# Modelling Adaptive Decision-Making of Farmer: an Integrated Economic and Management Model, with an Application to Smallholders in India

Marion Robert *±1*, Alban Thomas 2 & Jacques-Eric Bergez 1

## 1 Introduction

Farmers plan their cropping systems and farm operations depending on their expectations on economic, social and environmental contexts, and they make tactical adjustments to face uncertain rainfall, variable market prices, and changing resource availability. We use modeling to represent a farming system and we define its adaptation opportunities to drought events, delay in monsoon onset, limited irrigation water and market prices fluctuation in the Berambadi watershed, Karnataka, India. It is represented as three interacting sub-systems: the agent system, the operating system and the biophysical system (Martin-Clouaire & Rellier 2009; Le Gal et al. 2010). We focus on the agent system representation. Once the objectives and farm representation of the farmer have been identified, we use a double loop process to combine a proactive economic model with a reactive farm management model to simulate the planning and adapting process of the farmer under water constraint.

## 2 Materials and Methods

A case-based survey provided specific information on 27 farmers. Farmers were asked to detail their farm practices and management decisions. Changes and adaptation of their practices when facing downside climatic and price conditions were discussed to bring out adaptation decision-rules.

Individual farm representation were first formalized with a Belief-Desire-Intention architecture (Bratman 1987) and conceptualized with UML object diagrams. Genericity was allowed by introducing expert knowledge. An ontology was built with UML class diagrams.

Decisions on irrigation equipment and infrastructure investments were optimized using a stochastic dynamic programming approach. Our economic model described the strategic decision of the farmer to upgrade his irrigation capital stock (e.g., borewells, pumps) in order to optimize access to irrigation water.

Cropping system decisions and management practices were represented with UML sequence diagrams. Our farm management model used decision-rules to allow tactical adaptations in the crop choice and operational flexibility in the daily crop management tasks.

The agent system and its interactions with the biophysical system of crop growth and ground water level were implemented within the RECORD modeling platform (Bergez *et al.* 2013).

## 3 Results - Discussion

The conceptual approach is presented in Fig. 1. AMBHAS model (http://ambhas.com) provides information on the ground water (GW) volume available to the farmer for irrigation (1). Considering possible investments in irrigation equipment at the beginning of the year (e.g. dig a new borewell, buy a new pump, rebore a borewell), a converter combines the GW with other water resources (tank, canal, river), and irrigation equipment, and predicts the total available water for irrigation on the farm per irrigation investments (2).

Based on rainfall and crop price expectations, farm resources (equipment, labor, manure, and production techniques) (3), water available (2), the *tactical season bele* model provides to the *tactical season farm* model a matrix of crop yields per bele (i.e. per plot) and irrigation investment levels (4). Crops considered include any suitable crops that respect a crop rotation constraint, a preceding effect constraint, and a crop return time constraint. The *tactical season farm* model then selects the optimal cropping system that maximizes farmer's income for each irrigation investment level (5). The *strategical year farm* model selects the irrigation investment at the beginning of the year associated with the highest income. The *economic model* (*tactic season bele* + *tactic season farm* + *strategic year farm*) returns this value to the *farmer model* (6) and updates the available water volume on the farm at the beginning of the *Kharif season* that is sent to the second loop within the management model (7).

In the *tactical* part of the management model, decision rules (8) allow the farmer to adapt his cropping system to actual rainfalls and prices at the beginning of the season (9). Once crop choice is made, the *operational* management model applies production techniques (10) to trigger the daily farm operations. This model strongly interacts with a resource manager module (10') and crop model STICS (Brisson *et al.* 2003) (10') as a loop where rainfall, soil moisture, weed

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

<footnote n="2">² INRA, UMR 1081 LERNA, F-31000 Toulouse, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: marion.robert@toulouse.inra.fr</footnote>
and pest pressures, and crop stage are checked before each operation (10). After each irrigation or rainfall event, STICS sends the water abstracted and drainage to AMBHAS (11).

<figure><img src="image_237.png" /><figcaption>**Fig. 1.** Conceptual representation of the farm system, information flux between sub-systems and double loop in the agent model (loop 1 as continuous dark line in economic model, loop 2 as dotted line in management model).</figcaption></figure>

## 4 Conclusions

The double-loop process used in our approach models the sequential and continuous aspect of the farmer's decision-making process. As time passes and more information become available, the farmer is able to adjust his strategical choices (economic model and optimization) and adapt his farming practices (management model and tactical and operational decision-rules) to his changing environment.

This farm system design helps in modelling farm practices in a context of water scarcity and climate change and their impacts on the ground water level evolution.

*Acknowledgements*. The authors wish to thank all contributors to the AICHA project and RECORD platform. The AICHA project is funded by CEFIPRA and ACCAF INRA program.

## References

Bergez, J., Chabrier, P. & Gary, C., 2013. An open platform to build, evaluate and simulate integrated models of farming and agro-ecosystems. *Environmental Modelling & Software*, 39, pp.39–49.

Bratman, M., 1987. *Intention, Plans, and Practical Reason* Harvard Un., Cambridge.

Brisson, N. et al., 2003. An overview of the crop model STICS. *European Journal of Agronomy*, 18, pp.309–332.

Le Gal, P., Merot, A. & Moulin, C., 2010. A modelling framework to support farmers in designing agricultural production systems. *Environmental Modelling & Software*, 25(2), pp.258–268.

Martin-Clouaire, R. & Rellier, J., 2009. Modelling and simulating work practices in agriculture - Google Search. *International Journal of Metadata, Semantic and Ontologies*, 4(1/2), pp.42–53.
# The viability of small islands agro-systems: the case of the French West Indies

Angeon V. *±1,2*, H. Ozier-Lafontaine <fnref n="3" />, S. Bates <fnref n="4" />,<fnref n="9" />, E. Chia <fnref n="5" />, A. Desilles <fnref n="9" />, J.-L. Diman <fnref n="3" />, P. Andres-Domech ⁶,<fnref n="9" />, M.-H. Durand <fnref n="7" />,<fnref n="9" />, A. Fanchone <fnref n="2" />, A. Larade <fnref n="8" />, G. Loranger-Merciris <fnref n="3" />, B. Merlot <fnref n="3" /> & P. Saint-Pierre <fnref n="9" />

## 1 Introduction

Farm modeling has a long history from mainly microeconomic approaches looking to maximize an agricultural income to bio-economic models that take into account the functioning of various biological processes and to probabilistic models or simulation-based approaches that grasp the market or production uncertainties. Nowadays agriculture is required to provide income and food security, to preserve environmental resources and cultural identities while being able to adapt to climatic change or to abet its mitigation. The aim of this study is to conceive a new way of farm modeling providing the sequential and simultaneous decisions to undertake on different parcels of a farm in order to abide by environmental and economic concerns. This research is applied to the case of the French West Indies, small tropical islands, where a major challenge for the sustainability of agricultural systems is to preserve the quality of soils. Soil functioning and its interactions with the various agricultural practices are highly complex. We resort to GISQ (Global Indicator of Soil Quality, Velasquez et al., 2007) a synthetic and recognized global indicator of soil quality since the evolution of its value depending on crops and agricultural practices can be known for a variety of tropical soils. A farm is composed of several parcels with different GISQ values on which crops or livestock with semi-annual, annual, semi-perennial or perennial cycles and specific production costs can be implemented. A farmer may also decide between two practices: intensive agriculture with high level of inputs (fertilizers, pesticides, enclosed breeding ...) or reasoned agriculture with limited plant treatments. All these farmer's choices made successively at different times on each parcel determine the evolution of the farmer's global income and of the soil quality in each parcel. We used viability theory to obtain the *viable controls* that can be implemented at each date of the end of a production cycle on a given parcel. These *viable controls* (choice of a new production on a given parcel along with a practice and eventually a soil treatment) guarantee the preservation of the soil quality on each parcel and a minimum global income to the farmer. The exercise spans on all a farmer's active life with the objective to reach a given level of GISQ on all the parcels when the farm will be transmitted to the next generation.

