010) and from interdisciplinary approaches, such as the land change science (Rounsevell et al. 2012) and the ecoagriculture (Scherr & McNeely 2008), call for stronger integration of farming features in the research on land management systems. Accordingly, it is crucial to scale up the research on farming systems from plot/farm level to landscape level (Rizzo et al. 2013; Thenail et al. 2009) so as to build farming system design upon an improved understanding of the land patterns determined by the interactions between farming practices and natural resources (Benoît, Rizzo et al. 2012). This implies to address a spatially explicit way how farmers are choosing what to cultivate and the way to manage it, hence dealing with farming systems from a landscape agronomy perspective. Such an approach, however, largely depends from the availability of data over large areas and for long periods and on the methods to tackle them. Our aim hereby is to present a data-mining method to handle land cover sequences. In particular, we will discuss how segmenting a landscape by using the observed land cover sequences can help identifying flexible land units and their potential for cross-scale farming system studies.

## 2 Materials and Methods

The method we present provides a spatial wise synthesis of land cover sequences to get the most out of available datasets in terms of farming features. In this regard, we make the hypothesis that the observation and modelling of land cover sequences, with a focus on crops and pastures, can incorporate a relevant part of the farmers’ medium-term decision-making processes (Schaller et al. 2012). The method is based on stochastic segmentation with *hierarchical* Hidden Markov Models (HMM) whose spatial states are temporal HMM capable of assigning a probability to a time sequence. It was originally developed to handle large and labor-demanding survey datasets like TerUti (Mari & Le Ber 2006).

The method firstly identifies temporal regularities of the crop/grassland sequences, then use them to segment into homogeneous patches the study area (potentially ranging from farmland to region). In summary, the method includes three steps (Fig. 1a): data preparation, model topology choice and parameterization, model training on data and time-space segmentation (Mari et al. 2013). The method builds upon a Markov field hypothesis that considers the land cover of a given location as depending only on the land covers of the neighboring locations. Accordingly, the input data – sampled on a regularly spaced point grid – are scanned with a fractal curve where each point holds a time sequence of land covers. The key features of this approach and its potential for a landscape agronomy perspective on farming systems are illustrated referring to the Yar case study, a watershed of 61.5 km<fnref n="2" /> (Brittany, France) where the main crops – maize, wheat and grasslands – are related to industrial breeding. A 12-year time series (1997-2008) of satellite images was classified into 6 land cover classes for individual field polygons, then sampled with a 20m regular point grid.

<figure><img src="image_132.png" /><figcaption>Fig. 1. Schematic diagrams of the method: (a) steps and parameters, (b) model topology and hierarchy.</figcaption></figure>

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

<footnote n="2">² INRA, SAD-ASTER, Mirecourt, France</footnote>

<footnote n="3">³ LORIA, UMR 7503 & INRIA, Vandœuvre-lès-Nancy, France</footnote>

<footnote n="4">⁴ LETG-Rennes-COSTEL, UMR CNRS 6554 Université de Rennes 2, Rennes, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: ridavide@gmail.com</footnote>
## 3 Results – Discussion

The interpretation of results lead to revise the model parameters in an iterative and interactive process with domain specialists; for instance different sequence lengths and numbers of landscape segmentations were tested and compared. Although input maps derived from remote sensing were thematically less diverse than those derived from field surveys, time-space segmentation revealed some interesting spatial dynamics of land cover sequences. Finally, we distinguished permanent from temporary grassland and identified and localized the type of rotations in which temporary grasslands were involved. Of interest, these crop sequences faced major changes after 2003, identifying possible hotspot zones at the origin of the high nitrogen rates observed for several years in the local rivers. Hence, our approach provided relevant insights to better understand the agronomic relations between landscape patterns and natural resources that can be highlighted at the watershed level (cf. Dusseux et al. 2011). Generally speaking, farming system study requires the use of different data sources for being studied in a landscape perspective. Yet, available land cover maps rarely ensure continuity in time, seamless covering of large areas and detailed crop classification. Data about farming management are even more difficult to retrieve, also because of the continuous adaptations of agricultural techniques implemented by farmers in response to complex driving factors (Landais & Deffontaines 1988). Altogether, in-farm surveys still appear the best way to achieve an adequate description of local cropping systems, even though it is a highly time-demanding activity, thus generally limited to small areas or for short periods. However, some land cover dataset like the French TerUti (cf. Xiao et al. 2014) or the European Land Parcel Identification System (cf. Murgue et al., 2015) appear to provide promising bases to reconstruct agricultural land cover sequences. Advances in remote sensing can further help facing the lack of data, at least about agricultural land cover maps, although improvements are still needed to increase thematic detail (i.e., improving the list of identified crop types). In this perspective, the major novelty of our approach is to process time-space data in a time-dominant modeling framework, so as to propose an effective approach to visualize specific patterns that occur repeatedly or in sequence and constitute units that are geographically located. It already proved its applicability at different scales (cf. Mignolet et al. 2007; Xiao et al. 2014). Indeed, fostering patches of similar land cover sequences supports a multi-scale approach ranging from farmland to regional areas including multiple farmlands (Schaller et al. 2012) thus allowing to go beyond the inter-annual local variability of patterns and expected rotations.

## 4 Conclusions

The watershed segmentation into patches of land cover sequences is an innovative approach to identify land management units relating farmers’ choices and natural resource management. Accordingly, it could inform local planners’ decision-making by providing spatially explicit insights about farming system dynamics. In perspective, this can ultimately facilitate a shared landscape design allowing account to be taken of the diversity in farming systems.

## References

* Acevedo, M.F., 2011. Interdisciplinary progress in food production, food security and environment research. *Environmental Conservation*, 38(02), pp.151-171.

* Benoît, M. et al., 2012. Landscape agronomy: a new field for addressing agricultural landscape dynamics. *Landscape Ecology*, 10(27), pp.1385-1394.
Boiffin, J. et al., 2014. Agronomie, espace, territoire: travailler «pour et sur» le développement territorial, un enjeu pour l'agronomie. *Cahiers Agricultures*, 23(2), pp.72-83.

* Dury, J. et al., 2013. Cropping-plan decision-making on irrigated crop farms: A spatio-temporal analysis. *European Journal of Agronomy*, 50, pp.1-10.
Dusseux, P. et al., 2011. Identification of grazed and mown grasslands using a time series of high-spatial-resolution remote sensing images. In *Analysis of Multi-temporal Remote Sensing Images (Multi-Temp), 2011 6th International Workshop on the*. Analysis of Multi-temporal Remote Sensing Images (Multi-Temp), 2011 6th International Workshop on the. pp. 145-148.

