# Food security, income and agriculture in the new ruralities of Central America

Diego Valbuena*±<fnref n="1" />, Santiago López-Ridaura<fnref n="2" />, Mark van Wijk<fnref n="3" />, Pablo Siles<fnref n="1" />, Edwin García<fnref n="4" />, Orlando Téllez<fnref n="1" />
& Elvis Chavarria<fnref n="1" />

## 1 Introduction

Family agriculture in the sub-humid tropics of Central America faces multiple challenges that jeopardize the sustainability of rural livelihoods. Increased climate risk, high pressure on land, relatively expensive inputs and low agricultural profits, among others, are major threats to food security and income generation of smallholder farming in the region (FAO et al., 2012). Rural households experience and face these challenges to ensure their food and income in different ways depending on their socio-ecological context, objectives and resources. In Central America, many rural societies have moved from a completely agriculture-based to more diverse society where migration and rural-urban exchanges have shaped their current livelihoods and farming systems, the so-called new rurality (Grammont, 2004). The aim of this study was to understand the diversity of farming systems in the region, their main agricultural activities and performance, as well as their main sources and levels of income and food.

## 2 Materials and Methods

A short survey was conducted in 2014 with local partners to almost 800 farm households in five sites in sub-humid regions in Nicaragua, Honduras and El Salvador. Sites represented a gradient of market development and agroecology. Around 160 farmers were randomly selected accounting for 20-30% of the total rural households in each site. The survey contained 14 questions including information on household structure, cropping systems, livestock component, perceived food security, income and potential future strategies. Each survey was answered in ca. 40 minutes. Descriptive and statistical analyses were conducted to quantify the diversity of households between sites, as well as within sites using a structural household typology simply built on access to land (i.e. farm size). A balance was calculated to assess the level of self-sufficiency of the household in terms of energy in their diet based on their structure (i.e. demand of energy of the household), and the food produced in the farm and consumed by the household (i.e. availability). Preliminary results of the survey were presented to 10-20 participants from local organizations and rural population for discussion and validation and to identify potential future options for family agriculture in each site.

## 3 Results – Discussion

Analyses of the household survey data highlighted intra site diversity of the rural population in terms of their resources and livelihood activities (Table 1). For example, households in two sites in Nicaragua tend to have available less farming area per person with relatively larger farms and less area used for growing grains (i.e. maize and beans) than in the other sites. In contrast, households in the site in El Salvador tend to have more farming area available per person reflected in smaller farm sizes and higher maize yields. Animal husbandry tends to be more common among rural households in Nicaragua compared with the other two sites. Household total income tends to be the lowest in the site in Honduras compared to the other sites, while at least half of the households in one site in Nicaragua, Honduras and el Salvador obtain most of their salary (>80%) from off-farm activities. This reflects a strong diversification of household activities as a common feature of these small holder farming systems (Grammont, 2004).

<table><caption>Table 1. Median values for some key household and farm indicators</caption><thead><tr><th>Site</th><th>People per ha</th><th>Farm size (ha)</th><th>Grains (ha)</th><th>(%Maize yield (t ha<sup>-1</sup>)</th><th>TLU</th><th>Annual income (USD)</th><th>Share off-farm income (%)</th></tr></thead><tbody><tr><td>NI-Terrabona</td><td>1.4</td><td>3.5</td><td>50</td><td>1.2</td><td>0.7</td><td>973</td><td>49</td></tr><tr><td>NI-Somotillo</td><td>1.8</td><td>2.8</td><td>67</td><td>0.7</td><td>1.0</td><td>881</td><td>29</td></tr><tr><td>NI-Condega</td><td>2.9</td><td>1.4</td><td>100</td><td>1.0</td><td>0.6</td><td>1246</td><td>81</td></tr><tr><td>HN-Candelaria</td><td>3.8</td><td>1.3</td><td>83</td><td>1.0</td><td>0.2</td><td>428</td><td>100</td></tr><tr><td>SV-Chalatenango</td><td>5.7</td><td>0.7</td><td>100</td><td>1.3</td><td>0.2</td><td>1040</td><td>100</td></tr></tbody></table>

Diversification of household activities and farming systems can largely differ within sites. Fig. 1A illustrates differences between household strategies in one site in Nicaragua (NI-Terrabona) where more households owning farms less than 3,5 ha ensured a large part of their income (>50%) in 2013 from off-farm activities compared to households with larger farms. This confirms that although most households combine both on- and off-farm economic activities to ensure their

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

<footnote n="2">² International Maize and Wheat Improvement Center (CIMMYT), Mexico</footnote>

<footnote n="3">³ International Livestock Research Center (ILRI), Costa Rica</footnote>

<footnote n="4">⁴ International Center for Tropical Agriculture (CIAT), Honduras</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: d.valbuena@cgiar.org</footnote>
livelihoods, households with smaller farms tend to rely more on non-farm activities.
Diversification of activities and farming systems can also relate to the vulnerability of rural households in terms of poverty and food self-sufficiency. Fig. 1B shows that between 75-80% of households with farms smaller than 7ha earned less than 1.25 USD per person, while ~30% neither produce enough energy to fulfil the household needs in terms of food. For households with farmers larger than 7ha, these numbers drop to 63% and 12% respectively. This suggests that although households with larger farms tend to be less vulnerable in terms of income generation and food dependency than smaller farmers, most households still largely depend on their own food production.

<figure><img src="image_38.png" /><figcaption>**Fig. 1.** Households and farming system diversity among households with different farm sizes in NI-Terrabona, including: household distribution of the share of off-farm income of the household total income in 2013; B. comparison between balance of energy and daily income per person in 2013.</figcaption></figure>

Discussions with local partners confirmed these major differences between households within the same site. While diversification and intensification of home gardens seems to be a key option for the food and nutrient security of households with limited access to land, vegetable production for smallholder farming systems and agroforestry systems for more sustainable extensive production represent potential future development pathways. This reinforces the need to look for more integral research and development programs that account for the agricultural and non-agricultural components of the current and future rural societies.

## 4 Conclusions

The results of this approach combining short survey and discussion with farmers and local organization is a step forward to analyze and explore the interactions of food security, income and farming systems in the sub-humid tropics of Central America and to propose more contextualized future options for family agriculture in the region. Particularly, we need more integral research and development programs to better combine the agricultural and non-agricultural components of the new ruralities in Central America.

*Acknowledgements.* We would like to thank the farmers who participated in this study, as well as OCTUPAN, PCAC, ADEES and Mancomunidad la Montañona for their support in the field. This study was funded by the CGIAR Research Programmes: MAIZE, Humid tropics, WLE and CCAFS.

## References

FAO, ACF, & ECHO. (2012). *Estudio de Caracterización del Corredor Seco Centroamericano, Tomo II* (p. 90). Honduras. Grammont, H. C. de. (2004). *La nueva ruralidad en América Latina. Revista Mexicana de Sociología*, **66**, 279-300
# Ecological intensification in Río de la Platagrasslands

Pablo Modernel *±1,2,<fnref n="3" />, Walter Rossing <fnref n="1" />, Santiago Dogliotti <fnref n="3" />, Marc Corbeels <fnref n="2" />, Valentin Picasso<fnref n="3" /> & Pablo Tittonell<fnref n="1" />

## 1 Introduction

Understanding existing agro-ecosystems in which food production is based on intensive internal resource use might provide inspiration for re-designing external input based systems. The livestock production systems in the *Río de la Plata Grasslands* (RPG) in southern South America represent a good example of such model. Animal production in this vast region, which includes parts of Brazil, Uruguay and Argentina, co-evolved with plant biodiversity on semi-natural grasslands that received negligible amounts of external inputs since the introduction of domesticated livestock in the 16th century (Soriano, 1992), constituting a feasible form of land-sharing. During the last 15 years, high prices of grains (mostly soybean and wheat) prompted conversion of grasslands to arable land (Paruelo et al., 2006). Overgrazing due to high stocking rates on the remaining land caused loss of valuable grassland species (Overbeck et al., 2007), low grassland and meat productivity (Carvalho & Batello, 2009) and negative environmental impacts on soils and climate due to erosion and losses of soil carbon and high greenhouse gas (GHG) emissions (Modernel et al., 2013). This change in land use endangers the unique and 400 year old model of land sharing (Garnett et al., 2013) in which meat production is sustained by natural grassland biodiversity. Two intensification strategies can be distinguished in the region. The first one (conventional intensification) proposes to increase meat yields through replacing natural grasslands by ley and feed crops (Cohn et al., 2014). The second one(ecological intensification) proposes to increase meat yields by adjusting forage allowance<fnref n="1" /> to animal energy requirements in time and space through smart use of species diversity (C3 and C4) in native grasslands (Soca et al., 2008). The first strategy aims to intensify production to be able to save land and separate production and nature conservation areas (land sparing), while the second one aims to preserve the diversity of native grasslands while using them (land sharing)(Green et al., 2005). In this paper, we analyse the ecosystem services provision of both intensification pathways, compared to the traditional system with low productivity.

## 2 Materials and Methods

Environmental indicators were calculated based on the production of one steer slaughtered at 500 kg. Farms that produce this animal can specialize, or combine three production activities: calving, growing and fattening. Specialized farms include three types: cow-calf (produce 150 kg calves), backgrounding (receives 150kg steers and sells them at 350 kg) and fattening (fattens steers from 350 to slaughter weight). Intensification strategies can differ depending on farm specialization.

The impact of the intensification process on the ES provision was estimated from a review of published studies in the region. Meat productivity and GHG emissions were estimates from nine farm case studies in Uruguay (Becoña et al., 2014; Montossi, 2014; Picasso et al., 2014). Calculations on the impact on biodiversity and carbon sequestration of current and ecologically intensified systems was made from Brazilian experiments that evaluated the grazing pressure on natural grasslands on the soil carbon stock, considering 4% forage allowance (FA) as the traditional system and 12% FA for ecological intensification and crop-ley rotations for conventional intensification (Carvalho et al., 2009; Conceição et al., 2007; García Préchac et al., 2004). Fossil energy reduction, pesticide use reduction, GHG emissions reduction, erosion risk reduction and water use efficiency were calculated using published farm data (Picasso et al., 2014; Ran et al., 2013). In order to standardize the different impact categories, the system with the most positive (or least negative) impact on an indicator was considered as the reference and set to 100%; the other systems were expressed as fractions of the reference.

## 3 Results and discussion

While conventional intensification would increase meat yields and reduce greenhouse gas emissions compared to the ecological intensification strategy, the also occurring negative environmental consequences question this option for the RPG farming systems (Table 1 and Fig. 1). Production cycles in conventionally intensified systems produce meat in less time than the other two, resulting in greater productivity per hectare.

Ecological intensification shows synergies among a number of indicators by improving meat productivity, biodiversity conservation, carbon sequestration, GHG emissions reduction and water use efficiency. The use of fossil fuels, pesticides and erosion risk is higher than in the traditional low productive system, but (sometimes substantially) lower

<footnote n="1">¹ Farming Systems Ecology, Wageningen University, P.O. Box 430, Wageningen 6700 AN, The Netherlands.</footnote>

<footnote n="2">² Agro-ecology and Sustainable Intensification of Annual Crops, CIRAD, Avenue Agropolis, 34398 Montpellier cedex 5, France.</footnote>

<footnote n="3">³ Facultad de Agronomía, Universidad de la República, Av. E. Garzón 780, Montevideo, Uruguay.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: pablomodernel@gmail.com</footnote>

<footnote n="1">¹Weight of herbage per unit of animal live weight at a point in time (Allen et al., 2011).</footnote>
than under conventional intensification.

<figure><img src="image_39.png" /><figcaption>**Figure 1.**Impact of traditional, conventionally intensified and ecologically intensified livestock systems on the ecosystem services provided by natural grasslands in the RPG. Higher values (closer to 100) indicate better performance.</figcaption></figure>

<table><caption>**Table 1.** Indicators and their values considered for each farming system. NG: Natural grasslands; L: Ley; GR: Grains.</caption><thead><tr><th>Indicator</th><th>Traditional</th><th>Conventional intensification</th><th>Ecological intensification</th><th>Units</th></tr></thead><tbody><tr><td>Diet composition (dry matter %)</td><td>100% NG</td><td>32% NG; 37% L; 31% GR</td><td>70% NG; 30% L</td><td>ha</td></tr><tr><td>Stocking rate</td><td>0.7</td><td>1.6</td><td>1.3</td><td>Livestock Units·ha<sup>-1</sup></td></tr><tr><td>Meat productivity</td><td>124</td><td>342</td><td>233</td><td>kg LW ha<sup>-1</sup>yr<sup>-1</sup></td></tr><tr><td>Biodiversity conservation</td><td>2.6</td><td>1.3</td><td>3.5</td><td>No unit</td></tr><tr><td>Carbon sequestration</td><td>113</td><td>0</td><td>143</td><td>t C ha<sup>-1</sup>yr<sup>-1</sup></td></tr><tr><td>GHG emissions reduction</td><td>20</td><td>10</td><td>16</td><td>kg CO<sub>2</sub> eq kg LW<sup>-1</sup></td></tr><tr><td>Fossil energy reduction</td><td>0.0</td><td>12.1</td><td>3.4</td><td>MJ kg LW<sup>-1</sup>ha<sup>-1</sup></td></tr><tr><td>Pesticide use reduction</td><td>0.1</td><td>14.9</td><td>1.7</td><td>No unit</td></tr><tr><td>Soil erosion reduction</td><td>11</td><td>16</td><td>14</td><td>kg soil kg LW<sup>-1</sup></td></tr><tr><td>Water use efficiency</td><td>0.052</td><td>0.053</td><td>0.067</td><td>L kg LW<sup>-1</sup> yr<sup>-1</sup></td></tr></tbody></table>

## 4 Conclusion

The evidence presented in this article shows that the RPG is a region where combining agriculture and conservation of biodiversity is possible (land sharing), but under threat of change from use as grassland to soybean. Given the long history of land sharing, preserving livestock production systems based on native grasslands is key to the maintenance of regional biodiversity and the associated array of ecosystem services. The unique combination of production and resource conservation under ecologically intensive methods of producing meat should be further investigated to understand its benefits and promote low-input technologies that are adapted to the specific farming conditions.

## References

Allen, V.G., Batello, C., Berretta, E.J., Hodgson, J., Kothmann, M., Li, X., Mcivor, J., Milne, J., Morris, C., Peeters, A., Sanderson, M., 2011. An international terminology for grazing lands and grazing animals. *Grass Forage Sci.* 2–28.

Becoña, G., Astigarraga, L., Picasso, V.D., 2014. Greenhouse Gas Emissions of Beef Cow-Calf Grazing Systems in Uruguay. *Sustain. Agric. Res.* 3.

Carvalho, P.C.F., Batello, C., 2009. Access to land, livestock production and ecosystem conservation in the Brazilian Campos biome: The natural grasslands dilema. *Livest. Sci.* 120, 158–162.

Carvalho, P.C.F., Nabinger, C., Lemaire, G., Genro, T.C.M.C.M., 2009. Challenges and opportunities for livestock production in natural pastures: the case of Brazilian Pampa Biome, in: Feldman, R., Oliva, G.E., Sacido, M.B. (Eds.), Proceedings of the IX International Rangeland Congress: Diverse Rangelands for a Sustainable Society. Universidad de Rosario., Rosario, pp. 9–15.

Cohn, A.S., Mosnier, A., Havlik, P., Valin, H., Herrero, M., Schmid, E., O’Hare, M., Obersteiner, M., 2014. Cattle ranching intensification in Brazil can reduce global greenhouse gas emissions by sparing land from deforestation. *Proc. Natl. Acad. Sci. U. S. A.* 111, 7236–41.

Conceição, P.C., Bayer, C., Maria, Z., Castilhos, D.E.S., 2007. Estoques de carbono orgânico num Chernossolo Argilúvico manejado sob diferentes ofertas de forragem no Bioma Pampa Sul-riograndense, in: XXXI Congresso Brasileiro de Ciencia Do Solo.

García Préchac, F., Ernst, O., Siri-Prieto, G., Terra, J.A., 2004. Integrating no-till into crop-pasture rotations in Uruguay. *Soil Tillage Res.* 77, 1–13.

Hoffmann, I., Smith, P., Thornton, P.K., Toulmin, C., Vermeulen, S.J., Godfray, H.C.J., 2013. Sustainable Intensification in Agriculture: Premises and Policies. *Science.* 341, 33–34.

Green, R.E., Cornell, S.J., Scharlemann, J.P.W., Balmford, A., 2005. Farming and the fate of wild nature. *Science* 307, 550–5.

Modernel, P., Astigarraga, L., Picasso, V., 2013. Global versus local environmental impacts of grazing and confined beef production systems. *Environ. Res. Lett.* 8, 035052.

Montossi, F., 2014. INVERNADA DE PRECISIÓN: Pasturas, Calidad de Carne, Genética, Gestión Empresarial e Impacto Ambiental (GIPROCAR II). Montevideo.

Overbeck, G.E., Mu, S.C., Fidelis, A., Pfadenhauer, J., Pillar, V., D.D., Blanco, C.C., Boldrini, I., I., Both, R., Froneck, E.D., Mueller, S.C., Forneck, E.D., 2007. Brazil’s neglected biome: The South Brazilian Campos. *Perspect. Plant Ecol. Evol. Syst.* 9, 101–116.

Paruelo, J.M.M., Guerschman, J.P.P., Piñeiro, G., Jobbágy, E.G.G., Verón, S.R.R., Baldi, G., Baeza, S., 2006. Cambios en el uso de la tierra en Argentina y Uruguay: Marcos conceptuales para su análisis. *Agrociencia Uruguay* X, 47–62.

Picasso, V.D., Modernel, P.D., Becoña, G., Salvo, L., Gutiérrez, L., Astigarraga, L., 2014. Sustainability of meat production beyond carbon footprint: a synthesis of case studies from grazing systems in Uruguay. *Meat Sci.* 98, 346–354.

Ran, Y., Deutsch, L., Lannerstad, M., Heinke, J., 2013. Rapidly intensified beef production in Uruguay: Impacts on water related ecosystem services. *Aquat. Procedia* 1, 77–87.

Soca, P., Olmos, F., Espasandín, A., Bentancur, D., Pereyra, F., Cal, V., Sosa, M., Do Carmo, M., 2008. Impacto de cambios en la estrategia de asignación de forraje sobre la productividad de la cría con diversos grupos genéticos bajo pastoreo de campo natural, in: Quintans, G., Velazco, J.I., Roig, G. (Eds.), SEMINARIO DE ACTUALIZACIÓN TÉCNICA: Cría Vacuna. INIA, Montevideo, pp. 110–119.

Soriano, A., 1992. Río de la Plata Grasslands, in: Coupland, R.T. (Ed.), Ecosystems of the World. Elsevier, Amsterdam, The Netherlands, pp. 367–407.
# OPTIMIZING MILK AND BEEF PRODUCTION SYSTEMS ON FARM LEVEL

Martina Alig *±<fnref n="1" />, Pierre Mischler<fnref n="2" /> & Hélène Chambaut<fnref n="3" />

## 1 Introduction

Within the EU KP 7 project CANTOGETHER innovations are developed and explored to integrate plant production and animal production in mixed farming systems with the overall aim to design innovative sustainable mixed farming systems with crops and animals. This requires detailed knowledge about the advantages of mixed farming systems and the factors influencing their environmental performance. In our analysis of two existing farm data networks the environmental impacts of mixed and specialized farms were compared, and the factors responsible for a high or low environmental performance were explored to derive driving factors for an environmentally successful farming.

## 2 Materials and Methods

Data were derived from the network INOSYS of the French Institut de l'élevage (Idèle) and the LCA-FADN network of Agroscope (Hersener et al., 2011), containing farm management data, economic figures and results on environmental impacts on farm level for 622 and 87 milk and beef producing farms, respectively.

To ensure the comparability of the results, common environmental indicators were identified and used for the analyses: energy demand, global warming potential, eutrophication and acidification. For comparing the environmental impacts, reference units both related to the area and to the production were used: ha usable agricultural area (UAA) and kg milk and beef produced for both networks, as well as MJ digestible energy produced only for the Swiss farms.

As different inputs were considered and sometimes differents methods were used to calculate the environmental impacts in the two farm networks, the absolute numbers of the impacts calculated could not be compared. Therefore, a simplified method based on the formation of groups was used. For each indicator analyzed, the sample was divided in three groups: (1) the best third with 33 % of the farms with the lowest impact; (2) the worst third with 33 % of the farms with the highest impact; (3) the middle third with the remaining farms.

A score from 1 to 3 was attributed to each group, to lowest score being attributed to the group with the highest impact, the highest score to the group with the lowest impact. At the end, all indicators were aggregated and the scores of each farm were summed up. The final scores ranged between 4 and 12, the latter being attributed to farms located in the best third for all four indicators analyzed. Those farms were classified as "best farms" for the respective reference unit.