## 2 Materials and Methods

Viability theory is a set valued mathematical analysis designed for the control of dynamical systems with constraints and submitted to uncertainties (Aubin *et al.*, 2011). Unlike the usual approaches based on simulations techniques, viability theory relies on an inverse approach allowing to know the set of all the current states variables for which there exist controls such that the evolution of the states variables governed by these controls always comply with a set of constraints. The main drawback is the huge memory needed for the calculus of sets which limits the number of possible state variables, and, for the time being, the absence of a general and friendly viability calculus software. A software, specially designed for the purpose of this study, is presently in development. A mathematical and computational challenge is the handling of different durations for the cycles of various agricultural productions implemented on the different parcels of a farm. Some productions may span over years (e.g. bananas) while other can be produced in one month (e.g. salads). This is solved by introducing a time variable with the smaller unit (the month) and for each parcel an indicator of the remaining time before the end of a cycle in order to have the dates of possible changes of production which fatally increases the number of state variables. Currently, calculus have been done for a farm with only one parcel but the obtained results are more focused on the problem of long term soil management (sequential choice of production in order to achieve a satisfying value of GISQ at the end of the exercise while being profitable) than on a farm management (sequential choice of several productions simultaneously implemented with environmental and economic objectives).

<footnote n="1">¹ Université des Antilles-Ceregmia</footnote>

<footnote n="2">² INRA-UR 143 Unité de Recherche en Zootechnie</footnote>

<footnote n="3">³ INRA-UR 1321 ASTRO Agrosystèmes Tropicaux</footnote>

<footnote n="4">⁴ PSL Research University (Paris-Dauphine)-LEDa</footnote>

<footnote n="5">⁵ INRA-UMR Innovation ⁶AgroParisTech-LEF</footnote>

<footnote n="7">⁷ IRD-UMR GRED</footnote>

<footnote n="8">⁸ AgroParisTech-UMR Métafort</footnote>

<footnote n="9">⁹ LASTRE</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Valerie.Angeon@martinique.univ-ag.fr</footnote>
The data have been obtained from field research carried out for this research and from literature. For each possible production and practice we collected the costs per hectare and per month (various agricultural inputs, workforce, depreciation charges...), the subsidies (possibly attached to the quantity produced, the surface implemented or a onetime incitement for a given production) and the sale prices per ton. For each production, the quantities produced per hectare depend on the practice chosen and on the value of the GISQ at the beginning of the cycle. These parameters are given by the field experts. The evolution of the GISQ value depends on the production and practice that are implemented as well as on the GISQ value at the beginning of the production cycle. We considered that the GISQ decrease at the end of a production cycle is mainly determined by the percentage of exported biomass relatively to the total biomass produced which is characteristic of each combination of crop and practice. At the end of an agricultural season, soil amendment can be introduced to enhance the GISQ value before the beginning of a new campaign. It is possible to consider any number of production, they are only limited by the possibility to document the required parameters.

The control variables are the choices of production, practice and amendment. The system is depicted by three state variables: the GISQ (I), the time (t) and an indicator of economic performance (W). Earnings of each agricultural campaign are the difference between sales and all the expenditures occurred during the cycle. Economic performance is the sum of earnings cumulated all along a given period of time. It is required that at the end of the exploitation, the GISQ be over a target value considered as desirable. In viability theory this translates in the computation of a *capture basin of a target*. This capture basin of a target provides all the triples (I,t,W) of initial values of GISQ, exploitation period and economic performance for which there exists, from all the available range of productions and practices, at least one farm management suitable to restitute a soil with the desired quality. Furthermore, for a given period of exploitation, the software provides the best economic performance that can be obtained with a viable initial value of GISQ along with all the control rules that can be possibly or necessarily applied at each decision time in order to obtain both the desired soil quality and the best economic performance in final time. These optimal decisions may be reduced to a unique sequence of productions and practices imposed to the farmer but not necessarily, several optimal farm managements can lead to the same value of best economic performance and wished soil quality. In this case, it is possible to select a preferred pattern by defining some choice priorities in the range of possible productions and agricultural practices.

**3 Results - Discussion**

Six productions have been documented (plantain banana, tomato, yam, goat breeding, cattle breeding and fallow) for
two types of agricultural practice (intensive and reasoned). The exploitation period spans on a maximum of 40 years.

<figure><img src="image_238.png" /><figcaption>Fig. 1. Examples of farm conducts</figcaption></figure>

Figures on the left and center give examples of farm conducts guaranteeing the desired soil quality and the best economic performance at the end time when starting from either good or bad values of GISQ and with 40 or 20 years for the accomplishment. For the three cases, it is by beginning with a period of fallow that the best economic performance is achieved. The target level of GISQ is reached sooner when the initial GISQ level is higher. When 40 years are at disposal to realize the objective of soil restoration it is then more profitable to undertake an “intensive fallow” during the first years and an “extensive fallow” afterwards. This conduct must not be used if the time at disposal is only of 20 years since a better economic performance will be obtained with a succession of “extensive fallow” only. These trajectories are depicted on the right figure that shows the graph of the best economic performance as a function of time to elapse, spanning from 1 to 40 years, and of values of initial GISQ.

**4 Conclusions**

This one parcel farm model can also offer a new perspective to the “soil value evaluation” problem. As shown on the right figure, restoring the soil quality in only 20 years is possible but with a negative “best economic performance”. In this case, the “best economic performance” indicates the minimal cost that would be necessary to invest from the starting date to be able to restore the soil quality in only 20 years. Computations for a several parcels farm are currently in development.

**References**

Aubin, J.-P., Bayen, A. & Saint-Pierre P. (2011). Viability Theory, new directions, Springer
Velasquez E., Lavelle P. & Andrade M. (2007). GISQ, a multifunctional indicator of soil quality, *Soil Biology & Biochemistry*, 39, 3066–3080
# Agronomic, environmental and social assessment of soil management strategies limiting herbicide application in Mediterranean vineyards, at the catchment scale

Patrick Andrieux<fnref n="1" />, Anne Biarnès *±<fnref n="2" />, Jean-Marc Barbier<fnref n="3" />, Claude Compagnone<fnref n="4" />, Xavier Delpuech<fnref n="5" />, Christian Gary<fnref n="6" />, Aurélie Metay<fnref n="7" /> & Marc Voltz<fnref n="1" />

## 1 Introduction

Maintaining and restoring the ability of agricultural soils to limit water and pesticides fluxes by overland flow is a particularly important issue in the Mediterranean wine growing area where overland flow has been shown to be a major factor of the contamination of water resources by pesticides (Louchart et al., 2003). If the soil infiltration capacity is high, the soil exerts a buffering effect against floods, erosion and potential pesticide transfers. It is therefore important to identify soil management practices and their spatial distributions at the catchment scale that contribute to the preservation or restoration of soil infiltration properties throughout the year. Identifying these practices and their distribution implies to be able to assess their benefits and costs, ie: (1) the effects of those practices and of their spatial distribution on the soil's function to be preserved and (2) the constraints of implementation of these practices by the vine growers. The objective of the SP3A project was to identify and evaluate in Mediterranean viticulture, the soil management practices and their spatial distributions that limit the contamination of runoff waters by herbicides at the catchment scale while remaining acceptable by vine growers (Andrieux et al., 2014).

## 2 Materials and Methods

The study was carried out in the Rieutort catchment. This catchment covers 45 km<fnref n="2" />, one third of which is covered by vineyards that are managed by about 150 full or part time vine growers. It is located north of Beziers, 90 km west of Montpellier, on the edge of the foothills of the Massif Central in France. The climate is Mediterranean, characterized by rainy autumns and springs and hot dry summers with heavy rainfall and strong inter-annual variability of rainfall (the average annual rainfall over 20 years is 690mm with a maximum of 1585mm and a minimum of 311mm). Five major classes of soil can be distinguished including: (1) stony superficial soils, (2) clay soils of the transition zone between the northern slopes of shale and sandstone farther south; (3) sandstone soil; (4) fersiallitic soils; (5) sandy alluvial soils. Analysis of the quality of the water resources in this catchment used for the production of drinking water showed an almost permanent contamination by herbicides and placed the catchment in the list of the 500 most threatened water drinking resources in France according to the French Ministry of Ecology, Sustainable Development and Energie.

The scientific approach was in three steps. First, a group of scientists and local agricultural experts in viticulture identified adequate soil management strategies to reduce the use of herbicides across the catchment. These strategies define (i) targets in terms of herbicide use intensity at the plot and catchment scales, (ii) soil management practices to achieve these targets, while reducing the risk of runoff and (iii) spatial distribution rules of those practices according to the characteristics of the different plots of vines in the catchment. Second, an assessment of the environmental and production performances after implementation of these strategies was then simulated at the catchment and/or plot scales taking into account soil types and local climate. These evaluations were carried out for nine representative seasonal climate scenarios using an original chain of models. The chain of models was based on the use of (i) the DHIVINE decision model (Martin-Clouaire et al., submitted) to simulate timing of soil management operations at the plot resolution over the whole vine area, (ii) an extended version of the MHYDAS eco-hydrological model (Moussa et al., 2002) to simulate the evolution of soil surface features at the plot scale, and runoff and herbicide concentrations at the catchment outlet, (iii) the WaLIS water balance model (Celette et al., 2010) and a N balance model to simulate consequences of grass cover practices on yield reduction at the plot scale (Guilpart et al., 2014). Third, a survey of 31 full time vine growers in the Rieutort catchment was conducted in order to determine the current and possible future soil management practices and the dialogue network that structures the exchange of technical information among vine growers (Compagnone, 2014).