* Landais, E. & Deffontaines, J.-P., 1988. Les pratiques des agriculteurs. Point de vue sur un courant nouveau de la recherche agronomique. *Études rurales*, 109(1), pp.125-158.

* Leenhardt, D. et al., 2010. Describing and locating cropping systems on a regional scale. A review. *Agronomy for Sustainable Development*, 30(1), pp.131-138.

* Mari, J.-F. & Le Ber, F., 2006. Temporal and spatial data mining with second-order hidden markov models. *Soft Computing*, 10(5), pp.406-414.
Mari, J.F., Lazrak, E.G. & Benoît, M., 2013. Time space stochastic modelling of agricultural landscapes for environmental issues. *Environmental Modelling & Software*, 46, pp.219-227.

* Mignolet, C., Schott, C. & Benoît, M., 2007. Spatial dynamics of farming practices in the Seine basin: Methods for agronomic approaches on a regional scale. *Science of the Total Environment*, 375(1-3), pp.13-32.

* Moraine, M. et al., 2014. Farming system design for innovative crop-livestock integration in Europe. *animal*, 8(Special Issue 08), pp.1204-1217.

* Murgue, C., Therond, O. & Leenhardt, D., 2015. Toward integrated water and agricultural land management: Participatory design of agricultural landscapes. *Land Use Policy*, 45, pp.52-63.

* Rizzo, D. et al., 2013. Farming systems designing landscapes: land management units at the interface between agronomy and geography. *Geografisk Tidsskrift-Danish Journal of Geography*, 113(2), pp.71-86.

* Rounsevell, M.D.A. et al., 2012. Challenges for land system science. *Land Use Policy*, 29(4), pp.899-910.

* Schaller, N. et al., 2012. Combining farmers’ decision rules and landscape stochastic regularities for landscape modelling. *Landscape Ecology*, 27(3), pp.433-446.

* Scherr, S.J. & McNeely, J.A., 2008. Biodiversity conservation and agricultural sustainability: towards a new paradigm of “ecoagriculture” landscapes. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 363(1491), pp.477-494.

* Thenail, C. et al., 2009. The contribution of crop-rotation organization in farms to crop-mosaic patterning at local landscape scales. *Agriculture, Ecosystems & Environment*, 131(3-4), pp.207-219.

* Xiao, Y. et al., 2014. Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France. *Computers and Electronics in Agriculture*, 102, pp.51-63.
# Are crop sequence evolutions influenced by farm territory dynamics?

Clémence Bouty *±1,2, Florent Levavasseur 2,1, Philippe Martin 1,2 & Aude Barbottin 2,1

## 1 Introduction

Crop sequence can be defined as the temporal arrangement of crops and is influenced by four major types of constraint: the timing of agricultural operations, the minimum area of each crop, the minimum return period between the same crop, and the benefits or risks associated to preceding-following crop pairs (Castellazzi et al., 2008).

Crop sequence is a key factor for assessing the environmental impact of farming systems (Leteinturier et al., 2006). Thus, analyzing the evolution of crop sequences and its drivers (e.g. European regulations or changes in farm size) is essential to understand farming system dynamics. The aim of this work was to analyze the link between farm territory dynamics and crop sequence evolutions. We specifically analyzed the evolutions of crops preceding winter wheat on cereal farms with contrasted dynamics of their territory (growth or stability of their Used Agricultural Area (UAA)).

## 2 Materials and Methods

Our study was carried out in the Niort Plain in western France (53,000 ha). We used the spatially explicit data from the French Land Parcel Identification System (LPIS), called RPG, available from 2007 to 2012.

In the RPG, each mapped parcel is associated with a farm identifier which differs from one year to another. So, we established a link between the farm identifiers of two successive years by intersecting chronologically the RPG geo-referenced layers. We thus identified farms in which no change of the UAA has occurred over the 2007-2012 period (stable farms) and farms which have known an increase of their UAA in 2009, 2010 or 2011 (growing farms). We focused on 203 cereals farms, i.e. farms cultivating winter wheat and holding more than 50% of cereals or less than 5% of grasslands in their cropping plan. Among them, we identified 61 stable farms and 30 growing farms.

For these 91 cereal farms, winter wheat represents about 45% of the cropping plan. Therefore, crops preceding winter wheat play a key role in the lengthening and the diversification of crop sequences. We analyzed the evolution of the number (lengthening indicator) and the proportion of each crop preceding winter wheat (diversification indicator) on stable and growing farms between 2007-2008 and 2011-2012 (i.e. before and after the potential growth in farm size).

The preceding-following crop pairs were determined by intersecting RPG layers too (Leteinturier et al., 2006).

In addition, the evolutions of crops preceding winter wheat observed in the RPG were compared with those of 20 surveyed cereal farms (10 stable farms and 10 growing farms). Interviews were conducted to collect information about the composition and the allocation of crop sequences on the farm territory and to determine the drivers of changes.

## 3 Results – Discussion

On stable and growing farms, the average number of preceding crops remained quite the same between 2007-2008 and 2011-2012 (resp. 2.8 and 2.5 crops in stable farms; resp. 3.7 and 3.5 crops in growing farms). Nevertheless, we observed that the proportion of stable farms with more than 3 preceding crops before winter wheat decreased between 2007-2008 and 2011-2012 (Fig. 1). This highlight a potential slight shortening of crop sequences on stable farms. On growing farms, no specific behavior towards shortening or lengthening of crop sequences was identified: the proportion of farms with 4 preceding crops drastically decreased, but at the same time, both the proportion of farms with 3 or less preceding crops and the proportion of farms with 5 or more preceding crops increased. Besides, it appears that the crop sequence lengthening was often limited on the UAA of stable and growing farms. Actually, even when farms had 5 or more crops preceding winter wheat, two of them represented on average 70% of the farm area in winter wheat.

Rapeseed, sunflower and winter wheat were the 3 more frequent crops preceding winter wheat in 2007-2008 and in 2011-2012. Each of these preceding crops represented 15% to 40% of winter wheat area (Fig. 2). Between 2007-2008 and 2011-2012, the proportion of winter wheat preceded by rapeseed and winter wheat decreased (resp. -11.8% and -7.5 % on stable farms; resp. -12.1% and -11.0% on growing farms) whereas the proportion of sunflower/winter wheat area substantially increased (+20.4% on stable farms; +16.7% on growing farms). Over the same period, we also observed an increase of preceding crops with agronomic benefits (e.g. legumes/winter wheat). According to these changes, our results highlight a diversification of crop sequences with an expansion of crops whose crop management is less intensive (reduced needs in pesticides and nitrogen) in the Niort Plain.

