# Re-disigned farming system as a key for biodiversity conservation in Uruguay

Oscar Blumetto<sup>*±1*</sup>, Santiago Scarlato<sup>1</sup>, Guadalupe Tiscornia<sup>1</sup>, Andrés Castagna<sup>1</sup>, Felipe García<sup>1</sup>, Gerónimo Cardozo<sup>2</sup> & Andrea Ruggia<sup>1</sup>

## 1 Introduction

Only 0,6 % of Uruguayan land is under somehow protection such as National Parks or Protected Landscape. In this context wild life and biodiversity conservation in general depends on private areas which major part are under different production systems. Taking this into consideration, this work was developed in seven livestock farms (cattle and sheep) using a co-innovation approach (Dogliotti et al, 2014) that allows re-designing systems increasing meat productivity, reducing environmental crisis risk and reducing overgrazing. As main productive results, after two years of project, meat production increased 23%. Forage biomass availability increased 70%, from 1473 ± 644 kg DM•ha⁻<fnref n="1" /> to 2111 ± 979 kg DM•ha⁻<fnref n="1" />. These results are mainly based on livestock stocking rate reduction, a decrease of the sheep to cattle ratio and increase forage allowance, considering animal category and physiological state. Although productivity results are promissory, it is necessary to evaluate the effect of these management strategies on ecosystem functions. In the present article we are exposing the status of the measured environmental variables, relative to biodiversity conservation, in the redesigned systems.

## 2 Materials and Methods

Water was seasonally (four times a year, one on each season) sampled on streams and dams in at least six points of each farm. Turbidity, dissolved oxygen, total dissolute solid, nitrate, pH and temperature were measured with Hanna HI 9828 Multiparameter meter. Phosphorus was determined with Hanna HI736 Handheld Colorimeter -Phosphorus Ultra Low Range. A water quality index -WQI-(Michalos, 2014) was performed with media values of selected variables throw the following formula: $WQI = \sum ni=1 (Ci.Pi)(\sum ni=1 Pi)^{-1}$, where n represent the number of total variables, Ci the value assigned to variable i of the normalization and P is the value between 1 and 4, were 4 are assigned to the variable more important for aquatic life (e.g. dissolved oxygen).

Biodiversity was evaluated throw studying botanic composition of the herbaceous community, birds and spiders assemblages and an Ecosystem Integrity Index (EII).

Herbaceous plant communities of natural grassland were annually evaluated in the reference units using the point quadrate method. This method was used in each transect relieving all species present at contact points every 50 centimetres. Distribution of species was studied by calculating specific frequencies, richness and diversity indices (Daget & Poissonet, 1971).

Birds were monitored each season following line transects (Sutherland, 2006) at three reference units in each plot, two on natural grasslands (NG) and one in seeded pastures (SP). Transect was performed in 300 m segment, totalizing 900 to 1800 m long depending on the area of the reference unit. In every case the presence of species using habitat and number of individual was recorded.

Spiders sampling was done with sweep netting directly on grasses (Sutherland, 2006). Ten samples of 20 sweeps were taken in every reference unit, determining species presence, number of individuals and the belonging gremlies of each species. Natural grasslands and seeded prairies were sampled.

Ecosystem Integrity Index is a tool on developing phase and its main objective is to make a fast evaluation of the estate of the ecosystem relative to an "optimal" in a low intervention natural ecosystem. It is a 10 points scale index (from 0 to 5, 0.5 step) including four evaluated aspects: structure, species, soil erosion and state of streams including water, riparian zone and vegetation.

In order to display specific results and properly discussion, information of one case study is presented. This case is a livestock grazing system based on natural grasslands (364 ha) located in Eastern Uruguay.

## 3 Discussion

General water quality was good (>60) in both streams and dams, although we found turbidity and dissolved oxygen values at suboptimal levels for dams, which significantly reduce the index. WQI was 91 and 68, for streams and dams respectively. In table 2 selected parameters for the WQI are presented.

<footnote n="1"><sup>1</sup> Las Brujas Experimental Station, National Institute Agriculture Research (INIA), Uruguay <sup>1</sup>Trenta y Tres Experimental Station, National Institute Agriculture Research (INIA), Uruguay</footnote>

<footnote>* Speaker</footnote>

<footnote>‡ Corresponding author: oblumetto@inia.org.uy</footnote>
<table><caption>Table 2. Parameters registered for analysed water (mean± SD)</caption><thead><tr><th>Type</th><th>DO ppm</th><th>pH</th><th>Turbidity FNU</th><th>TDS ppm</th><th>NO<sup>3</sup></th><th>TP ppb</th><th>WQI</th></tr></thead><tbody><tr><td>Streams</td><td>7.1±2.7</td><td>7.5±0.4</td><td>5.5±10.8</td><td>87.8±21.2</td><td>0.42±0.1</td><td>47.5±18.8</td><td>91</td></tr><tr><td>Dams</td><td>5.3±2.9</td><td>7.6±0.4</td><td>122.0±33.4</td><td>49.7±43.8</td><td>0.40±0.1</td><td>49.3±21.8</td><td>68</td></tr></tbody></table>

References: DO=dissolved oxygen, TDS=total dissolved solids, NO3= nitrates, TP= total phosphorus and WQI=water quality index

Regarding to vegetal biodiversity, 61 species of herbaceous plants and 25 species of trees associated to grasslands were found. Ten species represent 74% of the soil covering in the reference plots at the beginning of the project, those species were: *Axonopus affinis*, *Cynodon dactylon*, *Piptochaetium montevidense*, *Chevreulia sarmentosa*, *Ciperus sp.*, *Richardia humistrata*, *Panicum sabulorum*, *Dichondra microcalix*, *Paspalum quadrifarium* and *Danthonia rhizomata*.

After two year of project implementation, the proportion and the ranking of species changed due to a combination of factors both grazing management and climate, specifically the high levels of rainfall recorded in the past years. There was an increase of summer grasses species, specially *Axonopus affinis* and others with similar functional type while *Ciperus sp.*, *Juncus sp.* and *Mimosa australis* also increased. It is also remarkable the reduction of the presence on *Cynodon dactylon* which is an alien plant considered a weed and the increase of *Coelerhachis selloana*, a high quality forage supplier. Other changes were registered in structure and average forage mass which increased from 1152 to 1718 kg.ha⁻¹. Besides of changes in species contributions, there was no evidence of any species disappearance.

Considering bird diversity, 59 birds species were found during the first year (transects method), and after two years 88 species were registered, and reach 100 species considering those registered out of transect. Accumulation curves showed differences in the estimated richness project in four years sampling for natural grasslands (89), and annual pastures (68). Eight species considered as national conservation priority (Soutullo et al, 2013) were recorded: *Cariama cristata*, *Coragyps atratus*, *Donacospiza albifrons*, *Gnorimopsar chopi*, *Lochmias nematura*, *Nothura maculosa*, *Picumnus nebulosus*, *Rhea americana* and *Rynchotus rufescens*. The effect of richness reduction in seeded area is considered to be low impact due to the low fraction of the total farm area dedicated to this used, only 11.3%. Most of the production areas are covered with natural vegetation communities providing birds with rich habitat and a well-preserved structural complexity.

In relation to spiders communities, nine families belonging to seven different gilds and a total of 20 species were registered. In both evaluated situations, natural grasslands and annual pastures, the most frequent gild was the orbicular web builders and *Larinia vivittata* the most frequent species, although in NG the population was three time higher. Irregular web builder and ambush hunters' gilds were found exclusively in NG.

General ecosystem integrity index of whole farm was 4.0, which result from the integration of "good" indexes in the majority of the area and lower values in a relative small zone of more intensive production used. In Fig.1 the distribution of index values for each paddock is shown.

<figure><img src="image_142.png" /><figcaption>Fig. 1. Map with Ecosystem Integrity Index for each plot of the farm.</figcaption></figure>

## 4 Conclusions

We found a general well preserved natural ecosystem that support the studied production systems, including biodiversity with a wide range of flora and fauna species. Initial records and in-course samplings showed the stability of wildlife species and favorable changes in the herbaceous species composition and structure.

## References

* Daget, P. and Poissonet, J. (1971) Une methode d'analyse phytologique des prairies, criteres d'application. Annales Agronomiques. 22(1): 5-41.

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

* Dupont, D., Fabris, P.H. & Quark, J. (2014). An improved sugar-use efficiency for leprechaun grown under boreal regions. Field of Gold Research, 67, 207-214. (Times New Roman, justified, pt. 8)

* Michalos, A. (2014). Water Quality Index (WQI). In Encyclopedia of Quality of Life and Well-Being Research. Springer, Netherlands.

* Soutullo A, Clavijo, C & Martínez-Lanfranco, JA (2013). Especies prioritarias para la conservación en Uruguay. Vertebrados, moluscos continentales y plantas vasculares. SNAP/DINAMA/MVOTMA y DICYT/ MEC, Montevideo. 222 pp.

* Sutherland, W. J. (2006) Ecological census techniques: a handbook. Cambridge University Press, Cambridge, 432p.
# Co-design of improved climbing bean technologies for smallholder farmers in Uganda

Esther Ronner *±1, Katrien Descheemaeker <fnref n="1" />, Conny Almekinders <fnref n="2" />, Peter Ebanyat <fnref n="3" /> & Ken Giller <fnref n="1" />

## 1 Introduction

Technologies to improve agricultural production in Africa need to address the needs and objectives of farmers in different bio-physical and socio-economic environments. A participatory process can facilitate the development of such technologies by taking into account farmers’ feedback and adaptations. However, participatory processes are time and data demanding, and efficient implementation and out-scaling in large-scale projects remains a challenge. In this study we used a participatory process to co-design a ‘basket of options’ of improved climbing bean technologies for specific recommendation domains (based on bio-physical factors and farm type), and to develop principles and guidelines for technology development in new areas or similar projects.

## 2 Materials and Methods

Two sites in the eastern and southwestern highlands of Uganda were selected with similar agro-ecologies but differences in market access, use of inputs and access to trees for staking. We conducted an iterative cycle of co-design, implementation and evaluation: a first set of improved climbing bean technologies was tested in on-farm demonstrations in the first season of 2014 (season A). This first set was based on initial interviews with a random selection of farmers. Staking was reported as main challenge, so improved technologies included alternative, low-cost staking materials: sisal string and banana fibre. We also included tripods, which were expected to enhance yields but were also more labour intensive. Besides, we compared a local and an improved (NABE 26C) variety, with and without manure and TSP fertilizer. Different types of farmers (men and women from low (LRE), medium (MRE) and high (HRE) resource endowed households) evaluated these technologies by pairwise comparison at planting, staking, podding and harvest. Farmers also indicated their reasons for preference of the treatments, feeding into an assessment of farmer criteria for appraisal of the new climbing bean technologies. The evaluations formed the basis for a session where farmers, researchers, extension officers and NGO staff co-designed treatments for new demonstrations in the second season of 2014 (season B). In this second season, the cycle of evaluations and co-design was repeated.

## 3 Results – Discussion

Among the staking methods, sisal strings and single stakes performed best in terms of yield (Fig. 1a). We hypothesized that LRE and MRE farmers would prefer technology options which maximize returns to labour, and HRE farmers options which maximize returns to land. Remarkably, farmers of all types preferred the more labour intensive tripods (Fig. 1b), which yielded less than single stakes. The most frequently mentioned reason for preference was that tripods were strong and prevented the stakes from falling over (Fig. 1c). LRE farmers preferred the low-cost banana fibre ropes relatively more often than MRE and HRE farmers, and mentioned costs and labour demand more frequently.

b.

<figure><img src="image_143.png" /></figure>

c.

<figure><img src="image_144.png" /></figure>

<figure><img src="image_145.png" /><figcaption>**Fig. 1a:** Climbing bean grain yield (kg/ha) for alternative staking methods in 2014A (n=7).</figcaption></figure>

<caption>**Fig. 1b:** Pairwise comparison of staking methods by high (HRE), medium (MRE) and low (LRE) resource endowed farmers; % is number of times method was preferred, divided by total number of comparisons.</caption>

<caption>**Fig. 1c:** Categories of reasons for preference staking methods; % is number of times the reason was mentioned divided by total number of reasons.</caption>

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

<footnote n="2">² Knowledge, Technology and Innovation, Wageningen University, The Netherlands</footnote>

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

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: esther.ronner@wur.nl</footnote>
We also hypothesized that female farmers would rank technology options with little capital and labour input higher than male farmers, as we expected female farmers to have less access to capital and labour within the household. Preference for staking methods did not differ between men and women: both ranked tripods highest and mentioned similar reasons (results not shown). But in the comparison of varieties with and without inputs (Fig. 2a), female farmers had a stronger preference for the local variety without inputs, and male farmers for the improved variety with inputs (Fig. 2b). Women more often than men mentioned that they could not afford fertilizer, and valued different characteristics of the varieties than men: both women and men mentioned yield as the most important criterion (27% vs 42%), but women placed relatively more emphasis on characteristics of the leaves (used as vegetable) (19% vs 14%), early maturity (8% vs 4%), performance without fertilizer (5% vs 2%) and taste (4% vs 0%). LRE farmers clearly preferred the treatments without inputs more strongly than MRE and HRE farmers (Fig. 2c). LRE farmers mentioned that they could not afford fertilizer and that they found the yield without fertilizer still acceptable, which is not surprising considering the similar yields of the treatments with and without inputs.

<figure><img src="image_146.png" /><figcaption>**Fig. 2a:** Climbing bean grain yield (kg/ha) for local and improved variety with and without inputs in 2014A (n=4). **Fig. 2b&c:** Pairwise comparison of local and improved variety with and without inputs by women and men (b.) and high (HRE), medium (MRE) and low (LRE) resource endowed farmers (c.); % is number of times treatment was preferred, divided by total number of comparisons.</figcaption></figure>

Results of the evaluations were used to co-design treatments for subsequent seasons. Notable contributions from farmers were refinement of treatments (cost-reduction for sisal string and banana fibre methods, labour and risk reduction by planting two seeds per hole instead of one to avoid gap-filling); solutions for locally identified problems (use of shorter stakes to reduce damage by birds); and new treatments to experiment with ("will cutting the growing tip of beans at a certain height enhance yields?"). Research, extension and NGO staff contributed with knowledge and technologies from elsewhere.

The co-design process resulted in a long list of about 20 promising treatments, including abovementioned suggestions from farmers. This long list can be used by extension agents or other projects to select options for cultivation of climbing beans in similar recommendation domains, and for further testing and tailoring. Offering a range of options rather than narrowing down to best yielding technologies is important considering the diversity of users. However, as it will be impossible to develop 'best-fit' technology packages for every type of farmer in every environment, 'flexible recommendations' that extension officers can pass on to farmers will also help to take into account different objectives and constraints (e.g. what is a good option for 'best yield' 'short maturity time', 'low costs' or 'little labour') (Collinson, 2000).

## 4 Conclusions

Farmers of different types (wealth, gender) have different preferences and use different criteria to evaluate new technologies. Researchers often focus on 'yield', but farmers use yield next to other criteria. Co-design of technologies helps getting insight in differences between users, but will remain challenging in many large-scale projects. Such projects may benefit from offering farmers a range of (stepwise) options to choose from, and from flexible recommendations that address different objectives and challenges of different types of users.

*Acknowledgements.* This research was carried out through N2Africa: Putting Nitrogen fixation to work for smallholder farmers in Africa (www.N2Africa.org) funded by the Bill & Melinda Gates Foundation and implemented in 11 countries in sub-Saharan Africa.

## References

COLLINSON, M. P. 2000. *A History of Farming Systems Research*, Rome & New York, FAO & CABI.
# Iterative design and *ex ante* assessment of cropping systems including energy crops in the Dijon plain (France)

Anabelle Laurent *±1,2*, Claire Lesur-Dumoulin <fnref n="3" />, Raymond Reau <fnref n="1" />,<fnref n="2" />, Laurence Guichard <fnref n="1" />,<fnref n="2" />, Marion Soulié <fnref n="1" />,<fnref n="2" /> & Chantal Loyce ±2,1

## 1 Introduction

The European Directive on Renewable Energy (2009) defined the reduction of greenhouse gas (GHG) emissions as one of the main criteria that must be fulfilled for biofuels produced from energy crops. As these crops have specific lifespans and short and long-term effects on the following crops of the crop sequence, an assessment at the cropping system (CS) scale is required. Our study aimed at designing and assessing CS including energy crops in the Dijon plain (Bourgogne region, France), where perennial (e.g. *Miscanthus x giganteus*, hereafter referred as *M. giganteus*), pluriannual (e.g. alfalfa) and annual (e.g. triticale) crops have already been grown and could be candidate to produce bioenergy.

## 2 Materials and Methods

The main target that energy-oriented CS aimed at fulfilling was to decrease GHG net emissions (i.e. N₂O and CO₂ emissions minus carbon storage in the soil) by 75% compared to the reference CS (REF) mostly practiced in the study area (i.e. characterized by the crop succession “rapeseed-wheat-barley” and an intensive crop management system for each crop). In addition, we also wanted these CS to decrease the Treatment Frequency Index (TFI) by 50%. We developed a four-step approach (Fig. 1), based on an iterative process including design and *ex ante* assessment steps (Meynard *et al.*, 2012): a design workshop, as described by Reau *et al.* (2012), involving scientific and local experts (step 1) was followed by an *ex ante* assessment step (step 2), where a set of tools were used, such as PERSYST (Guichard *et al.*, 2010). Eight scenarios combining minimum and maximum inputs prices, crops prices and yields were defined. The results of this first assessment were then presented to the experts, who provided us with modifications on CS1 (step 3). These new CS (CS1' and CS2') were then assessed in step 4.

<figure><img src="image_147.png" /><figcaption>Fig. 1. A four-step approach used to design and assess CS including energy crops</figcaption></figure>

## 3 Results – Discussion

Two innovative CS (CS1 and CS2) including *M. giganteus* as an energy crop were designed by the experts during the first step. CS1 is presented in Fig. 2 as an example. Its assessment showed that the main target (i.e. decreasing GHG net emissions by 75% compared to REF) has been reached (Fig. 3). This result is mainly due to *M. giganteus*, which uses low quantities of N fertilizer (involving low emissions of N₂O), and stores carbon in the soil: growing *M. giganteus* for 15 years indeed improves carbon storage of 6 t C ha⁻<fnref n="1" /> +/- 3 t C ha⁻<fnref n="1" /> (Poeplau

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

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

<footnote n="3">³ INRA, UE 411 Domaine Expérimental Alénya-Roussillon, F-66200, Alénya, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: anabelle.laurent@grignon.inra.fr & chantal.loyce@grignon.inra.fr</footnote>
& Don, 2014). Thanks to *M. giganteus* and alfalfa in the CS1, the second target (to decrease the TFI by 50% compared to REF) has been reached too. However, REF was more profitable, mainly because of the low price assigned to *M. giganteus* (Fig. 3).

<figure><img src="image_148.png" /><figcaption>Fig. 2. Description of the cropping system CS1. N= nitrogen fertilizer</figcaption></figure>

During the third step, experts recommended to reduce the lifespan of *M. giganteus* (10 years instead of 15 years) to improve the profitability of the energy-oriented CS while benefiting from the positive effect of *M. giganteus* on soil carbon storage. During the fourth step, CS1' (with the reduction of the lifespan of *M. giganteus* reduced) was assessed. Results showed that GHG net emissions of CS1' increased by 29.7% compared to CS1. However the semi-net margin of CS1' was not improved compared to the semi-net margin of CS1 (Fig. 3). Hence, results showed that CS1 and CS1' were beneficial regarding environmental and energy indicators, whereas REF was more profitable regarding semi-net margin in seven out of eight scenarios.

<table><thead><tr><td>Indicators</td><td>Unit</td><td>CS1</td><td>CS1'</td><td>REF</td></tr></thead><tbody><tr><td>GHG net emissions</td><td>kg CO<sub>2</sub> eq. ha<sup>-1</sup> year<sup>-1</sup></td><td>-128</td><td>38</td><td>2682</td></tr><tr><td>TFI</td><td>no unit</td><td>0.44</td><td>0.44</td><td>6.63</td></tr><tr><td>Average NO<sub>3</sub><sup>-</sup> losses</td><td>mg NO<sub>3</sub><sup>-</sup> l<sup>-1</sup> y<sup>-1</sup></td><td>8.16</td><td>10.30</td><td>28.33</td></tr><tr><td>Semi-net margin</td><td>€ ha<sup>-1</sup></td><td>927</td><td>927</td><td>1327</td></tr><tr><td>Energy efficiency</td><td>no unit</td><td>77</td><td>68</td><td>10</td></tr><tr><td>Energy cost</td><td>GJ ha<sup>-1</sup> year<sup>-1</sup></td><td>4.42</td><td>5.80</td><td>12.74</td></tr><tr><td>Food capacity</td><td>no unit</td><td>0.32</td><td>0.32</td><td>0.97</td></tr></tbody></table>

<figure><img src="image_149.png" /><figcaption>Fig. 3. Overview of the multicriteria assessment of CS1 and CS1' in comparison with REF. GHG = Greenhouse gas net emissions; TFI = treatment frequency index; SNM = semi-net margin; EE = energy efficiency; EC = energy costs; FC = food capacity. This scenario corresponds to maximum crops prices, maximum yield and minimum input prices.</figcaption></figure>

## 4 Conclusions

We observed in this case study that a tradeoff between (i) environmental and energetical impacts on one hand and (ii) profitability and food capacity on the other hand needs to be found to design mixed CS, i.e. CS including crops dedicated to provide food and bioenergy. Lastly, these prototypes of CS could contribute to feed an integrated assessment in the Dijon plain at the supply area scale.