## 3 Results – Discussion

The environmentally friendly soil management strategies that were identified are given in Table 1. They consist in four main types defined according to a target in terms of a treatment frequency index (TFI). Two strategies aimed at no or

<footnote n="1">1 INRA, UMR Lisah, bât. 24, 2 place Viala, 34090 Montpellier, France</footnote>

<footnote n="2">2 IRD, UMR Lisah, bât. 24, 2 place Viala, 34090 Montpellier, France</footnote>

<footnote n="3">3 INRA, UMR Innovation, bât. 27, 2 place Viala, 34090 Montpellier, France</footnote>

<footnote n="4">4 Agrosup Dijon, UMR Listo, 26 bd Dr Petitjean, 21079 Dijon, France</footnote>

<footnote n="5">5 IFV, Domaine de Donadille, 30230 Rodilhan, France</footnote>

<footnote n="6">6 INRA, UMR System, bât. 27, 2 place Viala, 34090 Montpellier, France</footnote>

<footnote n="7">7 Montpellier Supagro, UMR System, bât. 27, 2 place Viala, 34090 Montpellier, France.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: anne.biarnes@ird.fr</footnote>
rare herbicide applications whereas two others aimed at a medium rate of herbicide applications. All favoured, when possible, grass cover of the vineyard inter-rows.

The results of the environmental assessment of these strategies at the catchment scale (Table 2) showed the occurrence of herbicide concentration peaks at the catchment outlet exceeding the limit allowed for drinking water in the EU (0,1 µg/l) even for the strategy with a very small use of herbicides (Strategy 1b). Strategy 1a (no herbicide use) evidently respected the water quality requirements for drinking water, while the simulated herbicide concentrations for Strategy 2a and 2b were well above.

The agronomic assessment of water and nitrogen vine stress and consequences on grapevine yield at the plot scale (Table 3) highlighted the risks of decrease in grape production according to the spatial extent and duration of grass cover in the interrows. Permanent grass cover was shown to be possible only on deep alluvial soils whereas grass cover, even if limited to the winter period, was never possible on shallow stony soils due to the risks of water and nitrogen stresses. For the other soils types, grass cover is possible but there is a need to adapt its spatial and temporal extent to the annual climatic conditions.

Eventually, the analysis of the socio-technical networks in the Rieutort catchment revealed a collective ability of the wine grower community for technical change. Although the actual soil management techniques preferentially use herbicides for controlling weed, the community admit growers who apply alternatives practices.

<table><caption>**Table 1.** Description of the strategies selected to reduce the use of herbicides across the catchment. TFI: Treatment frequency index (number of full-dose treatments per hectare)</caption><thead><tr><th>Strategy</th><th>Target of herbicide use intensity at catchment scale</th><th>Associated soil management practices</th></tr></thead><tbody><tr><td>1a</td><td>TFI = 0</td><td>0 herbicide on the row (R) and inter-row (IR) – The soil is maintained by permanent grass cover (PGC) or winter grass cover (WGC) combined with tillage</td></tr><tr><td>1b</td><td>TFI ≤ 0,1</td><td>Idem strategy 1a with permission of post-emergence herbicide on the whole surface of very constrained plots (maxi 10% on the vineyards area in the catchment)</td></tr><tr><td>2a</td><td>TFI ≤ 0,3</td><td>The IR are maintained by PGC or WGC combined with tillage. Permission of post-emergence herbicide on 1/3 of the plot area (≈ on the Row)</td></tr><tr><td>2b</td><td>TFI ≤ 0,4</td><td>Idem strategy 2a with permission of post-emergence herbicide on the whole surface of very constrained plots (maxi 10% on the vineyards area in the catchment)</td></tr></tbody></table>

<table><caption>**Table 3.** Environmental performances of the strategies considering herbicide leaching at the outlet of the catchment.</caption><thead><tr><th rowspan="2">Simulated spatial distribution of practices and contamination by herbicide</th><th colspan="4">Strategy</th></tr><tr><th>1a</th><th>1b</th><th>2a</th><th>2b</th></tr></thead><tbody><tr><td>% of vine area maintained by PGC</td><td>28</td><td>28</td><td>27</td><td>27</td></tr><tr><td>% of vine area maintained by WGC and tillage</td><td>72</td><td>38</td><td>70</td><td>35</td></tr><tr><td>% of vine area maintained by WGC and herbicide</td><td>0</td><td>4</td><td>34</td><td>38</td></tr><tr><td>Average annual maximum peak of contamination (µg/l)</td><td>0</td><td>0.17</td><td>2.0</td><td>2.1</td></tr><tr><td>Maximum peak of contamination (µg/l)</td><td>0</td><td>0.33</td><td>3.4</td><td>3.5</td></tr></tbody></table>

<table><caption>**Table 2.** Acceptability of grass cover practices according to type of soil, at the plot scale.+: acceptable (the simulated yield reduction was under 15%); -: unacceptable (the simulated yield reduction was above 15%); ½ IR: 1 inter-row out of 2, ½ year: 1 year out of 2, 2½ years: 2 years out of 3</caption><thead><tr><th rowspan="2">Grass cover practice</th><th colspan="5">Type of soil</th></tr><tr><th>Alluvial</th><th>Fersial-litic</th><th>Sandstone</th><th>Clay</th><th>Stony</th></tr></thead><tbody><tr><td>PGC all IR</td><td>+</td><td>+</td><td>+ ½ year</td><td>-</td><td>-</td></tr><tr><td>PGC ½ IR<br>WGC ½ IR</td><td>+</td><td>+</td><td>+</td><td>+ ½ year</td><td>-</td></tr><tr><td>WGC all IR</td><td>+</td><td>+</td><td>+</td><td>+</td><td>-</td></tr></tbody></table>

## 4 Conclusions

Reducing herbicides while maintaining grapevine yield is possible in the Rieutort catchment by favouring tillage and grass cover on the inter-rows, and possibly on the rows, of vine plots. But agronomic and environmental assessments highlighted a strong inter-annual variability of performances (yields and contaminations) due to the strong variability of soil and climate conditions in the catchment. Consequently, both assessments converge on the issue of introducing more temporal flexibility in the definition of strategies to reduce herbicide use as well as on the associated modalities of soil management practices associated. The results of each of these assessments should help vine growers to choose new strategies best suited to both the environmental constraints and the agronomical and economical constraints they face.

*Acknowledgements.* The SP3A project was supported by the Gessol program of the French Ministry of Ecology.

## References

Andrieux, P., Biarnès, A., Barbier, J.M., Bonnefoy, A., Compagnone, C., Delpuech, X., Gary, C., Metay, A., Rellier, J.P. et Voltz, M., 2014. SP3A, Spatialisation de pratiques agricoles adaptées et acceptables. Préservation des sols viticoles méditerranéens et amélioration de leurs fonctions de régulation des flux d'eau et de matières associées. Andrieux, P. et Biarnès, A. (coord.). Rapport final GESSOL (MEDDE – ADEME). 166 p.

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), 1749-1759.

Compagnone C., 2014. « Les viticulteurs bourguignons et le respect de l'environnement. Réseaux de dialogues professionnels et dynamique de changements », Revue française de sociologie, 55, 2, p. 319-358.

Guilpart N., Metay A., Gary C., 2014. Grapevine bud fertility and number of berries per bunch are determined by water and nitrogen stress around lowering in the previous year. European Journal of Agronomy, 54 (Mar.), 9-20.

Louchart, X., Voltz, M., Andrieux, P. & Moussa, R. 2001. Herbicide transport to surface waters at field and watershed scales in a Mediterranean vineyard area. Journal of Environmental Quality, 30, 982-991.

Martin-Clouaire R., Rellier J.-P., Paré N., Voltz M., Biarnès A. (Soumis). Modelling management practices in viticulture taking resource limitations into account, Environmental Modelling & Software.