<footnote n="1">¹ AgroParisTech, UMR 1048 SAD APT</footnote>

<footnote n="2">² INRA, UMR 1048 SAD APT</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: clemence.bouty@agroparistech.fr</footnote>
<figure><img src="image_133.png" /><figcaption>**Fig. 1.** Proportion of farms according to the number of crops preceding wheat on stable farms (n=61) and growing farms (n=30) (1 - 2: short sequence / 4 - >5: long sequence). Sources: RPG (2007-2008 and 2011-2012).</figcaption></figure>

<figure><img src="image_134.png" /><figcaption>**Fig. 2.** Proportion of wheat area associated to each type of preceding crops on stable farms (n=61) and growing farms (n=30) (RS, W: intensive preceding crops / S, M, L, OS, tG, B: extensive preceding crops). Sources: RPG (2007-2008 and 2011-2012).</figcaption></figure>

RS: rapeseed, W: winter wheat, S: sunflower, M: maize, L: legumes, OS: other oilseed-crop, tG: temporary grassland, B: barley, O: others.

Farm interviews confirmed the evolutions observed on preceding crops of winter wheat (rapeseed, sunflower and winter wheat). Drivers of changes were similar on both stable and growing farms and can be classified as economic or environmental drivers. On the economic point of view, farms tended to lengthen their rotations with new break crops (sunflower, oilseed-linen, legumes or maize) in order to limit the economic risk related to a large area in one crop (mainly rapeseed). This was especially the case when the farm size increased between 2007 and 2012. However, the lack of irrigation can limit this diversification of crop sequences on shallow soils when irrigation is requested by downstream supply-chain for specific contract (e.g. seed contracts). On the environmental point of view, new break crops were often introduced in crop sequences to delay the return period of rapeseed and winter wheat and reduce the pest pressure and nitrogen requirement. According to interviews, Agro-Environmental Schemes (AES) could both contribute to lengthening and diversification of crop sequences (introduction of legumes to reduce nitrogen use) or limit it (suppression of rapeseed to reduce pesticide use). Alongside Schaller (2011), interviews showed that new crop pairs were introduced systematically or according to an opportunistic management. The latter was linked to the subscription to an AES or to the evolution of the collection area of a supply-chain. Duration of theses opportunistic management strategies without CAP subsidies is a new challenge for policy makers and raises the question of the durability of crop sequence changes on an agricultural territory.

## 4 Conclusions

Our results based on database analysis and interviews showed that farm enlargement was not the main driver of crop sequence changes: the number and the nature of crops preceding winter wheat changed on both stable and growing farms between 2007-2008 and 2011-2012. A slight shortening of crop sequences was observed on stable farms but no fixed orientation towards shortening or lengthening was identified on growing farms. On both stable and growing farms, the main observed evolution was the introduction of new preceding crops in order to replace or delay the return period of rapeseed and winter wheat. Strategies of crop sequence changes did not differ on stable and growing farms due to common environmental and economic drivers. These results might well be of importance either in identifying relevant crop sequence changes for environmental issues at large scale and in determining policy to deal with this issue.

*Acknowledgements.* The PhD of C. Bouty was founded by AgroParisTech and the ONEMA EMADEC project. RPG data were provided by the ASP.

## References

Schaller, N. (2011). Modélisation des décisions d'asselement des agriculteurs et de l'organisation spatiale des cultures dans les territoires de polyculture-élevage. AgroParisTech, 2011.

Castellazzi, M.S., Wood, G.A., Burgess, P.J., Morris, J. K., Conrad, F. & Perry, J.N. (2008). A systematic representation of crop rotations. *Agricultural Systems, 97*, no. 1-2: 26-33.

Leteinturier, B., Herman, J.L., de Longueville, F., Quintin, L., & Oger, R. (2006). Adaptation of a crop sequence indicator based on a land parcel management system. *Agriculture, Ecosystems & Environment, 112*, no. 4: 324-34.
# An Integrated Look at The Diversity, Sustainability and Dynamics of Argentina's Farming Systems

Mirna Mosciaro <fnref n="1" />, Gonzalo Carlos Bravo <fnref n="1" />, María Daniela Chavez *±1, Hernán Alejandro Urcola *±1, Víctor Brescia <fnref n="1" />, María Eugenia Van Den Bosch <fnref n="1" /> & Carlos Ghida Daza <fnref n="1" />

## 1 Introduction

Processes like intensification of agricultural production, advance of urban areas, growth of non-agricultural activities (mining, tourism) and climate change produce differentiation, fragmentation and concentration of the agricultural production which impact on farming systems of different regions. The design of development plans requires knowledge and understanding of diverse, complex and dynamic farming systems interacting among them through changing relationships. Specifically, in the center of Argentina (Córdoba province), economic changes have caused annually variations in the minimum required surface for production of 42 to 56 per cent in a period of eleven years, from 1998 to 2009 (Ghida Daza, 2010). Hence, issues like the sustainability of different farming systems in this region should be investigated for a better understanding of the dynamic of farming systems complexity in changing conditions. Sustainability is a broad conception that includes not only an economic side of production but also a balanced social development and environment protection (WCED, 1987; Cáceres, 2004). This paper describes an approach aimed at understanding the diversity and dynamics of farming systems of different regions of Argentina in a context of innovation and territorial transformations. The specific objectives of this approach are included: to adjust conceptual and methodological frameworks to study diversity, logics of functioning, dynamics and sustainability of farming systems, to improve the knowledge of diversity and operation of farming systems in Argentina, to evaluate global sustainability of farming systems (including social, economic and environmental dimensions), to characterize the dynamic of farming systems and their interactions, including a prospective vision and the impact of innovation and to enhance local skills for studying farming systems. The approach is illustrated through the evaluation of the global sustainability of farming systems in the south of Córdoba (center of Argentina) concerning economic, environmental and social indicators of sustainability.