*Acknowledgements.* This work has been funded under the EU seventh Framework Programme by the LogistEC project No. 311858: Logistics for Energy Crops' Biomass. The views expressed in this work are the sole responsibility of the authors and do not necessary reflect the views of the European Commission. We thank all the participants of the design workshop.

## References

* Directive 2009/28/EC (2009). Directive 2009/28/EC of the European parliament and of the Council on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77EC and 2003/30EC.

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

* Poeplau, C. & Don, A. (2014). Soil carbon changes under Miscanthus driven by C 4 accumulation and C 3 decompostion - toward a default sequestration function. GCB Bioenergy 6, 327-338.

* Meynard, J.M., Dedieu, B., Bos, A.P., 2012. Re-design and co-design of farming systems. An overview of methods and practices. In: "Farming system research into the 21st century: The New dynamic. Darnhofer, I., Gibon, D., Dedieu, B., 407-432.

* Reau, R., Monnot, L.-A., Schaub, A., Munier-Jolain, N., Pambou, I., Bockstaller, C., Cariolle, M., Chabert, A. & Dumans, P. (2012). Les ateliers de conception de systèmes de culture pour construire, évaluer et identifier des prototypes prometteurs. *Innovations Agronomiques, 20*, 5-33.
# Designing agroforestry systems for food production and provision of other ecosystems services: cases in the sub-humid tropics of Nicaragua

Diego Valbuena *±<fnref n="1" />, Pablo Siles <fnref n="1" />, Aracely Castro <fnref n="1" />,Steven Fonte <fnref n="2" />, Martín Mena <fnref n="1" />, Orlando Téllez <fnref n="1" />, Laurent Rousseau <fnref n="3" />, Reynaldo Mendoza <fnref n="4" />, Falguni Guharay <fnref n="1" /> & Rein van der Hoek <fnref n="1" />

## 1 Introduction

The provision of food and other key ecosystem services in rural landscapes of the sub-humid tropics of Central America are increasingly at risk as a result of unsustainable management practices, high potential for soil degradation and climate variability (FAO et al., 2012). A promising farming practice to restore ecological processes and the provision of ecosystem services is the use of agroforestry systems (AFS), which combine crops/pastures and trees to improve agro-ecosystem functioning, including biomass production, soil and water retention (Jose, 2009). AFS, however, can be difficult to implement and can generate trade-offs between ecosystem services, as well as between (limiting) resources such as labor, water, light and nutrients at both plot and farm scales (German et al., 2006). Better farming, development and research interactions and approaches are needed to develop AFS and farming systems that account for farmers' objectives, while restoring the provision of ecosystem services in degraded rural landscapes. The objective of this study was to evaluate the effect of AFS in restoring the provisioning of ecosystem services at the plot level, used on-farm experiments established with farmers and other partners in Nicaragua.

## 2 Materials and Methods

On-farm experiments were established with farmers and others partners at two sites in the sub-humid tropics of Nicaragua. Each site had 8 on-farm experiments comparing two major systems: AFS with maize/beans (i.e. Quesungual), and silvopastoral systems. In each system, different treatments or components were compared: soil cover (burning vs. mulching crop residues), maize/bean varieties (traditional vs. improved) and trees (with vs. without) in the Quesungual system (5 treatments); and grass species (traditional vs. *Brachiaria spp*) and trees (with vs. without) in a silvopastoral system (3 treatments). Different indicators were monitored during 2011 and 2013 to quantify the potential impact of these treatments in soil health, crop and livestock productivity, C sequestration and biodiversity. Non-parametric data analyses (Mann-Whitney Test) were conducted to evaluate potential differences between treatments of key selected indicators, specifically the potential influence of variety/species selection and tree presence in both Quesungual and silvopastoral systems.

## 3 Results – Discussion

These results indicate that the presence of trees and proper management (e.g. timely pruning, planting density) in croplands and pastures did not have a significant impact on maize and livestock productivity in the on-farm experiments (Fig. 1). In fact, treatments with trees had significantly higher bean productivity, particularly in 2013 (averages 0.9vs. 0.7t ha⁻<fnref n="1" />), likely a result of improved water use efficiency. In the case of grasslands, the use of *Brachiaria spp.* and regulated grazing showed higher livestock productivity compared with the traditional pasture management (averages 3.8 vs. 2.8 L cow day⁻<fnref n="1" /> in 2013), independent of the presence of trees. This indicates that improved pastures and livestock management can be combined with trees to enhance productivity and reduce potential environmental impact.

As expected, the presence of trees increased both tree diversity and above-ground C storage under Quesungual management (Fig. 1B). For tree diversity, the Quesungual system demonstrated similar values as some of the surrounding secondary forests. In terms of above-ground C, Quesungual systems stored 5-20 t ha⁻<fnref n="1" />. This reinforces the notion that AFS can bring multiple benefits to rural landscapes in this region, and maintains the soil functioning (Rousseau et al., 2013). A major challenge remains as to how farmers could benefit from ecosystem services that can have a positive impact for a larger population in emerging economies, where successful payment for ecosystem services is rare.

<footnote n="1">¹ International Center for Tropical Agriculture (CIAT), Nicaragua</footnote>

<footnote n="2">² University of California, Davis USA</footnote>

<footnote n="3">³ Université du Québec a Montréal, Canada</footnote>

<footnote n="4">⁴ Universidad Nacional Agraria (UNA), Nicaragua</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: d.valbuena@cgiar.org</footnote>
<figure><img src="image_150.png" /><figcaption>**Fig. 1.** Comparison of treatments: A. bean productivity in Quesungual across years; B. Livestock productivity in silvopastoral systems across treatments.</figcaption></figure>

These results suggest that the proper combination of a tree component in farming systems of the sub-humid tropics of Central America do not generate major trade-offs with food production. In fact, the combination of trees with other good agronomic practices (e.g. not burning) can enhance the overall provision of ecosystems services at the plot level, thus restoring soils and multiple ecological processes that can benefit farmers and rural communities (e.g. water regulation, C sequestration, biodiversity). However, the implications of these results will depend on the degree of agricultural intensification of the farming systems, which is also related to their ecological context and market integration.

<figure><img src="image_151.png" /><figcaption>**Fig. 2.** Comparison of tree diversity and above-ground C for three treatments or land use systems.</figcaption></figure>

## 4 Conclusions

The development of more sustainable farming and land use systems in rural landscapes of the sub-humid tropics of Central America requires a close interaction with farmers and other partners (i.e., co-design) to build common knowledge on social-ecological systems, as well as to facilitate the adoption and adaptation of technologies relevant to farmers and to the specific contexts associated with rural landscapes.

*Acknowledgements.* We would like to thank the farmers who participated in this study, as well as INTAfor their research support. This research is part of the CGIAR Research Programmes Humidtopics and WLE. This study was funded by the Austrian Development Agency.

## References

* FAO, ACF, & ECHO. (2012). *Estudio de Caracterización del Corredor Seco Centroamericano, Tomo II* (p. 90). Honduras.

* German, L. A., Kidane, B., & Shemdoe, R. (2006). Social and environmental trade-offs in tree species selection: a methodology for identifying niche incompatibilities in agroforestry. *Environment, Development and Sustainability, 8*(4), 535–552. doi:10.1007/s10668-006-9054-3

* Jose, S. (2009). Agroforestry for ecosystem services and environmental benefits: an overview. *Agroforestry Systems, 76*(1), 1–10. doi:10.1007/s10457-009-9229-7

* Rousseau, L., Fonte, S. J., Téllez, O., van der Hoek, R., & Lavelle, P. (2013). Soil macrofauna as indicators of soil quality and land use impacts in smallholder agroecosystems of western Nicaragua. *Ecological Indicators, 27*(0), 71–82. doi:http://dx.doi.org/10.1016/j.ecolind.2012.11.020
# Managing pasture-herd interactions in livestock family farm systems based on natural grasslands in Uruguay

Andrea Ruggia *±1, Santiago Scarlato <fnref n="1" />, Gerónimo Cardozo <fnref n="1" />, Verónica Aguerre <fnref n="1" />, Santiago Dogliotti <fnref n="2" />,
Walter Rossing <fnref n="3" /> & Pablo Tittonell<fnref n="3" />

## 1 Introduction

Livestock production in Uruguay involves the management of C4-species dominated natural grasslands, which cover more than 70% of the country's surface area. Almost 80% of the farms specialized in beef and wool production are family farms, which usually exhibit low sustainability due to low family income and grassland overgrazing. Historically, natural grasslands have been managed with low forage heights (2 to 4 cm), and consequently low leaf area indices, reducing the capacity of the sward to intercept light and photosynthesize, and providing niches for invasion by low productive grass species. As a result, grassland productivity has been low, and risk of erosion and loss of biodiversity high. Experimental evidence suggests that improving management of the pasture-herd interactions by seasonal modulation of animal density could improve natural grassland growth and increase meat production, which would contribute to the design of more resilient systems less vulnerable to droughts. To test this hypothesis at the scale of real family farms, a co-innovation approach was implemented on seven pilot farms in the east of Uruguay with the aim of improving family income and reducing natural resource degradation.

## 2 Materials and Methods

A co-innovation approach (Dogliotti et al., 2014) was implemented during two years in seven pilot farms involving family livestock grazing systems based on natural grasslands, located in the East of Uruguay. The approach involved characterization and diagnosis of the farm system's sustainability, followed by cycles of re-design, implementation, and monitoring of system evolution. Proposals for re-design were based on changes in management practices without adding external inputs and without increasing costs. The relevance of increasing the amount of standing biomass and improving pasture-herd management at farm level was discussed between technicians and farmers. Productivity (meat productivity, stocking rate, sheep to cattle ratio, kg of weaning calf per breeding cow and forage allowance), and economic (net income) indicators were estimated for the three previous years before starting the project from farmers' records, and records were kept during the project. After starting the project also forage mass (Haydock & Shaw, 1975) and forage height (Barthram, 1986) were measured twice per season in all farms.

## 3 Discussion

During characterization and diagnosis at farm level, we found low physical and economic results and natural resource degradation. Low values of standing biomass (1.183±335 kg of dry matter (DM) ha⁻<fnref n="1" />) were found and areas with low soil cover, presence of low productive grass species and problems of forage structure, revealing natural grassland degradations were observed. Low biomass, and as a consequence low leaf area index values reduced the capacity of the sward to intercept light and photosynthesize, affecting pasture growth. Inadequate feed supply resulted in low cattle sale weights and low animal reproductive efficiency, which led to low meat productivity and family income. Those problems were discussed with farmers and agreed. Historically, farmers managed their systems with large numbers of animals and high sheep to cattle ratio. Stocking rate is one of the most important management variables in grassland ecosystems, since it determines the relationship between forage offer and animal demand. Proposals for redesign were done by technicians and discussed with farmers. The main strategy elaborated with and implemented by the farmers was to increase standing biomass and forage production of the grasslands by reducing stocking rate and the sheep-to- cattle ratio, and adjusting allocation of animal categories to paddocks over the course of the year. Also the use of low cost breeding practices, such as allocation of different animal categories to different paddocks according to standing biomass, feed provision according to body condition, temporary weaning, definitive weaning of calves in autumn, and extra attention to feeding female calves and heifers during their first winter (Quintans & Scarsi 2013). After two years of implementation of the redesign proposal, significant improvements were achieved at farm level.

On average, farms decreased total stocking rate by 8% (from 0.92 Livestock Unit (LU) to 0.84 LU ha⁻<fnref n="1" />) and decreased the sheep to cattle ratio by 34% (from 2.6 to 1.4). The improved management of the pasture-herd interaction resulted in

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

<footnote n="2">² Faculty of Agronomy, Fagro, Udelar, Uruguay</footnote>

<footnote n="3">³ Farming Systems Ecology, Plant Sciences, Wageningen University, The Netherlands</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: aruggia@inia.org.uy</footnote>
an increase in standing biomass (Fig. 1). The average forage height at the beginning of the project (summer 2012-2013), the moment in which cows are lactating and should get pregnant again, was half the amount required according to Soca and Orcasberro (1992) (Fig. 2). The next summer (2013-2014) average forage height was already at the recommended level and it was kept above requirements till spring 2014.

<figure><img src="image_152.png" /><figcaption>Figure 1. Monthly average forage mass (kg DM ha⁻¹) for 7 pilot farms during the two years of the project. Dotted lines indicate standard deviation.</figcaption></figure>

<figure><img src="image_153.png" /><figcaption>Figure 2. Average forage height (cm) for 7 pilot farms per season and forage height (cm) suggested by Soca and Orcasberro (1992). Dotted lines indicate standard deviation.</figcaption></figure>

These increases in forage height and standing biomass resulted in an increase in forage and meat production per ha. Forage allowance, an instantaneous measure of the forage-to-animal relationship, increased on average, after two years of implementation of the project, from 3.5±1.2 to 6.1±2.0 kg of DM per kg Live Weight⁻¹. As a consequence, equivalent meat production (i.e. meat +wool) increased by 24% (from 99 kg ha⁻¹ to 123 kg ha⁻¹), and the weight of weaning calf per breeding cow increased by 39% (from 107 kg to 149 kg). Comparing the average of the two years before the beginning of the implementation of the re-design plans with the average of the two years after, the net income increased from 70 to 98 US$ ha⁻¹. We hypothesize that a higher standing biomass also had a beneficial effect in terms of reducing the risk of erosion, climate vulnerability and increasing soil carbon.

## 4 Conclusions

We presented a successful co-innovation process involving farmers and scientists showing that, even in slow- responding perennial systems, ecologically intensive strategies that better utilized system functionalities without extra inputs resulted in important improvements of system functioning within two years.

*Acknowledgements*. The authors would like to thank the farmers and their families for their dedication. Also A. Albin, M. Albicette, M. Benvenuto, I. Bortagaray, O. Blumetto, A. Castagna, P. Clara, C. Leoni, L. del Pino, F. García, S. Montaldo, G. Quintans, M. Scarlato, P. Soca and G. Tiscornia for their contributions.

## References

Barthram, G. T. (1986). Experimental techniques: the HFRO sward stick. Biennial Report. Hill Farming Research Organization 1984-85 pp. 29-30
Dogliotti, S.; García, M.C.; Peluffo, S.; Dieste, J.P.; Pedemonte, A.J.; Bacigalupe, G.F.; Scarlato, M.; Alliaume, F.; Alvarez, J.; Chiappe, M. & Rossing, W.A.H. (2014). Co-innovation of family farm systems: A systems approach to sustainable agriculture. *Agricultural Systems*, 126, 76-86.
Haydock, K. P. & Shaw, N. H. (1975). The comparative yield method for estimating dry matter yield of pasture. *Australian Journal of Experimental Agriculture and Animal Husbandry*. **15**: 663-670.

Quintans, G., Scarsi, A. (2013). Seminario de actualización técnica: Cría Vacuna. Serie Técnica Instituto Nacional de Investigación Agropecuaria N° 208. Treinta y Tres, Uruguay. p. 264.

Soca P. & R. Orcasberro. (1992). Propuesta de Manejo del Rodeo de Cría en base a Estado Corporal, Altura del Pasto y Aplicación del Destete Temporario. pp 54-56. En: Evaluación Física y Económica de Alternativas Tecnológicas en Predios Ganaderos. Estación Experimental M. A. Cassinoni. Facultad de Agronomía. Universidad de la República. 56 p.
# DESIGNING APPROPRIATE AGROFORESTRY SYSTEMS: A SYSTEMATIC UNDERSTANDING OF ADOPTION DECISIONS

Lieve Borremans *±<fnref n="1" />*, Bert Reubens <fnref n="2" /> & Erwin Wauters <fnref n="1" />,<fnref n="3" />

Corresponding author: lieve.borremans@ilvo.vlaanderen.be

## 1 Introduction

Many of the silvo-arable and silvopastoral agroforestry systems in Flanders have been abandoned because they are considered to be incompatible with today's agro-economy and intensive farming practices. Nevertheless, modernized agroforestry systems could enhance farm resilience and respond to several challenges in agriculture through diversifying production and supplying a wide range of ecosystem services. Because these opportunities are increasingly recognized by policy makers, consultants, researchers and educators, the Flemish government set up a subsidy program for the installment of agroforestry parcels in 2011. Since 2014 the subsidy covers up to 80% of the plantation costs if some requirements with respect to parcel size, crop type, amount, type and distribution of trees are fulfilled. Despite the existence of this subsidy program and the increasing interest of policy makers and researchers in agroforestry as a sustainable land use system, the adoption rate of agroforestry in Flanders remains very low. The results of a questionnaire distributed to a group of farmers in Flanders in 2012 showed that this low adoption rate is mainly related to farmers' limited knowledge of the practice and negative perceptions towards its' profitability and compatibility within their current farming system (Baeyens, 2012). Although the results of this questionnaire give some indications about how to make agroforestry more attractive to Flemish farmers in the short term, it does not give any information about the reasons behind the negative perceptions of farmers towards agroforestry and the processes by which trees have increasingly disappeared from the agricultural landscape from the 1960's onwards. Therefore the overall aim of this paper is to explore the main drivers and processes, in the past and in the present, that make Flemish farmers today reluctant with respect to tree-planting on the farm. The results are intended to aid in the design and study of appropriate agroforestry systems for Flanders. As such we hope to contribute in the long term to an increased uptake of agroforestry and more general to more tree-planting in and around farmers' fields in Flanders and the rest of Europe.

## 2 Materials and Methods

The framework for this research is a 5-year project in which a system perspective and an interdisciplinary and participatory approach towards agroforestry design, research and development is used. The project builds upon the farming systems research methodology, which comprises a family of approaches for the diagnosis of land management problems and potentials and the design of appropriate agricultural interventions. Although the different approaches under the FSR methodology have a lot of characteristics in common, this research project fits particularly well under the *systeme agraire* approach as used by francophone agronomists and described by Cochet (2012). He explains that the original *systeme agraire* approach was particularly focused on the relation between farmers' technical choices and their social and political context. To be able to understand this relation, different scales of analysis were necessary. This was different in the English literature in which the farming systems research was originally focused on solving technical questions in the short-term by analysing processes at field- or farm level. Furthermore, according to Cochet (2012) the francophone approach of farming systems research payed more attention to social aspects, long-term processes and historical aspects than was usual in the English approach.

With the *systeme agraire* approach in mind information and data are gathered about the social, financial, political and historical drivers behind the disappearance of trees out of the Flemish agricultural landscapes and the current negative perceptions of farmers towards agroforestry and tree-planting on the farm. A first part of the data is gathered through content analysis, a research technique used to make replicable and valid inferences from books, book chapters, essays, interviews, newspaper articles, historical documents, etc. (Krippendorff, 2013). The second part of the data collection exists of two series of in-depth interview with farmers. In a first series of 20 interviews the focus will be on the historical evolution from a natural agroforestry-situation in Flanders to an agricultural landscape that is largely without trees. The respondents will need to describe the historical and current situation with respect to trees on the farm and describe the most important processes and drivers that have contributed to and/or supported this evolution. As such we try to get a picture of trees and hedges in a historical perspective. We take here the methodology of Peterson (2005) as an example, who interviewed 20 farmers about trees as part of their production system during a walk through the farmers' own land. In the second series of 20 in-depth interviews the focus will be on the perceptions of the farmers

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

<footnote n="2">² Institute for Agricultural and Fisheries Research, Plant Sciences Unit</footnote>

<footnote n="3">³ University of Antwerp, Department of Veterinary Sciences</footnote>

<footnote>* Speaker</footnote>

<footnote>‡</footnote>
towards agroforestry and trees on the farm. The questionnaire that was distributed to a group of farmers in 2012 will be
used here as a starting point to explore why farmers believe in certain advantages or disadvantages in more detail.

3 Expected and preliminary results

The questionnaire about agroforestry that was distributed to farmers in 2012 already pointed out that the low adoption
rate of agroforestry in Flanders is mainly related to farmers' limited knowledge of the practice and negative perceptions
towards its' profitability and compatibility within their current farming system. Some test-interviews already showed
that farmers associate agroforestry systems mainly with crop production losses and have difficulties to estimate or
appraise the benefits of the tree component in the long term. Furthermore farmers consider time restrictions and
mechanization difficulties as very important drawbacks of trees on the farm.