In the first step the different reference units were analyzed separately. For identifying the most successful farms, the results related to the area used were crossed with those related to the production. The resulting scores ranged between 8 and 24. As the number of farms with per score was limited, five groups were formed from the least (score from 8 to 10) to the best performing farms (score from 22 to 24) and used for the comparison. To evaluate whether mixed farming systems perform better than specialized systems, three different definitions of mixity, proposed in the Cantogether working groups, were applied and compared with regards to their environmental impacts (results not shown).

## 3 Results

For Switzerland, the best performing farms per ha usable agricultural area (UAA) were more extensive farms with a lower use of external inputs. Typically they were organic suckler cow farms with a low stocking rate at rather higher altitudes. They had a high share of grassland and a low economic performance. Also in France the best performing farms per ha UAA were more extensive farms with a low use of external inputs such as concentrates, fertilizers, pesticides and fuel. They had a larger agricultural area, with more grassland and less maize and generally a lower productivity than the farms with high impacts.

Per kg milk produced, the best performing farms in Switzerland were rather larger, more intensive farms at lower altitudes, which combined milk and plant production. They had a higher share in open arable land, a higher stocking rate and milk yield per dairy cow than the less performing farms. Besides, their total return and income per family work forces was higher as well. The best performing farms per kg beef produced had a significantly higher stocking rate and less suckler cows than the less performing farms. They used more external inputs such as diesel and concentrates and had a higher overall return. As for the best farms in milk production, they tended to be larger with regards to UAA. In France the best performing farms per kg milk produced had a similar UAA as the less performing farms, with less maize and grassland, but more arable crops. The beef producing farms had a larger UAA with more maize and a higher meat production. They generally used less external inputs (nitrogen, concentrates for example) than the best performing

<footnote n="1">¹ Agroscope, Institute for Sustainability Sciences, Reckenholzstrasse 191, CH-Zurich</footnote>

<footnote n="2">² Institut de l'élevage, 19 bis rue Alexandre Dumas, F-80096 Amiens cedex 3</footnote>

<footnote n="3">³ Institut de l'élevage, 9 rue André Brouard, CS70510, 49105 Angers cedex 02</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: martina.alig@agroscope.admin.ch</footnote>
# Farming system models for supporting farm resilience: research needs, gaps and promising approaches

Quang Bao Le *±1,2

## 1 Introduction

It is important to increase the resilience of food production systems in the face of a changing climate, land scarcity, and changing demographics and market conditions. As farm resilience is a high-level system property emerged from social-ecological interactions, its direct measurement is difficult because it requires measuring the thresholds or boundaries that separate alternate stability regimes of the farm system. However, systems' modeling for supporting agricultural resilience is still in an early stage. Through critical review of state-of-the art literature, this study aims at highlighting the new requirements of agricultural system modeling as they apply to management for farm resilience, limitations of contemporary agricultural systems modeling approaches, and promising directions for future research on the field.

## 2 Materials and Methods

By review of previous conceptual works on socio-ecological systems' resilience, we conceptualized 11 criteria for evaluating models' suitability for farm resilience studies, which include the capability of the modeling approaches to (1) represent social-ecological complementariness, (2) have long-term perspective, (3) manage uncertainty, (4) capture global-local linkages, (5) mediating participation, (6) capture cross-scale feedback loops, (7) explain human behavior including (8) social learning and adaptation, (9) capture farm heterogeneity, (10) anticipate multiple farm performance allowing trade-off assessment, and (11) sensitive to biophysical, economic and social drivers. Using these criteria we evaluated a mass primary literature on farming systems models to assess strengths and weakness of six main farming systems modeling approaches, and conducted comparative analysis across the methods.

## 3 Results - Discussion

*Farm nutrient balance model* accounts farm nutrient balance based on the consideration of major material inputs and outputs. As several of these fluxes are difficult to measure (e.g., leaching, erosion), transfer functions are commonly used. Internal nutrient flows between farm production units are also measured (Smaling and Fresco, 1993; Den Bosch et al., 1998a; Den Bosch et al., 1998b; Lesschen et al., 2007), the popular nutrient balance accounting framework, produces farm nutrient balances and some farm agronomic and economic indicators. However, due to practical difficulties in measuring nutrient flows tied with soil processes, balance of soil nutrient reserve are still poorly considered. By capturing farm nutrient balance as a snapshot in time only, the analysis offers no long-term perspective. This ignores the residuals effects of fertilizer uses, long-term soil carbon cycling, and livestock or tree production cycles. Although human components exist as system entities and are connected to farm environment via nutrient flows and management activities, no decision making mechanism is included.

*Farm system dynamics model* deals with internal stocks (production units of the farm) and flows (nutrients and water), associated feedback loops and time delays that affect the behavior of the entire farming system. The substantive nature of feedback loops can be either material or information links, thus create multi-directional cause-effect relationship between biophysical and social observables (Shepherd and Soule, 1998; Sendzimir et al., 2011). These models can mimick the actual farm components and interinfluences, thus is perceivable by stakeholders. The models are able to perform nonlinear behavior and dynamic complexity of the farm in sensitive to change in values of observables. However, the structures of stock-and-flows and feedback loops are predefined and fixed during simulation runs, ignoring the adaptive farmers' decision on modifying the nutrient network structure to utilize subsidiary effects among farm components. Thus, the modeling approach cannot model structural adaptation of the farm to change that is essential in farm resilience. The model also can operate the system dynamics at one aggregated scale and less capability to capture heterogeneity within and between farms.

*Fixed-structure integrated farm modeling frameworks* couple the sub-model of static farm nutrient stock-and-flows with those of soil-crop dynamics and socio-economic processes that allow information exchange for forming feedback loops between farm nutrient cycles, crop and livestock productivity and socio-economic dynamics (Giller et al., 2006; Giller et al., 2011). However, its limitation to understanding farm resilience is that: the within-farm interactions and feedback loops are not the subject of farmers' adaptive decisions; they are rather fixed and unspecific to nutrient cycle management/design context. Thus, farm's structural adaptive behavior to major change in external drivers is not endogenous explained by this modeling type. Multi-agent system (MAS) models represent the coupled human-environmental system is described through autonomous 'agents', which can be defined to represent actors and acted-upon entities such as households, farm production units, offer a system tool for understanding the complexity of energy, nutrient and material flows that result from rich interactions and feedback among social and natural processes (Bousquet and Le Page, 2004; Gaube et al., 2009). As separate loci of control in the human-environmental system, agents act autonomously, and interact with other agents, in an ever-changing system. MAS is strong in supporting interdisciplinary between natural and social sciences. MAS is based on complex adaptive system theory that is nowadays well-suited for representing ecological systems, social systems, and human-environmental systems; thus it becomes a paradigm shared by ecological and social sciences (Bousquet and Le Page, 2004; Scholz, 2011). By mimicking actual entities in the real human-environmental system, MAS allows for an intuitive

<footnote n="1">¹ CGIAR Research Program on Dyland Systems, c/o International Center for Agricultural Research in Dry Areas (ICARDA)</footnote>

<footnote n="2">² Department of Environmental Systems Science, ETH Zurich</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author:</footnote>
representation of the environment and of the embedding of human actors in a socially, ecologically, and spatially explicit setting. As MAS displays large-scale outcomes that result from interactions and/or learning among individual entities, it allows an adequate representation of micro-macro relationships and a strong ability to model social learning and adaptation (Kelly et al., 2013). However, MAS models for understanding farm resilience and transition scenarios is still in a very early stage of development. To be able to assess farm resilience and support farms' transition to resilience, MAS models developed have to meet the following key requirements, which have not been addressed by current MAS research community: (1) capture resilience-relevant properties (i.e. buffering capacity, critical thresholds and tipping points), (2) model change in slow variables as the endogenous processes, (3) capture social-ecological feedback loops at different levels, (4) explain farming practices, which create subsidiary linkages between production units, or between farms as the subject of farmers' decisions, (5) parsimonious representation of socio-biophysical processes, (6) appropriate model validation and (7) better contextual robustness (i.e., less dependent on site-specific assumptions, more applicable to a wide range of contextual variation and management options).

<table><caption>**Table 1.** Comparative assessment of contemporary farming system modeling approach with respect to criteria for farm resilient research. Note: detailed narrative insights of the table cells do not show.</caption><thead><tr><th>Criteria</th><th>Nutrient balance models</th><th>System dynamics models</th><th>Bayesian Network models</th><th>Bio-economic models</th><th>Coupled component models</th><th>Multi-agent system models</th></tr></thead><tbody><tr><td>Interdisciplinary</td><td>weak</td><td>strong</td><td>medium</td><td>weak</td><td>weak</td><td>strong</td></tr><tr><td>Long-term perspective</td><td>no</td><td>strong</td><td>no</td><td>weak</td><td>strong</td><td>strong</td></tr><tr><td>Uncertainty management</td><td>no</td><td>weak</td><td>strong</td><td>no</td><td>unclear</td><td>medium</td></tr><tr><td>Local-global perspective</td><td>no</td><td>no</td><td>no</td><td>weak</td><td>strong</td><td>strong</td></tr><tr><td>Participation mediation</td><td>weak</td><td>strong</td><td>strong</td><td>weak</td><td>unclear</td><td>strong</td></tr><tr><td>Multi-scale feedback loops</td><td>no</td><td>no</td><td>no</td><td>no</td><td>unclear</td><td>strong</td></tr><tr><td>Actors' behavior</td><td>no</td><td>weak</td><td>strong</td><td>medium</td><td>no</td><td>strong</td></tr><tr><td>Social learning and adaptation</td><td>no</td><td>no/weak</td><td>no</td><td>no</td><td>no</td><td>strong</td></tr><tr><td>Farm heterogeneity</td><td>medium</td><td>medium</td><td>no</td><td>weak</td><td>strong</td><td>no</td></tr><tr><td>Multiple farm performance</td><td>strong</td><td>strong</td><td>no</td><td>medium</td><td>strong</td><td>strong</td></tr><tr><td>Driver sensitive - Biophysical</td><td>weak</td><td>weak</td><td>weak</td><td>weak/medium</td><td>strong</td><td>weak</td></tr><tr><td>- Economic</td><td>weak</td><td>strong</td><td>medium</td><td>strong</td><td>weak</td><td>strong</td></tr><tr><td>- Social</td><td>no</td><td>medium</td><td>strong</td><td>strong</td><td>weak</td><td>strong</td></tr></tbody></table>

## 4 Conclusions

Agro-ecosystems modeling has gone through more than 40 years of development. Although a great deal of knowledge and tools about economic and biophysical processes exist, agricultural system modelling science hardly ever seeks to develop modelling frameworks and tools to support farm resilience management. The result of our meta analysis found that none of developed farming system models are sufficient for supporting farm resilience regarding all criteria. The results can serve as a reference matrix that helps identifying research directions towards supporting the resilience of agricultural systems. Multi-agent systems (MAS) modeling has appeared as a promising approach for model farming system resilience. Using the above-mentioned criteria we also analyzed the current limitations of this model family and elaborate possible future developments as subjects of follow-up studies.

## References

* Bousquet, F., Le Page, C., 2004. Multi-agent simulations and ecosystem management: a review. *Ecological Modelling* 176, 313-332.

* Den Bosch, H.V., De Jager, A., Vlaming, J., 1998a. Monitoring nutrient flows and economic performance in African farming systems (NUTMON) II. Tool development. *Agriculture Ecosystems & Environment* 71, 49-62.

* Den Bosch, H.V., Gitari, J.N., Ogaro, V.N., Maobe, S., Vlaming, J., 1998b. Monitoring nutrient flows and economic performance in African farming systems (NUTMON). III. Monitoring nutrient fows and balances in three districts in Kenya. *Agriculture Ecosystems & Environment* 71, 63-80.

* Gaube, V., Kaiser, C., Wildenberg, M., Adensam, H., Fleissner, P., Kobler, J., Lutz, J., Schaumberger, A., Schaumberger, J., Smetschka, B., Wolf, A., Richter, A., Haberl, H., 2009. Combining agent-based and stock-flow modelling approaches in a participative analysis of the integrated land system in Reichraming, Austria. *Landscape Ecology* 24, 1149-1165.

* Giller, K.E., Rowe, E.C., de Ridder, N., van Keulen, H., 2006. Resource use dynamics and interactions in the tropics: Scaling up in space and time. *Agricultural Systems* 88, 8-27.

* Giller, K.E., Tittonell, P., Rufino, M.C., van Wijk, M.T., Zingore, S., Mapfumo, P., Adjei-Nsiah, S., Herrero, M., Chikowo, R., Corbeels, M., Rowe, E.C., Baijukya, F., Mwijage, A., Smith, J., Yeboah, E., van der Burg, W.J., Sanogo, O.M., Misiko, M., de Ridder, N., Karanja, S., Kaizzi, C., K'ungu, J., Mwale, M., Nwaga, D., Pacini, C., Vanlauwe, B., 2011. Communicating complexity: Integrated assessment of trade-offs concerning soil fertility management within African farming systems to support innovation and development. *Agricultural Systems* 104, 191-203.

* Kelly, R.A., Jakeman, A.J., Barreteau, O., Borsuk, M.E., ElSawah, S., Hamilton, S.H., Henriksen, H.J., Kuikka, S., Maier, H.R., Rizzoli, A.E., van Delden, H., Voinov, A.A., 2013. Selecting among five common modelling approaches for integrated environmental assessment and management. *Environmental Modelling & Software* 47, 159-181.

* Lesschen, J.P., Stoorvogel, J.J., Smaling, E.M.A., Heuvelink, G.B.M., Veldkamp, A., 2007. A spatially explicit methodology to quantify soil nutrient balances and their uncertainties at the national level. *Nutrient Cycling in Agroecosystems* 78, 111-131.

* Parunak, H.V., Savit, R., Riolo, R.L., 1998. Agent-based modeling vs. equation-based modeling: A case study and users' guide. *Lect Notes Artif Int* 1534, 10-25.

* Scholz, R.W., 2011. Environmental Literacy in Science and Society: From Knowledge to Decisions. Cambridge University Press, Cambridge, UK.

* Sendzimir, J., Reij, C.P., Magnuszewski, P., 2011. Rebuilding resilience in the Sahel: Regreening in the Maradi and Zinder regions of Niger. *Ecology and Society* 16, 1. http://dx.doi.org/10.5751/ES-04198-160301.

* Shepherd, K.D., Soule, M.J., 1998. Soil fertility management in west Kenya: dynamic simulation of productivity, profitability and sustainability at different resource endowment levels. *Agriculture, Ecosystems and Environment* 71, 131-145.

* Smaling, E.M.A., Fresco, L.O., 1993. A decision-support model for monitoring nutrient balances under agricultural land use (NUTMON). *Geoderma* 60, 235-256.
# Factors influencing the flexibility of farming systems – Case study of the Flemish beef farming sector

Laura Schotte<sup>±1</sup>, Jonas Hanssens<sup>2</sup>, Xavier Gellynck<sup>3</sup>, Fleur Marchand<sup>1,4</sup> & Erwin Wauters<sup>1,5</sup>

## 1 Introduction

In Western countries, most farming systems are highly specialized with a strong focus on increasing productivity, maximum biological control and technological optimization. Nonetheless, these farming systems experience economic and social difficulties and are confronted with the ecological boundaries of their environment. As a response, there is increasing attention for a shift in agricultural focus from optimization and control to one on resilience and adaptive capacity.

Flexibility is seen as an important aspect of a farm's resilience (Darnhofer et al., 2010) and can be defined as the room for change or the degrees of freedom (Lev & Campbell, 1987). It enhances the possibility to react to environmental changes and is part of the farm's adaptive capacity. These concepts are well described and operationalized in management science, but agricultural science focusses mostly on theoretical and conceptual aspects. There is a need to translate this theoretical knowledge into practical knowledge, so that flexibility can be used as an evaluation criterion over and above traditional concepts such as efficiency and productivity. The first step is to provide insight in the factors influencing the farmer's choice set. In this paper we present preliminary results regarding this insight, investigating the case of the Flemish beef farming sector.

## 2 Materials and Methods

We started this research with a comprehensive review of literature, both from the management and agricultural science domain, to understand the different interpretations and approaches on the concept flexibility. We selected the Flemish beef farming sector as case study because it is known for its heterogenic characteristics of management systems. This heterogeneity is assumed to result in a diverse range of flexibility strategies.

Based on literature research concerning flexibility aspects, we constructed a guide book for conducting semi-structured interviews (Evers, 2007). This type of interview allows you to focus in more detail on interesting aspects that emerge during interviews. Currently these interviews are being conducted and analyzed, resulting in a first indication of important factors influencing flexibility.

## 3 Results – Discussion

There is no scientific consensus on the definition or methodology to analyze flexibility in agricultural production systems. Authors make distinctions between different types of flexibility or focus on a specific aspect (Astigarraga & Ingrand, 2011; Ingrand et al., 2004; Lev & Campbell, 1987; Wauters & van Winsen, 2004; Weis C.R., 2001). Furthermore, different sources of flexibility are distinguished, ranging from input, output, technological and income flexibility (Wauters & van Winsen, 2004).

In the first phase of this research we do not focus on a single definition or source of flexibility, but approach the topic from a broad and general perspective. From the first analyses, we were able to extract a range of factors that significantly influence the flexibility of farm systems. These factors are presented in the first column of Table 1. The second column briefly illustrates how these factors influence the flexibility of farm systems.

<table><caption>**Table 1.** Overview of the factors influencing flexibility.</caption><thead><tr><td>Factor</td><td>Illustration / explanation</td></tr></thead><tbody><tr><td>Governmental policies</td><td>In the Flemish beef farming sector this is currently a much debated topic. A considerable amount of farmers has to reduce nitrogen deposition in the context of Natura 2000, influencing their management options.</td></tr></tbody></table>

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

<footnote n="2"><sup>2</sup> Faculty of Economics and Business Administration, University of Ghent</footnote>

<footnote n="3"><sup>3</sup> Department of Agricultural Economics, Faculty of Bioscience Engineering, University of Ghent</footnote>

<footnote n="4"><sup>4</sup> Ecosystem Management Research Group and IMDO, University of Antwerp</footnote>

<footnote n="5"><sup>5</sup> Department of Veterinary Sciences, University of Antwerp</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: laura.schotte@ilvo.vlaanderen.be</footnote>
<table><tr><td>Subsidies</td><td>Trying to qualify for subsidies requires complying with certain conditions, limiting the farmers choice set.</td></tr><tr><td>Financial resources</td><td>Loans from banks usually imply a large amount of money for one type of investment, favoring scale enlargement. Having own financial resources provides the farmer more decision freedom.</td></tr><tr><td>Income diversification</td><td>Focusing on only one income source or product makes farms more vulnerable to external influences. Diversification increases the range of possibilities to react to these changes.</td></tr><tr><td>Marketing channels</td><td>Having plural marketing channels enlarges choice options and possibility to react to price or market changes. There are also several gradations of autonomy in the marketing strategy.</td></tr><tr><td>Soil type</td><td>Type of soil determines for a large part which crops and fodder can be grown, influencing the flexibility in feeding strategy.</td></tr><tr><td>Infrastructure</td><td>Infrastructure is an important factor for the livestock sector, as it requires large investments. Overinvestment and -specialization of stables can reduce adaptive capacity.</td></tr><tr><td>Machine set</td><td>Machinery can either be very specialized or more general, determining the flexibility of the production process.</td></tr><tr><td>Cattle breed</td><td>Depending on the cattle breed animals are more robust, less susceptible to diseases, require less supervision and less concentrates. Especially the common Belgian Blue is a breed that requires specialized management, reducing flexibility.</td></tr><tr><td>Family situation</td><td>The family situation affects available labor force and possible succession. These factors influence management options and future investments.</td></tr><tr><td>Attitude farmer</td><td>Some farmers are very aware of the market situation, try to react proactive and handle risks by diversification. Others may react more conservative and are rather opposed to innovation.</td></tr></table>

Interpretation of these preliminary results show that factors are interrelated. For example, the choice of cattle breed may depend on available subsidies and/or the attitude of the farmer, in turn influencing the infrastructure. Through the construction of cognitive maps we will gain more insights in these relations. Cognitive maps are qualitative models of a system, consisting of variables and the causal relationship between these variables (Özesmi & Özesmi, 2004).

Typically the revealed factors are not straightforward reducing or enhancing flexibility (Table 1). Their effect is mostly the result of how farmers deal with these factors. An interesting application of these results is the characterization of different livestock management models by the way they cope with the different influencing factors.

In the second phase of this research we will compare the gained insight in the interdependencies of the emerging factors with the scientific literature. As written above, there's no consensus on how to analyze flexibility. By looking at our results from these different perspectives we will gain insight in the origin and the different types of flexibility. From the characterization of management models practical knowledge can be derived, as farmers can learn from more flexible systems and change their strategies.