Moussa, R., Voltz, M., Andrieux, P., 2002. Effects of the spatial organization of agricultural management on the hydrological behavior of a farmed catchment during flood events. Hydrol. Process. 16, 393-412.
# Pesticides pressure assessment using TFI (treatment frequency index) at the field, farm and watershed scale

Magalie Lesueur Jannoyer *±1*, PhilippeCattan 2, Marie Raimbault 1, Céline Gentil 2, Vincent Bonnal 3 & Marianne Le Bail 4

## 1 Introduction

In the French West Indies, pesticides highly impacts water bodies' quality (ODE Martinique, 2014). This is mainly due to farm practices associated with the high pest and disease pressure on crops, and also to the accelerated geochemical cycles increasing pesticide transfer (Mottes, 2013).

'Ecophyto 2018' Plan clearly targeted a reduction in pesticide use in all agricultural systems as a key option to reduce the contamination of the environment. The pesticide use reduction is monitored with a set of indicators, among them the treatment frequency index (TFI).

Little information is available on the phyto-sanitary practices according to cropping and farming systems in tropical areas. Our aim was to identify and assess the pressures sources at different space and time scales.

## 2 Materials and Methods

According to a farm typology, we surveyed 25 farms in Martinique and 23 farms in Guadeloupe, at two watersheds scale, accounting for contrasted agrosystems in terms of global farm strategy, of crop rotations and crop durations, of targeted markets (vegetables for farm consumption, sugarcane for transformation, export banana plantation...). To account for pesticide pressure for these agrosystems, we adapted the Treatment Frequency Index (TFI) (Brunet et al., 2008) initially build for annual crops to infra or supra annual crops. We defined cropping units with homogeneous crop production management (vegetable cropping systems; planting year, medium production year, fallow year in banana production...) to assess different level of TFI (equation 1).

$$TFI_{field, \text{cropj}} = \sum_{t=1}^{t=T} \frac{Dt}{DAt} \times \frac{St}{Sfield_i} \qquad (\text{equation 1})$$

with T: total number of pesticide treatments (in one or more category of pesticides); Dt: applied rate in active substances; DAt: approved rate for the active substances; St/Sfield_i part of the field_i with the treatment t; normalized on an annual basis.

For a multi-year crop with different type of management as the banana cropping system, we summed up the TFI for each homogeneous crop units (plantation, production and fallow periods) proportionally to their duration in the cropping system.

For a given farm, we summed up the TFI proportionally to the weight of each cropping system in the farm.

Spatializing these indices led to identify the contributive areas or cropping systems to pesticide pressure in both watersheds.

## 3 Results – Discussion

TFI values varied from 0 to 27 for one year between fields knowing that almost all practices complied with the regulation. Banana cropping systems had the highest TFI while sugarcane cropping systems had the lowest, because only herbicides are used. Horticultural and diversified cropping systems had intermediate TFI values. However, if farming systems including a high proportion of banana had the highest TFI they still showed a large variability of this index (Fig.1b). For part of them, animal or mechanical weeding was used instead of chemical mowing and the use field sanitation practices (use of in vitro plantlets after a fallow period) could explain the decrease of nematicides.

The TFI variability was also high considering the different phases of a crop cycle (Fig. 1a). For banana cropping systems, fungicides accounted for more than 80% of the TFI during the productive period, while herbicides accounted for the major part of TFI during fallow period. The plantation period differed with the productive period mainly because of a lowest TFI in insecticide. The variability depended on the duration of the productive period and fallow (Fig. 1b)

<footnote n="1">1 CIRAD, Research Unit HortSys, CAEC, Petit Morne, BP 214, 97285 Le Lamentin, Martinique</footnote>

<footnote n="2">2 CIRAD, Research Unit GECO (Ecological functioning and sustainable management of Banana and Pineapple systems), Station de Neufchâteau, Sainte Marie, 97130 Capesterre Belle Eau, Guadeloupe</footnote>

<footnote n="3">3 CIRAD, UMR Tétis, Station de Neufchâteau, Sainte Marie, 97130 Capesterre Belle Eau, Guadeloupe</footnote>

<footnote n="4">4 AgroParisTech, SIAFEE Departement (Sciences et Ingénierie Agronomiques, Forestières, de l'Eau et de l'Environnement), UMR SADAPT, 16 rue Claude Bernard, 75231 Paris Cedex 05, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: jannoyer@cirad.fr</footnote>
<figure><img src="image_239.png" /><figcaption>**Fig. 1.** TFI according to a. the cycle period for banana crop; b. the farm system; Perou river Guadeloupe</figcaption></figure>

At the watershed scale, the TFI spatialization according to pesticide use accounts for a diffuse high pressure of all systems for herbicides (Fig. 2a), compared to the local pressure of insecticides (Fig. 2b) and for a focused pressure of agro industrial systems for fungicides (Fig.2c). The contribution to the pesticides pressure of each system varied also according to the applied molecule: glyphosate pressure is the highest.

<figure><img src="image_240.png" /><figcaption>**Fig. 2.** TFIs at the Galion river (Martinique) watershed scale according to the pesticide target a. herbicides (green) b. insecticides (purple) c. fungicides (red)</figcaption></figure>

However, TFI is not enough to account for pesticide pressure and risk of river contamination (Bockstaller *et al*., 2008). First, when the applied quantities (kg of active matter) are considered at the watershed scale, herbicides are the major contributor to the pesticide pressure and are frequently found in surface water. Second when TFIs are related with the pesticides residues measured in the river, post-harvest fungicides are more frequently detected in water while foliar fungicides accounted for the highest TFI. Thus the inflow source is of importance: a concentrated source had a major effect than a diffuse field source. Finally, TFIs do not account for the molecule characteristics, i.e. the hypothetic pesticide transfer route through water bodies, and thus it is not possible to clearly link pressure with impact for water quality.

## 4 Conclusions

The pesticide pressure assessment using TFIs, is part of the agrosystem diagnosis in the order to improve pesticide management at the territory scale. Our results will help us to identify the major efforts of pesticide use reduction to focus on (herbicides and fungicides), the major contributors to the pesticide pressure and the ability of certain farming systems to reduce their pesticides. Those results will open to define a monitoring system for water resources quality, using additional information such as applied pesticides quantities, molecular characteristics and application modes.

*Acknowledgements.* We are very grateful to the UE FEDER funds, the ONEMA funds and the Cirad funds that have supported this project.

## References

Bockstaller C., Guichard L., Makowski D., Aveline A., Girardin P., Plantureux S. (2008) Agri-environmental indicators to assess cropping and farming systems. A review. *AgronSustainDev* **28**:139-149.

Brunet, N., Guichard, L., Omon, B., Pingault, N., Pleyber, E. & Seiler, A. (2008) L'indicateur de fréquence de traitements (IFT): un indicateur pour une utilisation durable des pesticides. *Courrier de l'environnement de l'INRA*, **56**, 131-141.

French Ministry of Agriculture and Fishery (2008) Ecophyto2018 Plan, Paris.

Mottes, C. (2013) Evaluation des effets des systèmes de culture sur l'exposition aux pesticides des eaux à l'exutoire d'un bassin versant. Proposition d'une méthodologie d'analyse appliquée au cas de l'horticulture en Martinique. AgroParisTech, Paris.

ODE, French Water Observatory of Martinique (2013) Valorisation des données de suivi de la qualité chimique de l'eau DCE. Année 2011. Fort de France.
# Food production typology of farms: an assessment of periurban farming systems.

R. Filippini *±1,2*, E. Marraccini <fnref n="3" />, E. Bonari <fnref n="1" /> & S. Lardon <fnref n="2" />,<fnref n="4" />

## 1 Introduction

In literature periurban agriculture has been assessed for its capacity to provide different services, especially social, educational and environmental (Allen, 2003; Zasada, 2011), in order to demonstrate the relevance of preserving agricultural activities in the periurban fringe. The recent debates about the development of Local Food Systems (Kneafsey et al., 2013) and the emergence in several countries of food policies and plans (Sonnino, 2014) have raised questions about the capacity of agriculture to assure food provision to urban consumers. In this context, several authors have considered the food production function of agriculture in periurban areas, also related to the new opportunities emerging with the development of short food supply chains (e.g. Zasada, 2011).

Nevertheless, few studies have been made on the food production of periurban farming systems (e.g. Soulard & Thareau, 2009). Moreover, considering the specific constraints and conflicts that periurban farming system needs to face (Darly and Torre, 2013), a specific attention needs to be paid on what kind of food production is possible around the cities. Literature has at first stressed the environmental constraints, that ask for new patterns of food production's intensity and evaluation of food quality (Cavailhès and WAVresky, 2007; Wortman and Lovell, 2013). Secondly, several studies have began to assess the food production capacity of periurban farming system in order to understand the real contribution that periurban farming system can offer in terms of production's yields (Filippini et al., 2014).