A literature review already indicated that the situation was different in the past. Flanders counted before much more
agricultural holdings which were much smaller. According to Platteau et al. (2013) the number of farm units in 2011
amounted up to 25.982, which is a decrease of 63% in comparison with 1980. At the same time average farm size
doubled over the past thirty years to 23.6 ha, which proves the current scaling up practice in Flemish agriculture. This
scaling up practice is related to the land consolidation processes that found place from the 1950's onwards with the
original goal to improve food productivity. This implied a clustering of the fragmented parcels into large units with an
optimal rectangle shape and located adjacent to the farm, resulting in the disappearance of traditional hedges and
bocage-elements separating different parcels (Pauwels, 2014). Also the initial EU Common Agricultural Policy (CAP)
from the 1960's till the 1980's has influenced negatively the amount of trees and hedges in agricultural fields by
focusing on maximization of agricultural production (Howarth, 2008). This is in contrast to the actual CAP, of which
30% is greening payment and depends on the implementation of agricultural policies beneficial for climate and the
environment (EU, 2013).

4 Discussion – Conclusion

The overall aim of this study is to explore the main drivers and processes, in the past and in the present, that make Flemish farmers today reluctant with respect to tree-planting on the farm. The preliminary results already indicate inter alia the scaling-up practices and land-consolidation processes in Flemish agriculture from the 1960's onwards, the increasing level of mechanization and the CAP as important drivers of the disappearance of trees and hedges out of agricultural parcels. Previous interviews also pointed out that farmers consider a lack of time, mechanization difficulties and possible crop production losses as important drawbacks to plant trees on their farmland. These findings will be complemented with the results of our interviews that are planned in the near future. As such we hope to give recommendations for the design of appropriate agroforestry systems that address Flemish farmers' needs.

*Acknowledgements*. This study is funded by the Agency for Innovation by Science and Technology (IWT-Flanders).

References

Baeyens, D. (2012). *Boslandbouwsystemen in Vlaanderen Kennis, ervaring en attitude van Vlaamse landbouwers*. Thomas More Kempen.

Cochet, H. (2012). The systeme agraire concept in francophone peasant studies. *Geoforum, 43*(1), 128–136. doi:10.1016/j.geoforum.2011.04.002

EU. (2013). *Regulation (EU) No 1307/2013 of the European Parliament and of the Council of 17 December 2013 establishing rules for direct payments to farmers under support schemes within the framework of the common agricultural policy and repealing Council Regulation*.

Howarth, R. (2008). The CAP: History and attempts at reform. *Economic Affairs, 20*(2), 4–10.

Krippendorff, K. (2013). *Content Analysis: an introduction to its methodology* (3rd ed.). Thousand Oaks Sage.

Pauwels, F. (2014). Land Development in Flanders in a Changing Perspective. *Zfv Zeitschrift Für Geodäsie, Geoinformation Und Landmanagement, 139*(3), 159–166. doi:10.12902/zfv-0018-2014

Peterson, A. (2005). Has the generalisation regarding conservation of trees and shrubs in Swedish agricultural landscapes gone too far? *Landscape and Urban Planning, 70*(1-2), 97–109. doi:10.1016/j.landurbplan.2003.10.007

Platteau, J., Van Gijseghem, D., Van Bogaert, T., & Maertens, E. (2013). *Landbouwrapport 2012* (p. 441).
# Participatory design of irrigated landscapes to limit the risk of water crisis

Clément Murgue *±<fnref n="1" />, Olivier Therond <fnref n="2" /> & Delphine Leenhardt ³

## 1 Introduction

In Europe, the recent development of environmental public policies (e.g. European Water Framework Directive) highlighted structural water deficit situations, of which many sub-basins of the Adour-Garonne watershed (France). In these rivers, flows are regularly observed below the regulatory low-water flow levels. These recurring “water crises” are mainly linked to agricultural withdrawals during periods of natural water shortage. The French law on water and aquatic habitats (LEMA 2006) seeks to balance water demand with resources and to promote territorial management through stakeholder involvement. In some basins, this means to significantly lower agricultural withdrawals while irrigation is a key production factor for farming systems. Opportunities for water storage development being limited, the current challenge is to help stakeholders design cropping systems and water management strategies that conciliate both water resources protection and economic viability of agriculture.

Our work aims at developing a generic and participatory design and assessment methodology to identify site-specific pathways to balance the dynamics of agricultural water demand and water resources availability. The methodology allows stakeholders to first model the socio-agro-hydrological system (SASH) based on “hard” and “soft” methods, knowledge and tools, and then to design and assess alternatives (Murgue et al., 2015). This communication presents the implementation of the methodology in a sub-basin of the Adour-Garonne watershed.

## 2 Materials and Methods

We used as case study a 800 km<fnref n="2" /> irrigated territory: the downstream part of the Aveyron watershed (water deficit is about 5 million m³ in dry years). We identified and interacted with two stakeholders’ collectives: one is representative of the agricultural stakes, the other of the water management and aquatic environments stakes. Our methodology is structured in 3 steps: (1) co-construction of a shared, fine and dynamic representation of the current SAHS, (2) co-design of potential options of change for cropping systems and their spatial distributions, (3) simulation and evaluation of alternatives using the MAELIA multi-agent simulation platform (Therond et al., 2014). Both collectives were involved in every step of the procedure, either separately or jointly, in individual open interviews, collective mapping or designing workshops. Our participatory approach was based on the use of models (representation, simulation and evaluation of the socio-environmental system) as boundary objects to facilitate the flow of information between science and society. To represent the current situation of the SAHS (step 1), in line with the concepts of (Yeager & Steiger, 2013), we enriched quantitative geo-datasets by mapping peoples’ spatial, qualitative knowledge on bio-physical features, hydrological dynamics and distribution of cropping practices in the territory. Co-design of options (step 2) was a participatory process where stakeholders were asked to specify changes in cropping systems and their distribution within the irrigated landscape. The facilitator provided a sequential framework to progressively elicit tacit knowledge, allowing to widen the possibility space and then to refocus it on key elements (Fig. 1). Eight options of change were specified each including an acceptability threshold expressed as a percentage on a farm's candidate area.

<figure><img src="image_154.png" /><figcaption>Fig. 1. Organization of the design process.</figcaption></figure>

<footnote n="1">¹ Compagnie d'Aménagement des Coteaux de Gascogne (CACG), chemin de l'Alette BP 449 65004 Tarbes, France</footnote>

<footnote n="2">² INRA UMR 1248 AGIR, 24 chemin de Borderouge BP 52627 F-31326 Castanet Tolosan, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: c.murgue@cacg.fr</footnote>
On the basis of the designed options, two alternatives (a combination of some options) were assessed using MAELIA simulations (step 3): the use of early flowering cultivars for maize planted earlier (AltPrec) and the reduction of monocropping areas using winter cereals (AltRot). For both alternatives, we simulated the proposed acceptability thresholds as well as a range of higher thresholds in order to evaluate their potential to solve the water crisis issue in comparison with the simulation of the current situation (CurSit).

## 3 Results – Discussion

The modelling step resulted into a dynamic representation of the current SAHS into the MAELIA platform (19.000 parcels, 4 water resources types, flow regulation norms). This model of the irrigated territory is a multi-agent, spatially explicit simulation platform which can reproduce farmers’ decisions (sowing date, soil management, irrigation) and water managers operations (dam releases and water use restrictions). The simulation can run day-to-day calculations, using ten years of observed climatic and hydrological data (2002-2012). The formal outcomes of the design process is a set of mapped cropping system distributions (rotation + cropping practices) in all fields of the landscape (Tab. 1).

<table><caption>Table. 1. Example of an option for change after the design workshop</caption><thead><tr><td>Options</td><td>Objectives</td><td>Change in practice</td><td>Location criteria</td><td>Acceptability threshold</td></tr></thead><tbody><tr><td>1. Adjust planting dates &amp; precocity of grain maize</td><td>1a. Advance peak water needs (flowering)</td><td>Early planting / early varieties</td><td>Terraces, except for hydromorphic boulbène* soil</td><td>20% of a farm's annual area of grain maize</td></tr></tbody></table>

The simulation of the formalized alternatives showed that using the proposed acceptability threshold, neither could solve the water crisis issue significantly. However both showed significant impact if applied on all candidate fields. AltPrec<sub>100%</sub> reduces late season irrigation withdrawals but also significantly raises the need for early season dam releases to sustain river flows. AltRot<sub>100%</sub> allows to reduce overall irrigation water volumes (39% of the deficit in a dry year -2009- ), but its hydrological impact is variable. In fact as awaited, AltRot tends to raise river flows where irrigation is abstracted directly from the streams. However opposite to what is commonly thought, in situations where water is abstracted from stored water resources (individual dams), AltRot may lower hydrological flows due to less drainage (Fig.2). Our simulations also show that due to more winter cropping of wheat with AltRot, soils are generally drier at the end of summer than with wide spread irrigated maize monocropping system, which induces a longer time period with no or few runoff to rivers and so a longer period with low river flows (Fig. 2).

<figure><img src="image_155.png" /><figcaption>Fig. 2. Hydrological impact of AltRot<sub>100%</sub> in the Lère sub-watershed, 2009</figcaption></figure>

## 4 Conclusions

The design process required persistent interactions with the collectives. Both tackling water management issue through land use as well as thinking change in cropping systems at the scale of a 800 km² territory were challenging exercises. Participants expressed their views mostly in parametrical space (Shi et al. 2009) (i.e. manipulating structural entities of the model to express spatial distribution). The model previously built eased the process by acting as “hub” for knowledge: it gave spatial meaning to formalize participants’ parametrical visions. The integrated model of the socio-agro-hydrological system and the assessed alternatives provides sound, bottom up base material for discussing agricultural land management alternatives through additional iterative design-and assessment cycles.

*Acknowledgements.* Agence de l’Eau Adour-Garonne, the INRA ACCAF metaprogram, Région Midi-Pyrénées, the MAELIA Project. Experts and stakeholders, interviewees and participants of the workshops: Local farmers, governments agents (Direction départemental des Territoires 82).

## References

Murgue, C., Therond, O., & Leenhardt, D. (2015). Towards sustainable water and agricultural land management: participatory design of social-agro-hydrological systems. *Land Use Policy*, **45**, 52–63.

Shi, X., Long, R., Dekett, R., & Philippe, J. (2009). Integrating Different Types of Knowledge for Digital Soil Mapping. *Soil Science Society of America Journal*, **73**(5), 1682.

Therond, O., Sibertin-Blanc, C., Balestrat, M., Gaudou, B., Hong, Y., Louail, T., ... Mazzega, P. (2014). Integrated modelling of social-ecological systems: The MAELIA high-resolution multi-agent platform to deal with water scarcity problems. , 8pp. In D. P. Ames, n N. W. T. Quin, & A. E. Rizzoli (Eds.), 7th Int. Congress on Env. Modelling and Software (iEMSs) (p. 8p). San Diego, CA, USA.

Yeager, C. D., & Steiger, T. (2013). Applied geography in a digital age: The case for mixed methods. *Applied Geography*, **39**, 1–4.
# PARTICIPATORY PROTOTYPING FOR COMPLEX RICE BASED ADAPTIVE SYSTEMS DESIGN IN EAST JAVA, INDONESIA

Uma Khumairoh *±1,2*, Egbert A. Lantinga<fnref n="1" />, Jeroen C.J.Groot<fnref n="1" />, Pablo A. Tittonell<fnref n="1" />& Didik Suprayogo<fnref n="2" />

## 1 Introduction

Despite the continuous development of improved rice varieties, severe pest outbreaks due to the combined impact of extreme weather conditions and increased use of agro-chemicals, are considered to be the major reason for the current unstable rice production in Indonesia (Sogawa, 2015; Baehaki, 2011; Soemarwoto and Conway, 1992; Khumairoh et al., 2012). The process of designing sustainable rice production systems requires integrated approaches taking into account the existing local natural resources and indigenous knowledge, and allowing step-by-step adaptations. Complex rice-based production systems incorporating azolla, fish, ducks and margin plants enable the promotion of ecosystem services, stabilise yield fluctuations and foster farm viability, whilst minimizing environmental pollution (Khumairoh et al., 2012). However, huge variations in biophysical and socioeconomic conditions challenge the implementation of complex designs due to the wide range of components integrated and the necessity for proper balancing and exact timing. To this end, scientists have developed participatory prototyping approaches to improve traditional crop production methods (Bellec *et al.*, 2012). The Farmer Field School (FFS) approach for Integrated Pest Management (IPM) in Indonesia successfully reduced the national pesticide use in the late 1990s. However, IPM practices dropped down due to pesticide subsidies which have been increased more than 30 times since the year 2000 (Sogawa, 2015). In order to find a formula to repeat their initial success, we complemented existing prototype design methods with FFS activities to attain robust complex agro-ecological rice production systems that can easily be adapted to specific locations and vice versa to develop FFSs that are more efficient and effective than before.

## 2 Materials and Methods

We formulated prototyping method to design complex rice based adaptive systems (Fig.1) by combining farm typology (Blazy et al., 2009) in step 1, simple experimentations on four pilot farms (Vereijken, 1997) by applying the FFS approach in step 2 and adjustment of the proposed design in step 3.

<figure><img src="image_156.png" /><figcaption>Fig. 1. Prototyping steps for complex rice-based system design. FFS: farming field school; FGD: focus group discussion.</figcaption></figure>

## 3 Results - Discussion

In **step 1** we sampled 99 farmer households across four districts in East Java from 2014 to 2015. Each in-depth interview lasted approximately 2.5 hours. We live in a farmer household in each district during the survey to understand their daily farming activities. Questionnaires were divided into three categories; farm performance, cultivation methods and farming context. The description of variables to establish a farm typology is provided in Table 1. In **step 2**, we conducted FFSs in four districts. Each FFS contained of seven meetings. The first meeting was started with Rapid Rural Appraisal (RRA), formulized the objectives namely stabilising rice yield, reduction of pollution, external inputs and operational costs and was followed by establishing a learning agreement. In 2nd-5th meetings we conducted a simple experiment in pilot farms consisted of three treatments; conventional, organic and complex rice plots that were replicated two times. Meeting 6 was a cross-visit for 2 FFSs and involved watching agro-ecological movies for another two FFSs. In the final meeting as part of **step 3**, we evaluated FFS processes to develop innovative, efficient and effective FFSs, followed by FGD on the proposed complex rice adaptive systems design to get suggestions for ideal

<footnote n="1">¹ Farming Systems Ecology Group, Plant Sciences Department, Wageningen University, Droevendaalssteeg 1, 6708 PB, Wageningen, NL</footnote>

<footnote n="2">² Faculty of Agriculture, University of Brawijaya, Jalan Veteran 65145 Malang, Indonesia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: uma.khumairoh@wur.nl/ uma.kh@ub.ac.id</footnote>
designs to be adjusted to local conditions and to available resources. At the time of writing, rice in experimental plots were harvested in two sites while two other sites are in the ripening stage. However, current plant performance visually shows the difference between treatments. In general, plots treated with complex elements performed better than organic, and similar or even better than conventional plots. Despite a devastating rat attack in one study site in Malang where many farmers failed to harvest their rice grain, the experimental plots were able to avoid high losses. Moreover, the side products from ducks and intercropped plants avert further deterioration. It implies that the proposed design successfully reduces pollution due to zero chemical application while increasing yield stability. FFS can be a useful tool to formalize farmer's roles in prototyping farm designs. However, conventional FFSs with high meeting frequencies, monotonous activities, sticking to only morning time and limited target need to be reformulated to further improve the sustainability, cost efficiency and effectiveness. One simple example is to replace cross visit sessions by watching movies on agricultural innovations which will greatly reduce costs while diverting tedium effects from some flat activities during FFS meetings.

<table><caption>Table 1. Variables used to set a typology of rice production systems in East Java</caption><thead><tr><th>Variables</th><th>Units</th><th>Variables</th><th>Units</th><th>Variables</th><th>Units</th></tr></thead><tbody><tr><td><strong>FARMING CONTEXT</strong></td><td></td><td><strong>Economic sources</strong></td><td></td><td>Marking</td><td>-</td></tr><tr><td><em>Bio-physical</em></td><td></td><td>Land ownership</td><td>-</td><td>Direct/ transplanting</td><td>-</td></tr><tr><td>Altitude</td><td>m.a.s.l</td><td>Off-farm income</td><td>USD year<sup>-1</sup></td><td>Planting distance</td><td>-</td></tr><tr><td>Precipitation</td><td>mm</td><td>Land fragmentation</td><td>#</td><td>Seedling age</td><td>days</td></tr><tr><td>Topography</td><td>-</td><td>Cash flow</td><td>-</td><td>Seedling number/hill</td><td>#</td></tr><tr><td>Soil types</td><td>-</td><td>Investment capacity</td><td>-</td><td>Amount of seed</td><td>kg ha<sup>-1</sup> cycle<sup>-1</sup></td></tr><tr><td>Soil texture</td><td>-</td><td><strong>CULTIVATION METHOD</strong></td><td></td><td><strong>PERFORMANCE</strong></td><td>-</td></tr><tr><td>Irrigation condition</td><td>-</td><td><strong>Inputs</strong></td><td></td><td><strong>Agronomy</strong></td><td></td></tr><tr><td>Polyculture</td><td>-</td><td>Organic fertilizers</td><td>kg ha<sup>-1</sup> cycle<sup>-1</sup></td><td>Cultivation number</td><td>Units year<sup>-1</sup></td></tr><tr><td>Rotation</td><td>-</td><td>Synthetic ferlitzers</td><td>kg ha<sup>-1</sup> cycle<sup>-1</sup></td><td>Rice yield</td><td>t ha<sup>-1</sup> cycle<sup>-1</sup></td></tr><tr><td>Rice field size</td><td>m<sup>2</sup></td><td>Fertilizer applications</td><td>#</td><td><strong>Economic</strong></td><td></td></tr><tr><td>Bund size</td><td>cm</td><td>Herbicides</td><td>kg ha<sup>-1</sup> cycle<sup>-1</sup></td><td>Input costs</td><td>USD ha<sup>-1</sup> cycle<sup>-1</sup></td></tr><tr><td><strong>Social</strong></td><td></td><td>Herbicide applications</td><td>#</td><td>Labor costs</td><td>USD ha<sup>-1</sup> cycle<sup>-1</sup></td></tr><tr><td>Contact with extension</td><td>-</td><td>Pesticides</td><td>kg ha<sup>-1</sup> cycle<sup>-1</sup></td><td>Rented machinaries</td><td>USD ha<sup>-1</sup> cycle<sup>-1</sup></td></tr><tr><td>Training</td><td>-</td><td>Pesticide applications</td><td>#</td><td>Rice margin</td><td>USD ha<sup>-1</sup> cycle<sup>-1</sup></td></tr><tr><td>Age</td><td>Year</td><td><strong>Planting</strong></td><td></td><td>Whole farm margin</td><td>USD ha<sup>-1</sup> year<sup>-1</sup></td></tr><tr><td>Engagement to farming</td><td>-</td><td>Pre-weeding</td><td>-</td><td>B/C</td><td>-</td></tr><tr><td>% of family involved</td><td>%</td><td>Weeding types</td><td>-</td><td><strong>Social-environmental</strong></td><td></td></tr><tr><td>% of family labor</td><td>%</td><td>Weeding frequencies</td><td>#</td><td>Chemical active ingredient</td><td>kg ha<sup>-1</sup> cycle<sup>-1</sup></td></tr><tr><td>Total work hours</td><td>Hours</td><td>Weeding equipment</td><td>-</td><td>M/F labor</td><td>-</td></tr></tbody></table>

## 4 Conclusions

Farmer-participatory prototyping appeared to be a successful approach for redesigning current rice production systems into more complex adaptive systems. However, the process is challenging due to the existing large variation in biophysical, social and cultural conditions. Nevertheless, valuable learning tools to gather information about indigenous knowledge and local natural resources for adjusting the proposed designs to a specific site were provided. In order to achieve multiple objectives the presented three-step participatory prototyping methodology could be considered as a promising tool. However, further developments are needed to increase its efficiency and effectiveness.

*Acknowledgements.* We are very grateful to the farmers and stakeholders who participated in the Farmer Field School meetings, in particular for their comments and suggestions.

## References

Baehaki, 2011). Normalisasi dan pengendalian dini hama wereng coklat pengaman produksi padi nasional. http://www.litbang.pertanian.go.id/download/one/113/file/Pengendalian-Wereng.pdf

Bellec, F.L., Rajad, A., *Lafontaine, H.*, Bockstaller, C. & Malezieux, E. (2012). Evidence for farmers' active involvement in co-designing citrus cropping systems using an improved participatory method. *Agron. Sustain. Dev.* **32**,703-714.

Blazy, J.M, Ozier-Lafontaine, H., Doré, T., Thomas, A., & Wery, J.(2009). A methodological framework that accounts for farm diversity in the prototyping of crop management systems. Application to banana-based systems in Guadeloupe. *Agricultural Systems.* **101.30-41**.