## 4 Conclusions

In this paper we presented an approach to analyze the flexibility of the Flemish beef sector and the factors influencing this flexibility. We illustrated our approach by some preliminary results. Results showed that factors are interrelated and that we need a systems approach to analyze flexibility of farming systems. We also concluded that the effect of influencing factors mainly depends on the coping strategy of the farmer in question, which may lead to a characterization of different management models. In the next phase we will construct individual cognitive maps of the factors influencing flexibility for each of the interviewed farmers. Through qualitatively analyzing these maps and comparing them between different management models, we will gain insight in the origins and the different types of flexibility.

*Acknowledgements.* The authors thank Frankwin van Winsen for his contribution and review of this paper.

## References

* Astigarraga, L. & Ingrand, S. (2011). Production flexibility in Extensive Beef Farming System. *Ecology and Society*, **16**(1):7.

* Cowan, L., Kaine, G. & Wright, V. (2013). The Role of Strategic and Tactical Flexibility in Managing Input Variability on Farms. *Systems Research and Behavioral Science*, **30**, 470-494.

* Darnhofer, I., Bellon, S., Dedieu, B. & Milestad, R. (2010). Adaptiveness to enhance the sustainability of farming systems. A review. *Agron. Sust. Dev.*, **30**, 545-555.

* Evers, J. (red.). (2007). Kwalitatief interviewen: kunst én kunde. LEMMA, Den Haag

* Ingrand, S., Bardey, H., Brossier, J. (2004). Flexibility of suckler cattle farms in the face of uncertainty within the beef industry: a proposed definition. (Pre)Proceedings of the 6th European IFSA Symposium, 259-268.

* Lev, L. & Campbell, D.J. (1987). The Temporal Dimension in Farming Systems Research: the Importance of Maintaining Flexibility Under Conditions of Uncertainty. *Journal of Rural Studies*, **3**(2), 123-132.

* Özesmi, U., Özesmi, S.L. (2004). Ecological models based on peoples knowledge: a multi-step fuzzy cognitive mapping approach. *Ecological Modelling*, **176**, 43-64.

* Wauters, E., van Winsen, F. (2014). What determines the flexibility of farming systems? A case-study of the bovine farming sector in Belgium. Proceedings of the 11th European IFSA Symposium, 1828-1835.

* Weiss, C.R. (2001). On flexibility. *Journal of Economic Behavior & Organization*, **46**, 347-356.
# Methods for designing farms that include a coal seam gas extraction enterprise.

Neil I. Huth$^{±1}$, Perry L. Poulton$^{*1}$, Brett Cocks$^{1}$ & Ethel Samalca$^{1,2}$

## 1 Introduction

Over 24,000 km<fnref n="2" /> of the Surat Basin in southern Queensland, Australia, has been approved for coal seam gas (CSG) development (Huth *et al.*, 2014) and this is driving significant landscape change. While regional scale economic benefits of CSG development are acknowledged, few studies have evaluated the impact on farmers that must now coexist in a “shared space” with large-scale resource extraction enterprises (Huth *et al.*, 2014). Much of this land is used for a broad range of agricultural purposes ranging from grazing, irrigated and dry land cropping and horticulture. Incorporation of a resource extraction enterprise into these farm lands requires the addition of extensive road networks, wells, pipelines, electricity transmission lines, water storages and processing facilities (Fig. 1a). To date, the design of these mixed gas-farm systems has been undertaken via negotiations between individual farmers and CSG companies looking to install infrastructure on their farms. Little information has been available to assist farmers in planning and implementing these significant changes to their farming enterprises. We outline ongoing research that provides insights on the farm redesign process that looks to maximize the benefits arising from CSG development (e.g. improved cash flow, investment into the asset base) whilst minimizing the costs (e.g. lost land, decreased yields, impacts on machinery efficiency).

## 2 Materials and Methods

Techniques used to evaluate costs and benefits include remote sensing, soil survey, farm economic and production modelling, GIS and participatory farmer discussions. Information on the impact of CSG infrastructure on machinery operations has been derived from tractor GPS logs (Fig. 1b). From these, changes in machinery efficiency (Ramin and Wan Ishak, 2012) have been calculated for well pads inserted into different locations within fields. Imagery from the ZY-3 Chinese Earth Observation Satellite has been used to study patterns in Normalised Difference Vegetation Index (NDVI) which can indicate impacts on crop and pasture production due to compaction or site disturbance (Fig. 1c).
Aerial photogrammetry was used to produce a digital elevation model at 20 cm pixel size for a 1200 km<fnref n="2" /> focal region. From this, a detailed map of water flows around CSG infrastructure has been developed and tested using ground-based measurements (Fig. 1d, Poulton *et al*, 2015). Simulation of farm production and cash flow are being undertaken with a farming systems simulation model (Holzworth *et al*, 2014). These simulations include reduced production from reduced cropping area or soil damage from CSG development. Simulations are also used to evaluate possible farm improvements that could be funded through increased income from CSG compensation payments. These data sets have been studied in consultation with landholders to understand areas of concern for farmers involving impacts of surface water flows and erosion, impacts on farm operations and safety, production losses and soil damage.

## 3 Discussion

Several lessons have risen from the various monitoring efforts and subsequent discussions with farmers. Many of these involve appropriate ways to position or re-use certain parts of the CSG infrastructure to minimise costs and grasp opportunities. The detail of these cannot be considered in this brief communication. However, some of the broader lessons can be summarised as follows:

1. Farmers need to develop a farm or business plan prior to CSG development to drive the CSG farm re- design processes.

2. Farmers need to be aware of their own values (e.g. financial, family, environmental, agronomic, place identity) and preferences and understand that the CSG company negotiators may not understand or share these values. They will need to clearly explain these during negotiations.

3. Individual farmers vary greatly in their values, goals and farm design preferences and so no single design approach will be suitable. Every farm will need to be designed individually.

4. Spatial information such as those used in this study is useful for informing farmer's designs, but also in communicating their ideas or concerns to CSG companies. For example, issues of surface water flows can be hard to demonstrate apart from during certain weather events. Maps of water flow or crop yields help communication with CSG staff who may have little experience with a particular farm.

5. Spatial data gathered in this study suggest that negotiations between farmers and CSG companies to date seem to

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

<footnote n="2">² University of Southern Queensland, Toowoomba, Queensland, Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>‡ Corresponding author: neil.huth@csiro.au</footnote>
have been successful in positioning CSG infrastructure effectively within farms. Most infrastructure has been positioned in areas of reduced production and with reduced impact on farm operations. This shows that sufficient flexibility exists in CSG design negotiations to place infrastructure in ways that it minimises impacts on agriculture.

<figure><img src="image_40.png" /><figcaption>**Fig. 1.** (a) Aerial image of a CSG development area. (b) Map of tractor movements around CSG well pads within a field (c) Map of NDVI for fields in a CSG development area showing pipeline impacts on yield. (d) Modelled accumulated water flow paths indicating low (cyan) to medium (yellow) to high (red) accumulation overlaid on aerial image.</figcaption></figure>

## 4 Conclusions

These techniques and the design principles derived from these studies will be of use in other agricultural landscapes which include a gas extraction industry. Future research will focus on 1) integrating these lessons into a simple series of design principles that can be used by landholders and 2) identifying areas within the CSG tenements where improved cash flow from CSG payments could be used to assist in farm improvement.

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

## References

Holzworth, D.P., Huth, N.I., deVoil, P.G., Zurcher, E.J., Herrmann, N.I., McLean, G., Chenu, K., van Oosterom, E., Snow, V.O., Murphy, C., Moore, A.D., Brown, H.E., Whish, J.P.M., Verrall, S., Fainges, J., L.W., B., Peake, A.S., Poulton, P.L., Hochman, Z., Thorburn, P.J., Gaydon, D.S.,

Dalgliesh, N.P., Rodriguez, D., Cox, H., Chapman, S., Doherty, A., Teixeira, E., Sharp, J., Cichota, R., Vogeler, I., Li, F.Y., Wang, E., Hammer, G.L., Robertson, M.J., Dimes, J., Carberry, P.S., Hargreaves, J.N.G., MacLeod, N., McDonald, C., Harsdorf, J., Wedgwood, S., Keating, B.A. 2014. APSIM - Evolution towards a new generation of agricultural systems simulation. *Environmental Modelling & Software* 62, 327-350.

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

Poulton, P., Huth, N. I., Caccetta, P., Cocks, B., Wallace, J. and Wu, X. 2015. Use of aerial photogrammetry to minimize erosion threats from broad scale resource developments on farmlands. 5th International Symposium for Farming Systems Design, 7-10 September 2015, Montpellier, France.

Ramin, S. and Wan Ishak, W.I. 2013. Exploring GPS data for operational analysis of farm machinery. *Research Journal of Applied Sciences, Engineering and Technology* 5, 3281-3286.
# Interview-based structuring of operational decision-making by farmers

Charlotte Daydé*±1, Stéphane Couture<fnref n="1" /> & Roger Martin-Clouaire<fnref n="1" />

## 1 Introduction

Farm management is a dynamic decision problem that requires a series of more or less independent decisions by the farmer about how to operate the farm system to achieve his goals. Such operational decision-making is difficult because it is highly dependent on uncertain factors such as weather, crop and disease development, prices, etc. Other sources of difficulty are the presence of time gaps between decisions and their resulting impacts, as well as the multiple side effects of each decision (e.g., preventing or delaying the execution of other decisions). Unlike in the manufacturing industries, strict production planning in agriculture is therefore not really possible. Operational Decisions (OD) daily decisions about which actions to carry out next and which modifications should be made to activities intended in the future are thus essential in farm management. It is our belief that by analyzing these decisions, we can better understand how farmers perceive operational management and cope with uncertainty, which explains, to a large extent, why performances differ among farmers.

More than only understanding farmer's reasons for a given choice, we must understand the mental process that leads to this choice, i.e., the decision-making process. Indeed, performance heterogeneity is observed among farmers even if farm systems are similar. This variability can be explained by differences in the mental process leading to the decision. Global knowledge of such a mental process is not available although many structuring features such as beliefs, goals, plans and preferences have long been identified by philosophers and artificial intelligence researchers (Pollock 2006) who have investigated decision-making foundations.

An initial fundamental step that is reported here concerns the use of interviews with farmers about their decision-making practices. The analysis of these interviews makes it possible to highlight the aspects that spontaneously arise from the farmer's discourse and those that are ignored or that remain implicit. On the basis of these outcomes, we plan to develop a refined interview-based survey to explore the decision behaviors of a larger panel of farmers and to focus on the aspects that were missing in the first phase.

## 2 Materials and Methods

Our analysis is based on a systematic examination of interviews to identify the decision-making features that appear determinant for the farmers, which information sources are looked at, and how they are used in the decision process. We conducted six interviews of grain crop producers located in the vicinity of Toulouse (in southwestern France). Each interview lasted from 2 to 5 hours and was recorded and transcribed. Content analysis, using Nvivo software, enabled us to systematically identify main themes and topics that emerged and to make a frequency analysis of the keywords used. The interviews are divided into three parts. A comprehensive part based on semi-directive interviews allows farmers to talk about their farm, their activities and management decisions, their sources of information, their constraints, and the risks and difficulties experienced in their work. Then, in a more directive part, farmers have to more precisely describe the nature and timing of their farming practices thanks to calendar-based positioning of management operations. The final part aims at investigating the role of events (e.g., incidents) in the dynamics of the decision process.

## 3 Results – Discussion

The interviews confirmed that regardless of the farm and its location, plan-based management, and actual situation monitoring are essential practices in operating a farm business (Martin-Clouaire and Rellier 2009). Farmers need to adopt plans for the future in order to allow their reasoning about what to do to extend beyond the present moment and to coordinate their activities with each other and with those of other farmers in the case, for example, of equipment sharing. Planning enables rough scheduling of the necessary time, materials and labor through anticipation of crop needs and threats.

The interviews also showed the need for flexibility and adaptability of the plans. Farmers form and commit to partial plans that roughly specify the activities that they intend to perform. Flexibility takes different forms. It may lies in the temporality of the activities declared in the plan. At most, timing is defined with windows of earliest starting time and latest finishing time (e.g., for sowing activities). The timing of activities may also be limited by temporal relationships of precedence (weeding before fertilization) or parallelism of execution. Plans may be logically complex; e.g., they can include conditions that provide them with additional flexibility. For instance, a farmer explained that he usually sows rapeseed the first week of September with a given technique, but if weather conditions are bad, he switches to an

<footnote n="1">1 UR875 MIAT, INRA, Castanet Tolosan, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: charlotte.dayde@toulouse.inra.fr</footnote>
alternative technique or delays sowing by two weeks. In this illustration, the flexibility also concerns the resources and means involved in the realization of the intended sowing activity and the fields potentially targeted by the activity. Tuning the execution of an activity to the actual situation is a common practice. Indeed, plans are partial because they concern intended activities in an uncertain future. Thus making a detailed plan at the beginning of the sowing season would simply be impossible. Having a partial plan makes it necessary to expand and revise it continuously. Expansion is needed to determine the executable actions that are appropriate in the current situation. Then, revision is triggered by events recognized as having an importance for management.

A structuring feature of the decision-making process thus concerns the identification and processing of these events. An event can either be a significant change in state (e.g., beginning of a new crop stage) or the occurrence of an incident that is external to the farm system (e.g., climatic event) but that affects it. Events are major drivers of change of intentions, including changes that result in actions to be executed immediately. Events constitute hazards as well as opportunities. On the basis of the interviews, we identified six types of events primarily defined with respect to: calendar or management landmarks (e.g., completion of winter wheat sowing), weather (e.g., wind), pest outbreak, crop development (e.g., harvesting stage), resource unavailability (e.g., the farmer is ill, equipment failure, etc.) and legislation (e.g., irrigation ban). For example, in the case of nitrogen fertilization activity that requires repeated applications of fertilizer separated by a time interval, if the first application has been delayed for some reason (e.g., bad weather), then the subsequent ones must also be delayed in the plan. These events are observed by farmers or reported to them (e.g., by an adviser) and can be more or less anticipated (e.g., with weather forecasts).

The farmers make operational decisions on the basis of their beliefs about both the current and predicted situations. These beliefs can result from direct observations or from indicators formed by making inferences from one or several observations. More generally, farmers process information given to them (e.g., by advisers), acquired through monitoring activities or available in their memory in order to evaluate the ongoing and upcoming situations. Most farmers also include this observation (or monitoring) of activities in their plans. The farmer's decision-making may consist in selecting the next action to carry out or in formulating or revising a plan and committing to it on the basis of the situation perceived. But the evaluation is highly subjective and uncertain; it partly relies on uncertain information but also depends on farmers' expertise, constraints, goals and preferences. Then, action selection requires resolving conflicts among competing goals, identifying alternative actions that contribute to the goals and that are coherent with the plan involved. At this stage, we need a better understanding of the role of farmers' characteristics (e.g., expertise, preferences, goals) in operational decision making.

## 4 Conclusions

By analyzing empirical data from qualitative surveys, we obtained a preliminary view of how grain crop farmers deal with operational decision-making. Using this information, we identified areas that require further investigation through subsequent interviews that we plan to carry out. Expertise role, goal reasoning and preference characterization and manipulation in an uncertain context are our next investigation priorities.

The motivational role of goals has long been recognized as a driver of decision-making behavior. Surprisingly, goals often remain implicit in the farmer's discourse, even if the farmer's problem is to frame future actions so as to achieve some desirable outcomes within a relatively short term. Actually, the justifications provided by the farmers indirectly point to goals that can be organizational (avoiding labor bottlenecks later on, having winter crop tillage activities completed by a predetermined date), agronomical (having pests under control), or circumstantial (saving money by having inputs be replenished before a predetermined date). The dynamics of creation and revision of goals is still to be examined.

Farmers often have more tasks to perform than they can do immediately. They have multiple information to consider, numerous goals and wishes to take into consideration, and several ways to move towards the goals. The various goals and wishes may be in conflict with each other (e.g., relaxing and meeting deadlines). The conflict may be between the short-term and long-term consequences that have opposite values in terms of their attractiveness, or because some goals are highly desirable but hardly feasible. Therefore, farmers have to somehow prioritize, which means mobilizing dedicated knowledge about preferences of various types. Such knowledge and the mental processes that can process preferences while taking matters of risks and urgency into account are poorly understood at this stage.

Ultimately, understanding how the various decision features are processed to yield operational decisions will require the exploration of the farmer's bounded rationality (Daydé *et al.* 2014) that accounts for limitations in the farmer's information and reasoning powers. The role of farmers' expertise in this context also needs further investigation.

## References

* Daydé C., Couture S., Garcia F., Martin-Clouaire R. 2014. Investigating operational decision-making in agriculture. In: Proceedings of the 7th International Congress on Environmental Modelling and Software, June 15-19, San Diego, CA.

* Martin-Clouaire R., Rellier J.-P. 2009. Modelling and simulating work practices in agriculture. Int. J. of Metadata, Semantics and Ontologies 4, 42-53. Pollock J. 2006. Thinking about Acting. Logical Foundations for Rational Decision Making. Oxford University Press.
# Strategies to manage crop planning complexity in very diversified direct selling farming systems: the example of organic market gardeners

Kevin Morel *±<fnref n="1" />& François Léger <fnref n="2" />

## 1 Introduction

Crop diversification and direct selling can be efficient ways to improve social, economic and environmental benefits of farming systems (Feenstra, 1997; Ponisio et al., 2014). However, they can increase the complexity of the farming system management and especially the cropping plan (Aubry et al., 2011, Lanciano et al., 2010) which can be defined as the acreage devoted to each crop and the spatial and temporal allocation of crops within the farming land along the production season (Dury et al., 2011). Small size organic market gardens often combine a high level of diversification and direct selling (Navarette, 2009). The aim of our work was to study the strategies they developed to deal with the complexity of crop planning.

## 2 Materials and Methods

We carried out a multiple-case study (Yin, 2009) on 12 organic market gardens in northern France producing from 30 to 80 plant species on an acreage going from 0,5 to 2 ha. These farms sold their vegetables directly to consumers through different commercial forms: vegetables baskets paid beforehand with a yearly subscription according to the principles of Community Supported Agriculture (10 farms), vegetables baskets retailed without any subscription (6 farms), vegetables retailed piece by piece on-farm or in producers markets or shops (8 farms). 8 farms combined 2 or 3 of these channels. The common characteristic of all these selling channels is that the market gardeners have to provide from 5 to 10 vegetables species every week all along the commercial season which lasts from 7 to 12 months depending on the farm. We carried out semi-directive interviews with market gardeners about their objectives, situations and practices in order to get a first global and systemic view of the farm and then we focused on the strategies implemented by farmers to manage crop planning complexity. We realized an inductive qualitative analysis of the rich collected material (Miles & Huberman, 1984).

## 3 Results – Discussion

Among the 12 market gardens, crop planning decision making is a systemic challenge because it has to satisfy simultaneously 3 main objectives: (i) matching selling requirements, (ii) limiting the complexity of technical intervention, (iii) respecting rotation criteria to maintain health and fertility of plants and soils. These objectives are related to commercial, technical and ecological aspects of the farming system. To manage the complexity of this systemic challenge, market gardeners have implemented organizational strategies at the same 3 levels of their system. Strategy A is to adapt their selling methods. It relies on the fact that in direct selling channels the producer controls the way he commercializes his vegetables. Selling vegetables baskets requires to produce every week a precise quantity of vegetables in right proportions to satisfy the customer whereas in retail selling systems the quantity of vegetables available every week and their proportion has not to be as precise. Some market gardeners choose to sell only through retail selling systems to be more flexible. Other use a retail selling system as a commercial buffer in combination to a vegetables baskets systems. In this case they can be less precise about proportions and quantities of vegetables sold in baskets because excess vegetables can be sold through the retail selling system. Some farmers use the heterogeneity of consumer's tastes to get more flexibility in planning species proportions in vegetables baskets systems. Instead of selling all baskets with the same proportion of vegetables, they can make baskets with different vegetables and different proportions and ask consumers to choose between them. They also can promote exchanges of vegetables between consumers if some of them wish a bigger or a smaller proportion of some vegetables.

Strategy B is to differentiate planning requirements in relation to the commercial function of the crops. Some crops are considered as “key vegetables” because they are strongly expected by consumers at different times of the year. The sowing or planting of these vegetables is therefore planned before the production season with safety margins. On the other hand, some vegetables may be not specifically expected by consumers but bring diversity to the commercial offer. These “complementary” vegetables can be planned with less safety margins and some of them may be planned not before but along the production season depending on opportunities. It is especially the case of short cycle species vegetables which can be sown/planted when there is an available surface area between two long cycle vegetables. When required these short cycle vegetables can also be sown/planted in multicropping with long cycle vegetables. The proportion and nature of vegetables considered as “key” or as “complementary” vary among farmers and have an

<footnote n="1">¹ INRA, UMR 1048 SADAPT, Paris, France</footnote>

<footnote n="2">² AgroParisTech, UMR 1048 SADAPT, Paris, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: kevin.morel@agroparistech.fr</footnote>
impact of the level of flexibility they can get from this strategy.