## 2 Materials and Methods

This nation-wide project works in association with regional projects and other national programmes of the National Institute for Agricultural Technology (INTA) and it is implemented by 60 researchers and extension agents who work in association with researchers from universities and national and international research institutes. Currently, case studies in 36 agro-ecological homogeneous zones are under analysis. The project's approach is shown by presenting the global sustainability performance of six systems belonging to two agro-ecological homogeneous zones of Córdoba, Argentina. More details about the systems evaluation can be found in Ghida Daza (2014).
The study area includes 6.3 million hectare in the humid (6) and sub humid (5) zones of the south of Córdoba. Around 11000 farming systems in this region mainly devote the land to agriculture production, especially soybean (MAGyA Córdoba, 2014). Productive systems for the analysis were classified in agricultural, cattle raising and mixed systems. Economic dimension of sustainability includes net benefits (total revenue – direct cost of activities- fixed expenditures) and global risk (coefficient of variation of net benefits). For the social dimension, the indicator total labour was calculated as hours of work in sowing and protecting crops and for livestock running. In the environmental dimension, the following indicators are included, nutrient balance (i.e., economic value of the balance of nitrogen, phosphorus and potassium, Manchado, 2010), organic carbon balance (Alvarez and Steinbach, 2006) and the risk from chemical contamination (i.e., EIQ coefficient, Kovach et al., 1992). Seven items were included in evaluating the solutions efficiency: average operative result, minimum operative result, coefficient of variation, nutrient balance, carbon balance, EIQ coefficient and total labour. Results for each indicator were normalized and evaluated under four different weighing schemes to represent different producer's objectives. In this way, the systems were classified according to five options of preference: net benefits, average of all indicators, risk minimization, lowest environmental lose and highest use of labour. A multicriteria or multiobjective method is used in this research to include the different indicators and dimensions of sustainability.

## 3 Results - Discussion

<footnote n="1">¹ Instituto Nacional de Tecnología Agropecuaria (INTA)</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: chavez.daniela@inta.gob.ar</footnote>
The performance in economic, social and environmental indicators in different farming systems of two regions of Córdoba are shown in Fig. 1. The multivariate assessment indicates that the agricultural and mixed systems are better in terms of net benefit, but do poorly in labour generation and in environmental indicators. The cattle raising systems improve the social and environmental performance, but the extensive cattle system of zone V C generates very low net benefits. Overall, the cattle raising system of zone VI A is the best considering all sustainability dimensions. The multivariate assessment also reveals the ecological effects of each zone over the evaluated systems. The zone VI systems show a better performance in terms of net benefits and in the variation of those benefits as a consequence of higher and more stable rainfalls. However, zone VI systems exhibit higher nutrient extraction and carbon losses than the zone V counterparts caused by higher crop yields. This diversity of indicators can also be weighted to rank the performance of each system according to different working logics or objectives. Therefore, considering only short-term economic rewards, the farming VI B system is the best. However, according to an average of all indicator criterion, the cattle raising VI A system is the best followed by the cattle raising V C system. Also, the cattle raising VI A system and the cattle raising V C system are the two best performers following a criteria that assigns 50% more weight to the risk aversion, environmental and social dimensions, respectively (Ghida Daza, 2014).

<figure><img src="image_135.png" /><figcaption>Fig. 1. Multivariate System Assessment (source: Ghida Daza 2014).</figcaption></figure>

**4 Conclusions**

The paper describes an approach aimed understanding the sustainability, diversity and dynamics of farming systems of Argentina. Preliminary results from six representative systems of the South of Córdoba have been presented. Although further work and methodological adjustments are needed, our results indicate that a wide diversity of systems is present even within relatively homogenous areas and that the ecological characteristics of each zone have important effects on system sustainability. The diversity of system performance can be combined with a diversity of producer objectives to increase the variety of situations. Furthermore, results presented revealed the old-standing conflict between short-term rewards, social inclusion and long-term resource conservation. Current processes such as intensification, advance of urban areas, growth of non-agricultural activities and climate change create growing complexities and interactions among productive systems, natural resources and the society as a whole. Therefore, there is a growing need for a more comprehensive evaluation of agricultural production systems, their innovation processes and dynamics. It is expected that the described approach can generate better knowledge to guide the design of practices and systems that can yield the best combination of multiple sustainability dimensions.

*Acknowledgements.* This study has received funding from the National Program for the Development and Sustainability of the Territories, INTA Argentina, through Project PNSEPT 1129023.

**References**

Alvarez, R. & Steinbach, H. (2006). Balance de Carbono en suelos cultivados, en Valor Agronómico y Dinámica en los suelos pampeanos. Ed Facultad de Agronomía.

Cáceres, D. (2004). Sostenibilidad como concepto situado: un marco conceptual para la construcción de indicadores. Revista de desarrollo rural y cooperativismo agrario 8, 189-200.

Ghida Daza, C. (2012). Evaluación de Sistemas Predominantes según Aspectos Económicos y Ambientales. IV Congreso Regional de Economía Agraria y XLV Reunión Anual de la AAEA. 21 a 23 de Octubre de 2014. Buenos Aires, Argentina.

Ghida Daza, C. (2010). Evolución de la unidad económica agrícola en el sudeste de Córdoba. INTA EEA Marcos Juárez, Información para Extensión N° 135, 11 p.

Kovach, J. & Petzoldt, C. (1992). A method to measure the environmental impact of pesticides. IPM Program Cornell University

MAGyA (2014). Ministerio de Agricultura, Ganadería y Alimentos de Córdoba. www.cba.gov.ar (22/07/2014).

Manchado, J.C. (2010). La sustentabilidad en la agricultura pampeana. Valoración económica del balance de nutrientes para las principales actividades agropecuarias extensivas del centro sur de la provincia de Buenos Aires. Anales de la 41º Reunión Anual de la Asociación Argentina de Economía Agraria, 17 pp.

WCED (1987). World commission on environment and development. Our common future. Oxford University press, Oxford.
# Linking landscape patterns and farm trajectories: a prerequisite to design eco-efficient landscapes in agricultural frontiers of Brazilian Eastern Amazon

Sophie Plassin *±1, René Poccard-Chapuis 1 & Jean-François Tourrand 2

## 1 Introduction

The Amazon plays an important role in providing global ecosystem services such as carbon sequestration, biodiversity conservation, water cycle regulation, etc. In agricultural frontiers of the Brazilian Eastern Amazon, the colonisation process by extensive cattle ranching led to landscape changes with a conversion of native forest to pastureland. Recently, legislation became more restrictive prohibiting deforestation and in this context making extensive cattle ranching unsuitable. In response, cattle farmers went into intensification movement and land use diversification. An agrarian transition process has been occurring but until today, it has poorly succeeded in leading to large scale innovative cattle systems, while promoting ecological services at landscape level.

Considering the isolation and the potential of this region, an increased efficiency of natural resources use through new farming practices and spatial reorganisation is relevant. In order to design such eco-efficient farming systems, we need to understand the mutual relationship between farmers' decisions (which lead to farming practices and farm spatial organisation) and landscapes. Therefore, this contribution presents the concepts and methodological framework to analyse the links between farmer's decision systems and landscape patterns in an agrarian transition process. Our hypothesis is that farmer decisions dealing with spatial organisation and natural resources use have changed leading to different landscape trajectories.

We refer to a bottom-up approach (Overmars et al., 2007) focused on farmer and farm. In a first step this approach consists to understand farmer decision-making processes (systemic approach) which led to landscape patterns. In a second step, farmer's decisions are modelled in order to be able to predict landscape patterns (spatial modelling).