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

Soemarwoto, O. & G. R. Conway.(1992). The Javanese home garden, *Journal for Farming Systems Research-Extension 2* (3): 95-118.

Sogawa, K.(2015). Planthopper outbreaks in different paddy ecosystems in Asia: Man-made hopper plagues that threatened the green revolution in rice. K.L. Heong et al. (eds.), *Rice Planthoppers*. Zhejiang University Press, Hangzhou and Springer Science+Business Media Dordrecht. DOI 10.1007/978-94-017-9535-7_2.

Vereijken, P. (1997). A methodical way of prototyping integrated and ecological arable farming systems (I/EAFS) in interaction with pilot farms. Eur J Agron. **7.235-250**
# Innovative design of smart farming systems: Some insights from the enhancement of native mycorrhizae in Martinique

Marie Chave *±1 & Valérie Angeon 2,3

## 1 Introduction

The consensus in support of agroecological principles gained more and more currency with the exacerbation of global changes (Tomich et al., 2011). The natural capacity of ecological systems to adapt to shocks is being exceeded (IPCC, 2014). It reaffirms the importance of human intervention to promote their viability through innovative methods that activate ecological processes. Mobilizing biodiversity offers new lines of action (Doré et al., 2011) and is likely to lead to *smart agriculture* (FAO, 2010). Unexplored potential of soil biodiversity may enhance crop health and productivity, reducing the use of chemicals. For instance, mycorrhizae (symbioses between more than 80% of plants and some soil fungi) provide multiple input services (provisioning, regulating, supporting) and are of growing interest for the ecologization of agriculture. Valorizing mycorrhizae requires to promote a holistic farming system approach and to develop agroecological engineering strategies based on knowledge-intensive sharing process (Angeon & Chave, 2014). An innovative design strategy based on the KCP® (*Knowledge Concepts Propositions*) methodology (Hatchuel et al., 2009) was experienced with horticulture producers in Martinique.

## 2 Materials and Methods

A partnership gathered researchers (4), technical advisers (4) and farmers (19) to build and take part in a new thinking area on the valorization of mycorrhizae. Three stages were implemented:

1. A collaborative workshop for sharing **Knowledge** and exploring the mycorrhization **Concept**.

Knowledge available in scientific literature shows deficient information on mycorrhizal networks mobilization in agriculture. From a scientific review two paradoxes were identified: i) Although mycorrhizal fungi are present in most of the soils worldwide, the inoculation of standard propagules (mycorrhized roots fragments or spores) is the most spread technology. In soil, these industrialized products are in competition with native mycorrhizae. ii) Few information on agricultural practices necessary for an efficient development of mycorrhizal networks is provided.

Hence, the partners were involved in exploring the concept of mycorrhizal networks mobilization. Three steps aimed at sharing knowledge on « How to enhance and benefit from mycorrhizal networks?» : i) an experimental design based on native mycorrhizal networks mobilization through chive (Allium fistulosum) and tomato (Lycopersicon esculentum) intercropping, ii) a pedagogical model -representing soil interactions- to understand how to mobilize latent mycorrhizae, iii) a card game to co-construct mycorrhizal networks friendly practices (limiting tillage, reducing chemical inputs etc.).

2. Surveys to assess **Knowledge** and **Concepts** appropriation

Surveys were conducted on farm to situate each of the 19 farmers in learning loops (Argyris & Schön, 1996). The interviews were run on two items: i) mycorrhizae nature and functions, ii) agricultural practices and farming system design. Farmers' answers were assessed in four levels of learning: 0. no learning; 1. simple knowledge acquisition (which does not result in any action); 2. incremental acquisition of knowledge resulting in action; 3. radical acquisition of knowledge (change of values that allows for redesigning farming systems).

3. A collaborative workshop to combine emerging **Propositions** in a design strategy

Two steps were proposed: i) the results of the experimental design and the survey were debated with farmers. ii) a concept-projector for mycorrhizae mobilization was introduced to orient collective creativity for the future. It consisted of the presentation of a virtual start-up producing local selected mycorrhizae strains.

## 3 Discussion

The farmers' learning processes were analyzed. Among the 19 farmers, two cohorts significantly differed (Student test α<0,05 p-value < 0,0003) regarding learning levels. 7 "best learners" (up to level 2) and 12 "in progress learners" (below level 2) were distinguished.

Experience and agronomic brakes were major constraints identified by both cohorts of learners (Fig. 1A). The agronomic practices and the organizational arrangements (Propositions) suggested by both cohorts of farmers to valorize mycorrhizae were capitalized (Fig. 1B).

<footnote n="1">¹ INRA UR 1321 ASTRO Agrosystèmes Tropicaux F-97170 Petit-Bourg France</footnote>

<footnote n="2">² Université des Antilles, CAEC, Petit Morne F-97232 Le Lamentin</footnote>

<footnote n="3">³ INRA UR 143 Unité de Recherche en Zootechnie F-97170 Petit-Bourg France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: marie.chave@antilles.inra.fr</footnote>
"Best learners" identified less brakes than "in progress learners". They were able to overwhelm agronomic brakes and moved more easily from the knowledge-sharing phase (K) to the proposition of levers (P). They stressed the importance of collective arrangements to implement agroecological farming systems.

<figure><img src="image_157.png" /><figcaption>**Fig. 1.** Brakes (A) and levers (B) identified by farmers exploring the concept of mycorrhizae valorization. "Best" and "In progress" learners significantly differed regarding learning levels (Student test α <0,05 p-value < 0,0003)</figcaption></figure>

All the partners' propositions were synthetized in a concept tree (Fig. 2) starting from an initial concept "C0: mycorrhizae valorization" which is progressively divided in sub-concepts. Most of the farmers' propositions rely on the concept of mycorrhizal networks mobilization (C1) rather than on the use of propagules. All the farmers rejected the inoculation of standard and local selected strains.

On farm native mycorrhizae amplification (C2) was appropriated. The farmers suggested experimenting native strains production through mycorrhizal crops cultivation in given pedoclimatic areas (C3). Hence, farmers proposed to implement collective arrangements to define the convenient technology. Indeed, on farm native mycorrhizae production is labor-intensive and uncertain (Douds et al., 2005), and the work organization is cost-consuming. It necessitates cooperation between farmers and technical advisers to be developed efficiently.

<figure><img src="image_158.png" /><figcaption>**Fig. 2.** Concepts tree from C0 to C3. Partners explore the concept of mycorrhizae valorization. **Bold letters** indicate farmers' proposals.</figcaption></figure>

The whole KCP® approach resulted in a common research-action project to help farmers to implement C3.

## 4 Conclusions

The innovative design of smart farming systems based on native mycorrhizae led in Martinique illustrates the pattern of a strong ecologization of agriculture. Further experiences in other contexts will help understand how the mycorrhizae valorization case study supports different agroecological transition pathways.

*Acknowledgements* We acknowledge the metaprogramme INRA SMaCH (Sustainable Management of Crop Health) for its support to the project: REACTION. We thank Jean-Marc Meynard for his precious advices.

## References

Argyris C and Schön DA (1996) Organizational learning. 2. Theory, method, and practice. Addison-Wesley.

Angeon V and Chave M (2014) Implementing the agroecological transition: weak or strong modernization of agriculture? Focus on the mycorrhiza supply chain in France. Communication in ERSA 54th congress. 25-28th August 2014, Saint Petersbourg.

Doré T, Makowski D, Malezieux E, Munier-Jolain N, Tchamitchian M, Tittonell P (2011) Facing up to the paradigm of ecological intensification in agronomy: Revisiting methods, concepts and knowledge. Eur J Agron 34:197-210

Douds DD, Nagahashi G, Pfeffer PE, Kayser WM, Reider C (2005) On-farm production and utilization of arbuscular mycorrhizal fungus inoculums. Canadian Journal Plant Science 85: 15-21

FAO (2010) "Climate-Smart Agriculture". Policies, Practices and Financing for Food Security, Adaptation and Mitigation. Rome. Italy

IPCC (2014) Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Field CB Barros VR et al. (eds.). Cambridge University Press, Cambridge, UK and NY, USA

Hatchuel A, Le Masson P, Weil B (2009) Design theory and collective creativity: a theoretical framework to evaluate KCP process. International conference of engineering design. 24 - 27 August 2009, Stanford University, USA

Tomich T P, Brodt S, Ferris H, Galt R, Horwath WR, Kebreab E, Leveau J, Liptzin D, Lubell, M, Merel P, Michelmore R, Rosenstock T, Scow K, Six J, Williams N, Yang J (2011) Agroecology: A review from a global-change perspective. Ann Rev Environ Resour 36: 193-222.
# Integrated Farming System for Sustainable Rural Livelihood of small and marginal farmers of North Eastern Transitional Zone (Zone-1) and North Eastern Dry Zone (Zone-2) of Hyderabad Karnataka Region

U. K. Shanwad<sup>*±1</sup>, R. L. Jadhav<sup>2</sup>, Santiago Lopez-Ridaura<sup>3</sup>, M. L. Jat<sup>4</sup>, Ivan Ortiz-Monasterio<sup>5</sup> & A. G. Sreenivas<sup>6</sup>

## 1 Introduction

Integrated Farming System (IFS) is a complex interrelated matrix of soil, plants, animals, implements, power, labour, capital and other inputs controlled in part by farming families and influence to varying degree by political, economic, institutional and rest factors that operate at farm level. Under the existing agrarian structure, most of the rural farm families are of small and marginal in nature that are living below the poverty line with the continued threats to their livelihood security characterized by low in food security and income, unemployment, health problems, education etc. Due to this reason, these categories of farmers are poorly adopted to the changed farming scenario especially in rainfed areas (Lal and Miller, 1990). Further, this section of farming community is very much susceptible to the natural vagaries (drought & flood) and resulting in large scale migration to urban areas for seeking livelihood opportunities. Keeping in view of these problems, the innovation on IFS developed by University of Agricultural Sciences, Raichur (UASR) addresses the following major constraints have been demonstrated on farmer's farms in Zone 1&2 of Karnataka state during the year 2010 and 2011. To ensure the consolidation of the natural resource base at farm level and offers better opportunities for adoption of improved technology/ies with the target of enhancement of overall production and productivity of the farm. To provide an opportunity to arrive at appropriate combination of the enterprise through interlinking of different farm enterprises for the effective use of natural resources available at farm level and for recycling of nutrients on the farm. And this technique ensures in the creation of better awareness on the adoption of technology/ies which can lead to sustainable production process with on-farm employment creation to support livelihood of the rural farm families.

## 2 Materials and Methods

On farm demonstrations (40 farm families) on integration of different components with crop in Integrated Farming system mode and recycling of resources within the system were organized in Zone 1 and 2 of northern Karnataka during 2010-2011. Villages and Farmers in the zone were randomly selected and rational information from Participatory Rural Appraisal (PRA). This information was used for redesigning the farming activities to develop tailor made IFS modules for different farming situations. The ToT centers under identified agro-climatic zone had selected the farmers having 1 to 2 ha of agriculture land. Based on the PRA analysis suitable action plan was prepared and executed on the farms during 2010-11 and 2011-12. Based on need, choice and resources available on the farm, different allied activities such as horticulture, dairy and vermi compost pits were suitably incorporated into the production system with an aim of generating income and employment for the farm family through economically friendly model to get regular income, employment and livelihood security. The crop and animal residues were recycled for vermi composting for use in the crop field. Budgeting and accounting of all the farm activities were calculated using standard procedures.

## 3 Discussion

North Eastern Transitional Zone (Zone-1) and North Eastern Dry Zone (Zone-2) of Karnataka cover Bidar and Gulbarga districts with moderate climatic conditions. Looking to the agro-climatic zone 1 & 2 features, to improve the productivity levels of the farm, the farmers were advised to follow the action plan in which the technologies involved laid greater emphasis on the cultivation of pulses followed by cereals, oilseeds and vegetables.

<figure><img src="image_159.png" /></figure>

<figure><img src="image_160.png" /></figure>

<footnote n="1">¹ Scientist (Agronomy), Main Agricultural Research Station, University of Agricultural Sciences, Raichur, Karnataka State, India</footnote>

<footnote n="2">² Subject Matter Specialist (Agronomy), Krishi Vigyana Kendra, Bidar, Karnataka State, India</footnote>

<footnote n="3">³ Principal Scientist, Global Conservation Agriculture Program, CIMMYT, El-Bation, Mexico</footnote>

<footnote n="4">⁴ Sr. Agronomist, CIMMYT-INDIA Program, NASC Complex, New Delhi, India</footnote>

<footnote n="5">⁵ Principal Scientist (Nutrient Division), CIMMYT, cd. Obregon, Mexico</footnote>

<footnote n="6">⁶ Associate Professor, Department of Entomology, College of Agriculture, University of Agricultural Sciences, Raichur, Karnataka State, India</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: shanwad@gmail.com</footnote>
The productivity enhancement after intervention and stability in crop productivity was noticed as indicated by higher sustainable yield index in crops adopted in all farms. In zone 1, 2 among cereals bajra recorded higher (0.60) sustainable yield index (SYI). Bengalgram recorded higher SYI than other pulses. The significant increase in productivity was recorded during the assessment year (2011) over the bench mark year in different food crops, commercial crops and vegetables grown. The increase in yield with different interventions in food grains mainly in bajra, sorghum, wheat was 41, 84, 80 per cent in zone 1 and 2, The change in productivity is variable and in constraint farming situations the interventions have greater impact and brought greater increase in yield (38 to 80 per cent) and stability in yield was noticed. This shows the impact of whole farm demonstration of IFS is significant because of improvement in natural resource base of the farmer and risk reduction. The tangible benefits of introduced vegetables production system were noticed in all the farm families and varied with crops and farming situations. In zone 1 and 2, brinjal, onion and cucumber noticed 36, 26 and 20 per cent higher productivity during 2011-12 as compared to benchmark year 2009-10. Sustainable yield index was higher with technological innovations in different vegetable crops. The new vegetable crops and varieties introduction in to the farms was one of the important interventions which enhanced the income of farm families, increased the cropping intensity, employment generation and nutritional security among farm families and surrounding rural house holds. Gill et al., (2009) also opined that, horticultural and vegetable crops can provide 2-3 times more energy production than cereal crops on the same piece of land and will ensure the nutritional security on their inclusion in the existing system. The whole farm analysis was done for different farming situations. The emphasis was given for the incremental changes with seasonal crops and with the other activities, with the introduction of new technologies, forced the farmers to re-organize substantial portions of their activities. Economic analysis was done by recording and the cost and income involved in crop production activities and for other farm enterprises. The monitory values used for comparing the alternatives that includes only those outputs sold for cash are those inputs purchased with cash. The sustainable value index in zone 1 & 2 ranges from 0.75 to 0.97. Higher sustainable value index were observed due to yield stability obtained with IFS interventions. Similar higher sustainable value index due to integrated farming system was also reported by Barik et al., 2010 and Jayanthi et. al., 2010.

<table><caption>Table 1. Productivity and Sustainable Yield Index in field crops as influenced by IFS interventions in Zone 1 & 2</caption><thead><tr><th>Zone 1 & 2</th><th>Before intervention (BI)</th><th colspan="2">After intervention (AI)</th><th colspan="2">% Increase over BI</th><th>Standard Deviation</th><th>SYI</th></tr></thead><tbody><tr><th scope="row">Food grains</th><td>BI</td><td>2010</td><td>2011</td><td>2010</td><td>2011</td><td>SD</td><td>SYI</td></tr><tr><th scope="row">Wheat</th><td>5.00</td><td>0.00</td><td>9.00</td><td>-</td><td>80.00</td><td>1.80</td><td>0.32</td></tr><tr><th scope="row">Bajra</th><td>4.60</td><td>6.25</td><td>6.50</td><td>35.87</td><td>41.30</td><td>1.86</td><td>0.60</td></tr><tr><th scope="row">Sorghum</th><td>3.90</td><td>7.03</td><td>7.19</td><td>80.26</td><td>84.36</td><td>1.82</td><td>0.59</td></tr><tr><th scope="row">Pulses</th><td>BI</td><td>2010</td><td>2011</td><td>2010</td><td>2011</td><td>SD</td><td>SYI</td></tr><tr><th scope="row">Redgram</th><td>4.90</td><td>7.72</td><td>8.00</td><td>57.55</td><td>63.27</td><td>1.98</td><td>0.61</td></tr><tr><th scope="row">Bengalgram</th><td>7.40</td><td>9.44</td><td>9.60</td><td>27.57</td><td>29.73</td><td>2.32</td><td>0.68</td></tr><tr><th scope="row">Blackgram</th><td>4.70</td><td>5.26</td><td>5.70</td><td>11.91</td><td>21.28</td><td>1.82</td><td>0.60</td></tr><tr><th scope="row">Greengram</th><td>4.30</td><td>5.00</td><td>5.10</td><td>16.28</td><td>18.60</td><td>1.74</td><td>0.60</td></tr><tr><th scope="row">Oilseeds</th><td>BI</td><td>2010</td><td>2011</td><td>-</td><td>-</td><td>SD</td><td>SYI</td></tr><tr><th scope="row">Soybean</th><td>0</td><td>10.06</td><td>11.2</td><td>-</td><td>-</td><td>1.537</td><td>0.496</td></tr><tr><th scope="row">Commercial crops</th><td>BI</td><td>2010</td><td>2011</td><td>2010</td><td>2011</td><td>SD</td><td>SYI</td></tr><tr><th scope="row">Cotton</th><td>17.5</td><td>24.5</td><td>36</td><td>40.00</td><td>105.71</td><td>3.81</td><td>0.62</td></tr><tr><th scope="row">Sugarcane</th><td>99.27</td><td>115</td><td>23</td><td>15.85</td><td>-76.83</td><td>7.71</td><td>0.62</td></tr></tbody></table>

## 4 Conclusions

The adoption of IFS results in reduction in the expenditure on external inputs namely, chemical fertilizers and plant protection chemicals along with the adoption of Integrated Crop Management Practices. In addition to this with the judicious use of critical inputs, the ill effects due to indiscriminate use of chemicals could be reduced to protect the environment. The IFS therefore refers to the farm as an entity with firm binding of inter departmental farming enterprises combination achieved to attain the overall development of the farm. In this regard IFS creates an opportunity to fine tune the agricultural technologies suiting to the farm situations.

## References

* Gill, M.S., Singh, J.P., and Gangwar, K.S. (2009). Integrated Farming System and Agriculture sustainability. *Indian Journal of Agronomy* 54(2):128-139.

* Lal, R. and Miller, F. P. (1990). Sustainable farming system for tropics. In: Sustainable agriculture: Issues and Prospective. Vol.I (Ed.) R.P.Singh.pp.68-89, Indian Society of Agronomy, IARI, New Delhi

* Jayanthi, C., Vennila, C., Nalini, K. and Myanavathi, V. S. (2010). Farmer participatory integrated farming system. In: XIX National symposium on 'Resource Management Approaches Towards Livelihood Security' 2-4 December, 2010.pp.175.

* Barik, K. C., Mohanty, D., Nath, S. K., Sahoo, S. K. and Laba Soren. (2010). Assessment of integrated farming system for rainfed ecosystem in Deogarh district of Orissa. In: XIX National symposium on 'Resource Management Approaches Towards Livelihood Security' 2-4 December, 2010.pp.172.
# Structuring data gathering on organic farms: the transdisciplinary development and use of a farm scan within a broader methodological framework

Jo Bijttebier *±<fnref n="1" />, Ludwig Lauwers <fnref n="1" />,<fnref n="3" /> & Fleur Marchand <fnref n="1" />,<fnref n="2" />

## 1 Introduction

In Flanders, organic suckler cow farmers meet at a regular basis to exchange experiences and knowledge. Within this network, an advisory service is involved to give advice on multiple aspects of the farming system. Research has shown that network organisation is rewarding to gain access to knowledge, to facilitate learning processes, and to foster knowledge creation (Van Wijk et al., 2003). Although effective management is linked to well-designed monitoring and evaluation (Stem et al., 2005), decision making of the farmers in this network is mainly based on practical experiences and perceptions. Comparable data and knowledge gathering by the farmers is lacking due to several reasons. For example, their accountancy systems differ. Furthermore, each farm is characterized by specific conditions. As a result, the farmers themselves are not able to organize efficient data collection to share within their network. However, monitoring and evaluation forms the basis for improved decisions making and improved management. Therefore, this study aims at the development of a monitoring and evaluating approach to improve decision making on organic beef farms. Through the use of a farm scan, we aim at gathering and structuring farm specific data in an efficient way to systemically examine interventions and to share this knowledge within their network. The development of farm scan is part of a broader methodological framework, which focusses on a system based, transdisciplinary approach. The system based approach aims at integrating knowledge on the complex relations within organic farms. The transdisciplinary approach focusses on the involvement of farmers, researchers and advisory services to facilitate the knowledge co-creation between stakeholders and scientists. A mixture of both quantitative and qualitative techniques for data collection is at the basis of this methodological framework and is described in depth by Marchand et al. (2015). In this paper, we focus on the iterative farm scan development and use in practice and discuss the experiences within the broader methodological framework.