Strategy C is to aggregate crops in similar management groups. It involves the determination of aggregation criteria to create groups of species which will be grown in the same space. Instead of thinking the spatial allocation of every species, the farmer has only to think the spatial allocation of a few groups. In the studied farms, the market gardeners use various grouping criteria: botanical family, cropping season (spring, summer, autumn or winter crops), irrigation or fertility needs (high demanding, medium demanding and low demanding). These criteria make both spatial allocation of crops and technical management easier.

Strategy D is to differentiate the importance of phytosanitary criteria in rotations according to species and other ecological technics at the farm level. It consists in being strict in the rotation criteria for some crops considered as "sensitive" for sanitary reasons and to be more flexible or even not to use any rotation criteria for other crops considered as "less sensitive". The market gardeners can release the pressure on rotation criteria because they implement a lot of other ecological technics at the farm level to promote the global immune function of the agroecosystem: high diversity of species and varieties on a small farm, use of resistant and locally adapted varieties, growing green manures with sanitary properties, multicropping, creation and management of ecological infrastructures such as ponds, hedgerows, woodlands, grass stripes, agroforestry. The nature and proportion of plants considered as "sensitive" or "less sensitive" vary among farmers and have an impact of the level of flexibility they can get from this strategy.

These 4 organizational strategies are not implemented and combined the same way among the farms (Table 1) but have been mentioned by farmers as allowing them to reduce the complexity of crop planning.

<table><caption>Table 1. Combination of crop planning strategies among the 12 studied farms (X means "presence")</caption><thead><tr><th rowspan="2">Strategy</th><th colspan="12">Farm</th></tr><tr><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>12</th></tr></thead><tbody><tr><th>A</th><td>X</td><td></td><td></td><td>X</td><td>X</td><td></td><td></td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><th>B</th><td>X</td><td>X</td><td></td><td></td><td>X</td><td></td><td></td><td>X</td><td>X</td><td></td><td></td><td></td></tr><tr><th>C</th><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><th>D</th><td>X</td><td>X</td><td>X</td><td></td><td></td><td></td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr></tbody></table>

In this study we have not associated these strategies with the economic, social and environmental performances of the farms. A multi-criteria assessment and more interviews could be carried out in order to determine in what extent certain strategies or combinations of strategies impact the performances of the farming system.

## 4 Conclusions

Innovative strategies have been developed on very diversified direct selling market gardens to manage the complexity of crop planning. These strategies can be combined and are implemented at different levels of the farming system: commercial, technical and ecological. They are mainly based on the opportunity farmers have to control their commercial methods in direct selling systems and on the sanitary advantage that a high level of plants diversity can bring to the farming system when associated with other ecological technics. This multiple-case study show that crop planning complexity has to be addressed as a systemic level and describe 4 strategies developed in the specific field of organic market gardening. Further investigation would be required in order to see in what extent these strategies could inspire the design of other types of farming systems such as cereal cropping or breeding farms.

## References

Aubry, C., Bressoud, F. & Petit, C. (2011). Le travail en agriculture: son organisation et ses valeurs face à l'innovation. Chapter: Les circuits courts en agriculture revisitent-ils l'organisation du travail dans l'exploitation ?, L'Harmattan Inc., France. http://hal.archives-ouvertes.fr/hal-00939711.

Dury, J., Schaller, N., Garcia, F., Reynaud, A., & Bergez, J.E. (2011). Models to Support Cropping Plan and Crop Rotation Decisions. A Review. *Agronomy for Sustainable Development*, **32**, 567-80.

Feenstra, G.W. (1997). Local food systems and sustainable communities. *American Journal of Alternative Agriculture*, **12**, 28-36.

Lanciano, E., Bon, N., Hérault-Fournier, C. & Aubrée, P. (2010). Diversité des logiques de travail dans les exploitations maraîchères en circuits courts. 4ièmes journées de recherche en Sciences sociales - INRA SFER CIRAD, Rennes, France. http://halshs.archives-ouvertes.fr/halshs-00676845.

Miles, M.B. & Huberman, A.M. (1984) Qualitative Data Analysis: A Sourcebook Of New Methods, SagePublications Inc., Beverly Hills

Navarrete, M. (2009). How do Farming Systems Cope with Marketing Channel Requirements in Organic Horticulture? The Case of Market-Gardening in Southeastern France. *Journal of Sustainable Agriculture*, **33**, 552-65.

Ponisio, L.C., M'Gonigle, L.K., Mace, K.C., Palomino, J., de Valpine, P. & Kremen, C. (2015). Diversification Practices Reduce Organic to Conventional Yield Gap. *Proceedings of the Royal Society of London B: Biological Sciences*, **282**.

Yin, R.K. (2009). Case study research: design and methods. Sage Publications Inc., Los Angeles, London, New Dehli.
# Labour Productivity Analysis in Mixed Family Farm Systems to support a Co-Innovation Process

Paula Colnago ±<fnref n="1" />, Gina Favretto <fnref n="1" /> & Santiago Dogliotti <fnref n="1" />

## 1 Introduction

Viability of family farming is strongly linked to labour productivity and opportunity costs of labour in the wider economy. Increasing labour productivity is as important as increasing land productivity in order to ensure the quality of life of family farmers (De Schutter, 2011). Labour productivity of most Uruguayan family farm systems is lower than could be achieved with their current resource availability. In some cases, it is even lower than the opportunity cost of labour, estimated as the cost of temporary hired labour in the region. A wide variability in labour productivity can be observed between farms with similar resource endowment. Quantifying and understanding the main causes of the gap between current and attainable labour productivity, and its variability among family farms is crucial to support the development of more sustainable family farm systems.

The **objective** of this work is to develop a method to study labour productivity in the framework of a co-innovation project. The *specific objective* is to identify and to analyze the main causes of low labour productivity in family farms, in order to propose strategies to increase it. This is an on-going project.

## 2 Materials and Methods

The co-innovation project involves a systemic process of characterization, diagnosis, redesign, implementation and evaluation (Dogliotti et al., 2014), of fourteen case studies selected to represent the variability on farm resource endowment present in a specific region in Uruguay. The method proposed to study labour productivity is part of the characterization and diagnosis phases of the co-innovation process and it integrates different hierarchical levels of analysis: the whole farm system, the production activities and the operational tasks. Data on labour allocation at each of these levels was collected by semi-structured interviews to farmers and their families, complemented with direct observations during specific moments of the production process. Based on this data plus the rest of the farm data collected during characterization, we built a 'problem tree' for each farm, explaining the main causes of actual labour productivity. All the farms studied combined vegetables and livestock production. During the interviews we identified the type of work (routine or seasonal) and the key questions asked to understand the organization of the work were when?, how much time?, who does the work? and in which way the task or activity is done?

## 3 Results & Discussion

Low labour productivity and high workload are common problems among family farmers in south Uruguay. We found that nine of the fourteen farms studied had in 2013-14 a labour productivity equal or lower than the opportunity cost of labour (4 U$S hour⁻<fnref n="1" />), and the maximum estimated was 8 U$S hour⁻<fnref n="1" />. We selected farm number 3, with a labour productivity of 6.4 U$S hour⁻<fnref n="1" />, to illustrate here the method proposed, summarized in three steps.

*First step: characterization of the farming system.* Farm size is 10 ha with 5.7 ha grown with field vegetables (0.5 ha irrigated), 1 ha of alfalfa, and the rest is used for cattle grazing. The cattle load is 0.8UG¹ha⁻<fnref n="1" />. Family is composed by a middle age couple (44 and 48 years) and their two daughters (17 and 20 years) who study and work occasionally at the farm. The level of mechanization is medium (tractor, crop sprayer and basic tools). The family contributed 5000 work hours per year and they hired 650 hours of temporary labour. Most of the labour was allocated to onion and sweet potato crops followed by sweet maize and melon. We found that 28% of available labour was allocated to general activities such as maintenance of infrastructure, repairs, buying inputs, etc. including time allocated to a carrot crop that was lost before harvest.

*Second step: the 'labour productivity tree'.* Based on the data collected during characterization phase, we calculated the technical coefficients for each crop and animal production activity in the farm. We calculated indicators for the different hierarchical levels of the system and drew the problem tree (Fig 1).

Labour productivity in vegetable crops (8.9 U$S hour⁻<fnref n="1" />; 67% of available labour) was higher than in cattle (5.1 U$S hour⁻<fnref n="1" />; 5% of available labour). The prices obtained for the products sold were similar to other farmers in the region, and the quality of the products was considered good, so this 'branch' of the tree was not developed. The yields of onion and sweet potato were 39 and 15% lower than top yields achieved by farmers in the region with similar resource

<footnote n="1">¹Plant Production Department, Facultad de Agronomía, Universidad de la República</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: pcolnago@fagro.edu.uy</footnote>

<footnote n="1">¹ Cattle Unit: 1 UG equivalent to 1 cow of 400 kg live-weight.</footnote>
endowment. Low yields were explained by deteriorated soil quality, timing of operations, low plant density and weed incidence. Low labour productivity in vegetable production was not only due to low yields but also was explained by excessive hours spent in some tasks.

<figure><img src="image_41.png" /><figcaption>Fig. 1. Labour problem tree of farm number 3</figcaption></figure>

We compared the time spent in this farm to perform the main tasks for each crop with data provided by the extension service of the MGAP<fnref n="2" />. Taking all crops together, the results showed that the time spent in direct operational tasks on crops was 33% higher in this farm. Classification and packing of products, and the sweet potato harvest were the most time consuming tasks at this farm and they were both done with low efficiency. Low labour productivity in cattle was explained by very low production of meat per hectare (45 kg live-weight ha⁻¹), due to low forage availability. The farmer sold part of his alfalfa bales, and over-grazing combined with deteriorated soil quality explained low forage production. The incidence of meat production in overall labour productivity was very low because time allocated to this activity was minimal.

*Step 3: quantifying the impact of improving labour productivity.* We estimated which could be the attainable labour productivity values in two different scenarios. The first scenario supposed increasing yields by adjustment of some crop practices (timing of operations, plant density and weed control) and by reducing 10% the vegetable crop area improving crop-pasture rotation. For the second scenario we added to the first scenario the effect of reducing 620 hours of labour through mechanization of classification and packing of products, and by improving sweet potato harvest practices (Table 1). The impact of increasing crops yields was much higher (43%) than reducing labour (14%). The labour productivity of cattle farming increased more than vegetable production (84 and 36% respectively) because the yield gap was significantly higher in the cattle production (Scenario 1, Table1). It seems feasible to increase family income and labour productivity, in a first step by increasing yields adjusting crop management and without investments. In a second step workload could be reduced and labor productivity further improved by investing in post-harvest machinery and harvest tools.

<table><caption>Table 1. Labour productivity indicators</caption><thead><tr><th></th><th>Actual</th><th>Scenario 1</th><th>Scenario 2</th></tr></thead><tbody><tr><td>Family income (U$S year<sup>-1</sup>)</td><td>31752</td><td>45277</td><td>45277</td></tr><tr><td>Labour productivity (U$S hour<sup>-1</sup>)</td><td>6.4</td><td>9.1</td><td>10.3</td></tr><tr><td>Labour productivity _vegetables (U$S hour<sup>-1</sup>)</td><td>8.97</td><td>12.2</td><td>14.8</td></tr><tr><td>Labour productivity _cattle (U$S hour<sup>-1</sup>)</td><td>5.1</td><td>9.4</td><td>9.4</td></tr></tbody></table>

## 4 Conclusions

The method proposed to study labour productivity at farm level was useful to aid the re-design step of the co-innovation process, because it allowed to identify the key factors to enhance labour productivity.

## References

* De Schutter O., (2011). How not to think of land-grabbing: three critiques of large-scale investments in farmland. The Journal of Peasant Studies, **38**(2): 249-279.

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

* Righi E., Dogliotti S., Stefanini FM. & Pacini GC. (2011). Capturing farm diversity at regional level to up-scale farm level impact assessment of sustainable development options. *Agriculture, Ecosystems and Environment*, **142**: 63-74.

<footnote n="2">² Ministry of farming and cattle farming. List of technical coefficients for the same crops with similar technological level.</footnote>
# Evaluation of sustainability of peasant farming systems in VallesCalchaquíes (NW of Argentina)

María Daniela Chavez ±<fnref n="1" /> & Gonzalo Carlos Bravo<fnref n="1" />

## 1 Introduction

Peasant farming systems of VallesCalchaquíes have different problems: low productivity, limited water availability for production, environmental degradation, scarce infrastructure and limited bargaining possibilities. They mainly produce vegetables, fruits, plants, maize, alfafa and goat milk for cheese production. They also produce: sheep, cows, pigs and chickens. In general, all the family relies on the farm and off-farm work for living. A question related to the sustainability of these farming systems strongly arises.

The concept of sustainable development was originally proposed by the World Commission on Environment and Development in 1987. That report support the idea that a sustainable development is a development that satisfies current generation requirement without affecting future generations needs and it includes the responsibility of preserving the environment and the consideration of a social dimension (WCED, 1987; Cáceres, 2004). A broad conception of sustainability is adopted in this study; sustainability implies an interdisciplinary view which includes economics, social-cultural and environmental aspects which help to a better understanding of the functioning of a farming system; they are a useful tool for designing policies and strategies for action in territories. To evaluate sustainability the productive unit and the family performance are included as a whole system. The objective of this work is to select and evaluate appropriate sustainability indicators to evaluate the sustainability of peasant farming systems of the VallesCalchaquíes. This knowledge will contribute to design research to develop practices to improve farming systems sustainability and to guide environmental and productive policies according to the situation of the territory.

## 2 Materials and Methods

This research focuses on farmers that live in a place called “La Aguadita”, located in Molinos department (25º 08’10” and 25º 58’ 20” Southern latitude and 66º 01’30” and 66º 58’10” Western longitude), in Salta province in the Northwest of Argentina. Molinos department has many mountains of 2000 to 5000 m and limited fertile area (Department Geography of Salta). The data for this study come from a questionnaire made to 15 farmers carried out in 2014 (INTA-SAF). Variables show information about characteristics of the family group, the area under production, production activities, labor, family income, social nets and problems of the farming system.

The use of specific indicators for each of the dimensions (economics, social-cultural and environmental) to measure sustainability is considered as a useful tool to show the functioning of a production system. The indicators synthesize, condense, quantify and communicate complex and complicated information (Singh et al., 2009). Indicators should be specific variables to reflect the situation of peasants producing for own consumption and selling. Different indicators are selected for each dimension. The indicators are expressed in different units depending on the variable to be quantified (economic, social-cultural, and environmental). A way to skip the problem of different expression of the units is to build a scale with 4 or 5 values (Sarandon & Flores, 2009). In this research we use 0 for the less sustainable category and 3 the most sustainable. Indicators were selected based on the availability of data from the questionnaire.

In Table 1 the selected indicators for each dimensions and the meaning of each category are presented.

## 3 Discussion

This research was the first step to explore the sustainability of peasant farming systems of the Northwest of Argentina. The use of a simple scale from 0 less sustainable to 3 or 4 more sustainable allows a direct quantification of the indicators and the comparison among different farming systems of different regions (Sarandon & Flores, 2009).

A broad idea of sustainability was adopted in this research. A limited number of indicators were selected based on available data and they were quantified following a scale. To get the value of an indicator the average of the questionnaire were used. A better evaluation requires to evaluate each indicator for each observation and to make a ranking of the most sustainable farming system to the worst. Moreover, in this study all the indicators were given the same weight; however the relative importance of some of them can be defined. Peasants’ opinion can be included in giving values of indicators that should contribute to their sustainability. Still, there is place for getting an overall evaluation of sustainability which means to combine all the indicators in a sustainability function (Van Calker, 2006).

<footnote n="1">¹ Economics and Rural Sociology Department, National Institute for Agricultural Technology</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: chavez.daniela@inta.gob.ar</footnote>
<table><caption>Table 1. Indicators for each dimension, scale and quantification</caption><thead><tr><th>Dimension</th><th>Indicator</th><th>Scale meaning</th><th>Quantification of the indicator</th></tr></thead><tbody><tr><td rowspan="3">Socio-cultural</td><td>Familiar continuity</td><td>0 = high age farmer without successor 2 = high age farmer with successor<br>1 = medium age farmer without successor3 = medium age farmer with successor</td><td>3</td></tr><tr><td>Formal education</td><td>0 = primary school incomplete2= secondary incomplete<br>1= primary school complete3= secondary complete or higher incomplete</td><td>0</td></tr><tr><td>Ownership of the land</td><td>0 = not owner2= owner in process of legal possession<br>1= owner without legal possession3= own with legal possession</td><td>3</td></tr><tr><td rowspan="3">Economic</td><td>Income diversification<sup>(1)</sup></td><td>0 = one source of income2= three sources of income<br>1= two sources of income3= more than three sources of income</td><td>2</td></tr><tr><td>Monthly total family income<sup>(2)</sup></td><td>0 = up to 1000 2= 1501-2000<br>1= 1001-15003= more than 2000</td><td>1</td></tr><tr><td>Self consumption diversification</td><td>0 = two products2= four products<br>1= three products3= more than four products</td><td>2</td></tr><tr><td>Environmental</td><td>Natural pastures management</td><td>0 = grazing all the year without supplementation<br>1 = grazing all the year with supplementation<br>2 = grazing part of the year without supplementation<br>3= grazing part of the year with supplementation</td><td>0</td></tr></tbody></table>

## 4 Conclusions

The methodology used in this research allows seeing the weakest and strongest points of the farming systems with respect to different dimensions of sustainability. A limited number of indicators for social-cultural, economic and environmental dimensions were included. Social-cultural dimension indicates a good performance for family continuity and ownership of the land; however an improvement of education is needed. Farmers have in average three sources of income, improving the sustainability of the system. Total income per capita is low, so there is place for improving quality of life in this aspect. Policies may be oriented to increase subsidies or credits to improve the production of the system. The management of natural pastures among farmers is poor, and probably it shows some kind of soil degradation. More and better indicators for environmental dimension are required; any other suitable one arises from the available questionnaire. In general terms, it seems that farmers systems have a better performance in the social-cultural dimension than the other two. There is still place to improve research in all the dimensions.

*Acknowledgments.* This work has been carried out under the National Program for Development and Sustainability of the Territories (INTA, Argentina).

## References

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

Department Geography of Salta Province.Sol Edition, Salta.

INTA& SAF.(2014). Instituto Nacional de Tecnología Agropecuaria and Secretaría de Agricultura Familiar Questionnaire to farmers. Sarandón, S.J.& Flores, C.C. (2009). Evaluación de la sustentabilidad en agroecosistemas : una propuesta metodológica. *Agroecología, 4*, 19-28.

Singh, R.K., Murty, H.R., Gupta, S.K.&Dikshit, A.K. (2009). An overview of sustainability assessment methodologies. *Ecological indicators 9*, 189-212.

Van Calker, K.J., Berentsen, P.B.M., Romero, C.,Giesen, G.W.J. &Huirne, R.B.M. (2006). Development and application of a multi-attribute sustainability function for Dutch dairy farming systems. *Ecological Economics, 57*, 640-658.

WCED (1987). World commission on environment and development. Our common future. Oxford University press, Oxford.

<footnote>(1) sources of income include income from: selling products, working outside, retirement, subsidies and remittance</footnote>

<footnote>(2) per capita. Argentinian pesos</footnote>
# Development of a trajectory to support sustainable choices at farm level

Ine Coteur ±<fnref n="1" />, Fleur Marchand <fnref n="1" />,<fnref n="2" />, Lies Debruyne <fnref n="1" />, Floris Dalemans <fnref n="1" /> & Ludwig Lauwers <fnref n="1" />,<fnref n="3" />

## 1 Introduction

Agriculture is more and more faced with extreme challenges such as climate change, scarcity of natural resources and specific demands from society. Both on farm and agricultural sector level, stakeholders express their need to evolve towards more sustainable farming practices to guarantee their future. To face these challenges many initiatives to identify, measure, evaluate and communicate sustainable development arise. The use of sustainability assessments is seen as an important instrument to move towards sustainability (Pope et al., 2004). Many definitions of sustainability assessment exist. It can be seen as “a range of processes that all have as their broad aim the integration of sustainability concepts into decision-making” (Pope, 2006). Sustainability assessment is “a process by which the implications of an initiative on sustainability are evaluated” (Pope et al., 2004). The initiative can range from an existing policy, a plan, programme, project or a current practice or activity. Hugé et al. (2011) describe it as a process that aims at operationalising sustainable development as a guide for decision-making by identifying the future consequences of current and planned actions.