The present work corresponds to the first step. To link farmer's decisions and landscape patterns, we borrowed concepts from farming systemic approach (agronomy) and spatial modelling (geography).

## 2 A systemic approach to understand farmer's decisions

The conceptual representation of farmer's decision process is based on a systemic approach which distinguishes a "production system" (or biotechnical system) made up of interacting crop, animal and soil compartments from a "decisional system" which guides and manage this production system to achieve management purposes. These two systems are connected through a cybernetic relationship: feedback from the production system may lead to a decision that influences future management action and changes, in turn, the production system (Bonneviale et al., 1989; Keating & McCown, 2001). The conceptual model defines internal (e.g.: characteristics of the farm household) and external (socio-economic, politic or regulation context, pedoclimatic conditions) determinants that can influence farmer's decision which therefore changes farmer's projects.

To understand farmer's decisions, it is important to note that farmers are not rational profit maximizers (Edwards-Jones, 2006). It was particularly true in the frontier of the Brazilian Amazon, where, migrants opted for cattle production not only for the profitability but also for land appropriation, work productivity, social promotion, security, etc. (Vaz et al., 2012).

To test our hypothesis and to embrace the heterogeneity of farming strategies, we established a typology based on expert knowledge. Two variables, considered as relevant indicators of the situation of farmer in the agrarian transition process, were chosen: the level of intensification and/or diversification and the respect or not of the Brazilian environmental legislation. From these variables, three types of farm were defined: (i) farms which have extensive farming systems (similar to migrant's strategies), (ii) farms which began changing some practices, (iii) farms which have been intensifying or diversifying. We also distinguished family farming and cattle ranching.

Semi-open interviews have been conducted aiming at understanding the current global functioning of the farm and spatial organization and at building farm trajectories. We based on three complementary approaches: farm global functioning approach (Bonneviale, et al., 1989), spatial organization approach (Naïtlho et al., 2003) and retrospective surveys (Moulin et al., 2008). The survey is conducted to both collect information about (i) specific facts (farm history and characteristics, livestock and crop system, spatial organization, farm projects) and (ii) farmer outlook. This data collection method, proposed by Girard (2006), aims at understanding the choice of practices of the farmers, adjustments made, and how they justify their choices.

<footnote n="1">¹ Umr Selmet, Cirad</footnote>

<footnote n="2">² Ur Green, Cirad</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: sophie.plassin@cirad.fr</footnote>
Quantitative and qualitative data are transcribed and analysed to build farmer decision rules and a conceptual model to illustrate the decision making process. These individual decisions are discussed and compared with another sample of farms.

### 3 Choremes: the use of graphic representation to design landscape patterns

Graphic representations have been used to represent the concept of landscape patterns. Choremes have already showed their ability to represent spatial and functioning organization of farms (Lardon & Capitaine, 2008). They are a relevant support to link farmer decision (the reasoning) and landscape (the result of farmer's action). Chorematic alphabets already exist to design generic graphic representations from structure and process (Piveteau & Lardon, 2002).

As we want to show how farmer's decisions and practices have had impacts on the landscape, we represent landscape spatiotemporal dynamic at relevant intervals. To do that, we use data from surveys and satellites from year 2000. We focus graphic representations on landscape composition (land-use and some landscape elements as riparian forests and trees on pasture) and configuration (spatial arrangement) which are both related at eco-efficiency level. We design the evolution of landscape on two levels: large individual farmer and smallholders community. The results of these retrospective evolutions are showed to farmers and leaders of the community. They are discussed which helps us to explicit farmer's decisions. Cross-analysis of results from systemic approach and graphic representations allow us to build conceptual decision model.

### 4 Application of the framework

We implemented this framework on two contrasted territories of Pará state (Eastern Amazon), Paragominas and Redenção. In these two municipalities, the process of land occupation by cattle ranching from the 1960s led to a predominance of agropastoral landscapes and livestock extensive farming systems. But, since the last years, the agrarian dynamic have become different. Paragominas is involved in an intensification process. Farmers experiment other options to extensive grazing and stakeholders got involved to get out government's blacklist municipalities with high deforestation rates. This municipality became the first Green Municipality of Pará state. In Redenção, innovation process is more tempered. Farmers didn't necessarily experiment alternatives to extensive grazing, which was a strength of livestock production in this region (Vaz, et al., 2012). The choice of these two heterogeneous situations allows us to outline the lever and impediments to design eco-efficient farming systems at farm and territorial scales.

### 5 Conclusions and perspectives

Designing eco-efficient farming systems requires understanding farmer's decision making process, in order to be able to promote practices or spatial organization suitable to farmers while improving landscape eco-efficiency.
In the agricultural frontiers of Brazilian Amazon, farmer's decisions were poorly studied in a cattle ranching intensification perspective. The global framework presented in this work will allow understanding and characterizing the links between innovation and landscape dynamics. The results will provide map support and knowledge to monitor an agrarian transition process towards efficient systems at territorial scale.

*Acknowledgements.* This work was carried out with the financial support of the French National Research Agency (ANR), under the project ECOTERA (Ecoefficiences et développement Territorial en Amazonie brésilienne; ANR-13-AGRO-0003) and the Rotary Foundation which have supported a Global Grant for doctoral scholarship.

### References

- Bonnevale, J.-R., Jussiau, R. & Marshall, E. (1989). Approche globale de l'exploitation agricole. Comprendre le fonctionnement de l'exploitation agricole : une méthode pour la formation et le développement. INRAP, Dijon, 329.

- Edwards-Jones, G., (2006). Modelling farmer decision-making: concepts, progress and challenges. *Animal Science, **82***, 783-790.

- Girard, N., (2006). Categorising farmers' practices to reformulate a problem in partnership: A method for building situation-specific typologies. *Cahiers Agricultures, **15***, 261-272.

- Keating, B.A. & McCown, R.L., (2001). Advances in farming systems analysis and intervention. *Agricultural Systems, **70***, 555-579.

- Lardon, S. & Capitaine, M., (2008). Processing and transforming spatial representations for agronomy. *Revue d'Anthropologie des Connaissances, **2***, 195-217.

- Moulin, C.-H., Ingrand, S., Lasseur, J., Madelrieux, S., Napoléone, M., Pluvinage, J. & Thénard, V. (2008). Comprendre et analyser les changements d'organisation et de conduite de l'élevage dans un ensemble d'exploitations: propositions méthodologiques. In Dedieu, B., E Chia, E., Leclerc, B., Moulin, C.-H. & Tichit, M. L'élevage en Mouvement. Flexibilité et adaptation des exploitations d'herbivores. QUAE, 181-197.