Several studies on farms typologies have been made (e.g. Andersen et al., 2007) but to our knowledge, none of them has focused on the characterization of the periurban farming systems and the practices that farmers put in place to produce food for urban dwellers. The purpose of this contribution is to develop a methodology for the assessment of periurban farming systems' food production through farms' typologies. Food production will be assessed in terms of quantity, quality and intensity of the on-farm food production.

## 2 Materials and Methods

The case study is the periurban farming system of Pisa, a medium-sized city of Tuscany (Italy). It is representative of the main dynamics of periurban agriculture in coastal plains of Mediterranean areas; moreover in the area local institutions have developed a food plan, *Piano del Cibo della Provincia di Pisa*, which one of the purposes was to recognise the contribution of local farming system to the local food demand. The methodology of this analysis is based on semi-structured interviews to 51 farmers selected considering the farm's size, the production and the distance from the main urban centre of Pisa. 50 farm-gate indicators were identified and calculated to estimate the food quality, the food quantity and food production's intensity. Throughout a principal component Analysis (PCA) we has selected 12 indicators able to describe the farm's sample. Thus, a cluster analysis has been performed, in order to analyse the principal characteristics of food producing for each types of farms. During the process two farms were considered outliers.

## 3 Results - Discussion

The PCA analysis' results are summarized in Fig. 1. The total variance explained is higher without quality's indicators (around 63%) than with them (around 50%); for this reason we have chosen not to take quality's indicators related to organic and certified production in the analysis. The lack of significance of quality's indicators may suggest that certified quality is not always related with the farms' performances in terms of intensity. In other words the presence of any kind of certifications doesn't seem to differentiate the sample's farms. Moreover, horticultural and olive farms have a percentage of primary products devolved to the human consumption (PFood) higher than the farms with forage and industrial crops. This result is explained by the fact in livestock production most of the primary produces are for animals. The percentages of olive production and of cereals have a negative correlation. PLocal seems to be more correlated to intensity's indicators as the percentage of fodder (percfod), or manure (Manure), than to the percentage of food production (PFood). This result seems to suggest that in this area, when the percentage of food production is higher in farms (especially horticultural, olive and cereal farms), farms allocate less production to the local market.

<footnote n="1">¹ Institute for Life Sciences, Scuola Superiore Sant.Anna, Pisa (Italy)</footnote>

<footnote n="2">² AgroParisTech, UMR 1273 Métafort, Aubière (France)</footnote>

<footnote n="3">³ UP 2012-10-103 PICART, Institut Polytechnique LaSalle Beauvais, (France)</footnote>

<footnote n="4">⁴ INRA, UMR 1273 Métafort, Saint-Genes Champanelle (France)</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: rosalia.filippini@sssup.it</footnote>
We identified from a cluster analysis five farms' types (Table 1). First of all the negative correlation between two indicators of food quantity: the percentage of production allocated locally (PLocal) and the percentage of food production in the farm (PFood) is better specified (see especially groups 1 and 4).

The cluster analysis helps in distinguish better among farms with industrial and fodder crops. Group 2 has the higher percentage of manure and fodder, for this reason it distinguishes itself from groups 3 and 5, because of its less intensive production.

<figure><img src="image_241.png" /><figcaption>**Fig. 1.** PCA analysis. PLocal refers to “percentage of food production devolved to local market”; PLegum, “percentage of leguminous plants”; POliv, “percentage of olive production”; Ha, “Total UAA of the farm”; PCereal, “percentage of cereal crops”; Omedica, “quantity of alfalfa in the rotation”; percfod, “percentage of fodder in the rotation”; Manure, “percentage UAA fertilised with organic manure”.</figcaption></figure>

<table><caption>**Table 1.** Average indicators values for each class. For the indicators' explanation see Fig. 1</caption><thead><tr><th rowspan="2">Group</th><th rowspan="2">Members</th><th colspan="7">Food Quantity</th><th colspan="2">Food production Intensity</th></tr><tr><th>Pfood</th><th>PLegum</th><th>POliv</th><th>PLocal</th><th>Ha</th><th>PCerea</th><th>Qmedica</th><th>percfod</th><th>Manure</th></tr></thead><tbody><tr><td>Small farms</td><td>20</td><td>87.6</td><td>6.4</td><td>37.2</td><td>33.2</td><td>16</td><td>10.1</td><td>3.0</td><td>5.3</td><td>15.7</td></tr><tr><td>Less intensive big farms</td><td>2</td><td>20.0</td><td>38.7</td><td>0.0</td><td>40.0</td><td>183</td><td>34.7</td><td>1500.0</td><td>55.2</td><td>83.7</td></tr><tr><td>More intensive big farms</td><td>3</td><td>25.7</td><td>50.6</td><td>0.0</td><td>30.0</td><td>254</td><td>27.4</td><td>3153.3</td><td>47.8</td><td>3.6</td></tr><tr><td>Cereals farms</td><td>15</td><td>78.5</td><td>9.3</td><td>0.1</td><td>3.9</td><td>252</td><td>40.6</td><td>8.0</td><td>12.8</td><td>3.7</td></tr><tr><td>Local markets farms</td><td>9</td><td>46.8</td><td>33.6</td><td>0.0</td><td>50.6</td><td>102</td><td>27.4</td><td>335.0</td><td>35.6</td><td>36.0</td></tr></tbody></table>

## 4 Conclusions

Considering the negative correlation between two quantity's indicators, the number of primary products and the percentage of production locally allocated, further studies could focus on the possible constraints for local markets, in order to properly assess the contribution of farms for the local food system (Kneafsey et al., 2013). Moreover the fact that not always a less intensive production is associated with a labelled production, and that quality's indicators are expected in almost all farm's groups, may suggest the need to consider the quality of productions by periurban farms beyond their labels. In conclusion our results suggest the need to accurately analyse the periurban farming systems' performance, characterised by a high degree of heterogeneity of food intensity's production and food quantity. This will finally help the development of more efficient food policies.

## References

* Allen, A. (2003). Environmental planning and management of the peri-urban interface: perspectives on an emerging field. *Environment and Urbaisation*, **15**, 135-148.

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

* Cavailhès, J., & Wavresky, P. (2007). Les effets de la proximité de la ville sur les systèmes de production agricoles. *Agreste Cahier*, **2**, 41-47.

* Darly, S., & Torre, A. (2013). Conflicts over farmland uses and the dynamics of “agri-urban” localities in the Greater Paris Region: An empirical analysis based on daily regional press and field interviews. *Land Use Policy*, **33**, 90-99.

* Filippini, R., Marraccini, E., Lardon, S., & Bonari, E. (2014). Assessing food production capacity of farms in periurban areas. *Italian Journal of Agronomy*, **9**, 63-71.

* Di Iacovo, F., Brunori, G., & Innocenti, S. (2013). Le strategie urbane: il piano del cibo. *Agriregionieuropa 32*, 9-16.

* Kneafsey, M., Venn, L., Schmutz, U., Balázs, B., Trenchard, L., Eyden-Wood, T., Bos, E., & Blackett, M. (2013). Short Food Supply Chains and Local Food Systems in the EU: a state of play of their socio-economic characteristics. JRC Scientifics and Policy Reports. Seveille, Spain.

* Sonnino, R. (2014). The new geography of food security: exploring the potential of urban food strategies. *The Geographical Journal*, doi: 10.1111/geoj.12129.

* Soulard, C & Thareau, B (2009). Les exploitations agricoles périurbaines: diversité et logiques de développement *Innovations Agronomiques 5*, 27-40

* Wortman, S.E., & Lovell, S.T. (2013). Environmental Challenges Threatening the Growth of Urban Agriculture in the United States. *Journal of Environmental Quality*, **42**, doi:10.2134/jeq2013.01.0031.

* Zasada, I. (2011). Multifunctional peri-urban agriculture—A review of societal demands and the provision of goods and services by farming. *Land Use Policy*, **28**, 639-648.
# W5. Silvo-pastoral systems

Chair: Bruno Rapidel, CIRAD

Co-chair: Marie Gosme, INRA
# Multi-scale studies of the relationships between cropping structure and pest and disease regulation services.