Literature on sustainability assessment and sustainability assessment tools to support decision making is still rapidly growing (Gasparatos & Scolobig, 2012; Marchand et al., 2014). However, despite this growing interest in sustainability assessments and the existence of numerous assessment tools, opinions differ on how to define, plan and measure the progress towards sustainability (Gasparatos & Scolobig, 2012). There is a lack in literature regarding tool choice, use of tools and use of specific methodologies in assessments (de Ridder et al., 2007). Not only the selection of a tool, but also its implementation plays a major role in the success of a sustainability assessment. Previous research on the implementation of assessment tools which support the decision making process of a farmer have led to multiple insights (Coteur et al. 2014; Marchand et al. 2014; Triste et al. 2014). Ownership, monitoring results over time, the attitude of the tool-users and the organization of discussion groups need to be taken into account when implementing an assessment tool. Nevertheless, insights on how various assessment tools can be used in a complementary way to support the needs of a farmer are lacking. We developed a two-dimensional assessment framework to support sustainable farm choices. The framework allows the farmer to follow a trajectory towards more sustainable farming making use of a set of complementary tools.

## 2 Materials and Methods

To develop the assessment framework, we have based our analysis on insights from empirical research and lessons learned from two other cases in which we were involved. This enabled us to identify what is important when implementing an assessment and which needs stakeholders have concerning the assessment of a farm.

First, Boerenbond, the biggest farmers' organisation in Flanders, requested the authors of this paper to design a sustainability assessment framework applicable to different agricultural sectors. This demand enabled us to set-up a participatory research and clearly grasp the needs of the stakeholders concerning the sustainability assessment and its implementation. In our research, we define the participatory approach as the collection and analysis of information on sustainable development of farming practices involving scientists, advisors, farmers and experts in all development steps of the assessment framework. The development steps included a context-specific assessment tool development phase, a reflection of this tool development phase (Coteur et al., 2014) and discussion meetings with experts, advisors, farmers and other stakeholders for the fruit production sector, the dairy sector, the meat production sector and the greenhouse horticulture sector. Concerns and needs were systematically written down in notes and reports during meetings. Second, we analysed lessons learned and reflections from two other cases in which the authors of this paper were involved. The first case is the development and implementation of the Monitoring Tool for Integrated Farm Sustainability (MOTIFS) (De Mey et al., 2011; Meul et al. 2011; Triste et al., 2014) and the second case is the Public Goods Tool (PGT) (Gerrard et al., 2011, Gerrard et al., 2012; Marchand et al. 2014).

## 3 Results – Discussion

The analysis resulted in five different needs regarding sustainability assessments. First, the sustainability assessment needs to be embedded in the surrounding context. Second, the framework should allow us to approach every agricultural sector differently. Third, communication about the sustainable development of an agricultural sector and

<footnote n="1">¹ Institute for Agricultural and Fisheries Research (ILVO)-Social sciences Unit, Burg. Van Gansberghelaan 115, box 2; 9820 Merelbeke, Belgium</footnote>

<footnote n="2">² University of Antwerp, Ecosystem Management Research Group and IMDO, Universiteitsplein 1, 2610 Wilrijk, Belgium</footnote>

<footnote n="3">³ University of Ghent; Department of Agricultural Economics; Coupure Links 653; 9000 Ghent, Belgium</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: ine.coteur@ilvo.vlaanderen.be</footnote>
their specific efforts needs to be possible. Fourth, the sustainability assessment framework needs to focus on the farmer and on the encouragement of sustainable practices on farm level. Fifth, the sustainability assessment framework needs to accommodate the goals of a farmer as every farmer should be able to set his own path during his decision process. There is a need for different types of tools as the function of a tool needs to reflect the goal of the farmer at a specific time. These five needs are incorporated in a two-dimensional assessment framework to support sustainable farm choices.

The first dimension of the framework describes five steps of the assessment. The first step involves the implementation and use of an assessment tool to gain insights on the sustainability of multiple farm aspects. After completing the assessment, the results are interpreted in a second step. To enhance the sustainable development of a farm, improvement strategies are developed based on the interpretation of the results. These improvement strategies will be implemented in a fourth step. The fifth and last step is the monitoring step. During this step the farm will be monitored over a longer period of time by for example completing the same assessment every year. In that manner a farmer can see if his farm is progressing towards a more sustainable farm system. The second dimension of our framework describes three levels of complexity of assessment tools. The first level contains basic tools, which consist of mainly sustainability measures a farm can implement. This is a very quick and easy way to assess a farm without the use of quantitative data. In level two both qualitative and quantitative data are used to assess a farm. Indicators used to measure sustainability can be simple or complex, but the data collection itself stays rather simple. In this level, the use of benchmarks allows us to compare farms within a specific agricultural sector. Level three consists of a more complex, time consuming and often more expensive data collection. To assess a farm at level three, expert judgment and/or specific monitoring are necessary.

Different combinations of an assessment phase and a specific level of a complexity form a farm specific trajectory. It is defined as a sequence of steps throughout the assessment framework. The steps a farmer chooses to follow have a different impact on the decision making process as different steps impose different insights in farm sustainability. The framework is flexible and each farmer can choose a specific trajectory within the framework.

## 4 Conclusions

The two-dimensional assessment framework enables us to support sustainable farm choices by fulfilling the needs of farmers and other stakeholders. It takes into account the general context in which each farmer operates, it can approach every agricultural sector differently, every farmer can choose its own path, communication about sustainable development is an option and different types of tools can reflect the goals of a farmer at a specific time. The flexibility of the framework allows a farmer to choose a farm specific trajectory which may change over time. Our future research will specify the farm specific trajectory for each combination of assessment phase and level of complexity.

## References

* Coteur, I., Marchand, F., Debruyne, L., Bijttebier, J., Triste, L. & Lauwers L. (2014). Development and evaluation of an on-demand sustainability tool in Flanders. *Accepted for publication in Proceedings of 11th European IFSA Symposium 1-4 April 2014 in Berlin, Germany*.

* De Mey, K., D'Haene, K., Marchand, F., Meul, M. & Lauwers, L. (2011). Learning through stakeholder involvement in the implementation of MOTIFS: an integrated assessment model for sustainable farming in Flanders. *International Journal of Agricultural Sustainability, 9*, 1-14.

* De Ridder, W., Turnpenny, J., Nilsson, M. & Von Raggamby, A. (2007). A Framework For Tool Selection And Use In Integrated Assessment For Sustainable Development. *Journal of Environmental Assessment Policy and Management JEAPM, 9*(04), 423-441.

* Gasparatos, A. & Scolobig, A. (2012). Choosing the most appropriate sustainability assessment tool. *Ecological Economics, 80*, 1-7.

* Gerrard, C. L., Smith, L.G., Padel, S., Pearce, B., Hitchings, R., Measures, M. & Cooper, N. (2011). OCIS public goods tool development. Organic Research Centre Report. Organic Research Centre, Berkshire, UK.

* Gerrard, C. L., Smith, L.G., Padel, S., Pearce, B., Hitchings, R., Measures, M. & Cooper, N. (2012). Public goods and farming. Pages 1-22 in Farming for food and water security. Sustainable Agriculture Reviews Volume 10. Springer, Dordrecht, The Netherlands.

* Hugé, J. & Waas, T. (2011). Converging impact assessment discourses for sustainable development: the case of Flanders, Belgium. *Environ Dev Sustain, 13*, 607-626.

* Marchand F., Debruyne L., Triste L., Gerrard C., Padel S. & Lauwers L. (2014) Key characteristics for tool choice in indicator-based sustainability at farm level. *Ecology and Society, 19*(3), 46.

* Meul, M., Van Passel, S., Nevens, F., Dessein, J., Rogge, E., Mulier, A. & Van Houwermeiren, A. (2008). MOTIFS: a monitoring tool for integrated farm sustainability. *Agron Sustain Dev, 28*.

* Pope, J., Annadale D. & Morison-Saunders, A. (2004). Conceptualising sustainability assessment. *Environmental Impact Assessment Review 24:595-616*.

* Pope, J. (2006). Editorial: what's so special about sustainability assessment? *Journal of Environmental Assessment Policy and Management, 8*, 5-10.

* Triste L., Marchand F., Debruyne L., Meul M. & Lauwers L. (2014) Reflection on the development of a sustainability assessment tool: learning from a Flemish case. *Ecology and Society, 19*(3),47.
# Evaluating the sustainability of urban agriculture projects

Agnès Fargue-Lelièvre ±1 & Anne-Cécile Daniel 1

## 1 Introduction

There is currently an important increase in urban agriculture (in and around the cities) in the North countries, including France and the Ile de France region (AC. Daniel, 2013). This development is greatly supported by Paris and by the Paris region policies (announcement of 33ha of productive agriculture on roof and walls in Paris for 2020 in the city council roadmap of September 2014, regional calls for projects in 2013 and 2014).

At the same time, the Agricultures Urbaines team has been organizing meetings for project holders in urban agriculture (UA) in the Paris region. The intra-urban projects involved in these meetings, projects either currently existing or in development, are very diversified and use different technical and economic models to take into account the constraints of an urban context. Among the reasons given for the necessity of developing UA are the services it provides to the city, such as biodiversity (Madre et al., 2014), food and social links (Pourias, 2014), waste recycling (Grard, 2013), climate change mitigation... (Lin et al., 2015). In this context, it seemed important to study these projects, the circumstances of their development and to evaluate more precisely the kind of services they render to the city. To this end, we have created a study grid for these innovating farming systems.

## 2 Materials and Methods

### Study grid

To our knowledge, there is no published study grid describing such urban farming systems and systematically measuring the services they render. However, these services are somewhat similar to those measured when evaluating the sustainability of farming systems.

We studied different indicators grids for evaluating the sustainability of farming systems at the farm scale: IDEA (Zahmet al., 2008), FADEAR (2013), IBEA (2013), Masc 2.0 (Craheixet al., 2012). We took note in these grids of which points were the most pertinent for our study, and of which indicators could be measured on urban farming systems. We then chose those most adapted to our context when several indicators were equivalent between methods, and added some indicators specific to UA and market gardening (which is the main production of these systems) using expert advice.

The indicators can be grouped in 3 categories (Table 1).

<table><caption>Table 1. Indicators retained for the study of urban farms.</caption><tr><td rowspan="6">Registration in the territory</td><td>Labelling and traceability</td></tr><tr><td>Implication in the local community</td></tr><tr><td>Marketing and exchange system</td></tr><tr><td>Autoconsumption</td></tr><tr><td>Waste management and recycling</td></tr><tr><td>Spatial organization</td></tr><tr><td rowspan="2">Economic and human resources</td><td>Working hours and workforce management</td></tr><tr><td>Origin of the income (sales, subventions, services...)</td></tr><tr><td rowspan="6">Cultural system</td><td>Cropped and wild biodiversity</td></tr><tr><td>Crop rotation</td></tr><tr><td>Fertility management</td></tr><tr><td>Diseases and pests management</td></tr><tr><td>Irrigation</td></tr><tr><td>Energy consumption</td></tr></table>

### Urban farming systems

We identified farming systems already in place or beginning in 2015 in the Paris region to evaluate the pertinence of our grid. The project managers of 8 diversified projects agreed to participate in this study.

These are:

- Agripolis: aeroponic project of 9 towers beginning in 2015;

- Ferme du Bonheur: association farming 4.5ha since 1995 on wastelands with a mixed production (plants and livestock);

- Ferme du Moultou: farm of 600m² belonging to the Resto du Cœur association (food assistance) in production since 2013;

<footnote n="1">¹ Département SIAFEE, UMR SAD-APT AgroParisTech-INRA</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: agnes.lelievre@agroparistech.fr</footnote>
- Paysanurbain: association beginning in 2015 the production of micro-greens in a 50m² greenhouse, for a 500m² production in the long term, supported by the Cocagne network;

- PlanèteLilas: association farming 2.5ha for vegetables in a public park since 2007;

- Pullman hotel-restaurant: roof vegetable garden of 60m², productive since 2014 and managed by the Topager company;

- Veni Verdi: association farming nearly 400m² since 2014 in a Parisian college;

- V'île fertile: association farming 600m² since 2014 in a public park.

In order to applyour grid on these projects, a regular monitoring (every 2 weeks) for a year is planned. All indicators present in the grid will be measured and every farming techniques applied on the production plots noted. Regular exchanges will also enable us to understand the reasons for the choice of techniques used and perhaps identify innovating practices if some are used.

**3 Results – Discussion**

Conventions have been signed with each project and in order to facilitate the monitoring, notebooks on 3 themes have been distributed to the project managers or to the person(s) in charge of the farming. These notebooks are: an activity notebook to study the share of each activity (cropping, trainings, interviews...) in the working schedule and the revenue when there is one; a harvest notebook to evaluate the production; a cropping notebook to note the management practices and cropping system.

**4 Conclusions**

The monitoring will begin in April and will enable us to measure the indicators on these urban farms and to study the operational and strategic levels of decision in these cropping systems. This monitoring is also an advice opportunity for the project managers from our team.

This monitoring and the analysis of the data acquired will enable us to evaluate our grid and its pertinence for the study of UA projects as well as its adaptability to other urban contexts.

The data from this year of monitoring will also help us clarify, qualitatively and quantitatively, the potential of urban farming in the Paris region.

**References**

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

FADEAR (2013). Agriculture paysanne, le manuel. 132 p.

Grard, B. (2013). Expérimentation des cultures maraîchères sur les toits de Paris par la valorisation de déchets urbains et l'ingénierie écologique.

Mémoire de M2, AgroParisTech, 68p.

Lin, B.B., Philpott, S.M. &Jah, S. (2015). The future of urban agriculture and biodiversity-ecosystem services: challenges and next steps. *Basic and applied ecology*, In press.

IBEA (2013). Notice scientifique. 127 p. http://ibea.portea.fr/

Madre, F. Vergnes, A., Machon, N. &Clergeau, P. (2014). Green roofs as habitats for wild plant species in urban landscapes :First insights from a large-scale sampling. *Landscape and urban planning*, **122**, 100-107.

Pourias, J. (2014). Production alimentaire et pratiques culturales en agriculture urbaine : analyse agronomique de la fonction alimentaire des jardins associatifs urbains à Paris et Montréal. PhD thesis. ABIES, Paris.

Zahm, F., Viaux, P., Vilain, L., Girardin, P. & Mouchet C. (2008). Farm sustainability assessment using the IDEA method. From the concept of farm sustainability to case studies on french farms. Working paper.
# Improve Nitrogen use efficiency in mixed crop-livestock system in North China Plain

Guiyan Wang Pengfei Shi *±1

## 1 Introduction

North China Plain (NCP) is one of the most important productive agricultural areas in China. In recent years, the intensive farming systems, from high-yielding agriculture and animal breeding to the segregation of crop and animal production at smallholder farm scale have led to a serious imbalance of the main nutrients (Oenema et al., 1998; Aarts et al., 2000) and limited the sustainable development of integrated agriculture. Linking cropping and animal production systems is an important to improve the nutrient use efficiency and reduce the resource use, and also an approach to sustainable development of agriculture in NCP.

Mixed crop-livestock system can provide the approach to construct the nutrition cycling and improve the nutrition use efficiency, thereby to increase the productivity of the whole farm, and is probably the most benign agricultural production system from an environmental viewpoint because it is at least partially closed to external inputs except energy (Egbert et al., 2004).

Nutrient budgets provide information about nutrient losses and nutrient use efficiency, and the information generated is readily communicable to farmers and policy makers. Nutrient budgets can also provide guidance to improving nutrient management (Oenema, 2006).

This study uses a framework for mixed crop-livestock system to evaluate nitrogen flows in a model farm in NCP at the farm-gate scale. The objective is to analyze N flows between crops and animals, and to evaluate the nitrogen use efficiency in crop and animal system, and to explore of better management means to improve N use efficiency.

## 2 Materials and Data collection

We selected a farm (Jinglong Company) in Jingxian County (37°58′N, 115°99′E) of Hebei Province, which is located in the northwest NCP. This farm is considered representative of the region's large-scale farm. There are 1000 ha land in this farm, and the crop production is mainly winter wheat-summer maize double cropping system and a few forage crops. This farm is considered as the typical of the livestock farms in the region: beef breeding, dairy cows, suckling cows and pig breeding. In this farm, the grain of maize and the residues of maize were all feed to the animal feeding, the grain of wheat are sold on the local market. In addition to the forage produced in this farm, they also bought some maize grain and synthetic feed outside the farm. The excreta of the livestock was first collected to compost until apply to the farmland as organic fertilizer. In this study, we focus on the nitrogen flow and use efficiency between crop and livestock system on the whole farm.

All data on the inputs and outputs of the crop and livestock production system were obtained using verbal estimations based on crop and animal production in 2013 by the formal survey questionnaires from the farm manager and workers and field observations during June-November 2014. The information provided by the administrator and workers was considered reasonably accurate since they managed and worked on the farm with the current production mode for many years, and they provided clear information on each production step. The data included the N inputs and outputs of wheat and maize production and animal breeding. The N outputs from crop production included the harvested grain and maize staw.

## 3 Results and Discussion

In cropping system, the grain of wheat and maize is harvest, wheat is sold in the market and maize is as fodder, and the straw of maize is also harvested as forage for animals. In this system, the inputs of N in purchased fertilizer is 44.7% of the total input of N, and the N from animal manure is 40.0% of the total. The surplus N per unit area is 647.0kg/hm², and the surplus N is as high as 64.5% of the total inputs of N per year, indicating the environment impacts. In animal system, feed purchased are considered as the main source of N inputs, and this part of N accounts for 80.4% of the total N. The input of N from cropping system is only 17.0% of the total inputs of N in animal system. There are much manure due to the large quantity of pig, beef and dairy cow breeding, and this part of N amounts to 6.2×10⁵kg/a, but the loss is 15.0% when storing in the barn and stack. 59.7% (3.6×10⁵kg) N from manure flows to the cropping field and part of manure are exported as commercial fertilizer. In this system, we harvest the pork, beef and milk, the total N output in these products is only 18.0% of the total inputs. In crop-livestock the whole system, the N use efficiency is 21.0%.

<footnote n="1">¹ Faculty of Agronomy, Agricultural University of Hebei, Baoding, Hebei 071001, China</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: guiyanwang@sina.com</footnote>
<figure><img src="image_42.png" /><figcaption>(in cropping system, the unit is kg/hm²; in animal system, the unit is kg/year)

Fig. 1 The nitrogen flow in the crop-livestock systems at the farm level</figcaption></figure>

**4 Conclusion**

It is concluded that the high surplus of N in cropping system are threatening the environment, and that is normal in the solo cropping system because of much fertilizer N. The N use efficiency is not satisfied in the whole crop-livestock system because of many young stocks and lacking of judicious feeding. So if the best ecotechnological means are combined in a balanced mixed farm system a multiple win situations will attain in terms of high food production capacity and environment quality.

**References**

Aarts, H.F.M., Habekotte', B., van Keulen, H., 2000. Nitrogen management in the ‘‘De Marke’’ dairy farming system. Nutr. Cycl. AgroEcosyst. 56, 231-240.

Oenema, O., Boers, P.C.M., van Eerdt, M.M., Fraters, B., van der Meer, H.G., Roest, C.W.J., Schro’ der, J.J., Willems, W.J., 1998. Leaching of nitrate from agriculture to groundwater: the effect of policies and measures in The Netherlands. Environ. Pollut. 102, 471-478.

Oenema, O., 2006. Nitrogen budgets and losses in livestock systems. International Congress Series 1293, 262–271.

Egbert A. Lantinga, Gerard J. M. Oomen, Johannes B. Schiere, 2004. Nitrogen efficiency in mixed farming systems. Journal of Crop Improvement, 12:1-2, 437-455.
# T3. Crop modeling and yield gap analysis for agricultural systems analysis and design

Chair: Sentold Asseng, University of Florida

Co-chair: Frank Ewert, Bonn University
# ARE OUR MODELLING TOOLS READY TO COPE WITH AGRICULTURAL SYSTEMS EVOLUTION?

Jacques-Eric Bergez *±1*, Marcello Donatelli 2, Frank Ewert 3, Dean Holzworth 4, Gerrit Hoogenboom 5, Hélène Raynal 6, Claudio Stockle 7

## 1 Introduction

Agricultural systems are continuously changing to better cope with demands by society, environmental concerns, and changing climate. As a result, new concepts have been developed such as agroecology, ecosystem services, multifunctional agriculture, and climate smart agriculture. These new agricultural concepts require tools to help with the analysis, design and evaluation of new systems. However, modeling the evolution of farming systems is challenging both for computer scientists and agricultural modellers. This paper discusses how these evolutions require new modelling concepts, tools and approaches to help improving the analysis, design and evaluation of farming systems. The key question is how the modeling community can evolve our models from cropping system models to an integrated platform capable of handling a continuously changing farming system.