- Naïlho, M., Lardon, S. & Yotte, M. (2003). Approche spatiale de l'exploitation agricole. Educagri Editions, Dijon,

- Overmars, K.P., Verburg, P.H. & Veldkamp, T., (2007). Comparison of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model. *Land Use Policy, **24***, 584-599.

- Piveteau, V. & Lardon, S., (2002). Choremes and territorial diagnosis: A training experiment. *Mappemonde, **2002***, 1-6.

- Vaz, V., Abreu de Carvalho, S., Barbosa, T., Thales, M.C., Mourão, M., Cialdella, N., Poccard Chapuis, R. & Tourrand, J.-F. (2012). A pecuaria na agenda ambiental da amazônia brasileira: Percepções e representações dos atores locais. In Almeida, J., Gerhardt, C. & Magalhães, S. B. Contextos rurais e agenda ambiental no Brasil: Práticas, políticas, conflitos, interpretações. Rede de Estudos Rurais, Belém, p. 65-90.
# Deciphering Corporate Governance and Environmental Commitment within South-East Asia Transnationals in strengthening Farming Systems interactions

Jean-Marc Roda <fnref n="1" />,<fnref n="2" />, Norfaryanti Kamaruddin *±<fnref n="2" /> & Rafael Palhiarim Tobias<fnref n="3" />

## 1 Introduction

Promoting tropical forest sustainability among corporate players in agro forest sector is a major challenge. Interactions among stakeholders in the farming systems need to be visualized and strengthen. Furthermore, in agro forest sector, resilient strategies are needed among the corporate players in balancing economic contribution and environmental sustainability. Corporate ownership in Southeast Asian conglomerate is dominated mainly by family, government, or private. Research has shown that there is a powerful relationship between the ownership structure of big business groups and their behaviour (Chandler, et. al, 1982; Almeida, et al., 2006; Todeva, 2006; De Masi, et al., 2012). In the specific case of South-East Asian conglomerates, much research has detailed their propensity to display an extremely complex and opaque structure, or interlaced cross-shareholdings, which need to be visualized and understood.

## 2 Materials and Methods

Our hypothesis is that financial factors such as ownership structure may have a fundamental role in shaping the corporations' behaviour (environmental policies, investment strategy, etc.). In order to understand if the corporate structure has an impact on the company's commitment to adopt sustainable practices in their farming systems, we adopt the main schemes of sustainable forest management and of sustainable oil palm plantation management as criteria of commitment (“a minima”). We analysed the audited accounts of 4 major Asian agribusiness transnationals. Using network analysis, we decipher how the 931 companies relate to each other and determine the behaviour of the transnationals which they form. We compared various metrics with the environmental commitment of these transnationals.

## 3 Results – Discussion

The visualization of the ownership structure of the transnationals and the computation of their ultimate shareholders is represented in Figure 1. Ta Ann and Sime Darby are connected through a common shareholder, the Malaysian government. This shareholder controls Sime Darby, but is an extremely minor shareholder of Ta Ann, which is controlled by the Wong family. We found that the size of the transnationals in terms of number of companies is not significant to clearly discriminate them.

<figure><img src="image_136.png" /><figcaption>Fig. 1. Ownership structures of WTK, Ta Ann, Olam, and Sime Darby</figcaption></figure>

A Principal Component Analysis was performed on the 931 companies of the data set, with ownership structure, financial factors and environmental commitment proxies as variables. Figure 2 present the factor map for Dimensions 1 and 2, which explains close to 50% of the variability. Dimension 1 ranks the business groups according to their listed capital and government involvement in their ownership structure. It opposes investment strategies of related diversification to unrelated diversification. It opposes also flexible and adaptable groups to Sime Darby, which appear heavily constrained by its lack of cross-shareholdings and its overwhelming government involvement. Dimension 2 ranks the business groups according to their relative number and proportion of subsidiaries, and to their level of decision delay or internal transaction cost. The fact that this dimensions opposes the two big corporations to the two smallest, suggests that the capital is nevertheless a major factor. Indeed, this dimension also ranks the companies according to their uptake of certification, producing a nice diagonal pattern of the companies across the graph of these 2

<footnote n="1">¹ UPR BioWooeB, CIRAD</footnote>

<footnote n="2">² INTROP, Universiti Putra Malaysia,</footnote>

<footnote n="3">³ CESSMA, Univ Paris Diderot, Sorbonne Paris Cité</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: norfaryanti@upm.edu.my</footnote>
dimensions. Dimension 3 mainly isolates the ownership structure specificities of Olam. We found that ownership structures reflect differences in flexibility, control and transaction costs, but not in ethnicities. Capital and its control, ownership structure, and flexibility explain 97% of the environmental behaviour. It means that existing market-based tools do not engage transnationals at the scale where most of their behaviour is determined. For the first time, inner mechanisms of corporate governance are deciphered in agriculture and forest sustainability. New implications such as the convergence of environmental sustainability with family business sustainability emerged.

<figure><img src="image_137.png" /><figcaption>**Fig. 2.** Factor map of environmental commitment proxies for the companies belonging to WTK, Ta Ann, Olam, and Sime Darby transnationals</figcaption></figure>

**4 Conclusions**

Ownership structures definitely influence corporate strategies. It is an intrinsic feature of the South-East Asian transnationals analysed here. It enables them to decide to quit a market as easily as they decide to adapt to new economic conditions (such as more or less demand for sustainable products). Basically they constantly choose between various relative transaction or organization costs. The extremely intertwined corporate structures appear to be a fundamental adaptation to country risks and unpredictable conditions. Some uncertainties remain regarding some details of the mechanisms of corporate governance of agribusiness transnationals in South-East Asia. More transnationals with various sets of parameters should be studied to draw conclusions on the role of governments, investment strategies, or the mass of stock exchange smallholders in shaping or influencing environmental corporate governance.

*Acknowledgements*. CIFOR, member of the CGIAR “Research Programme 6 Forests, Trees and Agroforestry”, as well as CIRAD, Universiti Putra Malaysia (INTROP), and the SPOP project, co-funded the study which leads to this paper.

**References**

Chandler, A. D. Jr.(1982). The M-form: Industrial groups, American style. *Eur. Econ. Rev.*, **19**, 3-23.

Almeida, H. V., and Wolfenzon, D. (2006). A theory of pyramidal ownership and family business groups. *J. Financ.*, **61**, 2637-2680.

Todeva, E. (2006). *Business networks: strategy and structure*; Routledge studies in business organization and networks. Routledge: London, New York.