## 2 Methodological framework and data collection

The first objective within the broader methodological framework was to reveal some determinative elements for successful organic meat production. As the active role of a stakeholder plays an important role in the success of a development process, participation was ensured from the start (Reed, 2008). Through observations and group discussions with five farmers (qualitative approach), the advisory service, and the network facilitators, main issues perceived as determinative for successful organic farming, were identified. In a second step, which is the focus of this study, a monitoring farm scan was developed to get more insights on the farmers' performance on each of these key aspects. A combination of quantitative and qualitative indicators are structured in a rapid farm scan, which can be considered as a tool to facilitate monitoring and evaluation. For data gathering and processing, researchers and advisors used a simple excel file. Benchmarking of the data was introduced through data from literature or by identifying best performances within the farmer group.

Through an iterative process the scan was validated and improved. This includes an evaluation of the scan data by the advisor followed by an in-depth discussion in a network meeting with the farmers. With this information, the researcher adjusted the farm scan and started with the following round of farm visits to collect new data. The iterative process thus encompasses an initial step of data gathering and structuring by the researcher, followed by a phase of data interpreting by the advisor and a final sharing and discussion step with the farmers.

## 3 Results - Discussion

We developed a methodology for structured monitoring and evaluation within an existing network of organic beef farmers. In contrast to conventional farms, where Belgian Blue is commonly used, organic holdings use different breeds, often from French origin. Therefore, the first determinative elements for successful farming, indicated by the farmers, were the cows' performances and the potential of the breed with respect to fertility, growth and carcass quality. Through discussions with advisors, relevant indicators were included in the scan and farm data on these key aspects were collected. Within the group of participating farms, animals are slaughtered at varying ages which results in differences with respect to carcass quality. On each of these five farms, a share of grassland is owned by nature

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

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

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

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: jo.bijttebier@ilvo.vlaanderen.be</footnote>
organizations. On this grassland, fertilization and tillage activities are under strict regulations, which limits productivity of the grassland. To reveal to what extent farmers are capable of using this area efficiently in their production system, total meat production/ha utilized agricultural area, which is the area that is cultivated for roughage production without this grassland, was calculated. Despite each farm system has its very specific approach, adjusted to the breed and other farm characteristics, sharing and discussing data was perceived as very valuable. Compared to the potential growth and carcass quality of the breeds used, which was obtained from data in the literature, none of the farms seem to reach this potential. Further insight into the production method and cost, might reveal whether carcass quality might be improved without increasing the production cost. Therefore, as a following step, more insight into the cost structure at each farm, might clarify to what extent optimizing farm management is valuable to positively affect carcass quality.

These examples illustrate how the structure and content of the scan is continuously evolving, dependent on the needs of the participants. This flexibility has been recognized earlier as an integral aspect of participatory monitoring and evaluation processes (Estrella & Gaventa, 2000). Another prospect of successful stakeholder participation is creating confidence between all stakeholders, with a sense of mutual respect between stakeholders involved (Reed et al., 2008). We had the advantage of starting a process within an existing network of farmers that were very open towards each other and towards data shearing and including researchers in this process. However, one of the challenges will be to maintain this trust when they want to extend the existing network towards other farmers. Data gathering has been done by the researchers so far. However, the transdisciplinary approach aims at increasing ownership of the tool both by the advisors and the farmers (Binder et al., 2010). As our approach progressed, farmers became more and more confident with the tool for continuing monitoring on the farm. In addition, the advisors became aware of the content and the what's behind the data of the tool. As a result, they are able to interpret the data and use it in their advisory task during the network meetings. However, although discussions on the data might reveal some interactions and trade-offs between different indicators, this knowledge is not structured in the farm scan. Nevertheless, as a system based approach is characterized by considering bio-physical processes within their socio-economic context, other methods can be applied to structure these relationships and trade-offs. This is also acknowledged by Stem et al. (2005), emphasizing that monitoring and evaluation approaches cannot solely be based on quantitative information. Additional qualitative data might help to provide a more complete understanding of the processes, which is further described in the framework of Marchand et al. (2015).

## 4 Conclusions

This paper focusses on the transdisciplinary development of a farm scan, as a part of a broader methodological framework. With this farm scan, we aim at structuring monitoring and evaluation on organic farms through involving stakeholders during the development and implementation process. This did not occur as a linear process, however, development and implementation almost occurred at the same time through an iterative transdisciplinary approach. This study illustrates how we succeeded at initiating a process of data gathering on the farm level and using this data within an existing network of organic beef farmers. Furthermore, this process permits farmers to get further insight into strengths and weaknesses and reveals opportunities for potential new farm strategies. However, continuing this process warrants enough discipline and commitment of both advisors and farmers to use this tool as a monitoring tool on the farm level and as a basis for exchanging knowledge in the further network activities. It might evolve towards a database for other farmers and attract new farmers to the network.

*Acknowledgements:* We are grateful to all the farmers, advisors and Bioforum for their contribution and participation. This work was funded by the Department of Agriculture and Fisheries, Government of Flanders.

## References

Binder, C., Feola, G., Steinberger, J. (2010). Considering the normative, systemic and procedural dimensions in indicator-based sustainability assessments in agriculture. Environmental impact assessment review, **30**, 71-81.

Estrella, M. & Gaventa, J. (2000). Who counts reality? Participatory monitoring and evaluation: a literature review. IDS working paper 70, Institute of Development Studies, Brighton.

Marchand, F., Bijttebier, J., Van Meensel, J., Strubbe, M., Lauwers, L. (2015). A transdisciplinary approach to structure knowledge gathering on organic farm systems: evaluation of organic farm strategies in the case of Flanders. Submitted.

Reed, M.S. (2008). Stakeholder participation for environmental management: a literature review. *Biological conservation*, **141**, 2417-2431.

Stem, C., Margoluis, R., Salafsky, N., Brown, M. (2005). Monitoring and evaluation in conservation: a review of trends and approaches. *Conservation biology*, **19**, 295-309.

Van Wijk, R. Van Den Bosch, F.A.J., Volderba, H.W. (2003). The Blackwell handbook of organizational learning and knowledge management. Blackwell Publishing, Malden, Oxford, Carlton.
# Co-innovation as an effective approach to promote changes in farm management in livestock systems in Uruguay

Santiago Scarlato<sup>*±1</sup>, María Marta Albicette<sup>1</sup>, Isabel Bortagaray<sup>1</sup>, Andrea Ruggia<sup>1</sup>, Mariana Scarlato<sup>1</sup> & Verónica Aguerre<sup>1</sup>

## 1 Introduction

In Uruguay, livestock production involves 65% of the family farmers and more than 70% of the area of the country. Low levels of sustainability were diagnosed in livestock family farming systems based on natural grasslands in Uruguay, being the main causes low meat yield and income. Scientific evidence shows that it is possible to increase production while preserving natural resources and enhancing ecosystem services through changes in management practices of pastures and animals (Nabinger et al., 2011). However, during the last decades low levels of technological innovation has been applied in livestock systems. Lack of improvement by farmers could be explained by the weakness and the traditional approach of the extension service. From a traditional approach, innovations are designed externally to the systems and farmers adopt those innovations by an “extension” process. Extension linearly involves awareness of the problem by the farmer, interest in the solution, evaluation, experimentation and finally adoption (Cramb, 2000). Nevertheless, the active participation of the farmers in the diagnosis and redesign might maximize the impact of the proposals generated, promoting learning processes that support innovation in practices in the long term (Leeuwis & Van der Ban, 2004). The co-innovation approach combines complex systems theory, social learning and dynamic project monitoring and evaluation to stimulate strategic re-orientation of family farm systems (Rossing et al., 2010). We hypothesized that a systemic and participative approach such as this one is necessary for re-designing productive systems in order to improve their sustainability, being the learning process in farmers as important as the bio-physical changes in their production systems.

## 2 Materials and Methods

The co-innovation approach was implemented in 7 family livestock farms located in eastern Uruguay between 2012 and 2015, in order to generate and evaluate changes in systems sustainability. The approach involved characterization and diagnosis of the farm system’s sustainability, re-design, implementation, and monitoring and evaluation of system evolution (Dogliotti et al., 2014). By using the systems approach, an agreed baseline of farms sustainability was generated based on farm information of the three previous years before starting the project. To explore alternatives for re-designing, simple models of farm operation, scientific information and information obtained from other production systems were used. Feeding and financial budgets were performed to assess the impact of re-design. This process was carried out with farmers and their families, letting them choose the final alternative. Monthly visits to the farms were done to implement, support and monitor the process.

This project was based on a multiple case study design in which each farm constituted a case study. The study of multiple cases does not attempt to represent family farmers, rather it is based on replication logic: each case replicates a broader theoretical framework (Yin 2003). The research strongly relies on primary data, which emerges from the combination of different techniques: monthly visits to the farms to monitor and gather data on a series of environmental and economic-productive indicators, and a series of in-depth interviews throughout the project, and a historical analysis of the main milestones that have shaped the life of these farmers to study the main changes and learning processes that have taken place at the micro (family) level.

## 3 Results – Discussion

After two years of project implementation, significant improvements were achieved at different levels. Adjusted stocking rate and sheep-to-cattle ratio, combined with improved grazing management of the natural grasslands allowed increasing on average 20% the cow’s pregnancy rate and 24% the meat yield per hectare. Farm income increased 40%, as the proposals did not increase productive costs and improved animals’ sales strategies. Improved management resulted in an increase in standing biomass of natural grassland, minimizing soil erosion risk and preserving biodiversity.

Changes in farmers' vision and in the way they think and decide on their farms were identified, being a key element for the project approach. The way farmers expressed main changes reflects an understanding and belief in what they are doing, as well as a consciousness about why they have made changes, and its implications (Table 1).

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

<footnote>* Speaker</footnote>

<footnote>‡ Corresponding author: sscarlato@gmail.com</footnote>
The improvements achieved at farm level, based on farmers learning, were also recognized, being critical for supporting innovation in practices in the long term.

Furthermore, farmers' shared perception is that the project has changed their life at the farm. Their perception regarding the use of time has changed, as well as the type and complexity of the tasks associated to farm management, their future prospects, their goals and their overall approach to the farm.

<table><caption>**Table 1.** Farmers' perception about the main changes in their farms</caption><thead><tr><th>Sustainability dimension</th><th>Improvement area</th><th>Farmers' opinions related to changes in their farms</th></tr></thead><tbody><tr><td>Bio-physical and Economic</td><td>Increased forage and meat production, improved reproductive efficiency. Increased net income.</td><td>"We have learned that we should not count how many animals we have, but how much meat we produce, and how many animals we need to produce as many kg of meat per hectare. Because we used to have too many animals per hectare, though with very low production."</td></tr><tr><td rowspan="3">Environmental</td><td rowspan="3">Soil, natural grasslands and biodiversity conservation.</td><td>"We have wasted and wasted the soil so much... now it is so difficult to get it back. Planting a pasture, as green manure. Always trying to leave some residue, not like in the past when we left only the ground and we kept nothing for restoration of the soil."</td></tr><tr><td>"In pastures, for instance, the appearance of certain species that are indicators of good management of the natural grassland... besides the animals, the birds..."</td></tr><tr><td>"I notice that we are now more organized. And suddenly being more organized means working more comfortable... We are less demanded in winter, it used to be more complicated, but now as we have more forage, we are better prepared... We would have more time to do other things."</td></tr><tr><td rowspan="3">Social</td><td>Use of the time.</td><td>"The pasture management has been very important. I learned a lot of pasture management, about forage height, how to see things I have not seen before. We just used to walk on the grass. We knew it was high or low. But now we can see other indicators in the grassland that are very good and sometimes we didn't realize they were so important."</td></tr><tr><td>Farm management.</td><td>"The technician is talking about soils but also talks about the body condition of the animals, they cover a broad range of topics. The project has not put money, instead it has been about thinking."</td></tr><tr><td>Methodological approach.</td><td>"On the technical approach, it has not only been about economics... being in close interaction has been key, and that technicians are so open, so sociable. It is essential that part of knowledge we have of each other... it allows to build confidence"</td></tr></tbody></table>

## 4 Conclusions

Farms' sustainability increased through a systems re-design. The involvement of farmers and their families in the farm diagnosis, in the development of alternatives for improvement and in monitoring progress was essential in order to promote learning processes to support changes. Changes in the farmers' vision of their own farm, and a new way of facing management decisions were found. The co-innovation approach was an effective tool for promoting changes in farms.

*Acknowledgements.* This work would not have been possible to do without the contribution of the farmers and their families. We wish to thank also to A. Albín, M. Benvenuto, O. Blumetto, G. Cardozo, A. Castagna, S. Dogliotti, P. Clara, F. García, C. Leoni, S. Montaldo, and G. Quintans.

## References

Cramb, R. A. (2000). Processes influencing the successful adoption of new technologies by smallholders. In ACIAR Proceedings (pp. 11-22). ACIAR; 1998.

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

Leeuwis, C. & Van der Ban, A. (2004). Communication for rural innovation: Rethinking agricultural extension. Oxford, Blackwell Science. 412 p.

Nabinger, C., De Faccio Calvalho, P.C., Cassiano Pinto, E., Messalira, J.C., Martins Brambilla, D. & Boggiano, P. (2011). Ecosystems services from natural grasslands: It's possible to enhance them with more productivity? *Archivos Latinoamericanos de Producción Animal*, **19** (3-4), 27-34.

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

Yin, R. K. (2003). Case studies research: designs and methods. Thousand Oaks, Sage Publications.
# How to co-build a viable farming model? Guadeloupe and Martinique Cases

Bérengère Merlot *±1*, Arnaud Larade 2, Valérie Angeon 3 & Eduardo Chia 4

## 1 Introduction

The implementation of agroecological transition involves the creation of a societal agreement that must be shared with all the stakeholders of a given territory. This implies co-building processes for an effective transition and for perennial effects. To that extent, identifying and analyzing socio-technical conditions for the implementation of agroecological transition is of first importance. Taking as an example the French Caribbean, we wonder about the ability of these areas dominated by the productionist model of farms and facing exacerbated global changes (IPCC, 2014) to invent new futures by implementing agrotechnical and organizational innovations.

We make the hypothesis that agroecological transition comes not only from the ability of stakeholders to build a common paradigm on the viability of farming systems but also from the ability of each stakeholder to transform these production systems. This may involve the establishment of a specific model of territorial governance (both vertical and horizontal) that comes from market and non-market coordination between stakeholders. We then take viability as a « social construct » (Latour, 2010) arisen from collective and individual strategies. The aim of this article is to reveal and draw a map of the actors' understandings of what is necessary to reach viability of farming systems. Our reasoning unfolds in three steps. First, by reviewing the literature, we tackle the scientific context of viability. We identify three key dimensions constituting viability (economic, agroecological, socio-cultural and organisational). Second, we characterize the representations of the stakeholders questioned (Are the three dimensions mentioned? Which variables discriminate them?). Third, we explain in what extent the representations of the different categories of stakeholders diverge and the consequences that may occur on the propensity of the agroecological transition to occur in the studied territories.

## 2 Material and methods

To tackle how the viability of farming systems is described, our study focuses on two categories of stakeholders: farmers and institutional representatives. Both of them were asked to define viability in terms of objectives and constraints. To quickly collect views of the numerous farmers, we used the focus group method (spring 2013). Only came farmers with the most concerns, they were mainly the small and diversified ones. Using the stakeholders analysis (Mitchell *et al.*, 1997) combined with the snowball non-probability sampling method, we conducted 20 comprehensive interviews with institutional stakeholders (local government officers and regional authorities) and non-profit organisations (autumn 2014).

The whole empirical collected material is explored through content analysis. We use qualitative rating scale to illustrate the position of each category of actor. The representations of the farmers from the focus groups (Fig. 1a, b) are presented below as well as those of the institutional stakeholders (Fig. 2a, b).

<figure><img src="image_161.png" /><figcaption>**Fig. 1.** a (objectives, left) and 1b (constraints, right) Farmers' conception of viability according to agroecological dimension</figcaption></figure>

<footnote n="1">1 INRA - Astro, Domaine Duclos, 97 171 Petit-Bourg, Guadeloupe,</footnote>

<footnote n="2">2 AgroParisTech - UMR Métafort, 9 avenue Blaise Pascal, CS 70054, 63178 Aubière Cedex, Clermont-Ferrand</footnote>

<footnote n="3">3 Université des Antilles - CEREGMIA, Faculté de droit et d'économie, Campus de Schoelcher, BP 7209, 97275 Schoelcher Cedex, Martinique, et INRA URZ, Domaine Duclos 97 171 Petit-Bourg, Guadeloupe</footnote>

<footnote n="4">4 INRA - UMR Innovation Inra Supagro, 2 place Viala, 34060 Montpellier</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: bmerlot@antilles.inra.fr</footnote>
<figure><img src="image_162.png" /><figcaption>**Fig. 2. a (objectives, left) and 2b (constraints, right)** Institutional representatives' conception of viability according to environmental dimension</figcaption></figure>

**3 Discussion**

Our results show that: 1/ stakeholders do not have a common view on viability. The three dimensions of viability (economic, agroecological, socio-cultural) are addressed differently. Farmers have a more “holistic” perception of viability. They have no difficulty to fill in the different dimensions. Regarding for example to the agroecological dimension farmers have concerns about improving the environment (identity, pollution, natural capital) while institutions only tend to enforce laws. Then, farmers quickly initiate responses to agroecological practices in concrete terms. As a constraint, they point out that the structures that frame agriculture (rules, skills, training and finances) do not enable to implement traditional practices, still applied on the territory. From their side, institutional stakeholders identify limits to understand cultural and production systems that enable to operate that environmental efficiency of agriculture. Regarding to the other dimensions (economical, socio-cultural), results show the same prominences. Then, 2/ for some stakeholders, farming systems in the French Caribbean are viable while for others the end of an era has been reached. This is a threat to the transition toward agroecology. Finally, 3/ it appears that the viability of farming systems derives from negotiations, tensions and conflicts between agricultural stakeholders, a fact that impede the effectiveness of agroecological transition. 4/ last but not least, our study makes appear a fourth, and unforeseen, dimension of viability which is the organizational one.

**4 Conclusion**

Our study shows that agricultural stakeholders have to negotiate new modalities on the ways and means to reach viable farming systems, by creating collective mechanisms to build new rules of actions within their territories: coordination of actors, project implementation, structuring space, creation of regulations (Leloup, 2010). Thus, there are some specific socio-technical conditions for the implementation of agroecological transition leading to a viable farming system in the French Caribbean. In that sense, these stakeholders could participate in the design of a territorial agroecological system (Duru *et al.*, 2014) which would be initiated through collective activities in 2016. This is a major challenge to ensure the resilience of those territories in the context of global changes.

*Acknowledgements* We thank the agricultural stakeholders as well as the institutional leaders for their time and answers.

**References**

Duru, M., Fares, M. & Therond, O. (2014). Un cadre conceptuel pour penser maintenant (et organiser demain), la transition agroécologique de l'agriculture dans les territoires. *Cahiers Agricultures, 23*, 84-95.

IPCC (2014). *Climate Change 2014: Impacts, Adaptation, and Vulnerability*. *Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change*. Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., Girma, B., Kissel, E.S., Levy, A.N., MacCracken, S., Mastrandrea, P.R. and L.L.White (eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,

Latour, B. (2010). *Changer de société, refaire de la sociologie. La Découverte/Poche, Paris*.

Leloup, F. (2010). Le développement territorial et les systèmes complexes : proposition d'un cadre analytique. *Revue d'Economie Régionale et Urbaine, 4*, 687-705.

Mitchell, R.K., Agle, B.R., Wood, D.J. (1997). Toward a theory of stakeholder identification and salience: Defining the Principle of Who or What Really Counts. *Academy of Management Review, 22*, 853-886.
# Agronomic knowledge for cropping system design: characterization and dynamics of mobilization

Quentin Toffolini*±1,2,3, Marie-Hélène Jeuffroy 1,2 & Lorène Prost 3

## 1 Introduction

Redesign of cropping systems has been recognized as necessary to implement the agronomic principles required for an agroecological transition (Hill & MacRae, 1995; Meynard et al. 2012). Namely, the systems targeted are biodiversity based, locally adapted, and more efficient in nutrient use and recycling. To carry out this design, agronomists have mainly worked on a de-novo redesign approach (e.g. Vereijken, 1997), and little research concerns step-by-step design processes although they seem to better correspond to the reality of the changes implemented by farmers (Chantre et al., 2015). Specificities of step-by-step redesign processes are (1) a distant objective (e.g. reducing herbicides use by 50%, or reducing working time), by contrast with a definite target cropping system, (2) a necessary systemic reasoning, (3) numerous uncertainties related to emergent dynamics in natural processes mobilized and feedback loops, and dependency of techniques' results on local environments, (4) a necessary combination of short-term (imminence of the decision) and long-term time scales (e.g. time required for regulation establishment) in reasoning of actions. These specificities require an adaptation of the agronomic knowledge produced and mobilized in agronomic science. Several authors agree on the necessity to mobilize both scientific and local expert knowledge (Doré et al., 2011; Faugèreet al. 2011). Beyond this legitimation of diverse sources of knowledge, very few theoretical tools exist that make possible an analysis of the specific knowledge content that efficiently equips farmers' action during step-by-step redesign processes. We propose a characterization tool for agronomic knowledge and identify possible attributes of actionable knowledge all along technical changes applied by farmers.