## 2 Scientific challenges

### New biophysical knowledge

The first challenge is the new biophysical knowledge that has to be acquired in order to cope with change. This concerns different evolutions:

* Climate change: how to integrate new processes regarding the effects of extreme temperatures and responses to tropospheric ozone?

* Smart agriculture: how to better represent mitigation processes and related greenhouse gases emissions?

* Integrated pest management: how to integrate biotic regulation functioning? How to represent pest and pest natural enemies typically represented by different modelling approaches? How to predict potential spreading of alien pests and diseases facilitated by changing climatic conditions?

### New practices

The second challenge concerns the development of new types of agricultural systems. Representing the decision process of these new agricultural systems and the impact of the agricultural operations on the biophysical system is therefore an important issue:

* How to represent new crop management as conservation agriculture, intercrops, agroforestry systems, mixed crops?

* How to represent interactions with the field environment that provides natural enemies such as hedges?

### Integrating diverse production on the same farm

There is a general movement towards more diversified farming systems. More research focuses on integrating livestock and cropping systems together.

* How to represent an increasing diversity of production systems and related interactions?

* How to integrate flows of resources within the farm?

* How to share resources among competing activities?

### Changes of scales

A big challenge in implementing new farming practices is to integrate the surrounding area of the farm either as a biological reservoir or in order to link it with the agri-food chain or to analyse complementarity within a territory.

* How can models deal with scales and change of scales for crop production, processing and management?

### Working with farmers, resources managers and policy-makers

Agroecological farming systems target specific environmental and social conditions. There is a real need to work with farmers and to integrate specific local conditions:

<footnote n="1">1 INRA/Université Fédérale de Toulouse, UMR 1248 AGIR, F31326 Castanet</footnote>

<footnote n="2">2 CRA, CRA-CIN, 40128 Bologna, Italy</footnote>

<footnote n="3">3 Univ. of Bonn, INRES, Crop Science Group, Katzenburgweg 5, 53115, Bonn, Germany</footnote>

<footnote n="4">4 CSIRO Agriculture Flagship, Toowoomba, Australia</footnote>

<footnote n="5">5 AgWeatherNet, Washington State University, Prosser, Washington 99350, USA</footnote>

<footnote n="6">6 INRA/Université Fédérale de Toulouse, UR 0875MIAT, F31326 Castanet</footnote>

<footnote n="7">7 Dep. of Biological Systems Engineering, WSU, Pullman, WA, USA</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: jebergez@toulouse.inra.fr</footnote>
* Do we have the tools to interact with stakeholders while developing and applying simulation models; how to create ad hoc models (library of modules, repositories of parameters)? Do we need web interfaces?

* How to integrate partial knowledge and stakeholder knowledge?

* How to represent simulation results in a meaningful way?

* How to run models in digitally-isolated rural areas?

*Issues regarding data*

Not only farming systems are changing, but also data acquisition. Data flow has increased dramatically during the last ten years due to the availability and use of remote data among others, and new options are made available even by smartphones.

* How to deal with large increases in the amount of data?

* How to integrate data from satellite images (from initialization to real-time process) in running processes?

**3 Evolutions required in our modelling platforms**

When checking the different scientific challenges, some general challenges for the modelling platforms emerge:

* Integrating new formalisms

* Integrating complex management options

* Integrating different production systems

* Integrating cross spatial and temporal scales

* Allowing on-site development or parametrisation

* Using real-time data

Some of these challenges will not require considerable modification of actual modeling platforms. For example, integrating new formalisms to take into account extreme temperature or CO2 impacts on crop production is not something that requires modifying the architecture of modeling platforms.

Some other challenges are more complicated such as representing complex management options and their impacts on the biophysical systems. Diversifying production systems or integrating pests is also complex as some modeling platforms are more or less dedicated to a specific type of agricultural production (e.g. cereals).

Other challenges may need much more substantial changes such as integrating hedges or landscape structures because this will require representing the system in a 2-D (or 3-D) dimension. The other important example is the use of the platform in a participatory manner and allowing for the integration of farmer's knowledge while developing the appropriate model.

We conclude that the main problem is not lack of scientific understanding in most cases, or lack of software architecture and approaches amenable to deploy the platforms and capabilities that are needed. What is lacking is more a concerted effort that brings together many disciplines, including software engineering, to create the conditions to make progress in this area.

We point to a common misunderstanding about the ease to develop and use of simulation tools. Resources are made available to produce specific analyses, but very rarely to develop new tools, infrastructure, and even for targeted data collection. In the same way as the demand for integrated simulation has grown, the complexity of tools required has increased as has the need for verifying quality and reproducibility of results. All of this cannot be performed without an articulated approach in which there are dedicated resources available to reconsider tools and modelling frameworks development.
# Putting the coherence between crop processes and model inputs at the core of model development: a case study for water stress in intercropped systems

Nicolas Bertrand ±<fnref n="1" />, Sébastien Roux <fnref n="2" />, Maé Guinet <fnref n="1" /> & Jacques Wery <fnref n="1" />

## 1 Introduction

Modeling the dynamics of water stress is a recurrent issue for many crops, either for the quantification of yield gap due to water or for the management of Regulated Deficit Irrigation. The demand for such models increases especially for intercropped systems, but data required to build, parametrize and evaluate these models is limited. Moreover dealing with a diversity of agrosystems with one model highlights the questions of parametrization protocols and model uncertainty estimates. These aspects appear insufficiently handled by existing intercropped systems models.

## 2 A modeling approach putting the coherence between crop processes and model inputs

We put the biophysical processes conceptualization and parametrization at the core of the model development by following several principles. Firstly, each model input has a functional relevance and refer to a biophysical process. This coherence between crop processes and model inputs opens the possibility to use expert knowledge to estimate parameters in situations where data are lacking while knowledge on plants and soils are rich. Secondly, parameter calibration is excluded, this procedure being both particularly demanding in data and potentially delicate when evaluating the predictive quality of the model. Thirdly, several protocols specifying how to get each model input are defined. These protocols take into account different accuracy levels depending on data availability and allow to assess the associated uncertainty in model outputs (Roux et al., 2013). Fourthly, some critical model concepts are integrative in order to cover a range various intercropped systems while trying to contain both the potential error resulting from using simplified hypotheses and the parametrization error coming from using more detailed descriptions of crop processes.

## 3 Illustration of the modeling approach through the BIS_Wat model development

We applied these principles to develop a new water balance model called BIS_Wat (Bispecific Intercropped Systems – Water balance model) built to simulate the dynamics of water stress experienced by each crop of an intercropped system. This new model extends to others types of bispecific agrosystems the WaLIS model (Celette et al., 2010) developed for intercropped vineyard. BIS_Wat is a 2D bispecific tipping-bucket model working at a daily time step that is based on the Fraction of the Transpirable Soil Water (FTSW) concept (Sinclair & Ludlow, 1986) used as water stress indicator. The use of this generic concept allows to take into account both soil and crops characteristics for the transpiration regulation process. The soil is sampled into layers and columns with roots representation based on the effective rooting depth concept (Lacape et al., 1998). Moreover, the model is based on three main hypotheses: i) homogeneity along the row and symmetry on either side of tree (useful to extrapolate results to field scale); ii) the four water fluxes which impact the water balance are soil evaporation, plants transpiration, surface runoff and deep percolation, implying that others water fluxes are neglected; iii) the evaporation and transpiration fluxes are directly driven by the intercepted radiation and by the soil water deficit experienced by plants, each plant of the association having its specific FTSW. Making explicit these assumptions allows to define a theoretical validity domain and ensure model transparency but evaluation on contrasted situations will lead us to define more precisely the model limits.
The use of integrative concepts allows to simulate both monospecific and bispecific systems for a wide range of crops in a Mediterranean climate. Consequently, it is theoretically possible to simulate with the model the water stress dynamics of contrasted agrosystems ranging from annual crop to intercropped orchards (Fig 1.). Two main concepts are at the core of the BIS_Wat parameterization: the Radiation Interception Efficiency (RIE) and the Total Transpirable Soil Water (TTSW) for each of the associated species. These model inputs have to be set or not, depending on which kind of scenario is modelled (Table 1.).

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

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

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: nicolas.bertrand@supagro.fr</footnote>
<figure><img src="image_43.png" /><figcaption>**Fig. 1.** Example of 4 contrasted scenarios where A is the dominant crop or structure and B is the dominated crop for the radiation competition: (a) intercropped vineyard (b) durum wheat (c) irrigated peach orchards and (d) food crops under photovoltaic panels. For each example, the 2D modeling pattern is highlighted.</figcaption></figure>

<table><caption>**Table 1.** Illustration of BIS_Wat parametrization for 4 contrasted scenarios
(0: model input not required; 1: model input required)</caption><thead><tr><th></th><th>Intercropped vineyard<br>(a)</th><th>Durum wheat<br>(b)</th><th>Irrigated peach orchards<br>(c)</th><th>Food crops under<br>photovoltaic panels<br>(d)</th></tr></thead><tbody><tr><td>TTSW (A)</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>TTSW (B)</td><td>1</td><td>1</td><td>0</td><td>1</td></tr><tr><td>RIE (A)</td><td>1</td><td>0</td><td>1</td><td>1</td></tr><tr><td>RIE (B)</td><td>1</td><td>1</td><td>0</td><td>1</td></tr></tbody></table>

## 4 Conclusion and future work

We have presented several modeling principles to build specific crop model and a new model of bispecific cropping systems named BIS-Wat, putting at the core of the model development the model inputs and their coherence with crop processes. A possible model use would to assess the level of water stress of each of the associated crop compared to a water stress target as defined for example in vineyards by (Pellegrino et al., 2006). We have already verified that the model can be parametrized on various systems thanks to the choice of two central integrative inputs and made an evaluation on vineyards based on soil water content measurements. We are currently working on error level assessment on the four different systems described in Figure 1 (e.g. wheat, intercropped vineyard, salad and peach orchards) using quantitative metrics adapted to the diagnosis of water stress.

*Acknowledgements.* The authors want to thank the business chair AGROSYS which provide a part of the required funds for this study and also all scientists who help us by providing their data to enable the BIS_Wat model evaluation.

## References

* Celette, F., Ripoche, A. & Gary, C. (2010). WaLIS—A simple model to simulate water partitioning in a crop association: The example of an intercropped vineyard. Agricultural Water Management, 97(11), 1749–59

* Lacape, M. J., Wery, J. & Annerose D. J. M. (1998). Relationships between plant and soil water status in five field-grown cotton (Gossypium hirsutum L.) cultivars, Field Crops Research 57: 29-43.

* Pellegrino, A., Gozé, E., Lebon, E. & Wery, J. (2006). A model-based diagnosis tool to evaluate the water stress experienced by grapevine in field sites, Europ. J. Agronomy 25: 49–59.

* Roux S., Brun F. & Wallach D. (2013). Combining input uncertainty and residual error in crop model predictions: A case study on vineyards. Europ. J. Agronomy 52: 191–197.

* Sinclair, T. R., Ludlow M. M. (1986). Influence of Soil Water Supply on the Plant Water Balance of Four Tropical Grain Legumes. Australian Journal of Plant Physiology 13, 329–341.
# How could yield gap assessment contribute to cropping system design?

Nicolas Guilpart<sup>±1</sup>, Patricio Grassini<sup>1</sup>, Victor O. Sadras<sup>2</sup> & Kenneth G. Cassman<sup>1</sup>

## 1 Assessing yield gaps at the cropping system level

Yield gap analyses have been used to estimate differences between yields achieved by farmers (Ya) and potential (Yp) or water-limited (Yw) potential yields. Yield gap estimates provide crucial information to evaluate scenarios of global food security given limited land suitable for crop production, the world population soon to exceed 9 billion, and the need to decrease the environmental footprint of agriculture (Van Ittersum et al., 2013). A central issue is how to reduce the yield gap while maximizing efficiency in use of limited resources (e.g. water, nitrogen, phosphorus)(Van Noordwijk & Brussaard, 2014). Yield gap analysis assumes that Yp is achieved with optimum or recommended sowing dates, sowing density and cultivar, which determines growing period to maturity (Van Ittersum et al 2013). The focus of this approach is thus on single crops for a given cropping system. Cropping systems are defined by the temporal and spatial organization of crops. Cropping intensity can be below 1 (e.g. 0.5 in crop-fallow systems), 1 (i.e. a single crop per year) or greater than 1 in multiple cropping systems involving two or more crops per year that can be grown sequentially, or with some degree of simultaneity; the yield of multiple cropping systems thus needs to account for the time dimension, i.e. kg per ha per year (Evans 1993). Moreover, there is often a trade-off between cropping intensity and the yield of individual crops in the system (Table 1). Therefore, closing the cropping system yield gap may trade-off with closing the yield gaps of individual crops within the cropping system. Hence, developing a robust quantitative framework for evaluating biophysical performance of cropping systems involving new crops and/or new arrangements (in space and time) of crops would inform research on designing new systems (Gaba et al., 2014) and help devising benchmarks to assess yield and efficiency gaps at the cropping system level.

<table><caption>Table 1. Publications documenting trade-offs between cropping intensity and yield of individual crops</caption><thead><tr><th>CROPPING SYSTEM</th><th>CLIMATE</th><th>REFERENCES</th></tr></thead><tbody><tr><td colspan="3"><strong>A – Sequential(crops are not grown simultaneously)</strong></td></tr><tr><td>wheat-maize</td><td>temperate (Argentina, New-Zealand)</td><td>Monzon et al. 2007; Fletcher et al. 2011</td></tr><tr><td>wheat-soybean</td><td>temperate (USA)</td><td>Shapiro et al. 1992</td></tr><tr><td>maize-maize</td><td>semi-arid (Chile)</td><td>Meza et al. 2008</td></tr><tr><td>maize-soybean</td><td>temperate (Argentina)</td><td>Monzon et al. 2014</td></tr><tr><td>rice-rice-rice</td><td>tropical (Asia)</td><td>Evans 1993</td></tr><tr><td colspan="3"><strong>B – Simulatneous (crops are, at least partly, grown simultaneously, e.g. intercropping)</strong></td></tr><tr><td>maize-barley</td><td>semi-arid (China, Gansu)</td><td>Li et al. 2011</td></tr><tr><td>maize-wheat</td><td>semi-arid (China, Gansu)</td><td>Li et al. 2011</td></tr><tr><td>maize-faba bean</td><td>semi-arid (China, Gansu)</td><td>Li et al. 2011</td></tr><tr><td>maize-pigeon pea</td><td>sub-humid (Brazil)</td><td>Balde et al. 2011</td></tr><tr><td>maize-soybean</td><td>temperate (Argentina)</td><td>Monzon et al. 2014</td></tr><tr><td>maize-cassava</td><td>tropical</td><td>Mutsaers et al. 1993</td></tr><tr><td>maize-potato</td><td>tropical</td><td>Wu et al. 2012</td></tr><tr><td>wheat-pea</td><td>temperate (France)</td><td>Bedoussac et al. 2010a,b</td></tr><tr><td>vegetable-vegetable</td><td>-</td><td>Yildirim and Guvenc 2005</td></tr></tbody></table>

## 2 How to focus on cropping system options more likely to be useful?

Despite the existence of cropping system level trade-offs as mentioned above, yield gap analyses have usually been performed on existing cropping systems under the assumption that farmers are good at allocating land, labor, and time (Van Ittersum et al., 2013). But there may be barriers to adoption of crops not currently included in the cropping system due to lack of markets, infrastructure or information which could be overcome if a new crop or cropping system is well adapted to the environment in terms of yield and resource use efficiencies. However, the number of possible alternative crops and cropping systems for a given location is very large such that it is not feasible to evaluate all of them, even through simulation with crop models. Therefore we propose a framework, presented in Figure 1, to narrow the focus on those more likely to be useful. This framework is based on two principles: (i) new crops, if introduced, must have an existing market and be adapted to the target environment; (ii) the option of increased cropping intensity depends on the total growing period as constrained by photoperiod, temperature, light, and water. Enabling tools include reliable crop models to simulate the cropping system of interest (this will require further attention regarding intercropping) and a long-term weather database, or at least 3 years of observed weather data (Van Wart et al., 2015) to quantify climate-

<footnote n="1">¹ Department of Agronomy and Horticulture, University of Nebraska – Lincoln, P.O. Box 830915, Lincoln, NE 68583-0915, USA</footnote>

<footnote n="2">² South Australian Research and Development Institute, Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: nguilpart2@unl.edu</footnote>
related risks. Finally, the performance of the system is evaluated in terms of production and yield variability expressed per unit land and time (Egli 2011), as well as in appropriate metrics allowing comparisons of different crops, e.g. monetary value of the production, energy or protein content (Hochman *et al.*, 2014). At some point an economic assessment will also be required, but having estimates of yield and yield variation for the different cropping system options is essential to such an analysis.

<figure><img src="image_44.png" /><figcaption>Fig. 1. A framework to assess yield gaps at the cropping system level.</figcaption></figure>

## References

Bedoussac, L., & Justes, E. (2010a). The efficiency of a durum wheat-winter pea intercrop to improve yield and wheat grain protein concentration depends on N availability during early growth. *Plant and soil, 330*(1-2), 19-35.

Bedoussac, L., & Justes, E. (2010b). Dynamic analysis of competition and complementarity for light and N use to understand the yield and the protein content of a durum wheat-winter pea intercrop. *Plant and soil, 330*(1-2), 37-54.

Egli, D. B. (2011). Time and the productivity of agronomic crops and cropping systems. *Agronomy Journal, 103*, 743-750. Evans, L.T., 1993. Crop Evolution, Adaptation and Yield. Cambridge University Press, Cambridge.

Fletcher, A.L., Brown, H.E., Johnstone, P.R., de Ruiter, J.M., & Zyskowski, R.F. (2011). Making sense of yield trade-offs in a crop sequence: A New Zealand case study. *Field Crops Research, 124*(2), 149-156.

Gaba S, Lescourret F, Boudsocq S, Enjalbert J, Hinsinger P, Journet E-P, Navas M-L, Wery J, Louarn G, Malézieux E, Pelzer E, Prudent M, Ozier-Lafontaine, H (2014). Multiple cropping systems as drivers for providing multiple ecosystem services: from concepts to design. *Ag.Sus. Dev.*

Hochman, Z., Prestwidge, D., & Carberry, P. S. (2014). Crop sequences in Australia's northern grain zone are less agronomically efficient than implied by the sum of their parts. *Agricultural Systems, 129*, 124-132.

Li, L., Li, S.M., Sun, J.H., Zhou, L.L., Bao, X.G., Zhang, H.-G., & Zhang, F.S. (2007). Diversity enhances agricultural productivity via rhizosphere phosphorus facilitation on phosphorus-deficient soils. *PNAS, 104*(27), 11192-11196.

Li, Q. Z., Sun, J. H., Wei, X. J., Christie, P., Zhang, F. S., & Li, L. (2011). Overyielding and interspecific interactions mediated by nitrogen fertilization in strip intercropping of maize with faba bean, wheat and barley. *Plant and Soil, 339*, 147-161.

Meza, F. J., Silva, D., & Vigil, H. (2008). Climate change impacts on irrigated maize in Mediterranean climates: Evaluation of double cropping as an emerging adaptation alternative. *Agricultural Systems, 98*, 21-30.

Monzon, J.P., Sadras, V.O., Abbate, P.A., & Caviglia, O.P. (2007). Modelling management strategies for wheat-soybean double crops in the south-eastern Pampas. *Field Crops Research, 101*, 44-52.

Monzon, J.P., Mercau, J.L., Andrade, J. F., Caviglia, O.P., Cerrudo, A.G., Cirilo, A.G., Vega, C.R.C., Calviño, P.A. (2014). Maize-soybean intensification alternatives for the Pampas. *Field Crops Research, 162*, 48-59.

Mutsaers, H. J. W., Ezumah, H. C., & Osiru, D. S. O. (1993). Cassava-based intercropping: a review. *Field Crops Research, 34*(3-4), 431-457.
Shapiro, B. I., Brorsen, B. W., & Doster, D. H. (1992). Adoption of double-cropping soybeans and wheat. *Southern J. of Ag. Economics, 24*, 33-40.

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

Van Wart, J., Grassini, P., Yang, H.S., Claessens, L., Jarvis, A., Cassman, K.G. 2015. Creating long-term weather data from the thin air for crop simulation modelling. Agric. For. Meteoro. *In Press*.

Noordwijk, M. Van, & Brussaard, L. (2014). Minimizing the ecological footprint of food: closing yield and efficiency gaps simultaneously? Current Opinion in Environmental Sustainability, 8(August), 62-70.