De Masi, G., and Gallegati, M. (2012). Bank-firms topology in Italy. *Empir. Econ.*, **43**, 851-866.
# Observatories of territorial practices: a tool to contribute to sustainable development of territories and performance of production systems

Javier Alejandro Vitale ±<fnref n="1" />, Cecilia Inés Aranguren <fnref n="2" /> Marcelo Saavedra <fnref n="3" />, Sandra Elizabeth Ledesma <fnref n="4" />, Erika

Zain El Din <fnref n="5" />, Eduardo Daniel Cittadini *<fnref n="6" />, Roberto Arnaldo Cittadini <fnref n="7" /> & Marc Benoît <fnref n="8" />

## 1 Introduction

In 2014, INTA<fnref n="1" /> launched the National Program for Development and Sustainability Territories (PNDST), including its strategy of implementing Observatories of Territorial Practices (OTP) as a socio-technical device to contribute to the definition and implementation of sustainable territorial development projects<fnref n="2" />. The aim is to better understand the pathways of landscape and farming system at territorial scales (Benoît et al., 2012).

### Thinking about the observatories on territorial practices

Following the typology of observatories given by Dobois (2006), it is promoted that the OTP understand and influence the practices of the diversity of social subjects and the development of the required skills for managing the territorial complexity. In this sense, the OTP are a strategic device for understanding the complexities (Morin, 1973) and the territorial transformations, and for the design of strategies, policies and planning of INTA itself (Fig. 1).

<figure><img src="image_138.png" /><figcaption>Fig. 1. Dimensions of the OTP.</figcaption></figure>

The implementation of the OTP as PNDST strategy is a complex process involving multiple views of various working groups, interdisciplinary, contexts and different territorial scales, inter- and intra-institutional coordination and participation of social subjects of the territory. The development of the OTP is closely related to the theoretical-conceptual approach from which it the territory is conceived as a web of social relations, within which conflicts occur and materialize, and results of previous actions that reveal disputes, conflicting interests and correlations force. As a result of previous actions the territory reveals forms of the past, while also configures a range of opportunities for future territorial action.

<footnote n="1">¹ INTA-Centro Regional Mendoza-San Juan, Mendoza (ARGENTINA)</footnote>

<footnote n="2">² INTA-EEA Balcarce, Balcarce (ARGENTINA)</footnote>

<footnote n="3">³ INTA-Instituto de Investigación en Prospectiva y Políticas Públicas, Ciudad Autónoma de Buenos Aires (ARGENTINA)</footnote>

<footnote n="4">⁴ INTA-AER Cruz del Eje, Cruz del Eje (ARGENTINA)</footnote>

<footnote n="5">⁵ INTA-Centro Regional Tucumán-Santiago del Estero, San Miguel de Tucumán (ARGENTINA)</footnote>

<footnote n="6">⁶ INTA -EEA Chubut, Trelew (ARGENTINA)</footnote>

<footnote n="7">⁷ INTA -Labintex, Montpellier (FRANCE)</footnote>

<footnote n="8">⁸ INRA-SAD ASTER, Mirecourt (FRANCE)</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: vitale.javier@inta.gob.ar</footnote>

<footnote n="1">¹ Instituto Nacional de Tecnología Agropecuaria (Argentina).</footnote>

<footnote n="2">² For OTP, we mean the INTA's Territorial Observatories of the Argentinean Agro-food and Agro-industry System, located in the Regional Centres and articulated with the Regional Projects with Territorial Focus, developed as environments in which INTA facilitates the processes of knowledge construction with territorial social subjects (where Universities have a relevant role) to promote territorial innovation.</footnote>
INTA's strategy for the development and consolidation of OTP consists of a series of steps or stages (Fig. 2). Among them, during 2014 there have been two important activities: a mission of experts from INTA to France (October) in order to know experiences of observatories and subsequently the organization in Argentina of the First International Seminar and Workshop for Motivation/Awareness on Observatories (December), where about 70 researchers and extension workers from various regions of the country participated.

<figure><img src="image_139.png" /><figcaption>Fig. 2. INTA's strategy for the OTP.</figcaption></figure>

## Dialog and reflections

In the Workshop the key debates were centered about the implausibility of the political neutrality of observatories as tools for development and sustainability of the territories; inclusion and participation forms of social subjects to address the complexity and territorial transformations; commitment and institutional support; building political and technical leadership for the implementation of observatories; resource availability and quality of relevant information; articulation and coordination between the various programmatic instruments of INTA and the development of local capacity. Given these tensions, the purpose of PNDST is to understand the complexities and territorial transformations and to influence them from the actions of INTA.

The event allowed us to observe the diversity of perspectives and observatories at INTA and the need to study the territorial practices of social subjects embedded in power and conflicting relations. The need for the establishment of a National Observatory Network of Territorial Practices was also explored, whose main challenge will be to contribute to the development and sustainability of territories from tools collectively constructed for integrating knowledge and action.

In 2015, meetings with management teams of the Regional Centers of INTA are planned, in order to reflect and discuss about the objectives and types of observatories, and the key stages of the cycle, defining an agenda for the implementation of the observatories in different locations and to initiate a process of coordination between the participating institutions.

*Acknowledgements.* This work has been carried out under the National Program for Development and Sustainability of the Territories (INTA, Argentina), and as a part of Agriterris Project.

## References

Benoît, M., Rizzo, D., Marraccini, E., Rapey, H., Thenail, C., Moonen, A.C., Lardon, S., Galli, M., Caron, P., Silvestri, N. & Bonari, E. (2012). Landscape Agronomy: Conceptualizing a new research field in landscape ecology. *Landscape Ecology* 10 (27): 1385-1394.

Dubois, E. (2006). Observatoires de pratiques agricoles et enjeux territoriaux: élaboration d'une typologie, INRA SAD, Rapport de Maîtrise des Sciences et Techniques en Aménagement et Environnement, Version 2, 70 p.

Elverdín, J., Ledesma, S.E., Zain El Din, E. & Cittadini, E. (2014). Programa Nacional para el Desarrollo y la Sustentabilidad de los Territorios: Documento base y estructura organizativa. Ediciones INTA, Ciudad Autónoma de Buenos Aires, Argentina.