## 2 Materials and Methods

We first based our analysis of knowledge attributes on a diversity of written documents which concerned three specific techniques (i) diversification strategy with the introduction of a leguminous species into the crop sequence; (ii) implementation of cover crops with diverse objectives (e.g. recycling of nutritive elements, soil structure conservation, weeds regulation); and lastly (iii) functional biodiversity conservation through the establishment of floral strips into the fields or at borders. Seventy-eight documents were analysed, combining scientific articles (15), agricultural press articles (38), and publications of technical institutes (25). A framework of attributes of knowledge was built from this analysis. Then, we applied the framework of attributes to analyse knowledge mobilized by farmers involved in a redesign process. Thirteen semi-directive interviews were held. Interviews were focusing on one or two specific technical changes identified with the farmer as salient in his management evolution and sufficiently recent in order to avoid bias linked with historical reconstruction.

## 3 Results and Discussion

### A framework for knowledge characterization:

The characterization referred to seven main aspects: (1) formatting of evidence, (2) the temporality and dynamics, (3) uncertainties, (4) the objectives explicitly related to knowledge, (5) the elements of agronomic reasoning (explanation), (6) the references to agronomic situations, and (7) the monitoring and assessment of actions. In each of these categories, we identified the diversity of specific attributes (Fig. 1).

Although some attributes were equally abundant in the different types of sources (either present in more than 40% of documents for each type of source, or in less than 25%), some were unequally represented. For instance, descriptions of dynamics were much more common in scientific articles (73%) than in press or technical institutes' articles (24% for both). The same differences were found for factors of sensitivity as form of uncertainty (60%, 21% and 32% for scientific, press and technical articles respectively). By contrast, indicators for monitoring the action were identified in average ratios in press and technical articles (29% and 40% respectively), but never in scientific articles.

The analysis of interviews using the seven main aspects of knowledge previously described showed general trends in dynamics of knowledge mobilization all along technical changes. We could formalize these dynamics in three successive steps, and associate main attributes of knowledge mobilized in each step (Fig. 1). First step corresponds to the choice of a specific technique and the decision to apply it. Second step corresponds to an adaptation of the specific monitoring and management of the technique, toward a relative stabilization of action modalities. The third step

<footnote n="1">¹ INRA, UMR211 Agronomie, 78850 Thiverval-Grignon, France</footnote>

<footnote n="2">² AgroParisTech, UMR211 Agronomie, 78850 Thiverval-Grignon, France</footnote>

<footnote n="3">³ INRA, UMR 1326 LISIS, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Quentin.toffolini@grignon.inra.fr</footnote>
corresponds to the confirmation of the viability of the practice within a system of practices, and according to other technical changes it would require. Evaluation seems to occur at all steps, however in different forms (consistency of the agronomic principle related to the technique, efficiency of a specific actions sequence, viability of a new combination of practices). Surprisingly, quantifications were used only in the second step mainly for adaptation of the practice and its monitoring. Indicators were mainly mobilized in the first and second steps, and very rarely in the third step as evaluation tool of the technique. As a consequence, the value of our work was not to describe unique attributes of actionable knowledge, but rather to reveal necessary dynamics in combinations of different knowledge attributes.

<table><caption>**Figure 1.** Framework for knowledge characterization. The first column corresponds to large categories or themes of distinction. The second column reports more specific modalities within these themes. The three last columns show the knowledge attributes which were mobilized in more than 50% of cases in different steps of technical change.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">Steps of technical change</th></tr><tr><th>1<br>Choice of the<br>technique<br>Decision to apply it<br>Preparation for<br>implementation</th><th>2<br>One or several tests,<br>Adaptation of<br>specific monitoring<br>of action<br>Stabilization of<br>operational method,<br>Amplification</th><th>3<br>Evaluation of<br>consequences on<br>the system<br>Other related<br>and/or necessary<br>changes</th></tr></thead><tbody><tr><td rowspan="5">1.</td><td rowspan="5">formatting of<br>evidence</td><td>1</td><td>to measure or validate a mechanism, or its effect</td><td></td><td></td><td></td></tr><tr><td>2</td><td>relative quantifications, orders of magnitude</td><td></td><td></td><td></td></tr><tr><td>3</td><td>accurate, absolute quantification</td><td></td><td></td><td></td></tr><tr><td>4</td><td>photographs</td><td></td><td></td><td></td></tr><tr><td>5</td><td>narratives</td><td></td><td></td><td></td></tr><tr><td rowspan="3">2.</td><td rowspan="3">temporality and<br>dynamic</td><td>1</td><td>dynamic explicitly addressed</td><td></td><td></td><td></td></tr><tr><td>2</td><td>time step of the main concerned object</td><td></td><td></td><td></td></tr><tr><td>3</td><td>larger time scale than concerned object</td><td></td><td></td><td></td></tr><tr><td rowspan="5">3.</td><td rowspan="5">Uncertainties</td><td>1</td><td>limits of a farming practice</td><td></td><td></td><td></td></tr><tr><td>2</td><td>ignorance about the technique</td><td></td><td></td><td></td></tr><tr><td>3</td><td>sensibility</td><td></td><td></td><td></td></tr><tr><td>4</td><td>probabilities of results (losses, failure)</td><td></td><td></td><td></td></tr><tr><td>5</td><td>probabilities of results (losses, failure)</td><td></td><td></td><td></td></tr><tr><td rowspan="3">4.</td><td rowspan="3">objectives explicitly<br>related to knowledge</td><td>1</td><td>numeric objective</td><td></td><td></td><td></td></tr><tr><td>2</td><td>logical objective</td><td></td><td></td><td></td></tr><tr><td>3</td><td>elements to (re)define objects concerned</td><td></td><td></td><td></td></tr><tr><td rowspan="3">5.</td><td rowspan="3">agronomic reasoning</td><td>1</td><td>interactions between functions, or mechanisms, or variables</td><td></td><td></td><td></td></tr><tr><td>2</td><td>mechanisms (ecophysiology, biology, ecology, physics...)</td><td></td><td></td><td></td></tr><tr><td>3</td><td>comparisons of technical options</td><td></td><td></td><td></td></tr><tr><td rowspan="5">6.</td><td rowspan="5">reference to<br>agronomic situations</td><td>1</td><td>local agronomic context</td><td></td><td></td><td></td></tr><tr><td>2</td><td>specific/limitant characteristics of the situation</td><td></td><td></td><td></td></tr><tr><td>3</td><td>type of system and interactions with existing practices</td><td></td><td></td><td></td></tr><tr><td>4</td><td>historical and initial situation</td><td></td><td></td><td></td></tr><tr><td>5</td><td>homology/analogy of problems and ways to solve them</td><td></td><td></td><td></td></tr><tr><td rowspan="4">7.</td><td rowspan="4">Monitoring and<br>assessment of actions</td><td>1</td><td>operating method, decision rule for implementation of action</td><td></td><td></td><td></td></tr><tr><td>2</td><td>indicators to confirm that action performed is the expected one</td><td></td><td></td><td></td></tr><tr><td>3</td><td>indicators for adjustment/monitoring of the action</td><td></td><td></td><td></td></tr><tr><td>4</td><td>indicators to evaluate the effects of a specific action</td><td></td><td></td><td></td></tr></tbody></table>

## 4 Conclusions

The characterization we proposed for agronomic knowledge in step-by-step redesign processes revealed the necessity to focus on the content of knowledge, beyond the fact that it is an expert or scientific knowledge. The framework built showed a dynamic of mobilization of knowledge in three successive steps when applied to farmers' technical changes. The framework could be useful for identifying mismatches and hindrances in redesign processes, and to help organizing dynamic advisory support service along time courses of redesign processes. Specific attributes analysed as necessary, for instance concerning references to agronomic situations, also give agronomists guidelines concerning the knowledge that should be produced.

## References

Chantre, E., Cerf, M., Le Bail, M., (2015). Transitional pathways towards input reduction on French field crop farms. Int. J. Agric. Sustain. 13, 69–86.
Doré, T., Makowski, D., Malézieux, E., Munier-Jolain, N., Tchamitchian, M., Tittonell, P., (2011). Facing up to the paradigm of ecological intensification in agronomy: Revisiting methods, concepts and knowledge. Eur. J. Agron. 34, 197–210.

Faugère, E., Navarrete, M., Charles, M., Étienne, M., Fauriel, J., Lasseur, J., Lécrivain, É., Napoléone, M., Paratte, R., (2011). Des connaissances scientifiques en quête de connaissances d'acteurs. Nat. Sci. Sociétés 18, 395–403.

Hill, S.B., MacRae, R.J., (1995). Conceptual framework for the transition from conventional to sustainable agriculture. J. Sustain. Agric. 7, 81–87.

Meynard, J.-M., Dedieu, B., Bos, A.P. (Bram), (2012). Re-design and co-design of farming systems. An overview of methods and practices, in: Darnhofer, I., Gibbon, D., Dedieu, B. (Eds.), Farming Systems Research into the 21st Century: The New Dynamic. Springer Netherlands, pp. 405–429.
# The need for agronomic indicators to monitor and assess action and to enhance learning loops during cropping system redesign process

Quentin Toffolini *±1,2,3, Marie-Hélène Jeuffroy 1,2 & Lorène Prost 3

## 1 Introduction

The re-design of cropping systems towards less dependence on synthetic inputs generates strong uncertainties for the farmers who are confronted to incomplete knowledge on their growing processes (Duru, 2013). The results of actions are highly dependent on local biotic and abiotic conditions (Horlings & Marsden, 2011), which are often unpredictable themselves. In an agroecological paradigm, farmers handle local environment characteristics relying on biological regulations, which are steered thanks to techniques with partial and complementary effects. The farmers need to act within short term of decision making, but with the aim to make the system evolve over the long term, which necessitate to now if the system is evolving in the targeted direction, but also to re-adapt the actions according to the results obtained, to validate new combinations of techniques. However, specific indicators that make possible such monitoring of systems" evolutions are seldom described in the scientific literature. In agronomic sciences, we notice a steady progress in analysing and understanding natural processes and dynamics of agroecosystems, and indicators have been formalized to get easy access to information about complex systems. However, these indicators have mostly been used to assess the impacts of existing or simulated practices, according to several dimensions such as agronomic, economic, environmental and social (Bockstaller et al., 2008). As a consequence, their definition and specificities are not dedicated to farmers" actions support. Hence, we propose an analysis of indicators actually mobilized during step-by-step redesign processes and compare them to the indicators usually produced by agronomists.

## 2 Materials and Methods

In order to reach the widest variety of indicators used in redesign actions, we gathered a panel of case studies. First, we interviewed 11 farmers identified as being in a redesign process, located in Ile de France, Pays de la Loire and Centre regions (France). We realised semi-directive interviews focusing on specific technical changes to identify indicators in narratives about recent actions. We then completed by interviews of actors (5 farmers, 2 advisors, and 2 project managers) from a redesign project which lasted from 2003 to 2011. In this case, we could ask farmers about specific indicators that were used for group animation, which gave access to long term related roles of indicators. Finally, we observed meetings of farmers. Most of them were visits of experimental fields with tested cropping systems. We identified the indicators mentioned in their comments about the situations and the states of the observed systems. For each indicator identified in the interviews, we defined on one hand the role(s) it played in the farmer's action and on the other hand the attributes that characterized it.

## 3 Results and Discussion

We collected 294 statements of indicators from the various case studies. 22 specific roles can be distinguished (Fig. 1), spread along the time course of the technical change, which implies that they are much more diverse than strict evaluative scoring of actions or techniques. Some roles correspond to a classical use of indicators proposed by agronomists (notably from advisory services) as a basis for decision rules and crop management plans specifications: trigger the action, refine a modality of action, identify specific feature of situations to adapt a technical choice. We also found roles corresponding to the classical evaluative use of indicators. Some correspond to the farmers" need to evaluate the results of specific technique: verify the viability of a technic, interpret the viability of a strategic choice. But the farmers also need to evaluate the consequences of this choice on the rest of their cropping system. This level of evaluation corresponds to the roles labeled "identify a direction of development of the system", "know that the system remains in (situations) states that one knows how to manage", "identify a strategic cause-effect link". Furthermore, we identified roles related to the learning process that occurs in action thanks to retrospective analysis of actions. Associated to identification of dynamics in the system and intermediary states, they are either observations made without the capacity to interpret directly (identify an intermediary state of the system to reinterpret the effects of action a *posteriori*), or identifications of a known necessary state of the system to obtain certain results ("identify an intermediate state of the system necessary to reach a goal", "validate that intermediate state of system is reached", "assess a potential to provide a specific function").

<footnote n="1">¹ INRA, UMR211 Agronomie, 78850 Thiverval-Grignon, France</footnote>

<footnote n="2">² AgroParisTech, UMR211 Agronomie, 78850 Thiverval-Grignon, France</footnote>

<footnote n="3">³ INRA, UMR 1326 LISIS, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Quentin.toffolini@grignon.inra.fr</footnote>
<figure><img src="image_163.png" /><figcaption>Fig. 1. Roles of indicators for farmers" action along the time of technical change</figcaption></figure>

Attributes were specified according to several categories. We distinguished their nature (visual, physico-chemical feature, calculated, measured), form of description (binary, relative, quantified in absolute terms, in reference to an initial situation, or in reference to a value in a group of farmers), their time scale (multi-year, time scale of the object of action, on information acquired subsequently to the object of action, long term evolution), their spatial scale (object of action or plant individual scale, other object than the one targeted through action, field, farm), their mode of acquisition („active” meaning that an instrumentation is settled, e.g. the double-density sown strip, by contrast with „passive”, meaning that spontaneous phenomenon are observed: Fränzle, 2006), and their frequency of assessment (static, repetitive).

We illustrate relations between roles and attributes with two examples. First, whereas indicators for triggering the action are usually related to quantified variables in reference to threshold, or date, this role was associated to very diverse attributes, such as for instance, visual and relative observations of state of the object (e.g. relative height of cereal and legume crop in intercrop), or tendencies deduced from repeated observations. Second, a unique indicator may have distinct roles at different time for a farmer. Yield (quantitative, absolute) was used to validate the viability of a technique, but also to reinterpret the effect of a specific action or identify a key step in crop management, to identify strategic cause-effect links.

## 4 Conclusions

Rather than propositions to improve the quality and reliability of existent indicators, we bring propositions to explore and develop new types of indicators. Three types of indicator seem to be particularly lacking among those produced by agronomists: the indicators that make possible to monitor an action supposed to produce result mainly over the long term, the indicators that facilitate learning loops required for application of innovative and poorly known techniques, and the indicators that make possible to evaluate the effects of new actions implemented and the adaptation of specific modalities of operational methods. We emphasise that „visual”, „relative”, „related to dynamics”, and „passive” attributes should be regarded as contributing to these new roles of indicators.

## References

* Bockstaller, C., Guichard L., Makowski D., Aveline A., Girardin P., and Plantureux S.. (2008). "Agri-Environmental Indicators to Assess Cropping and Farming Systems.A Review." Agronomy for Sustainable Development 28 (1): 139-49.

* Duru, M. (2013). "Combining Agroecology and Management Science to Design Field Tools under High Agrosystem Structural or Process Uncertainty: Lessons from Two Case Studies of Grassland Management." Agricultural Systems 114 (January): 84-94.

* Fränzle, O. (2006). "Complex Bioindication and Environmental Stress Assessment." Ecological Indicators, Theoretical fundamentals of consistent applications in environmental management, 6 (1): 114-36. doi:10.1016/j.ecolind.2005.08.015.

* Horlings, L.G., and T.K. Marsden.(2011). "Towards the Real Green Revolution? Exploring the Conceptual Dimensions of a New Ecological Modernisation of Agriculture That Could „feed the World.”" Global Environmental Change 21 (2): 441-52.
# *De novo* design workshop: a method for co-designing innovative cropping systems

Raymond Reau<sup>*±1,3*</sup>, Michael Geloen<sup>2</sup>, Anabelle Laurent<sup>1,3</sup>, Chantal Loyce<sup>3,1</sup>, Anne Schaub<sup>4</sup> & Maximino Caballero<sup>5</sup>

## 1 Introduction

As agriculture is facing challenges such as environmental degradations, climate change and demographic increase, strong changes in farming systems are needed. Among prototyping and co-design methods useful to prepare system changes, co-design workshop method (Reau *et al.*, 2012) aims at *de novo* designing cropping systems (CS) (Meynard *et al.*, 2012). Based on its successful implementation in several case studies in France and Spain, we present the method, the goals of each step and examples of results from a Spanish case study.

## 2 Materials and Methods

The method begins by the definition of the main goal that the CS needs to fulfill (e.g. achieving low Green House Gas emissions): a single goal, and a distant and ambitious target are a way for getting out of a routine and to stimulate the imagination of the participants. This goal could be defined from the negative impacts of the CS mostly practiced in the study area. Then, the design workshop is organized in three steps:

**Step 1-**Sharing knowledge. Scientific experts specialized in the area covered by the goal provide the other participants (e.g. agronomists working as advisors in extension services, farmers) with generic knowledge about the main processes involved in the goal previously defined (e.g. C and N cycle if the main goal is to achieve low-GHG emissions) and about the way the several elements of the CS (e.g. nature of the crop in the crop sequence, crop techniques of each crop, residues management, etc.) influence them.

**Step 2-**Expressing technical options or functions (ideas). Participants make single proposals aiming at contributing to the main goal previously defined, and using the located knowledge as well as the generic knowledge shared in step 1.

**Step 3-**Designing CS prototypes through a collective discussion. Facilitators summarize the individual ideas, and organize a collective discussion aiming at designing one or several CS prototypes. They ensure that each CS is described with enough details to allow its *ex ante* assessment with chosen tools.

<figure><img src="image_164.png" /><figcaption>**Fig. 1.** Main steps, inputs and deliverables of a *de novo* design workshop</figcaption></figure>

## 3 Results – Discussion

In the province of Caceres (Extremadura, Spain), the main negative impacts of the irrigated arable CS were the high consumption of water and the high use of fossil energy, directly with agricultural mechanical operations, or indirectly with nitrogen fertilizer. As a result, the main goal was to reduce by half the fossil energy consumption and the water consumption (fossil energy consumption less than 15 GJ.ha⁻<fnref n="1" /> year⁻<fnref n="1" />, and irrigation water consumption under 3000 m<fnref n="3" />.ha⁻<fnref n="1" /> year⁻<fnref n="1" />), which represents both an ambitious and distant goal.

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

<footnote n="2">² Chambre d'agriculture de la Nièvre, BP 80, F-58028, Nevers, France</footnote>

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

<footnote n="4">⁴ ARAA, BP 30022 Schiltigheim, F-67013 Strasbourg, France</footnote>

<footnote n="5">⁵ ACCIONA, Apartado de correos 76, SP-10100 Miajadas (Caceres), Spain</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: raymond.reau@grignon.inra.fr</footnote>
From the individual ideas proposed by all participants, a facilitator mapped the proposals on a white board, in order to organize them and to link them to the main goal, and to clarify the strategies underlying the choice of a given technical option. A figure linking the aim, the functions targeted and the proposed technical options was built, and could be used as a resource for the following CS design step (Fig. 2).

During the third step, the participants designed one CS prototype. It was first described through a conceptual diagram indicating the main technical options mobilized in order to provide the expected service. Then, a first *ex ante* assessment using two simple indicators (water and energy use) was implemented to check off the main goal was achieved.

<figure><img src="image_165.png" /><figcaption>**Fig. 2.** Concept map summaring and organizing individual ideas and technical options gathered during step 2</figcaption></figure>

After the workshop, a facilitator realized the description of the CS practices (Table 1), using bibliography and local experts' knowledge among the participants of the workshop, in order to achieve a wide *ex ante* sustainability assessment.