Wu, K., Fullen, M. a., An, T., Fan, Z., Zhou, F., Xue, G., & Wu, B. (2012). Above- and below-ground interspecific interaction in intercropped maize and potato: A field study using the —target! technique. *Field Crops Research, 139*, 63-70.

Yildirim, E., & Guvenc, I. (2005). Intercropping based on cauliflower: More productive, profitable and highly sustainable. *Eur. J. of Agr.*, 22, 11-18.
# Explaining rice yield gaps at farm level in Central Luzon, Philippines: An application of stochastic frontier analysis and crop modelling

João Vasco Silva ±<fnref n="1" />,<fnref n="2" />, Pytrik Reidsma<fnref n="1" /> Alice Laborte<fnref n="2" /> & Martin K. van Ittersum<fnref n="1" />

## 1 Introduction

Large rice yield gaps have been reported for rice farming systems in Central Luzon, Philippines (Laborte et al., 2012). Understanding the reasons behind rice yield gaps is crucial in identifying strategies to increase production of this staple crop in Central Luzon, to meet the demand of a growing population and to enhance resource use efficiency. The objective of this study is to introduce an innovative framework to decompose the rice yield gap into efficiency, resource and technology yield gaps (Fig. 1) and to explain those yield gaps at farm and farming system level using information related to crop management, farmers’ objectives and production technology employed. The aforementioned gaps can be explained by integrating concepts of production ecology into methods of frontier analysis and applying this to individual farm level data.

## 2 Materials and Methods

Stochastic frontier analysis based on the specification of Battese and Coelli (1995) is used to compute and explain the efficiency yield gap for each farm. Rice yield (kg ha⁻<fnref n="1" />) is used as output variable. Input variables include N, P and K application rates (kg ha⁻<fnref n="1" />), seed rate (kg ha⁻<fnref n="1" />), and herbicide and insectide use (kg a.i. ha⁻<fnref n="1" />). In addition, 'dummy' variables are used to control for variety, season and year random effects. The effect of crop management referring to the timing of application of nutrients and pesticides on the efficiency yield gap is assessed in a 2nd stage multiple regression. The resource yield gap is estimated from a production perspective based on the difference between highest farmers' yields (i.e. mean actual yields above the 95th percentile) and technical efficient yields computed with stochastic frontier analysis. Finally, ORYZA v3 (Bouman et al. 2001) is used to estimate the potential yield in Central Luzon and hence, the technology yield gap, which is defined as the difference between potential yield and highest farmers' yields. We test the framework using the Central Luzon Loop Survey, an unbalanced panel dataset of about 100 farm households collected by the International Rice Research Institute (IRRI) every 4 to 5 years during the period 1966 – 2012. The dataset contains detailed information on rice crop management (input – output coefficients and timing of nutrient and pesticide applications), farm (e.g. land quality and tenure status) and household characteristics (e.g. off-farm activities and income) and it allows the analysis of changes in rice yield gaps over the past half-century.

<figure><img src="image_45.png" /><figcaption>**Fig. 1.** Theoretical framework to explain rice yield gaps in Central Luzon, Philippines. Y<sub>P</sub> is the potential yield as defined by van Ittersum & Rabbinge (1997). Y<sub>HF</sub> refers to the highest farmer's yield in the farm survey, Y<sub>TEX</sub> is the technical efficient yield at a specific input level, and Y<sub>A</sub> is the actual yield observed in a particular farmers' field. Each dot represents an individual field in a well-defined biophysical environment. A single input-output relationship is shown for illustration purposes.</figcaption></figure>

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

<footnote n="2">² Social Sciences Division, International Rice Research Institute, The Philippines</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: joao.silva@wur.nl</footnote>
## 3 Results – Discussion

<figure><img src="image_46.png" /><figcaption>**Fig. 2.** Estimates of a) efficiency, b) resource and c) technology yield gaps for rice farming systems in Central Luzon. Dashed lines in b) refer to linear regressions fitted to the data and each year corresponds to one season (i.e. wet or dry season). See legend of individual figures and text for further explanation of the results.</figcaption></figure>

Rice yield gaps of 5.0 t ha⁻¹ were reported for the irrigated rice farming system of Central Luzon (Laborte *et al*., 2012).

A mean efficiency yield gap of 24.5% was calculated for all the rice fields included in the Central Luzon Loop Survey (Fig. 2a). Although low efficiency yield gaps (< 20%) were estimated for the majority of the fields, large efficiency yield gaps (> 50%) still persist in many of the farmers’ fields analysed. As shown in Fig. 2b, the mean efficiency yield gap (difference $Y_{TEx}$ and $Y_A$) narrowed over the period of the analysis from about 1.5 t ha⁻¹ to 1.0 t ha⁻¹. Efficiency yield gap and the timing of the first and second applications of fertilizers and pesticides (data not shown) have a statistically significant relationship highlighting the contribution of timeliness of crop inputs to prevailing yield gaps. The resource yield gap over time is also shown in Fig. 2b. For most of the years, the pattern observed for $Y_{TEx}$ and $Y_{HF}$ is rather similar which results in a rather constant resource yield gap over time of about 1.0 t ha⁻¹. Such estimate indicates that approx. 1 t ha⁻¹ can be gained in case all farmers would raise their input levels to the one resulting in $Y_{HF}$. Further analysis is required to analyse the opportunities and trade-offs offered by alternative farmers’ objectives such as profit maximisation, risk minimisation or resource use efficiency maximisation.

An estimation of the technology yield gap is presented for the dry season of 2004 in Fig. 2c. Although N application rates in some of the farmers’ fields are high enough (i.e. 150 – 200 kg N ha⁻¹) to realize the $Y_p$ of ca. 9 t ha⁻¹, $Y_{HF}$ remains 3 t ha⁻¹ lower than $Y_p$. Such large differences are consistent over the years and tend to be larger in the dry season than in the wet season (data not shown). In order to fully understand and disentangle this technology yield gap it is important to further decompose it and assess whether such differences are caused by technologies (input combinations such as irrigation, nutrients and pest management) not analysed or not occurring in the farm survey.

## 4 Conclusions

In this analysis, the rice yield gap of 5.0-5.5 t/ha was decomposed into efficiency, resource and technology yield gaps which could be explained based on timing of application of fertilisers and pesticides (ca. 1.0 t ha⁻¹), farmers’ objectives and constraints referring to resource use (ca. 1.0 t ha⁻¹) and current technologies (in the survey) not being able to exploit full climatic potential due to sub-optimal input use (roughly 3 t ha⁻¹).

Further research is required to a) explain the technology yield gap, b) assess the role of off-farm income and marginal returns to labour to prevailing yield gaps and c) quantify possible trade-offs among different farmers’ objectives.

## References

* Battese, G.E. & Coelli, T.J. (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20, 325-332.

* Bouman, B.A.M., Kropf, M.J., Tuong, T.P., Wopereis, M.C.S., ten Berge, H.F.M. & van Laar, H.H. (2001) ORYZA2000: Modelling lowland rice.

* Los Baños (Philippines): International Rice Research Institute, and Wageningen: Wageningen University and Research Centre.

* Laborte, A.G., de Bie, K.C., Smaling, E.M., Moya, P.F., Boling, A.A. & van Ittersum, M.K. (2012) Rice yields and yield gaps in Southeast Asia: Past trends and future outlook. European Journal of Agronomy, 36, 9-20.

* Van Ittersum, M.K. and Rabbinge, R. (1997) Concepts in production ecology for analysis and quantification of agricultural input-output combinations. Field Crops Research, 52, 197-208.
# Modelling the population dynamics of root lesion nematodes

Jeremy Whish<sup>*±1</sup></sub>, Darren Kriticos<sup>2</sup> & John Thompson<sup>3</sup>

## 1 Introduction

The root lesion nematode *Pratylenchus thornei* (Pt) is a major pest of cereal and pulse crops on the heavy clay textured soils of the northern grains region of eastern Australia. *P. thornei* has a broad host range covering many cereals and pulses (Castillo and Vovlas, 2007; Nicol and Rivoal, 2008) highlighting its economic importance as a major pathogen of grain production worldwide. In Australia yield losses in susceptible varieties as a result of *P. thornei* have been estimated at between 44 and 80% (Thompson, 2008; Thompson *et al.*, 2012), resulting in an estimated annual cost to the industry of $38 million (Murray and Brennan, 2009). Genetic control by breeding tolerant and resistant varieties has been considered the best long-term approach for this pathogen (Thompson *et al.*, 1999). Wheat lines with superior tolerance have been developed, which has meant the regional wheat yield potential has continued to be achieved. However, tolerant varieties continue to increase the nematode population, creating high pathogen levels in the soil and creating a serious risk to other host crops that do not have tolerant and resistant lines available.

The grains region of north-eastern Australia is summer rainfall dominated and the majority of crops are rain fed, with winter crops being grown on stored moisture supplied from the heavy clay soils. A population model was designed and linked to the farming systems modelling framework APSIM to better understand the dynamics of *P. thornei* within this highly variable environment and to understand the interactions between the farming

system, the environment and nematodes,(Holzworth *et al*., 2014). This paper will focus on the survival of *P. thornei* between host crops, and the length of time required to reduce the population below the accepted damage threshold of 2 nematodes per cm<fnref n="3" />. Previous studies have shown that the population of nematodes at the time of sowing has a significant impact on yield loss, with susceptible varieties suffering no significant yield loss

when planted into low populations of *P. thornei* at the beginning of the season (Whish *et al*., 2014b). Current rotations in the northern grains region aim to provide a break in the form of a long fallow to a non-host summer crop (sorghum or cotton) followed by a second long fallow back to a susceptible winter crop. However, when conditions are favourable the addition of a second crop to reduce the length of unproductive fallow is often favoured. An understanding of the rate of mortality and population decline of *P. thornei* in the absence of host crops will offer some insight into the trade-off between reducing the time between host crops and the decline in population numbers.

## 2 Materials and Methods

**Observed data:** Two consecutive susceptible wheat crops were grown in succession to increase the nematode population to high levels. The maximum nematode population was achieved just prior to harvest of the second successive wheat crop. The soil was then fallowed to a non-host sorghum crop and fallowed again, totalling 30 months duration. Over this period regular sampling of the nematode population determined the mortality rate of the nematode population. Nematode sampling involved the collection of soil samples and manual counting of nematodes following the methods described in Owen *et al.* (2010).

**Modelling:** A population model of *P. thornei* was constructed in the population-modelling framework DYMEX (Sutherst *et al*., 2000). This model is focused around two lifecycles. The first lifecycle occurs in the roots of the host plant where reproduction occurs. The second lifecycle takes place in the soil where the nematodes survive between host crops. The model was linked to APSIM following the linking method described by Whish (2014a). The coupling point used to join the two models was at the root/soil interface with the nematode population reducing the water and nutrient uptake of the roots. This coupling point relates to the turgor reducers described by Boote (1983)and effectively reduces the supply of water and nutrient to the plant as observed by Whish *et al*. (2014b). The APSIM soil temperature model provided the environmental information to DYMEX. Simulations were initiated within the model with the highest population of nematodes measured in the field. The distribution of nematodes between life stages followed the ratio of 33% Adults, 33% J4 juveniles, 17% J3 juveniles and 17% J2 juveniles. Following the validation of the model a scenario analysis was completed to see how initial population influenced the time required to reduce the population below the damage threshold of 2 nematodes per cm<fnref n="3" /> soil.

## 3 Results - Discussion

The mortality functions within the DYMEX population model simulated the three years of observed data quite well (Fig. 1). The longest decay curve (Fig 1a) took 765 days before the population reduced below the damage threshold. Neither of the other two shorter curves managed to reduce the population below the damage threshold. The two

<footnote n="1">¹ CSIRO Agriculture Flagship, 203 Tor St Toowoomba, QLD 4350, Australia</footnote>

<footnote n="2">² CSIRO Agriculture Flagship, Box 1600, Canberra, ACT 2601, Australia</footnote>

<footnote n="3">³ Centre for Crop Health, Institute for Agriculture and the Environment, University of Southern Queensland, Toowoomba, QLD, Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>‡ Corresponding author:</footnote>
longer cures (Fig 1a and 1b) reproduced the observed data well. However, the final curve (Fig. 1c) did not fit the data as well. This may be improved as more data is added, but it may also be a result of the drought conditions experienced before and after harvest. The observed predicted regression (Fig. 1d) shows the model accounted for 95% of the error with a RMSE of 3.9. The model appeared to slightly over-predict the rate of decline especially when the population was high, but generally improved the prediction at low population numbers.

The scenario analysis (Fig. 2) highlighted the importance of the initial population when reducing nematode populations below the damage threshold. High population of 80 nematodes per $cm^3$ took three years to reduce below the threshold. A moderate initial population of 50 nematodes per $cm^3$ took two and a half years and a population of 20 nematodes per $cm^3$ took 20 months. The long survival mechanisms of this pathogen highlight the importance of knowing the size of the population at the beginning and the end of each season. Once a population increases, non-host, or resistant crops or fallows are required to reduce the population below the damage threshold. Planting susceptible or tolerant crops within this time period will increase populations to higher levels that will take longer to reduce, thereby limiting cropping options, and potentially reducing the profitability of the overall farming system.

<figure><img src="image_47.png" /><figcaption>Fig. 1. The observed (points) and predicted (line) population data declining from maximum population at harvest of the preceding wheat crop and the decline over the break. The longest decay curve (a) included a non-host sorghum crop sown in October 2012. The shorter curve (b) commenced in November 2012 and there was no sorghum crop planted due to drought. The final curve (c) commenced in 2013 and had a sorghum crop planted in October 2014. The dashed horizontal line represents the damage threshold below which minimal yield loss will occur in a susceptible wheat crop. The observed predicted regression (Fig. 1d) shows good correlation between the observed and predicted data, $y = 0.93x + 0.28$, $R^2=0.94$.</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>Fig. 2. The effect of starting population (80 pt/cm$^3$, solid black line; 50 pt/cm$^3$, dashed red line; 20 pt/cm$^3$, dotted green line) on the time taken for the *P. thornei* population to reduce below the economic threshold.</figcaption></figure>

The simulation model described works within a single 30cm soil layer, the maximum soil temperature is calculated at the centre of this layer and does not vary much outside the cardinal survival temperatures for *P. thornei*. However, surface temperatures of these soils will be hostile to nematode development and survival. A new model that works across these soil layers is under construction and will improve the prediction of nematode population dynamics within the soil.

***Acknowledgements.*** This research was undertaken as part of the Northern nematode management project funded by the Grains Research and Development Corporation (GRDC), Australia.

## References

* Boote, K.J., 1983. Coupling Pests to Crop Growth Simulators to Predict Yield Reductions. Phytopathology 73, 1581–1587. doi:10.1094/Phyto-73-1581

* Holzworth, D.P., Huth, N.I., deVoil, P.G., et al. 2014. APSIM – Evolution towards a new generation of agricultural systems simulation.

* Environmental Modelling & Software. doi:10.1016/j.envsoft.2014.07.009

* Murray, G.M., Brennan, J.P., 2009. Estimating disease losses to the Australian wheat industry. Austral. Plant Pathol. 38, 558–570. doi:10.1071/AP09053

* Nicol, J.M., Rivoal, R., 2008. Global Knowledge and its Application for the Integrated Control and Management of Nematodes on Wheat, in: Mukerji, K.G. (Ed.), Integrated Management and Biocontrol of Vegetable and Grain Crops Nematodes. Springer, pp. 251–283.

* Owen, K.J., Clewett, T., Thompson, J.P., 2010. Pre-cropping with canola decreased *Pratylenchus thornei* populations, arbuscular mycorrhizal fungi, and yield of wheat. Crop Pasture Sci. 61, 399–410. doi:10.1071/CP09345

* Sutherst, R.W., Maywald, G.F., Russell, B.L., 2000. Estimating vulnerability under global change: modular modelling of pests, in: Presented at the Agriculture Ecosystems & Environment, pp. 303–319.

* Thompson, J.P., 2008. Resistance to root-lesion nematodes (*Pratylenchus thornei* and *P. neglectus*) in synthetic hexaploid wheats and their durum and *Aegilops tauschii* parents. Aust. J. Agric. Res. 59, 432. doi:10.1071/AR07222

* Thompson, J.P., Brennan, P., Clewett, T., Sheedy, J.G., Seymour, N., 1999. Progress in breeding wheat for tolerance and resistance to root-lesion nematode (*Pratylenchus thornei*). Austral. Plant Pathol. 28, 45–52.

* Thompson, J.P., Zwart, R.S., Butler, D., 2012. Inheritance of resistance to root-lesion nematodes (*Pratylenchus thornei* and *P. neglectus*) in five doubled-haploid populations of wheat. Euphytica 188, 209–219. doi:10.1007/s10681-012-0689-x

* Whish, J.P.M., Herrmann, N.I., White, N.A., Moore, A.D., Kriticos, D.J., 2014a. Integrating pest population models with biophysical crop models to better represent the farming system. Environmental Modelling & Software. doi:10.1016/j.envsoft.2014.10.010

* Whish, J.P.M., Thompson, J.P., Clewett, T., Lawrence, J.L., 2014b. *Pratylenchus thornei* populations reduce water uptake in intolerant wheat cultivars. Field Crops Research 161, 1–10. doi:10.1016/j.fcr.2014.02.002
# Combining yield gap and efficiency analysis to analyze farmers' fields network: a case study on nitrogen in durum wheat in Tunisia

Y. BenZekri ±1,4, K. Barkaoui 2, I. Mekki 3, H. Marrou 1, H. Belhouchette 4 & J. Wery 1

## 1 Introduction

In most Mediterranean regions, durum wheat yield is variable with time and location, depending on several factors, primarily water and nitrogen supply (Boussen et al., 2005). Nitrogen (N) is one of the most important nutrients applied as a fertilizer and responsible to a great extent for the large yields obtained from high input agriculture (Latiri, 1998). However, the increased use of N fertilizer can affect farmers' margins, causes excessive losses to the environment and make agriculture dependent of non-renewable energy. The simultaneous increase in both yield and N-efficiency is at the core of eco-efficient agriculture which targets efficient and sustainable use of resources in agricultural production and land management (Keating et al., 2010). It adds to the concept of ecological intensification (Cassman, 1999), the hypothesis of eco-efficiency frontiers that are determined by the genetic nature or by the agrobiodiversity of the cropping systems. This study aims to analyze the efficiency of nitrogen supply on durum wheat and its drivers in different regions in Tunisia and try to identify eco-efficiency frontiers, associated with constrasting rotation sequences. We addressed the following question: Can higher yields and greater N-efficiency be expected in wheat-based cropping systems by diversifying rotation sequences?

## 2 Materials and Methods

Assessing the prospects for eco-efficiency on durum wheat production in Tunisia requires compiling and analyzing available data on crop performance and resource inputs as they are currently managed by farmers. Farm surveys were conducted on a sample of 576 farmers' fields which are representative of the diversity of farmers' practices in four grain-producing regions: 182 in Jendouba, 62 in Siliana, 198 in Kairouan and 134 in the Lebna catchment of the Cap Bon. Among these fields 310 were irrigated and 266 were rainfed. The resulting database contains information on wheat yields, N inputs, climatic parameters, previous crop in rotation sequences and other information about the crop management system.

The analysis of yield variability and N-efficiency is based on the concept of efficiency frontier (Keating et al., 2010; Carberry et al., 2013). This approach involved analyzing durum wheat responses to N supply over a boundary curve which represents the maximum achievable yield when the input (here N) is the only limiting factor, using the following model (Fermont et al., 2009):

$$Y = Y_{max} / (1 + K * \text{Exp}(-R * X))$$

Where Ymax is the maximum level observed in the target variable (durum yield), X is the independent variable (nitrogen), and K and R, are two constants obtained by minimizing the root mean squared error (RMSE).

In order to sort the points that were used to define the boundary curves, we chose a window of fixed size, on the x-axis, and we calculated the maximum yield for each value of the independent variable (N). Then, we formed pairs of points, within the window, where each value of yield was replaced by maximum yield (X, Ymax). In this way, we created a new data set, to which a global boundary curve was first fitted for all the fields together. Then, distinct curves were fitted and tested according to rotation type. The different crop rotations included: (i) vegetable crops-wheat rotation (CM-Dw), (ii) legumes-wheat rotation (LG-Dw), and (iii) cereal (including wheat)-wheat rotation (CE-Dw).

## 3 Results and discussion

The performance of durum wheat grown in the four sites is benchmarked against a boundary curve that represents achievable yield (boundary points) when only nitrogen is limiting yield (Fig. 1a). The observed achievable yield (Ymax) was 75 q/ha. However, a large variability was observed in yield and yield gap. This variability was, in part, explained by water supply and its distribution. As indicated by the difference in boundary curve between irrigated and rainfed wheat (Fig. 1b). Other biotic and abiotic factors may also partly explain yield gap, such as poor soil quality, or lack of crop protection.