Morin, E. (1973). Le Paradigme perdu: la nature humaine. Rééd. Seuil, coll. «Points essais», 1979.
# T8. Co-design and co-innovation with farmers and stakeholders: methods, results and challenges

Chair: Michel Duru, INRA

Co-chairs: Santiago Dogliotti, Universidad de la Republic
Marie-Hélène Jeuffroy, INRA
# Co-design ecologically intensive fish farming systems using agroecology and ecosystem services

Joël Aubin *±1,2, Hélène Rey-Valette <fnref n="3" />, Syndhia Mathé <fnref n="4" />, Aurélie Wilfart <fnref n="1" />,<fnref n="2" />, Marc Legendre <fnref n="5" />, Jacques

Slembrouck <fnref n="5" />, Edurdo Chia <fnref n="4" />, Gérard Masson <fnref n="6" />, Myriam Callier <fnref n="7" />, Jean-Paul Blancheton <fnref n="7" />,
Aurélien Tocqueville <fnref n="8" />, Domenico Caruso <fnref n="5" /> & Pascal Fontaine <fnref n="6" />

## 1 Introduction

The last few decades have shown high growth in global fish farming, whose quantity of production now equals that of fisheries(FAO, 2014). This change is associated with varying degrees of intensification of fish farming among countries. Therefore, many conflicts arise, and the place of fish farming is regularly discussed within regions, both as an opportunity and a risk. Therefore, it is necessary to reconsider the development of fish farming in the context of a growing human population on a planet with limited resources, but also in relation to local issues. Our study aimed to adapt to aquaculture, the concept of ecologically intensive agriculture (Griffon, 2013): use of ecological processes and functions to control pests, reduce pollution, make an efficient use of resources and improve services provided by ecosystems. This approach offers options to re-design aquaculture systems using biophysical and social mechanisms.

## 2 Materials and Methods

We defined the ecological intensification of aquaculture as a process that considers agroecological principles as defined by Altieri (1995), ecosystemic services supplied by the aquaculture ecosystem (MEA, 2005), and issues facing different types of aquaculture worldwide. The objective is not to propose a pre-existing model for implementing ecological intensification, but to identify a variety of possible pathways and describe driving factors, mainly environmental and technico-economic, but also those related to issues in the coordination and governance. In a first step, we built an expended framework to define the aquaculture ecosystem combining various ecosystem and territorial levels (Fig. 1). Then, we performed biophysical and ecological assessments (in particular, Life Cycle Assessment and Emergency Accounting (Wilfart et al., 2013)) coupled with the analysis of the regional stakeholders (fish farming, value chain and administration) perceptions of ecosystem services. Then, we used participatory approach at various stages of our work, in particular, to co-construct with stakeholders various scenarios involving new practices of ecological intensification and performed experiments with the selected practices. The approach is presented in Fig.1. We implemented this approach within six regions selected to cover a variety of aquaculture production systems (from extensive polyculture to monoculture activities in ponds or in recirculating water systems), ecosystems and socio-economic contexts in France, Brazil and Indonesia.

## 3 Results – Discussion

The project generated different levels of results. We first defined an ecologically intensive aqua-ecosystem, based on the flows of inputs, the variety of services provided and the different ecosystem involved (Fig. 2). We identified seven objectives to guide the adoption and the implementation of ecological intensification combining technical, environmental and social considerations:

1. Minimize dependence on external resource

2. Increase performance of aquaculture production systems and product quality

3. Improve robustness, flexibility, and resilience of systems via integration and functional complementarity

4. Diversify market-oriented ecosystem services of aquaculture systems

5. Promote recognition of services and better use of skills and know-how

6. Improve territorial integration of aquaculture systems by promoting production of non-market ecosystem services

7. Adapt mechanisms and instruments of territorial governance and help stakeholders participate

<footnote n="1">¹ INRA, UMR1069 Sol Agro et hydrosystème Spatialisation, F-35000 Rennes, France</footnote>

<footnote n="2">² AGROCAMPUS OUEST, F-35000 Rennes, France</footnote>

<footnote n="3">³ Université de Montpellier 1, LAMETA, F-34000 Montpellier, France</footnote>

<footnote n="4">⁴ CIRAD, UMR Innovation, F-34000 Montpellier, France</footnote>

<footnote n="5">5 IRD, UMR ISEM, F-34000 Montpellier, France</footnote>

<footnote n="6">⁶ Université de Lorraine, F-54000 Nancy, France</footnote>

<footnote n="7">⁷ IFREMER, UMR ECOSYM, F-34250 Palavas les Flots, France</footnote>

<footnote n="8">⁸ ITAVI, Service Aquaculture, F-76000 Rouen, France</footnote>

<footnote>* Speaker</footnote>

<footnote>‡ Corresponding author: joel.aubin@rennes.inra.fr</footnote>
Then, we proposed a set of indicators to monitor the application and effects of ecological intensification in aquaculture.

<figure><img src="image_140.png" /><figcaption>**Fig. 1.** Approach to co-design ecologically intensive aquaculture</figcaption></figure>

<figure><img src="image_141.png" /><figcaption>**Fig. 2.** Framework for ecologically intensive aquaculture</figcaption></figure>

Different profiles of fish farmers were defined based on their ability to adopt the concept and on the way they apply it: only modifying input/output of the farming system, using the ecosystem services concepts to redefine the goals of their farming system, or to redesign the farming system. Regardless of the situation, adoption of the concept involves concerted efforts that depend on conditions for adopting innovations but also processes of collective engagement. Through learning processes during the various interactions with the stakeholders, the project also helped to modify the perception of the roles of aquaculture at the territorial level.

At the site level, experiments showed the environmental and economic potential of practices based on nutrient recycling, the association of fish and/or plant species, and new production systems were proposed. In Indonesia, a monoculture of panga (*Pangasianodon hypophthalmus*) was changed into a polyculture associating, a cage of panga, a floating plant (*Lemna minor*) and a high value herbivorous species (*Ophronemus goramy*). In France, the reuse of nutrients from outlet of intensive farming of salmon (*Salmosalar*) or carp (*Cyprinuscarpio*) was proposed for macrophytes co-production and for the creation of remediation spaces as a support for biodiversity. In Brazil, the concept of ecological intensification based on ecological services, leaded to propose new practices in pond effluent management and helped to conduct negotiations with territories managers for the sustainability of the activity.

## 4 Conclusions

The approach developed in this project, offers new perspectives to reconsider fish farming development, taking account its ecological and territorial integration. The generic characteristic of the approach provides a broad potentiality of application for various terrestrial systems. The approach and main conclusions of the project were synthetized in a guide for ecological intensification of fish farming (Aubin *et al.*, 2014).

*Acknowledgements.* The authors acknowledge particularly Ediwarman, Joni Haryadi, Tri HeruPrihadi, Jorge de Matos Casaca, and Sergio Tamassia, The fish farmers of Brenne, Lorraine, Muara Jambi (Indonesia), and Santa Catarina (Brazil), as well as Emmanuel Delcroix (BDV company), Arnaud Lefevre, Sandrine Marchand, the students who conducted a part of the study and the SYSTERRA program of the French National Research Agency (ANR-09-STRA-08) for its financial support.

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