<table><caption>**Table 1.** Main practices and yield results of the new cropping system proposed during step 3</caption><thead><tr><th colspan="2">Crop practices</th><th>Maize</th><th>Soya</th><th>Wheat</th><th>Sorghum</th><th>Tomato</th><th>Zucchini</th><th>Broccoli</th></tr></thead><tbody><tr><td rowspan="2">Agricultural machine</td><td>Ploughing</td><td>1</td><td>-</td><td>1</td><td>-</td><td>-</td><td>-</td><td>1</td></tr><tr><td>Other tillage</td><td>3</td><td>4</td><td>2</td><td>3</td><td>3</td><td>4</td><td>23</td></tr><tr><td rowspan="3">Fertilization (kg/ha)</td><td>N</td><td>280</td><td>15</td><td>75</td><td>147</td><td>220</td><td>200</td><td>220</td></tr><tr><td>K20</td><td>-</td><td>76</td><td>-</td><td>43</td><td>73</td><td>350</td><td>84</td></tr><tr><td>P205</td><td>-</td><td>23</td><td>-</td><td>23</td><td>-</td><td>150</td><td>34</td></tr><tr><td rowspan="2">Pesticides (passing/ha)</td><td>Herbicide</td><td>2</td><td>-</td><td>1</td><td>2</td><td>3</td><td>-</td><td>2</td></tr><tr><td>Insecticide, Fungicide</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>5</td><td>2</td></tr><tr><td>Irrigation (m³/ha)</td><td>Irrigation</td><td>6 500</td><td>-</td><td>-</td><td>5000</td><td>5500</td><td>2700</td><td>2000</td></tr><tr><td>Harvest</td><td>1 passing/crop</td><td>Machine</td><td>Machine</td><td>Machine</td><td>Machine</td><td>Machine</td><td>Manual</td><td>Manual</td></tr><tr><td rowspan="3">Yield (T/ha)</td><td>minimum</td><td>10</td><td>2,7</td><td>1,0</td><td></td><td>60</td><td></td><td></td></tr><tr><td>average</td><td>14</td><td>3,5</td><td>2,6</td><td>7,8</td><td>80</td><td>26</td><td>22</td></tr><tr><td>maximum</td><td>18</td><td>6,0</td><td>3,0</td><td></td><td>100</td><td></td><td></td></tr></tbody></table>

In addition to the design of new CS, this design workshop delivers useful resources for agronomists and farmers to locally adapt their own CS: a map of ideas and technical options linked to a goal, a conceptual diagram indicating how the proposed CS could fulfill the main goal, and at last, the expected practices and results of the innovative CS.

## 4 Conclusions

This design workshop approach appeared to be suited to situations where knowledge is poorly synthesized in scientific literature and/or crop models. It was successful to explore new CS, and also to help designing CS before their test through on station experiment (this example in Extremadura, Spain) or directly on farm implementation (non shown example of a case study involving farmers in Burgundy, France). Lastly, in addition to the design of innovative CS, design workshop could also serve as a platform for knowledge exchange and know-how.

*Acknowledgements.* This work has been funded under the EU seventh Framework Programme by the LogistEC project No. 311858: Logistics for Energy Crops' Biomass. The views expressed in this work are the sole responsibility of the authors and do not necessary reflect the views of the European Commission. We thank M-H. Jeuffroy for her thorough re-reading of this article.

## References

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

* Reau R., Monnot L.-A., Schaub A., Munier-Jolain N., Pambou I., Bockstaller C., Cariolle M., Chabert A., Dumans P., 2012. Les ateliers de conception de systèmes de culture pour construire, évaluer et identifier des prototypes prometteurs, Innovations Agronomiques 20, 5-33.
# Forages for Reduced Nitrate Leaching – a cross sector approach

Paul Edwards *±1, Ina J.B. Pinxterhuis 1 & Denise Bewsell 2

## Introduction

In 2013 the New Zealand Government set a target to double agricultural exports by 2025. At the same time, the Government required regional councils to set nutrient discharge limits under the National Policy Statement for Freshwater Management (Ministry for the Environment, 2011). Achieving these dual goals of increased production and mitigating environmental impact will require a substantial effort to achieve on-farm change and will require the input of multiple agricultural sectors. This paper describes how the Forages for Reduced Nitrate Leaching programme (FRNL) aims to support farmers to achieve these demanding objectives through integrated research, development and extension.

## FRNL programme logic

Farmer adoption of new technologies and farm systems is the ultimate indicator of the FRNL programme's success. Traditionally, linear information transfer has been less effective at achieving on-farm change for more complex issues, such as increasing production within nutrient limits. The FRNL programme (running from 2014 to 2019) incorporates principles of co-innovation (Klerkxet al., 2012) and participatory modelling (Vanclayet al., 2006), and recognises that a systemic approach to facilitating on-farm practice change is needed. Co-innovation recognises that interactions between stakeholders are as important as integrating different research disciplines.

The FRNL programme logic developed for the programme reflects these principles (Fig. 1). Research will focus on technical issues to reduce nitrate leaching in the context of New Zealand farming systems and developing models to reflect a variety of crops and pasture and impact of management on productivity, profitability and environment. Stakeholders, including farmers, are involved from the beginning of the programme to develop relationships and exchange information to support the research and learning for all involved. An important aspect of FRNL is the involvement of a group of farmers – the monitor farmers – who will guide the development and adoption of sustainable farming systems.

<figure><img src="image_166.png" /></figure>

*Farm Environmental plans are action plans developed in a regulatory setting to assess environmental risks on-farm and to define and implement actions to mitigate these risks.

<caption>**Fig. 1.** Diagram of programme logic for Forages for Reduced Nitrate Leaching.</caption>

<footnote n="1">1 DairyNZ, PO Box 85066, Lincoln University, Lincoln 7647, New Zealand</footnote>

<footnote n="2">2 AgResearch, Private Bag 4749, Christchurch 8140, New Zealand</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Paul.Edwards@dairynz.co.nz</footnote>
Role of monitor farmers in guiding FRNL programme development

A network of nine monitor farmers is a key feature of the FRNL programme. They represent the arable, dairy and sheep & beef industries in the Canterbury region of New Zealand. The purpose of the network is threefold. First, the monitor farmers will be involved in guiding research direction (co-develop) using their existing knowledge through experiences and innovation on-farm. Second, current practice and practice change will be demonstrated through monitoring and reporting on-farm performance (technical and financial) and modelling nutrient balance and nitrate leaching. Third, whilst demonstrating current practice and change, the monitor farmers will help identify barriers to adoption, in terms of risks, unintended consequences, or whether new skills and resources are required.

With the imposition of targets and limits for nitrogen discharges from farm land, farmers are eager to see commercial scale examples of nutrient management strategies that can be readily incorporated on-farm. Farmers learn more readily from other farmers, therefore it is important to showcase successful colleagues rather than focussing on modelled benchmarks or the average performance of a farmer group (Ondersteijn, 2002). For example, in The Netherlands this approach was successfully adopted in a project involving 17 dairy farms to improve nutrient management. This project supported substantial environmental improvement on-farm and had an additional impact on policy development, which benefitted from reliable data of commercial farms (Oenema, 2014). Another example is an earlier New Zealand project with monitor farmers; the Southern Wintering Systems project which confirmed that farmers feel they learn most from other farmers and prefer to see results from colleagues rather than from modelling or regional benchmarking exercises (Dalley et al., 2014).

Assessing success of the FRNL programme

A meeting with local farm consultants was held to outline the proposed programme logic. Using an interactive voting system, the 33 participants were asked about the expected value of monitor farms in influencing the FRNL research direction. Forty eight percent of the participants agreed that having monitor farmers involved would make solutions practical, with a further 44%saying it would be crucial to achieving relevant results. Similarly, 48% agreed that the monitor farm network would be crucial to achieving adoption of new mitigation options on commercial farms and a further 48% though it would help increase adoption of mitigation options. Forty-one percent wanted to be involved in the network and a further 44% wanted to be updated regularly on the programme. Overall, the survey results suggested that this group believes the FRNL programme is well structured to achieve its goals.

Measures for monitoring and evaluating the impact of the FRNL programme were explored at another meeting involving governance and programme management committee members, key researchers and monitor famers. Several quantifiable measures of the programme's success were identified. These included: the monitor farmers successfully implementing new practices; new mitigation options being included in nutrient budget models; improved awareness and understanding of nutrient cycling and applicability of mitigation options; and level of their adoption as monitored through annual farmer surveys. Other measures included the number of actions in Farm Environmental Plans relating to the programme; an environmental trait being included in the national forage evaluation system; and changes in the ratio of seed of different pasture and crop species being sold by retailers.

With these first two stakeholder meetings the FRNL programme has a clear mandate to involve monitor farmers in guiding research and farmer uptake activities and an agreed approach to assess the success of the programme.

Conclusions

The Forages for Reduced Nitrate Leaching programme (2014-2019) has been established to co-develop solutions to achieve the twin challenge of increasing primary sector productivity, while mitigating environmental impact, through direct engagement between researchers, farmers and other stakeholders. Early engagement with stakeholders has clarified expectations and provided a framework for monitoring and evaluating the impact of the programme.

*Acknowledgements*. Forages for Reduced Nitrate Leaching is a DairyNZ-led collaborative research programme across the primary sector delivering science for better farming and environmental outcomes. The aim is to reduce nitrate leaching through research into diverse pasture species and crops for dairy, arable and sheep and beef farms. The main funder is the Ministry of Business, Innovation and Employment, with co-funding from research partners DairyNZ, AgResearch, Plant & Food Research, Lincoln University, Foundation for Arable Research and Landcare Research.

References

Dalley, D.E., Pinxterhuis, J.B., Hunter, M., Geddes T. & Tarbotton I. (2014). A co-development approach to investigating wintering options on dairy farms in southern New Zealand. Proceedings of the 11th European IFSA Symposium, 636-643.

Klerkx, L., van Mierlo, B. & Leeuwis, C. (2012). Evolution of systems approaches to agricultural innovation: concepts, analysis and interventions. In Darnhofer, D. Gibbon & B. Dedieu (Eds.), *Farming Systems Research into the 21st Century: The New Dynamic*, 457-483. Springer, The Netherlands.

Ministry for the Environment (2011). National Policy Statement for Freshwater Management 2011. Ministry for the Environment, Wellington, New Zealand.

Oenema, J. (2014), Transitions in nutrient management on commercial pilot farms in The Netherlands. PhD thesis Wageningen University, Wageningen, The Netherlands.

Ondersteijn, C.J.M. (2002). Nutrient management strategies on Dutch dairy farms - an empirical analysis. PhD thesis Wageningen University, Wageningen, The Netherlands.

Vanclay, J.K., Prabhu, R. & Sinclair, F. (2006). Realising community futures: a practical guide to harnessing natural resources. Routledge, Florence.
# A research trajectory driven by scaling out: from a detailed farm model (SEDIVER) to a participatory board game (Forage Rummy)

Guillaume Martin*±1

## 1 Introduction

Under the influence of various factors (climatic, economic, social, etc.), the context of agricultural production is increasingly changing and erratic. Farmers keep trying to adapt their farming systems to this context in order to preserve the sustainability, in particular the production ability, of such systems. Adaptation refers to a process, action or outcome in a system in order for the system to better cope with, manage or adjust to experienced or expected events e.g. climatic. The pace, scale and even the direction of contextual changes being plagued with uncertainties, it is particularly difficult for farmers to make decisions about adaptation measures. To address this adaptation challenge, technology transfer has long been dominant in agricultural research and development. Over recent years, unlike the technology transfer approach, approaches seeking to develop farmers' adaptive capacity have increasingly been developed. They often rely on the modeling of data from physical, chemical, physiological and ecological processes. These approaches are criticized among other things for being unable to cope with different production and management contexts, i.e. for being hard to scale out. In this article, I elaborate on my own research trajectory that has been driven by the scaling out features of the decision support systems I have developed.

## 2 A first period of detailed simulation modeling

During my PhD, I developed a very detailed dynamic and mechanistic simulation model of grassland-based livestock systems called SEDIVER (Martin et al., 2011b). SEDIVER reproduced the interactions on grassland-based beef-cattle farms between the biophysical and management processes in response to weather conditions. It was intended to be used with farmers in order to design farm management strategies enhancing exploitation of farmland and grassland diversity to promote management flexibility against interannual weather conditions. At that time, in the French community of agricultural scientists, the failure of simulation-based agricultural decision support systems was often related to the poor or even lack of modelling of farmers' decision-making processes leading to too unrealistic simulation outputs to inform practice (Garcia et al., 2005). As a consequence, in SEDIVER, emphasis was put on modelling (i) the heterogeneous nature of the biophysical processes occurring in the system and the subsequent constraints on grassland use, and ii) the farmer's management behaviour on a daily scale to coordinate the work activities that are constrained by this diversity over time and space.

Modelling farmland and grassland diversity and their consequences on biophysical processes required to provide a detailed description of topography (altitude, exposition, etc.), micro-climate (temperatures, rainfall, etc.), soil (water capacity, nutrient status, etc.) and plant types (distribution of grass functional groups, legumes, etc.) for each field as model input parameters. For management-related parameters, the basic modelling unit was an activity. In its simplest form, an activity denoted something to be done (e.g. hay-making) to a particular biophysical object or location, e.g. a field, by an executor, e.g. a worker. It was characterized among other things by local opening and closing conditions, defined by time windows and/or predicates (Boolean functions) and a speed of execution (e.g. number of hectares which can be harvested in a unit of time). Activities were further constrained by using programming constructs enabling specification of temporal ordering, iteration, aggregation and optional execution (e.g. a sequence of two harvests on a field constrained by earliest and latest starting dates). All the activities were connected in a plan. Adjustments to the plan were any change such as the removal or insertion of activities as particular events occur, e.g. a lasting drought event.

To calibrate and validate SEDIVER, we used data from pluriannual surveys on two farms in the French Pyrenees. Simulations accurately reproduced the chronology of farming practices and the resulting production performances (forage production, meat production, self-sufficiency for forage, etc.; see Martin et al., 2011b and 2011c for more details). It also proved informative for farmers in exploring the scope for adaptation of their management practices through enhanced exploitation of farmland and grassland diversity to promote management flexibility against interannual weather conditions. Then, calibration and validation confirmed the capacity of SEDIVER to improve the realism of simulation outputs. Still, SEDIVER was so detailed and it was so hard to get into farmers' mental models that it took 6 months to conduct the calibration and validation work for these two farms (Martin et al., 2011c). It appeared unrealistic to consider scaling out SEDIVER for further projects.

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

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: guillaume.martin@toulouse.inra.fr</footnote>
## 3 A change towards participatory modeling

Following this experience, I started a participatory project to develop a completely different approach called Rami Fourrager® (Forage Rummy, Martin et al., 2011a; Martin, 2015). It is a board game supported by a computer model. It is intended to be used by agricultural consultants and/or researchers with small groups of 2 to 4 farmers during workshops lasting from 2 to 4 hours. Workshops include collectively and iteratively designing (with material objects e.g. cards) and evaluating (with a simulation model) livestock systems able to be adapted to new contextual challenges (e.g. climate change) and new farmers' objectives (e.g. transition to organic farming). Throughout these iterations, it aims at developing farmers' adaptive capacity by stimulating their reflections and discussions.

Forage Rummy has been developed following a participatory approach that resulted in interactions with nearly 200 farmers and agricultural consultants. Modelling choices have been strongly influenced by the outcomes of these interactions. Forage Rummy builds on process-based models that have been selected or developed because (i) they display robustness when being scaled out e.g. the grassland model has been evaluated satisfactorily in a wide number of French regions; and (ii) they require a low number of input parameters to be informed e.g. the management model requires informing about 10 times less parameters than with SEDIVER. Moreover, these parameters are expressed in units used by farmers and agricultural consultants. This way, while management-related parameters were extremely difficult to inform with SEDIVER, they are directly provided by farmers representing their farm on the board game using cards and felt tip with Forage Rummy.

Therefore, scaling out Forage Rummy mostly relies on farmers' and agricultural consultants' experiential knowledge in checking during calibration and validation that the outputs are consistent considering a range of input parameters representing their farm. When a discrepancy is found, the input parameters are modified based on farmers' suggestions until satisfying outputs are obtained. The agreement of farmers and agricultural consultants with the model behaviour is then the key indicator of the validity of the model.

<figure><img src="image_167.png" /><figcaption>Fig. 1. Overview of a Forage Rummy workshop with farmers.</figcaption></figure>

## 4 Conclusions

Scaling out turns out to be much quicker (half-a-day for a NUT 3 region) and easier with Forage Rummy than with SEDIVER thanks to the modelling choices made during the development stage. Moreover, involving farmers and agricultural consultants in the calibration and validation process promotes transparency of the whole approach and develops mutual trust and understanding among researchers and farmers. Since 2013, about 30 French agricultural consultants have been trained to using Forage Rummy. They manage its scaling out by themselves.

*Acknowledgements*. This work was funded by the French ANR SYSTERRA program as part of the project O2LA (ANR-09-STRA-09), by the French ANR Agrobiosphère program as part of the TATABOX project (ANR-13-AGRO-0006), by the ACCAF Farmatchproject and by the PSDR Midi-Pyrénées program.

## References

* Garcia, F., Guerrin, F., Martin-Clouaire, R., Rellier, JP. (2005) The human side of agricultural production management - the missing focus in simulation approaches. In: A.Zerger, RM. Argent(Eds.), Proc. of the MODSIM 2005 Congress, Melbourne, 12-15 December 2005, pp. 203-209.

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

* Martin, G., Felten, B., Duru, M. (2011a) Forage rummy: A game to support the participatory design of adapted livestock systems. *Environ. Model.Softw.*, **26**, 1442-1453.

* Martin, G., Martin-Clouaire, R., Rellier, J.P., Duru, M. (2011b) A simulation framework for the design of grassland-based beef-cattle farms. *Environ. Model.Softw.*, **26**, 371-385.

* Martin, G., Theau, J.P., Therond, O., Martin-Clouaire, R., Duru, M. (2011c) Diagnosis and Simulation: a suitable combination to support farming systems design. *Crop Pasture Sci.*, **62**, 328-336.
# Photovoltaic Water Pumping System –Sustainable Water Irrigation for Best Farming System

P. S. Shehrawat *±1

## 1 Introduction

Photovoltaic Systems based Power Plants have emerged as viable power sources for applications such as lighting, water pumping and telecommunications and are being increasingly used for meeting electrical energy needs in un-electrified locations such as remote villages, hamlets, hospitals and households. The country today has the world's largest program for deployment of decentralized PV systems. Photovoltaic-based water pumping system is eco-friendly in nature and pollution free technology can be more appropriate to the needs of the developing countries like India than solar/thermal energy conversion (STEC).

## 2 Material and Methods

The study was undertaken in the Hisar, Rohtak and Jhajjar districts of Haryana state (India) to know the constraints faced by the farmers in the use of photovoltaic water pumping system. A total number of 282 respondents i.e. 141 beneficiaries and 141 non-beneficiaries were interviewed for the study. To measure the constraints encountered by the farmers in the use of PWPS, a schedule was developed. The responses were obtained on three point continuum i.e. „very serious“, „serious“ and „not so serious“ and the weights of 3, 2 and 1 were assigned, respectively. The scores so obtained on all items were summed up and the rank orders were assigned to each constraint.

## 3 Results and Discussion

### Constraints faced By the Farmers' in the Use of PWPS.

**Technical Constraints:** The PWPS adopted respondents reported that "This technology only works in less than 8 meters water table" and "It does not works in cold / winter days" consider as major technical constraint, moreover the similar results were also obtained in case of non adopted respondents. Further, "High cost of PWPS" found the most serious financial constraint as observed by both adopted and non-adopted respondents. On the other hand, "Lack of extension literature" and "Lack of package of practices for PWPS irrigation farming system" were perceived as the major extension constraints by the adopted respondents. However, in case of non-adopted farmers, "Lack of attention of mass media" found to be the most serious extension constraint (Table 1). These findings were supported Kaur *et al.* (1998), Prasad and Singh (2000), Hazarika and Palit (2001).

### Best farming system recommended by extension functionaries for crop production under PWPS.

Extension functionaries suggested that 16.25, 8.25, 7.58, 5.00, 4.50, 4.16, and 3.66 per cent from area (Table 2) should be covered under vegetable, mushroom, horticulture, floriculture, spices, medicinal plants and fishries, respectively, by PWPS adopted farmers who were having less than 5 acres land holding. The PWPS owning farmers who were having 5 to 15 acres of land holding should covered 12.16, 8.00, 5.50, 4.50, 3.16, 2.25 and 1.82 per cent area under vegetable, horticulture, mushroom, floriculture, fishries, spices and medicinal plants, respectively. The recommended area under different crops the farmers having more than 15 acres was highest (70.65 %) under traditional crops followed by area should be covered under horticulture (9.67%), vegetable (8.16%), mushroom (3.17 %), floriculture (2.75 %), fishries (2.25 %), spices (2.00 %) and medicinal plants (1.33 %).