Cynthia Gidoin *±<fnref n="1" />, Régis Babin <fnref n="2" />,<fnref n="3" />, Leïla Bagny Beilhé <fnref n="2" />,<fnref n="4" />, Corentin Barbu <fnref n="1" />, Marie Gosme <fnref n="5" />, Marie-Hélène Jeuffroy <fnref n="1" />, Marie-Ange Ngo Bieng <fnref n="6" />, Muriel Valentin-Morison <fnref n="1" /> & Gerben Martijn ten Hoopen <fnref n="2" />,<fnref n="4" />

## 1 Introduction

Farmers need to regulate numerous pests on each of their crops, using as little pesticide as possible. Several practices can be used to reach this target, yet most of them focus on a single pest. Some control practices implemented for one pest, however, can have antagonistic effects on the development of another pest. Today, with the will to decrease pesticide use while increasing production, studies must take into account the diversity of pests and must focus on tradeoffs in their regulations.

The concept of agroecology proposes to use natural ecological mechanisms in agroecosystems. Plant diversity impacts pest regulation services through biological mechanisms and physical mechanisms. Biological mechanisms typically depend on the species composition of the communities of crop and associated plant. Physical mechanisms typically depend on the spatial structure of crop and associated plant (Schroth et al., 2000). These mechanisms are both supported by empirical research and epidemiological models, yet their relative importance and independent effects are not well known. In the present study, our aim was to assess the relative importance and the independent effects of plant communities' composition and spatial structure on pest communities in tropical and temperate areas. Here we postulate that i) crop composition, sensitive host tissue amount and crop spatial configuration impact pest presence through resource availability and accessibility, ii) associated plant composition and spatial configuration impact pest presence by providing other resources or shelters and through microclimatic variations. The scale necessary to observe such mechanisms would depend on the scale necessary to observe a given level of diversity. In tropical areas, many species can be found in association within small plots, while in simplified temperate agroecosystem a much larger scale would be needed to observe a similar diversity. We first investigate biological and physical effects of biodiversity observationally on tropical agroecosystems in cameroon and second assess through modelling if such mechanisms could be of importance in temperate agroecosystems at a much larger scale.

## 2 Materials and Methods

In the center region of Cameroon, we did field measures and point pattern analysis in 20 cacao-based agroforest plots (50 x 50 m) to evaluate the impact of plant composition and spatial structure on mirid and black pod regulation, i.e. a pest and a disease of cacao (Gidoin et al., 2014). For the temperate region, numerical simulations with population dynamic models were used to study the potential impact of landscape (5000 x 5000 m) composition and configuration on the pollen beetles and phoma stem canker dynamics, i.e. a pest and a disease of oilseed rape. Two models were used: i) Mosaïc-Pest (Vinatier et al., 2012) to study the spatio-temporal dynamics of Meligethes aeneus, and ii) SIPPOM-WOSR (Lô-Pelzer et al., 2010) to study the spatio-temporal dynamics of stem canker. Finally, we used hierarchical partitioning to quantify the observed or simulated impact of plant structure variables on i) mirid density and black pod prevalence at the plot scale and ii) pollen beetle density and phoma stem canker severity at the landscape scale.

## 3 Results – Discussion

At the plot scale, in cacao-based agroforests, we found mirid density to linearly increase with sensitive host tissue amount. This relationship explained 18.9 % of mirid density variance independently of the other variables (Fig. 1). On the contrary, host (cacao tree) abundance had a negative relationship with black pod prevalence and explained 20.3 % of the variance independently of the other variables (Fig. 1). This was not coherent with the dilution hypothesis (Keesing et al., 2006) as a decrease in host abundance did not correspond to a decrease in disease infection. In addition, the rarely studied horizontal structure of forest trees explained 14.5 % of mirid density variance independently of the other variables. Finally, mirid density was lower in plots with aggregated forest trees than in plots with low forest tree density

<footnote n="1">¹ UMR Agronomie, INRA-AgroParisTech, Thiverval-Grignon, France.</footnote>

<footnote n="2">² UPR Bioagresseurs, CIRAD, Montpellier, France.</footnote>

<footnote n="3">³ Plant Health Division, icipe, Nairobi, Kenya.</footnote>

<footnote n="4">⁴ Regional Center Nkolbisson, IRAD, Yaoundé, Cameroon.</footnote>

<footnote n="5">⁵ UMR System, INRA, Montpellier, France.</footnote>

<footnote n="6">⁶ UMR System, CIRAD, Montpellier, France.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Cynthia.Gidoin@grignon.inra.fr</footnote>
and it was even lower in plots with forest trees distributed randomly. This is coherent with the known aggregation of mirids on cacao trees that are exposed to direct sun light, a situation favored when high forest trees are aggregated (Babin *et al.*, 2010). Interestingly, mirid density and black pod prevalence were impacted by different features of the biodiversity, respectively spatial aggregation of forest trees and host relative abundance. This opens perspectives in reducing mirid density through forest tree spatial structure optimization without increase in black pod prevalence.

<figure><img src="image_242.png" /><figcaption>**Fig. 1.** Results of the hierarchical partitioning analyses: independent contributions of host composition (AbCa: cacao tree abundance, Dca: cacao tree density, Alter: presence/absence of alternative hosts), sensitive host tissues (Prod: amount of pods, Flush: new leaves presence) and shade tree spatial structure variables (Dsha: shade tree density, Inter: proportion of shade trees in the intermediate stratum, HSFO: horizontal structure of forest trees, HSFU: horizontal structure of fruit trees) on A) mirid density and B) black pod prevalence. \*Significant contributions (Z-score value).</figcaption></figure>

At the landscape scale, in temperate areas, the first results showed that crop rotation (2 or 10 years, *i.e.* proportion of oilseed rape was about 50% or 10% in the landscape each year) would have a major effect on pollen beetle explaining 86.6% of pollen beetle density variance independently of the other variables tested, *i.e.* trap crop (present or absent) and the forest proportion (less than 3% or more than 25%, which is a wintering site for pollen beetles). This result emphasizes the importance of hosts composition and is consistent with the dilution hypothesis: an increase in rotation length leads to a decrease in oilseed rape proportion and thus a decrease in resource availability for pollen beetles. As changing the crops composition might comes with a very high cost for the farmer, it is important to assess the potential of spatial structure modifications. Aggregation of colza fields has been shown to decrease stem canker severity (Lô-Pelzer *et al.*, 2010). A broader factorial simulation plan is currently designed to test the impact of structural aggregation jointly on pollen beetle density and phoma stem canker severity (with SIPPOM-WOSR).

## 4 Conclusions

Based on our observations and modelling results, we showed that the dilution of pest resources acts on pest infestation but potentially in opposite directions for black rot on cacao tree in tropical areas and pollen beetles on oil seed rape in temperate areas. Other plant communities' structure variables can affect the pest regulation service and at least in the specific case of black rot and mirid on cacao tree could be used to optimize natural regulations. The impact of plant structures on oil seed rape is still under study but the great diversity of observed relationships between plant diversity and pests natural control suggests that general rules can not be used to guide agronomical practices. In consequence, future research should aim to model the specific characteristics of the main pests of an agrosystem to predict the effect of composition and spatial structures at the relevant scale (Gosme *et al.*, 2013).

## References

* Babin, R., Hoopen, G.M.T., Cilas, C., Enjalric, F., Yede, Gendre, P. & Lumaret, J.P. (2010). Impact of shade on the spatial distribution of *Sahlbergella singularis* in traditional cocoa agroforests. *Agricultural and forest entomology, 12*, 69-79.

* Gidoin, C., Babin, R., Bagny Beilhe, L., Cilas, C., ten Hoopen, G.M. & Ngo Bieng, M.A. (2014) Tree Spatial Structure, Host Composition and Resource Availability Influence Mirid Density or Black Pod Prevalence in Cacao Agroforests in Cameroon. *PLoS ONE* 9(10): e109405.

* Gosme, M., Vinatier, F., Valantin-Morison, M. & Jeuffroy, M.H. (2013). PODYAM: a modeling framework for simulating population dynamics in agricultural mosaics. Presented at the *11th International Epidemiology Workshop*, Beijing, China.

* Keesing, F., Holt, R.D. & Ostfeld, R.S. (2006) Effects of species diversity on disease risk. *Ecology letters, 9*, 485-498.

* Lô-Pelzer, E., Bousset, L., Jeuffroy, M.H., Salam, M.U., Pinochet, X., Boillot, M. & Aubertot, J.N. (2010). SIPPOM-WOSR: A simulator for Integrated Pathogen Population Management of phoma stem canker on Winter OilSeed Rape. I. Description of the model. *Field Crops Research, 118*, 73-81.

* Schroth, G., Krauss, U., Gasparotto, L., Aguilar, J.A.D. & Vohland, K. (2000). Pests and diseases in agroforestry systems of the humid tropics. *Agroforestry Systems, 50*, 199-241.