The effect of the preceding crop was a key factor in wheat yield and nitrogen efficiency relationship. Boundary curves of

<footnote n="1">¹ Montpellier SupAgro, UMR System, 2 Place Viala 34060 Montpellier France</footnote>

<footnote n="2">² CIRAD, UMR System, Montpellier France</footnote>

<footnote n="3">³ INRGREF, Laboratoire Génie Rural, 2080 Ariana, Tunisia 4: IAMM, 3191 Route de Mende, 34093 Montpellier, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: yosser.ben-zekri@supagro.fr</footnote>
wheat response to nitrogen supply were found significantly different according to rotation type (Fig. 1b). The highest Ymax (75 q/ha) was found for irrigated wheat following vegetables crops (CM_Dwi). In addition to water supply, the significantly higher performance and N-efficiency of this rotation compared to the other can be explained by fertilization and weeding practices on the vegetables, which altogether resulted in favorable growing conditions for wheat. By contrast, the lowest achievable yield (37q/ha) was found for rainfed wheat in cereal-wheat rotation (CE_Dwr), revealing the poor N-efficiency of rotation sequences based on cereals only. At intermediate positions were the rainfed wheat in rotation with vegetable crops (CM_Dwr, 44q/ha) and legumes (LG_Dwr, 58q/ha), illustrating the range of achievable durum wheat yields depending on the preceding crop. Apparently, legumes had higher fertilizing effects than did vegetable crop, probably due to greater soil N enrichment through legume N-fixing processes than N-credit from vegetables. Furthermore, the large gap between irrigated and rainfed wheat in wheat-vegetable crop rotations suggested important interactions between the effects of water and N supply on durum wheat performances.

<figure><img src="image_49.png" /><figcaption>**Fig. 1.** The relationship between nitrogen supply and durum wheat performances in irrigated (Dwi) and rainfed (Dwr) system compared to a boudary line representing maximum yields or boundary points (Bp)(a) and eco-efficiency frontier with each type of rotation: CM_Dwi: irrigated wheat after crop vegetable, CM_Dwr: rainfed wheat after crop vegetable, CE_Dwr: rainfed wheat after cereals, LG_Dwr: rainfed wheat after legumes (b)</figcaption></figure>

## 4 Conclusions

In this study, grain yield responses of durum wheat to nitrogen supply, in a Mediterranean environment was used as a framework in the diagnosis of yield gap and nitrogen efficiency frontiers. In addition to the water supply and distribution, potential yields (Ymax) and yield gap can be explained by crop rotations. This analysis validate the hypothesis of higher eco-efficiency frontier determined by diversified crop rotations compared to cereal-based rotations and allow to identify strategies to reduce yield and N efficiency gaps.

## References

* Boussen H, Asr N.Z, Ben Mechlia N.2005. "Efficiency d'utilisation de l'eau et de l'azote chez le blé dur: Etude comparative de deux variétés à haut potentiel de production." Food Security Options and Perspective: 1-11.

* Carberry P. S, Liang W, Twomlow S, Holzworth D. P, Dimes J. P, McClelland T, Huth N. I. 2013. "Scope for improved eco-efficiency varies among diverse cropping systems". Proceedings of the National Academy of Sciences of the United States of America, 110(21) 110 (21) (May 21): 8381-8386.

* Cassman K.G. 1999. "Ecological intensification of cereal production systems: yield potential, soil quality and precision agriculture". Proceedings of the National Academy of Sciences (PNAS) 96: 5952-5959.

* Fermont A.M, Van Asten P.J.A, Tittonell P, Van Wijk M.T, Giller K.E. 2009. "Closing the Cassava Yield Gap: An Analysis from Smallholder Farms in East Africa." Field Crops Research 112 (1): 24-36.

* Keating, B. a., P. S. Carberry, P. S. Bindraban, S. Asseng, H. Meinke, and J. Dixon. 2010. "Eco-efficient Agriculture: Concepts, Challenges, and Opportunities." Crop Science 50 (Supplement 1) (March 8): 109-119. doi:10.2135/cropsci2009.10.0594.

* Latiri-Souki K, Nortcliff S, Lawlor D.W. 1998. "Nitrogen fertilizer can increase dry matter, grain production and radiation and water use efficiencies for Durum wheat under semi-arid conditions." European Journal of Agronomy 9 (1): 21-34.
# EXPLAINING YIELD VARIABILITY BETWEEN FARMERS AS A FIRST STEP TO REDUCE YIELD GAPS

Mariana Scarlato<sup>1</sup>, Santiago Dogliotti<sup>*±1</sup></sup>, Cecilia Berrueta<sup>3</sup>, Carlos Barros<sup>1</sup>, Facundo Reherman<sup>1</sup>,

Alejandra Borges<sup>2</sup>, Margarita García<sup>1</sup> & Gustavo Giménez<sup>3</sup>

## 1 Introduction

Reducing yield gaps is one of the major pathways identified to meet the future food demand (Keating *et al.*, 2014). A prerequisite to design strategies to reduce the yield gap of crops is to understand its causes. The sustainability of most vegetable farms in south Uruguay is threatened by low family income and deteriorating soil quality. The main cause of low income is that most farms obtain 50% or less of the attainable crop yields in the region, with similar production resources and proper management (Dogliotti *et al.*, 2014). Low yields are the main cause of low labour productivity and resource use efficiency. There is a huge variability between farmers in crop yields, product quality and economic results. To explain the main causes of this variability and to identify strategies to reduce the distance between the average yield and the top yielding fields and farms, we started a project to study important vegetable crops in south Uruguay (onion, tomato, sweet potato and strawberry). In this paper we present the method developed to explain variability in physical and economic results, the main causes identified in the seasons studied on strawberry and onion, and discuss strategies to reduce the observed gaps.

## 2 Materials and Methods

We adapted and combined the methods of the Regional Agronomic Diagnosis (Dore *et al.*, 2008) and Yield Gap Analysis (Lobell *et al.*, 2009; Van Ittersum *et al.*, 2013). The region under study is 60 km around Montevideo, radiation and temperature was considered homogenous within this radius, rainfall was measured at each farm. Based on Census data, we built a typology of vegetable farms growing the selected crops using cluster analysis. Combining farm types and location, we selected a representative sample of 10% of the farms in strawberry (13) and 5% in onion (30). From each farm we selected one to three fields to be monitored and evaluated throughout the growing season. Bio-physical and economic data was gathered at the farm system and at the crop/field level. Growth and yield of the crop was evaluated in 4-6 plots per field. Variables were classified as growth-defining, growth-limiting, and growth-reducing, according to Van Ittersum & Rabbinge, (1997). Statistical analysis combined different tools: path analysis, boundary lines, and regression trees. We studied two seasons of strawberry crop (76 fields) and one season of onion crop (69 fields).

## 3 Results - Discussion

The strawberry average commercial yields were 18.6 ± 12.2 and 24.9 ± 8.1 Mg ha⁻<fnref n="1" />, the top 10% average yields were 41.3 and 40.0 Mg ha⁻<fnref n="1" /> and the average yield gap was 55% and 38% in 2012 and 2013, respectively. Strawberries are planted end of summer and early autumn. To reduce costs, farmers use one third of the plants required to complete the target plant density. Planting is completed with the first two daughter plants of each plant.

<figure><img src="image_50.png" /><figcaption>**Fig. 1.** Relationships between strawberry yield and soil cover at early spring (A), and onion yield and leaf area index at bulbing initiation (B). Boundary line models fitted to all observations, A (n=76): $y_i=49,27/(1+20,40 * \exp(-0,17 * x_i))$; B (n=69): $y_i=46,82/(1+5,81 * \exp(-3,5 * x_i))$.</figcaption></figure>

<footnote n="1"><sup>1</sup> Plant Production Systems Department, Faculty of Agronomy, Universidad de la República, Montevideo, Uruguay</footnote>

<footnote n="2"><sup>2</sup> Biostatistics Department, Faculty of Agronomy, Universidad de la República, Montevideo, Uruguay</footnote>

<footnote n="3"><sup>3</sup> Vegetable Production Programme, National Institute for Agriculture Research, INIA, Las Brujas, Uruguay</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: sandog@fagro.edu.uy</footnote>
Strawberry yield was determined by soil cover at early spring (Fig. 1A), which was explained by the initial planting date, the date planting is completed, and the final plant density. We found that to yield over 30 Mg ha⁻¹ strawberries should be planted before April 15th, the plant density should be over 40 thousand plants per ha and this number of plants should be completed before May 31st (Table 1). Using cluster analysis we divided the fields in three groups according to the soil cover at early spring to study the causes of yield gap within each group. We found that relative yield gap ((boundary line yield-observed yield)/boundary line yield) on the medium and high soil cover groups was mainly explained by N and K fertilization management and water balance. Most farmers applied more than enough N and K, but they applied them mostly at pre-planting. Farmers applying more N and K by fertigation since August had the best results. We couldn't identify causes of relative yield gap in the low cover group due to the reduced number of fields in this group.

<table><caption>**Table 1.** Strawberry soil cover, yield, relative yield gap and management variables associated to determinant factors for each soil cover group (Date 1st January = 1).</caption><thead><tr><th>Group</th><th>N</th><th>Soil cover at early spring (%)</th><th>Crop yield (Mg ha<sup>-1</sup>)</th><th>Relative yield gap</th><th>Planting date</th><th>Complete crop density date</th><th>Plant density (10<sup>3</sup> ha<sup>-1</sup>)</th></tr></thead><tbody><tr><td>1 (low)</td><td>14</td><td>11 ± 3</td><td>8,7 ± 6,1</td><td>0,30 ± 0,30</td><td>117 ± 47</td><td>160 ± 34</td><td>32 ± 5</td></tr><tr><td>2 (medium)</td><td>32</td><td>20 ± 3</td><td>20,0 ± 8,1</td><td>0,29 ± 0,23</td><td>80 ± 17</td><td>144 ± 30</td><td>41 ± 7</td></tr><tr><td>3 (high)</td><td>30</td><td>34 ± 5</td><td>28,9 ± 8,5</td><td>0,37 ± 0,18</td><td>85 ± 19</td><td>125 ± 28</td><td>45 ± 6</td></tr></tbody></table>

<table><caption>**Table 2.** Onion Leaf Area Index at bulbing initiation, yield, relative yield gap, length of period from planting to bulbing initiation and plant density.</caption><thead><tr><th>Group</th><th>N</th><th>Crop yield (Mg ha<sup>-1</sup>)</th><th>LAI at bulbing initiation</th><th>Relative yield gap</th><th>Leaf area per plant (cm<sup>2</sup>)</th><th>Planting to Bulbing initiation (days)</th><th>Plant density (10<sup>3</sup> plants ha<sup>-1</sup>)</th></tr></thead><tbody><tr><td>1 (low)</td><td>33</td><td>19,2 ± 7,6</td><td>0,78 ± 0,24</td><td>0,41 ± 0,23</td><td>448 ± 190</td><td>87 ± 16</td><td>216 ± 49</td></tr><tr><td>2 (medium)</td><td>23</td><td>26,4 ± 6,8</td><td>1,36 ± 0,18</td><td>0,40 ± 0,15</td><td>671 ± 165</td><td>84 ± 15</td><td>223 ± 40</td></tr><tr><td>3 (high)</td><td>13</td><td>41,4 ± 8,8</td><td>2,30 ± 0,37</td><td>0,15 ± 0,12</td><td>1045 ± 162</td><td>97 ± 8</td><td>237 ± 49</td></tr></tbody></table>

Onion crops in south Uruguay are mostly installed by transplanting in winter. The onion average commercial yield was 25.8 ± 11.1 Mg ha⁻¹, the top 10% average yield was 47.0 Mg ha⁻¹, and the average yield gap was 45% in 2014. Crop yield was determined by Leaf Area Index at bulbing initiation (Fig. 1B), which was explained by the initial plant density, the plant density at harvest and the length of the period from planting to bulbing initiation, which depends on the planting date (Table 2).

## 4 Conclusions

The average commercial yield of strawberry and onion in south Uruguay could be improved by more than 40% by adjusting timing of operations and crop management without significant increase in inputs and production costs. The first step to reduce yield gap would be to increase soil cover at early spring and LAI at bulbing initiation by adjusting planting dates and planting densities, which requires better planning of soil preparation and of plant nurseries. Second step to improve strawberry yield is to reduce relative yield gaps within the high soil cover group by adjusting crop fertilization management and irrigation. In the onion crop the relative yield gap within the group with high LAI at bulbing initiation was only 15%.

*Acknowledgements.* Without the help of all the farmers that gently contributed their time and fields, and the help of the technical advisers and experts to select a representative sample of farms, this research would have not be possible, so our sincere gratitude to all of them. This research was funded by the National Institute of Agricultural Research (INIA), Project FPTA 288.

## References

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

Doré, T., Clermont-Dauphin, C., Crozat, Y., David, C., Jeuffroy, M.H., Loyce, C., Makowski, D., Malézieux, E., Meynard, J.M. & Valentin-Morison, M. (2008). Methodological progress in on-farm regional agronomic diagnosis. A review. *Agronomy for Sustainable Development*. **28**, 151-161.

Keating, B.A., Herrero, M., Carberry, P.S., Gardner, J. & Cole, M.B. (2014). Food wedges: Framing the global food demand and supply challenge towards 2050. *Global Food Security*, **3**, 125-132.

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

Van Ittersum, M.K. & Rabbinge, R. (1997). Concepts in production ecology for analysis and quantification of agricultural input-output combinations. *Field Crops Research*, **52**, 197-208.

Van Ittersum, M.K., Cassman, K.G., Grassini, P., Wolf, J., Tittonell, P. & Hochman, Z. (2013). Yield gap analysis with local to global relevance - A review. *Field Crops Research*, **143**, 4-17.
# On methodology of deciding nitrogen intensity: a case with dynamic typology in the small scale wheat-maize system in the North China Plain

Wei-li Liang *±1*, Gui-yan Wang 1, Fu Chen 2 & Ai-ping Xia 1

## 1 Introduction

In face of great challenges in increasing demand for increasing food grain production and resource-environmental issues, sustainable intensification has become the only way that Chinese agriculture must follow. There is a big yield gap in the wheat-maize cropping system in the North China Plain (Laing *et al.*, 2011) and at meanwhile the system has been criticized for applying too much nitrogen (N) (Chen *et al.*, 2013). This presentation tries to reveal the whole story behind the confusing phenomena and to propose a more relevant approach of deciding N intensity of the system.

## 2 Materials and Methods

Farm survey data that have partly published (Liang *et al.*, 2011; Carberry *et al.*, 2013) are further explored and analyzed. N efficiency frontier of the wheat-maize system built with APSIM was used as a bench mark in classifying N intensity. Analysis and typology is based on individual farm fields.

## 3 Results – Discussion

**How attainable yield was achieved** Yields of 9.1 t/ha for wheat and 10.5 t/ha for maize were obtained by careful land preparation, adequate soil moisture storage before wheat sowing, sowing timely at appropriate seed rate, timely irrigation, balanced fertilization (i.e. rate and ratio of nutrients; 234 kg N/ha on wheat and 236 kg N/ha on maize were applied in this case), effective disease and pest control, and harvest at mature (not too late for wheat and not too early for maize).

**What happened to those fields with unsatisfactory yield** Average farm yield was 6.5 t/ha for wheat and 7.3 t/ha for maize with corresponding N rates of 260 kg/ha on wheat and 112 kg/ha on maize, respectively. Coefficient of Variation (CV) of wheat yield was 12%, of maize yield was 15% while CV for N rate on wheat was 49% and on maize was 125%, indicating a great variation of N rate among fields. Problems limiting the productivity of the system include unsatisfactory land preparation before wheat sowing, small maize population density, untimely sowing (too early of wheat and too late of maize), unbalanced nutrient application, untimely irrigation, untimely and improper pest and diseases control, etc. (Liang *et al.*, 2011). As a result, only 11% of the surveyed fields obtained satisfactory yield (above 80% of the attainable yield) with rational N rate (within 80-120% yield corresponding N rate on the efficiency frontier), 1% of the fields obtained satisfactory yield with surplus N (higher than 120% yield corresponding N rate on the efficiency frontier), 46% of the fields obtained low yield (bellow 80% of the attainable yield) with inadequate N (bellow 80% yield corresponding N rate on the efficiency frontier), and 42% of the fields obtained low yield (bellow 80% of the attainable yield) with surplus N (above 120% yield corresponding N rate on the efficiency frontier).

Then, a subsequent question is: *Would it be possible for the lower yielding fields to achieve attainable yield by eliminating the management problems?* The answer is ‘yes and no’. Most of the mis-implemented management practices could be improved by better extension service, etc, while timing of irrigation could only be better set till irrigation facilities being well developed. Current percentage of fields irrigated at water critical stages of the crops is: 42% of the wheat fields get irrigated at stem-elongation and 36% at heading-anthesis, while 30% of the maize fields irrigated at booting. This means that about two thirds of the fields would not be able to obtain attainable yield even if all the other management practices be perfectly implemented. And these timely and untimely irrigated fields shifts between crop seasons since the sequence of each irrigation is decided by drawing lots in most cases—This makes the situation complex, adding that the field size is very small (0.35 ha for wheat and 0.61 ha for maize in average).

The most popular practice, being considered effective and efficient by most agronomists, is to apply all phosphorus and potassium and around half of the total N on wheat before sowing (basal application) and top-dress another half N at stem elongation incorporated with irrigation; apply all the phosphorus and potassium and 30% N on maize at sowing and top-dress the left 70% N at booting incorporated with irrigation or rainfall. Thus the actual total N rate is decided by the amount of top-dressings which is also the basis for typology of N intensity. Hence all the fields fall into four

<footnote n="1">¹ Faculty of Agronomy, Agricultural University of Hebei, Baoding, Hebei 071001, China</footnote>

<footnote n="2">² School of agronomy and biotechnology, China Agricultural University, Beijing 100094, China</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: lwl@hebau.edu.cn</footnote>
categories (table 1). A great potential of improving N efficiency, either by improving yield or by reducing N rate, exists in the last three categories by replacing the blanket recommendation for high yield with most possible yield targeted dosing.

<table><caption>**Table 1** Typology for N intensity of the wheat-maize system in the Hebei Plain, China</caption><thead><tr><th>Crop status, wheat at stem elongation and maize at booting</th><th>Timeliness of irrigation at critical stage</th><th>Typology</th><th>N intensity recommend</th></tr></thead><tbody><tr><td rowspan="2">I Good</td><td>A timely</td><td>IA</td><td>Full</td></tr><tr><td>B untimely</td><td>IB</td><td>Sub-full</td></tr><tr><td rowspan="2">II Not so good</td><td>A timely</td><td>IIA</td><td>Sub-full</td></tr><tr><td>B untimely</td><td>IIB</td><td>Semi-full</td></tr></tbody></table>

Policy and support systems of the government should realize this fact and effort on improving agricultural infrastructure, especially irrigation facilities and effective-efficient machines to move more farm fields into type IA in the long run and on strategic operation of current facilities in the short term for more efficient use in the meanwhile.

This approach of sustainable intensification is essential for national food security.

Recommending N rate by soil testing is not practical because it is not possible to test soils in each field. Short term field trials are also not suitable to provide fertilization recommendations for their intrinsic feature of inadequate yield and especially of inadequated time period exhausting soil fertility. Well calibrated simulations can serve this purpose with good satisfaction, as proved by Yield Prophet, commercial version of APSIM in Australia. However, before this kind of approach can be used to provide N recommendation, being limited by farm/field size and infrastructure, a role of thumb approach employing useful practical 'modern' technology as simulation may help.

## 4 Conclusions

Not all wheat-maize fields in the Hebei Plain are able to obtain attainable yield, and hence full N intensity, presently by improving management practices owing to inadequate irrigation facilities, thus it is not reasonable to adopt a blanket recommendation of N rate aiming at high yield or at low environmental risk. N dosing should be based on mostly probable yield that is determined by crop population quality at stem elongation of wheat and booting of maize together with timeliness of irrigation at those critical stages. Better decision tools incorporating advanced modern technology and cutting edge scientific breakthroughs need to be developed, to serve bigger farms that are emerging by 'land transfer' policy.

*Acknowledgements.* The study presented in this paper was financially supported by the the national special research fund for public good agricultural program 'Establishment of modern farming systems and development of corresponding techniques' (project ID: 201103001) and CSIRO Agricultural Flagship Collaboration Fund (2010-2011). Sincerely thanks go to Dr Peter Carberry, Dr Li Yanming and many other colleagues in the Toowooba groups of CSIRO, Department of Agriculture Fisheries and Forestry of Quensland, Australia, Agricultural University of Hebei and friends and colleagues in Hebei province for their crucial support in providing scientific knowledge, skill and data.

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