### Best Farming System Recommended by Extension Functionaries for livestocks production under PWPS.

The large majority of the Extension Functionaries recommended 2 to 3 buffaloes / cows, 5 to 10 sheep / goats, 16 to 32 number of layers and 9 to 18 numbers of honey bee boxes for those farmers who are having less than 5 acres land holding for effective management with other farm enterprises. Whereas the study further showed that the significant majority of Extension Functionaries recommended 10 to 15 buffaloes / cows, 10 to 20 sheep / goats, 63 to 106 number of layers and 41 to 57 number of honey bee boxes to those farmers having 5 to 15 acres land holding. More than half of Extension Functionaries recommended 13 to 19 buffaloes / cows to the PWPS owning farmers having more than 15 acres land holding. While more than, two-third of them recommended 20 to 40 sheep / goats to the farmers having more than 15 acres land holding. 100 to 125 numbers of layers for poultry production and 106 to 177 number of honeybee boxes to the farmers having more than 15 acres land holding for effective handling with other enterprises cited in Table 3.

<table><caption>Table 1. Constraints being faced by the farmers' in the use of PWPS</caption><thead><tr><th rowspan="2">Sr No.</th><th rowspan="2">Constraints</th><th colspan="2">Adopted</th><th colspan="2">Non adopted</th></tr><tr><th>TWFS</th><th>R</th><th>TWFS</th><th>R</th></tr></thead><tbody><tr><td>A</td><td>Technical constraints</td><td></td><td></td><td></td><td></td></tr><tr><td>1</td><td>PWPS spareparts are not available in market</td><td>272</td><td>III</td><td>291</td><td>III</td></tr></tbody></table>

<footnote n="1">¹ Department of Agriculture ExtensionCCS Haryana Agricultural University, Hisar-125004.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: psshehrawat1965@gmail.com</footnote>
<table><tr><td>2</td><td>It does not works in cold/winter days</td><td>309</td><td>II</td><td>311</td><td>II</td></tr><tr><td>3</td><td>This technology only works in less than 8 meters water Table</td><td>327</td><td>I</td><td>333</td><td>I</td></tr><tr><td>4</td><td>Farmers are not aware about the PWPS</td><td>245</td><td>IV</td><td>268</td><td>IV</td></tr><tr><td>5</td><td>The water available through the PWPS is not sufficient for farming</td><td>238</td><td>V</td><td>245</td><td>V</td></tr><tr><td>B</td><td>Financial constraints</td><td></td><td></td><td></td><td></td></tr><tr><td>1</td><td>Lack of money to buy a PWPS</td><td>358</td><td>III</td><td>342</td><td>II</td></tr><tr><td>2</td><td>Lack of credit facilities</td><td>354</td><td>IV</td><td>289</td><td>V</td></tr><tr><td>3</td><td>Less subsidy on PWPS</td><td>347</td><td>V</td><td>296</td><td>IV</td></tr><tr><td>4</td><td>Less number of PWPS are available on subsidy</td><td>380</td><td>II</td><td>341</td><td>III</td></tr><tr><td>5</td><td>High cost of PWPS</td><td>385</td><td>I</td><td>347</td><td>I</td></tr><tr><td>C</td><td>Extension constraints</td><td></td><td></td><td></td><td></td></tr><tr><td>1</td><td>Lack of extension literature</td><td>327</td><td>I</td><td>316</td><td>IV</td></tr><tr><td>2</td><td>Lack of attention of mass media</td><td>279</td><td>VII</td><td>343</td><td>I</td></tr><tr><td>3</td><td>Lack of knowledge of extension agencies</td><td>298</td><td>VI</td><td>332</td><td>II</td></tr><tr><td>4</td><td>Lack of adequate manpower from state extension agencies</td><td>311</td><td>V</td><td>316</td><td>IV</td></tr><tr><td>5</td><td>Lack of fellow farmers co-operation</td><td>271</td><td>VIII</td><td>260</td><td>VIII</td></tr><tr><td>6</td><td>Lack of motivational programme for adoption of PWPS</td><td>267</td><td>IX</td><td>278</td><td>VI</td></tr><tr><td>7</td><td>Lack of information after effects of PWPS installation on the farm</td><td>232</td><td>X</td><td>310</td><td>V</td></tr><tr><td>8</td><td>Lack of information regarding the profitability of PWPS on the farm</td><td>318</td><td>IV</td><td>271</td><td>VII</td></tr><tr><td>9</td><td>Lack of package of practices for PWPS irrigation farming systems</td><td>326</td><td>II</td><td>310</td><td>V</td></tr><tr><td>10</td><td>Lack of feedback programme</td><td>322</td><td>III</td><td>321</td><td>III</td></tr></table>

$$TWFS = \text{Total Weighted Frequency score, R = Rank}$$

<table><caption>Table 2. Best farming system recommended by Extension Functionaries</caption><thead><tr><th>Sr. No.</th><th>Enterprises</th><th>Less than 5 acres land holding (N=27)</th><th>5 to 15 acres Land holding (N=27)</th><th>More than 15 acres land holding (N=27)</th></tr></thead><tbody><tr><td>1</td><td>Horticulture</td><td>7.58</td><td>8</td><td>9.67</td></tr><tr><td>2</td><td>Vegetable</td><td>16.25</td><td>12.16</td><td>8.16</td></tr><tr><td>3</td><td>Floriculture</td><td>5</td><td>4.5</td><td>2.75</td></tr><tr><td>4</td><td>Spices</td><td>4.5</td><td>2.25</td><td>2</td></tr><tr><td>5</td><td>Medicinal Plant</td><td>4.16</td><td>1.82</td><td>1.33</td></tr><tr><td>6</td><td>Fishries</td><td>3.66</td><td>3.16</td><td>2.25</td></tr><tr><td>7</td><td>Mushroom</td><td>8.25</td><td>5.5</td><td>3.17</td></tr></tbody></table>

<table><caption>Table 3. Best farming system recommended by Extension Functionaries for Live stock production under PWPS</caption><thead><tr><th>Name of Enterprises</th><th>Categories</th><th>Score Range</th><th>land holding</th><th>Score Range</th><th>land holding</th><th>Score Range</th><th>land holding</th></tr></thead><tbody><tr><td>Livestock</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Buffallo/ Cow</td><td>Low</td><td>Less than 2</td><td>Nil (0)</td><td>Less than 10</td><td>6 (22.22)</td><td>Less than 13</td><td>7 (25.92)</td></tr><tr><td>Middle</td><td>2 to 3</td><td>25 (92.59)</td><td>10 to 15</td><td>19 (70.37)</td><td>13 to 19</td><td>16 (59.25)</td></tr><tr><td>High</td><td>Above 3</td><td>2 (7.40)</td><td>Above 15</td><td>2 (7.40)</td><td>Above 19</td><td>4 (14.81)</td></tr><tr><td rowspan="3">Sheep / Goat</td><td>Low</td><td>Less than 5</td><td>3 (11.11)</td><td>Less than 10</td><td>3 (11.11)</td><td>Less than 20</td><td>3 (11.11)</td></tr><tr><td>Middle</td><td>5 to 10</td><td>22 (81.48)</td><td>10 to 20</td><td>20 (74.07)</td><td>20 to 40</td><td>21 (77.77)</td></tr><tr><td>High</td><td>Above 10</td><td>2 (7.40)</td><td>Above 20</td><td>4 (14.81)</td><td>Above 40</td><td>3 (11.11)</td></tr><tr><td rowspan="3">Poultry</td><td>Low</td><td>Less than 16</td><td>3 (11.11)</td><td>Less than 63</td><td>2 (7.40)</td><td>Less than 100</td><td>5 (18.51)</td></tr><tr><td>Middle</td><td>16 to 32</td><td>21 (77.77)</td><td>63 to 106</td><td>24 (88.88)</td><td>100 to 125</td><td>18 (66.66)</td></tr><tr><td>High</td><td>Above 32</td><td>3 (11.11)</td><td>Above 106</td><td>1 (3.70)</td><td>Above 125</td><td>4 (14.81)</td></tr><tr><td rowspan="3">Bee - Keeping</td><td>Low</td><td>Less than 9</td><td>4 (14.81)</td><td>Less than 41</td><td>6 (22.22)</td><td>Less than 106</td><td>1 (3.70)</td></tr><tr><td>Middle</td><td>9 to 18</td><td>18 (66.66)</td><td>41 to 57</td><td>18 (66.66)</td><td>106 to 177</td><td>25 (92.59)</td></tr><tr><td>High</td><td>Above 18</td><td>5 (18.51)</td><td>Above 57</td><td>3 (11.21)</td><td>Above 177</td><td>1 (3.70)</td></tr></tbody><tfoot><tr><td colspan="8">Figure in parentheses indicate percentage</td></tr></tfoot></table>

References:

Hazarika, Nandita and Palit, Debajit (2001). „Renewable Energy Development programmes in North East India.” *Kurukhatra*.Nov:36-40.

Kaur, Inderdeep; Bakshi, Pupa and Arora, Sadhana (1998). „Evaluation of problems faced by biogas plant users of Ludhiana District”. *Indian J.Social Research*.29(3&4):201-208.

Prasad, R.B. and Singh, S.(2000). „Constraints in adoption of biogas technology”. *Journal of Applied Biology*.9(1):100-102.
# Gender and wealth influence how smallholder farmers make on-farm changes: a case study from Uganda

Skye Gabb *±1,2*, Peter Dorward <fnref n="1" /> & Graham Clarkson <fnref n="1" />

## 1 Introduction

Innovation is key to increasing agricultural productivity, however the processes involved in establishing productivity enhancing changes on smallholder farms are poorly understood. In Uganda, the government has recognised the importance of innovation, undertaking significant reforms to extension services in an attempt to promote on-farm innovation (Kjær & Joughin, 2012). However, investment in science and technology alone are not enough to enable innovation (Hall et al., 2006) and, in Uganda, there is little evidence that key policies to promote innovation and mainstream gender concerns are being translated into practice. Consequently, a better understanding of the actual processes smallholder farmers engage in to make productivity enhancing on-farm change is required. Given the gendered nature of agriculture and influence of gender and wealth on farmers’ access to and control over resources (Moser, 1994; Scoones, 1995), this research expected to find that the gender and wealth of smallholder farmers’ influences how they make on-farm changes.

## 2 Materials and Methods

This case study of seven villages in Nakaseke district, central Uganda, assessed farmers’ social networks and the drivers and constraints to agricultural innovation. Initial research focused on crop innovations using participatory methods with farmer focus groups (11 groups with 6-11 participants each). To provide contextual background to innovation activities, agricultural timelines were constructed at three villages by either male, female or mixed groups (Garforth, 2001). At each of the remaining four villages either one male and one female or two mixed groups each completed two innovation histories and a communication map (Douthwaite & Ashby, 2005). These activities provided a more comprehensive analysis of innovation processes anchored in specific examples, as well as a visual representation of linkages within farmers’ innovation systems.

Of the seven villages involved in the participatory research, Namasujju village was selected for the questionnaire survey as farmers had identified complex innovation networks and were actively adapting innovations. Four key informants from Namasujju completed a village wealth ranking; dividing farmers into wealthy, medium and poor farmers based on assets, income, education and provision of basic needs (Grandin, 1988). This was used to select a stratified random sample of 99 respondents for a questionnaire which focused on both crop and livestock innovations. Farmers were asked to explain the three key changes they had made in the past ten years and to identify the drivers, constraints and actors involved in making those changes.

## 3 Results

Smallholder innovation networks were influenced by gender and wealth. Gender analysis indicated that men and women interacted with different types of farmers; women relied on informal interaction with farmers who were in their social network, while men consulted model farmers and engaged with farmers outside the village. Wealth also influenced farmer-farmer interactions, as neighboring farmers’ information was considered ‘very useful and reliable’ by 52% of poorer farmers but only by 23% of wealthy farmers (X<fnref n="2" />=14.80, p=0.022). Wealthy farmers instead relied on extension services and NGOs, finding extension staff ‘very helpful’ in 21% of changes they made, compared to 9% of changes made by poor farmers (X<fnref n="2" />=10.93, p=0.012). Gender analysis also indicated that men had more geographically

dispersed innovation networks and more ways of accessing information, with men identifying 13 different communication channels and women only 9. In addition, sometimes only one gender received a specific type of information, for example only men said they received information about livestock production. In such cases, if the recipient didn’t share the information with their spouse, then the spouse commonly resorted to copying other farmers’ practices. Consequently, gender and wealth affect on-farm innovation by influencing which stakeholders farmers engage with, how farmers access information and what information farmers receive. Gender also influenced the drivers of on-farm change, as men were predominantly driven by income, labor saving and cost reduction and women by food security, taste and market. These differences often persisted even when men and women adopted the same technology. For example, in one village, women adopted a disease resistant banana variety because banana disease was affecting food security, in contrast men adopted the same variety because disease had destroyed their coffee crops and they were endeavoring to replace lost

<footnote n="1">¹ School of Agriculture, Policy and Development, University of Reading, Reading, UK.</footnote>

<footnote n="2">2 Agriculture Flagship, Commonwealth Scientific and Industrial Research Organisation, Toowoomba, Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: skye.gabb@csiro.au</footnote>
income. Supporting this example, the importance of income differed with gender, as women considered income 'not important' in 21% of changes they identified compared to only 3% of changes identified by men ($X^2=17.34, p=0.001$). Wealth also influenced drivers, with food security driving 39% of changes made by poor men but only 23% made by wealthy men ($X^2=11.01, p=0.007$). In contrast, food security was consistently important for women, as it was considered a 'very important' driver in over 40% of changes made by women across all wealth categories. Therefore, while men and women often respond to different drivers, the interaction between gender and wealth indicated that poor male farmers may respond to the same combination of drivers as female farmers.

Gender and wealth also influenced constraints to on-farm innovation, with key constraints including labour, finance, access to inputs, pests and disease and household support. Gender analysis showed that financial constraints predominantly affected women, as 95% of women were affected by financial constraints compared to 78% of men ($X^2=25.185, p=0.007$). In addition, women were also more frequently constrained by labour, as it affected 32% of women compared to only 2% of men ($X^2=25.185, p<0.001$). While women often had better access to information, men commonly controlled the resources to use this information, including income and access to planting material. Wealth also influenced constraints to innovation, with poorer farmers more constrained by finance, labour availability and production risk, which constrained 17% of changes made by poor farmers but only 9% of changes made by wealthy and medium farmers ($X^2=12.01, p=0.01$). Finally, these constraints are embedded within intra-household interaction, with spousal support able to exacerbate or reduce constraints to innovation.

## 4 Discussion

The gender and wealth of smallholder farmers' affect their innovation networks and, consequently, how they make on-farm changes. Results indicate that male and wealthy farmers are able to take advantage of consultative relationships with model farmers, extension services and NGOs, while women rely on informal interaction with farmers in their social networks. This suggests that pooling of information within women's networks involves a higher level of reciprocity than in mens' networks and that both gender and wealth influence whether the resources required for innovation are freely available or diffuse through social networks. In addition, intra-household relationships also influence innovation activity, as the disconnect between access and control over information and resources within households and the withholding of information from spouses can significantly constrain on-farm innovation.

Within these innovation networks, the gender and wealth of farmers also influenced the drivers and constraints to on-farm innovation. Men were more frequently driven by income and women by food security, with such differences often persisting even when farmers were adopting the same innovation. Farmers' ability to respond to such drivers was constrained by a range of factors including access to and control of financial resources, land, labour and information. Although both men and women faced constraints to innovation, promotion of household innovation activity requires special consideration of the constraints that women face, such as labour and finance, as on-farm changes that increase household labour may be hampered unless women are able to access some of the benefits.

## 5 Conclusions

Findings suggest that smallholders farmers' gender and wealth influence their innovation networks and social learning processes. Such differentiation also influences the drivers and constraints to on-farm innovation, resulting in individual technologies being adopted for a range of reasons and adapted to address a range of constraints specific to the farmer's gender and wealth. Consequently, gender and wealth are two vital elements that need to be understood if practitioners in agricultural development wish to promote productivity enhancing on-farm change.

*Acknowledgements.* This work was supported by Biosciences for Farming in Africa, which was funded by the John Templeton Foundation and the Malaysian Commonwealth Studies Centre. The authors would like to thank: the staff and students from Makerere University, farmers from Bulyake Parish and Mr Frances Kalumba of National Agricultural Advisory Services.

## References

* Douthwaite, B & Ashby, J. (2005). Innovation histories: A method for learning from experience. Institutional Learning and Change (ILAC), Brief No. 5, CGIAR, Rome.

* Garforth, C. (2001). Agricultural Knowledge and Information Systems in Hagaz, Eritrea. SD Dimensions. Retrieved 01/08/2013 from http://www.fao.org/sd/2001/KN1001_en.htm

* Grandin, B. (1988). Wealth ranking in smallholder communities: A field manual. Intermediate Technology Development Group, Rugby, UK.

* Hall, A., Janssen, W., Pehu, E. & Rajalahti, R. (2006). Enhancing agricultural innovation: how to go beyond strengthening of agricultural research. World Bank, Washington D.C.

* Kjær, A. & Joughin, J. (2012). The reversal of agricultural reform in Uganda: Ownership and values. *Policy and Society, 31*, 319-330.

* Moser, C. (1994). Gender roles, the family and the household. Gender planning and development. Routledge, London.

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# SCALING UP AGRO-ECOLOGICAL INNOVATION ADOPTION AMONG FARMING SYSTEMS. APPLICATION TO IMPROVED FALLOWS IN MARTINIQUE

Laurent Parrot<sup>*±1</sup></sub> 1, Laurent Hennig<sup>2</sup>, Eric Roux<sup>3</sup>, Lucile Vantard<sup>3</sup>, Alexandra Jestin<sup>4</sup>, François Ratye<sup>2</sup> & Paula Fernandès<sup>4</sup>

## 1 Introduction

Agro-ecological innovations face low adoption rates among farmers. Agro-ecological innovations are basically new farming practices whereas agrochemical inputs can be considered as objects. Farming practices are dependent on spatial variables (agro-ecological zoning), the farmer's profile (agronomic expertise), the farming system (mechanized or not, access to labor). Agro-ecological innovations are also dependent on a time frame spanning over several years. Therefore agro-ecological innovations case studies have a poor generic range outside their timeframe, their agro-ecological zoning or even commodity chain in which they have been studied.

The objective of this paper is to suggest a methodology for improving the generic range, and therefore a scaling up, of case studies focusing on agro-ecological innovations. We articulate this methodology around participatory approaches with the key stakeholders, the adaptability of the agro-ecological innovations to the farmer's and farming systems constraints, and the access to census databases.

We tested this methodology with a case study focused on improved fallows in Martinique and the vegetable sector. Martinique is a tropical island of the French West Indies representative of the constraints faced by resource limited and import dependent economies. Martinique faces the challenges of a declining agricultural sector, a large dependence on imports, and environmental degradation (Agreste, 2011, 2013). One alternative to agro-chemical inputs is the promotion of improved fallows. The current practice for fallows in Martinique is to let spontaneous grass and weeds expand on the plots. Improved fallows consist in introducing annual leguminous species to restore the biological and chemical properties of the cultivated soil, respond to local soil borne diseases (nematodes), and compete against weeds (Fernandes et al., 2009). The underlying agronomic principle is to take advantage of the existing and prevalent 2 to 3 months current fallows between the cropping periods to add green manure for a chemical and biological soil improvement.

## 2 Materials and Methods

A participatory approach hypothesized the adoption potential of improved fallows among farmers and validated the statistical classification of farmers.

A statistical classification from the 2010 agricultural census database was conducted among all farms involved in vegetable production (N= 1382). The participatory approach suggested two variables as potential determinants for the adoption of improved fallows: stable land tenure and total cultivated land area. We conducted a first computation of a Principal Component Analysis (PCA) and an Ascending Hierarchical Classification (AHC) based on those two variables. This first computation excluded farms with unstable land tenure. We conducted a second PCA and AHC computation among farms with stable land tenure for the five following variables related to different crops (in percentage of total farm cultivated area): vegetable, permanent grassland, banana and sugarcane, fallow, orchard. The correlations between these five variables defined the typology of farms.

We surveyed 80 farmers with a quota sampling from the results of the classification of farms. 47 variables were recorded on the profile of the farmers (socio demographic variables, network participation), agronomic practices, farm characteristics (area, mechanization potential of the land, labor supply, etc.).

We used the R 3.1.1. software and the —glmnetl package to establish an econometric model with a set of 47 explanatory variables at farm and farming system level (Tibshirani, 1996). The dependent variable was the (yes/no) willingness to test improved fallows. This package uses a penalized regression method to choose coefficients of the model. A first nmodel using the 47 variables eliminated variables which have no linear link to the dependent variable; this is the Model 1. Then the economic model (Model 2) is obtained by using the variables which are meaningful for the Model 1. The Model 2 confirms each selected variable from the Model 1.

<footnote n="1">1 UPR HORTSYS, Cirad, 34398 cedex 5, Montpellier, France</footnote>

<footnote n="2">2 Istom, 95000, Cergy, France</footnote>

<footnote n="3">3 SISEP, Ministère de l'agriculture de l'agroalimentaire et de la forêt, BP 642 – 97262 Fort-De-France cedex, Martinique</footnote>

<footnote n="4">4 UPR HORTSYS, Cirad, BP 214, 97285 Le Lamentin Cedex 2, Martinique</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: laurent.parrot@cirad.fr</footnote>