* Vinatier, F., Gosme, M. & Valantin-Morison, M. (2012). A tool for testing integrated pest management strategies on a tritrophic system involving pollen beetle, its parasitoid and oilseed rape at the landscape scale. *Landscape Ecology, 27*, 1421-1433.
# Ecosystem services provided by coffee agroecosystems across a range of topo-climatic conditions and management strategies

Rolando Cerda *±1,2*, Clémentine Allinne <fnref n="1" />,<fnref n="2" />, Louise Krolczyk <fnref n="3" />,<fnref n="4" />, Charlie Mathiot <fnref n="2" />,<fnref n="3" />, Eugénie Clément <fnref n="4" />,<fnref n="5" />,
Celia A. Harvey <fnref n="6" />, Jean-Noel Aubertot <fnref n="7" />, Philippe Tixier <fnref n="2" />,<fnref n="8" />, Christian Gary <fnref n="9" /> & Jacques Avelino <fnref n="2" />,<fnref n="4" />,<fnref n="10" />

## 1 Introduction

There is an urgent need to ensure that farming systems not only provide high yields, but also the provision of ecosystem services (ES) on which agriculture and farmer households depend. We compared the provision of four ES indifferent types of coffee agroecosystems: i) regulation of pests and diseases (P&D); ii) provision of agroforestry products (coffee, bananas, fruits, timber); iii) maintenance of soil fertility; andiv) carbon sequestration. We provide key insights on how coffee agroecosystems could be most effectively managed to ensure the continued provision of ES.

## 2 Materials and Methods

We established a coffee research network (69 coffee plots) in Turrialba, Costa Rica for two years of field measurements (2014-2015). Coffee agroecosystems were selected according to the combination of three factors: i) Altitude: low (<850m.a.s.l.) and high (>850m.a.s.l.); ii) Shade: full sun coffee, simple shade (dominated by *Erythrinapoeppigiana*) and diversified shade (musaceas, service trees, fruit trees and timber trees); iii) Management: low (few cropping practices and low inputs) and high (many cropping practices and high inputs). We calculated the areas under the disease progress curve (AUDPC) of P&D, registered the severity, and counted the number of dead branches. We also assessed the effectiveness of coffee agroecosystem in regulating P&D by estimating the coffee yield losses (=attainable yields minus actual yields; estimated by modelling). Yields, costs and incomes of agroforestry products were used to calculate economic indicators and to assess their overall contribution to farmer households (Cerda *et al.*, 2014). Soil fertility was determined by laboratory analysis. Above-ground biomass carbon was estimated with the use of allometric equations.

**Statistical analysis:** analyses of variance using general linear mixed models and the test LSD (Fisher) with p < 0.05 to compare the effect of the three factors (altitude, shade and management) and their interactions on the provision of ES.

## 3 Results – Discussion

The interaction of shade and management was the most important for explaining the regulation of P&D. Coffee leaf rust (*Hemileiavastatrix*), the severity of P&D attacks and the number of dead branches were higher in full sun coffee plantations with high management as well as in coffee under diversified shade with low management; indicating that none of those extremes are good for avoiding P&D. Coffee under diversified shade with high management showed fewer P&D impacts, suggesting that complex agroforestry systems can contribute to the regulation of P&D (Fig. 1).

<figure><img src="image_243.png" /><figcaption>Fig. 1.A) Coffee leaf rust, B) Maximum severity, and C) Dead branches, according to shade and management.</figcaption></figure>

Attainable yields and actual yields of coffee were similar among coffee in full sun with high management and coffee agroforestry systems, but coffee under diversified shade with high management tended to have the lowest yield losses

<footnote n="1">¹ CIRAD, UMR System, SupAgro Montpellier bat 27, 2 place Viala, 34060 Montpellier, France</footnote>

<footnote n="2">² CATIE, Program of Sustainable Agriculture and Agroforestry, 7170 Turrialba, Costa Rica</footnote>

<footnote n="3">³ AgroParisTech, F-75231 Paris, France</footnote>

<footnote n="4">⁴ CIRAD, UR Bioagresseurs, TA A-106 - Avenue Agropolis, 34398, Montpellier, France</footnote>

<footnote n="5">⁵ INSA Toulouse 135, Avenue de Rangueil - 31077 Toulouse- France</footnote>

<footnote n="6">⁶ Conservation International, 2011 Crystal Drive Suite 500, Arlington, VA, USA</footnote>

<footnote n="7">⁷ NRA-Centre de Recherches de Toulouse, 31326 Castanet Tolosan Cedex, Toulouse, France</footnote>

<footnote n="8">⁸ CIRAD, UPR GECO, TA B-26 / PS4 - Boulevard de la Lironde - 34398 Montpellier, France</footnote>

<footnote n="9">⁹ INRA, UMR System, SupAgro Montpellier bat 27, 2 place Viala, 34060 Montpellier, France</footnote>

<footnote n="10">¹⁰ IICA-PROMECAFE, AP 55, 2200 Coronado, San José, Costa Rica</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: rcerda@catie.ac.cr</footnote>
(Fig 2). These results reinforce the idea that diversified shade systems can help to regulate P&D (Fig 1). These findings become important knowledge for the development of agroecosystems that are capable of balancing high yields and reduce the impacts of P&D (Avelino *et al.*, 2011).

<figure><img src="image_244.png" /><figcaption>Fig. 2.A) Attainable yields, B) Actual yields, and C) Yield losses of coffee, according to shade and management.</figcaption></figure>

Only high coffee yields would not always be the best for farmer households. Cash costs of coffee in full sun were high and therefore its cash flow tended to be lower than in agroforestry systems. Besides, the agroforestry products of shade canopies increased even more the cash flow, the domestic consumption, and therefore, the family benefit (Fig. 3).

<figure><img src="image_245.png" /><figcaption>Fig. 3.A) Gross income, B) Cash costs, C) Cash Flow, and D) Family Benefit, according to shade and management.</figcaption></figure>

The sole effect of shade was the most important on soil and carbon. Most elements of soil fertility were better in coffee under diversified shade, as in the case of acidity and potassium (Fig. 4), two key indicators of soil quality. Finally, agroforestry systems had at least double the above-ground carbon compare to coffee in full sun (Fig. 5).

<figure><img src="image_246.png" /><figcaption>Fig. 4.A) Acidity, and B) Potassium in soil.</figcaption></figure>

<caption>Fig. 5. Above-ground biomass carbon.</caption>

4 Conclusions

The provision of ES varies across different types of coffee agroecosystems. The best ES are provided by coffee agroforestry systems. Coffee farming systems should be designed with the inclusion of productive shade canopies and managed with constant cropping practices, trying to reduce as much as possible the cash costs for a higher family benefit; being also the best alternative to reduce yield losses.

*Acknowledgements*. This research is part of CASCADE project "Ecosystem-based Adaptation for Smallholder Subsistence and Coffee Farming Communities in Central America", funded by the International Climate Initiative (ICI).

References

Avelino, J., ten Hoopen, M., &DeClerck, F. (2011). Ecological mechanisms for pest and disease control in coffee and cacao agroecosystems of the Neotropics. In: DeClerck, F., Le Coq, J.F., Beer, J. (Eds.), Ecosystem services from agriculture and agroforestry measurement and payment. Earthscan, 91-117. London.

Cerda, R., Deheuvels, O., Calvache, D., Niehaus, L., Saenz, Y., Kent, J., Vilchez, S., Villota, A., Martinez, C. & Somarriba, E. (2014). Contribution of cocoa agroforestry systems to family income and domestic consumption: looking toward intensification. *Agroforestry Systems*, **88**, 957-981.
# Evaluation and design of multispecies cropping systems with perennials: are current methods applicable?

Bruno Rapidel *±1,2*, Delphine Mézière <fnref n="3" />, Raphaël Metral <fnref n="4" />, Christian Dupraz <fnref n="3" />, Anne Mérot <fnref n="3" />, Clémentine Allinne <fnref n="1" />,<fnref n="2" /> & Christian Gary <fnref n="3" />

## 1 Introduction

Mixing simultaneous crops, including perennials is repeatedly mentioned as a way towards ecological intensification in agricultural fields (Malézieux et al., 2009). Research on these systems has developed recently and show that this mixing is not a silver bullet: practices have to be adapted locally in order to build on synergies and minimize tradeoffs between diverging functions, productions and services. For example, the introduction of shade trees in coffee plantations has contrasting effects on disease regulation, depending on the location, on the disease considered, or even on the epidemiological stage of the disease (Bedimo et al., 2012). The same difficulties arise when considering the use efficiency of water or of nutrients. These requirements for local adaptations make the outscaling of innovations relatively difficult: the same cover crop in vineyard can have positive outcome on grape production in a location, and a negative outcome in another one, due to climatic or soil differences (Ripoche et al., 2010).

Moreover, these systems usually rely on various products that enter into different value chains and are delivered at different time scales; the systems, due to the inclusion of perennials, have to be planned on the long term, with low transformability; as they are supposed to provide services as well as goods, their evaluation is complex, relying on multiple indicators.

Methods for cropping system evaluation and design have been developed for annual systems principally. Do they apply to cropping systems with perennials, are adaptation required or do we need to develop new methods?

## 2 Existing methods and their applicability to multispecies cropping systems with perennials

We discuss the applicability to these systems of common methods developed for the design of simple and annual cropping systems, based on several temperate and tropical case studies (Table 1).

<table><caption>Table 1: case studies selected for each method</caption><thead><tr><th>Method used for the design<br>Systems</th><th>Prototyping</th><th>Participatory diagnosis of current systems</th><th>Modelling/simulations</th></tr></thead><tbody><tr><td>Tropical Agroforestry</td><td>Design of two long term coffee agroforestry experiments with experts (Haggar et al., 2011)</td><td>Design of agroforestry systems based on the study of trade offs between Ecosystem Services, (Notaroet al., this congress),</td><td>Modeling for the design of coffee based agroforestry systems, (Meylan et al., 2014)</td></tr><tr><td>Temperate agroforestry</td><td>Design of pesticide-free agroforestry system without pesticides with experts (Grandgirard et al., 2014)</td><td>Commercial agroforestry systems in temperate regions are too scarce to allow for meaningful diagnosis</td><td>Virtual experiments to identify optimal tree density and organization in the field (Talbot, 2011)</td></tr><tr><td>Mediterranean grapevine</td><td>A prototyping method for the re-design of intensive perennial systems: the case of vineyards in France (Metralet al., this congress)</td><td></td><td>Cropping system design for including cover crop in vineyards (Ripoche et al., 2010)</td></tr></tbody></table>

A first approach is prototyping, based on the integration of general and local knowledge to elaborate hypotheses on the factors influencing the performances to be improved and build solutions according to these hypotheses (Lançon et al., 2007). These methods apply well to complex systems, as the expert knowledge mobilized is often integrative, as shown in vineyards (Metral et al., this congress) or in trees/arable crops systems (Castel et al., 2013; Grandgirard et al., 2014)

<footnote n="1">¹ CIRAD, UMR SYSTEM, Montpellier, France</footnote>

<footnote n="2">² CATIE, PAAS, Turrialba, Costa Rica</footnote>

<footnote n="3">³ INRA, UMR SYSTEM, Montpellier, France</footnote>

<footnote n="4">⁴ Montpellier SupAgro, UMR SYSTEM, Montpellier, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: bruno.rapidel@cirad.fr</footnote>
in France. However, the evaluation and iterative design improvement of the resulting prototypes is problematic, as it relies on long lasting experimentation with results only validated locally (Stamps and Linit, 1999). Some experiments have been set, both in tropical regions and in EU and the US, which design were usually decided following this method. Few of them have already produced the expected results (Haggar et al., 2011). The timing of the iterative adjustment of prototypes is always problematic in these perennial systems.

A second approach is based on diagnosis of existing cropping systems. The objective is to identify and rank environment and cropping system variables related to performance variation, and then to identify leeways and stepwise improvements through participatory research (Doré et al., 1997). Many experiences of complex cropping system design rely on related methods. However this method requires an important number of commercial fields, implemented since long time enough, which might result difficult particularly in case of poorly disseminated complex systems. This is particularly true in vineyards in Europe, where monocropping has been the rule, particularly with the advent of mechanization. It is still very common in tropical agroforestry, where research has followed, rather than preceded, these practices. Millions of hectares of coffee or cocoa plantation are managed as agroforestry systems, and the potential of these methods for innovation is great (Notaro et al., this congress).

The third set of methods relies on simulations with numerical models, to evaluate or design new combinations of practices that better fulfill a limited number of objectives. This method allows the exploration of very numerous solutions to select those that satisfy best the criteria; it can be used with stakeholders (Martin et al., 2013). Models simulating multiple species and perennial cropping system are relatively scarce. Moreover, these models have a narrow validity domain: for example, they rely on strong hypotheses on soil exploration by the roots of mixed species, which can be hardly transposed to new environmental conditions. When used in collaboration with farmers, these models have proved very useful to explore scenarios and trigger new, more precise questions and hypotheses from participants (Meylan et al., 2014). The recent uptake in silvoarable temperate agroforestry systems (STAFS) (about 3000 ha planted each year in France since 2012) was stimulated by the publicity about some key features of STAFS that were NOT measured on the field, but produced by simulating STAFS with process-based numerical models (i.e. high Land Equivalent Ratio (Talbot 2011); deep nitrate capture (Adriannarisoa et al., 2015); good light transmission (Molto and Dupraz, 2014); enhanced resilience to extreme weather event (Schuller et al., 2015)). Waiting for field experiments to deliver the same outputs would have required decades. Nevertheless, there is a need to refine and validate the modelling tools that were used in order to avoid stakeholders to take wrong decisions for wrong reasons

## 3 Conclusions

We conclude that the existing methods are applicable to multispecies cropping systems with perennials. However, the particular features of these cropping systems highlight the drawbacks of each of them. Therefore, combining these approaches, where and when it is possible, should be preferred. Whatever the method, evaluation of the new systems requires new indicators development, to account for the multiple productions with very different timescales and serving varying objectives. Development of simple and effective sets of indicators adapted to these systems is a powerful tool to boost the design realm, for practitioners and researchers alike.

## References

* Andrianarisoa, K.S., Dufour, L., Bienaime, S., Zeller, B. and Dupraz, C., 2015. The introduction of hybrid walnut trees (Juglans nigra x regia cv. NG23) into cropland reduces soil mineral N content in autumn in southern France. Agroforestry Systems, accepted for publication.

* Bedimo, J.A.M., Dufour, B.P., Cilas, C., Avelino, J., 2012. Effects of shade trees on Coffea Arabica pests and diseases. Cah Agr 21, 89-97.

* Castel, L., Sieffert, A., Warlop, F., Tchamitchian, M., 2013. Participative co-design of innovative agricultural systems mixing fruit trees and annual crops. DINABIO congress, 10-12 November 2013, Tours, France.

* Doré, T., Sébillotte, M., Meynard, J., 1997. A diagnostic method for assessing regional variations in crop yield. Agr Syst 54, 169-188.

* Grandgirard, D., Oheix S., Lançon L., Liagre F., Dupraz C., Mézière D., Poulain J.L., Wartelle R., 2014. SCA0PEST, a pesticide-free agroforestry cropping system: ex-ante performance evaluation. In: EURAF (Editor), 2nd European Agroforestry Conference. 4-6 June 2014, Cottbus, Germany.

* Haggar, J., Barrios, M., Bolaños, M., Merlo, M., Moraga, P., Munguia, M., Ponce, A., Romero, S., Soto, G., Staver, C., Virginio, E., 2011. Coffee agroecosystem performance under full sun, shade, conventional and organic management regimes in Central America. Agrofor Syst 82, 285-301

* Lançon, J., Wery, J., Rapidel, B., Angokaye, M., Gérardeaux, E., Gaborel, C., Ballo, D., Fadegnon, B., 2007. An improved methodology for integrated crop management systems. Agron Sustain Dev 27, 101-110.

* Malézieux, E., Crozat, Y., Dupraz, C., Laurans, M., Makowski, D., Ozier-Lafontaine, H., Rapidel, B., de Tourdonnet, S., Valantin-Morison, M., 2009. Mixing plant species in cropping systems: concepts, tools and models. A review. Agron Sustain Dev 29, 43-62.

* Martin, G., Martin-Clouaire, R., Duru, M., 2013. Farming system design to feed the changing world. A review. Agron Sustain Dev 33, 131-149
  Metral R., Lafond D., Gary C., Mero A., Metay A., Wery J., 2012. "How to design and experiment new cropping systems with low pesticide inputs for perennial crops: framework development and application to vineyards". ESA Congress 2012 (Helsinki)

* Meylan, L., Sibelet, N., Gary, C., Rapidel, B., 2014. Combining a numerical model with farmer participation for the design of sustainable and practical agroforestry systems. IIIrd World Congress of Agroforestry. ICRAF, Delhi, India, 10-14 Feb 2014.

* Molto, Q. and Dupraz, C., 2014. Is light competition between trees and crops a limiting factor for agroforestry systems at high latitudes? In: EURAF (Editor), 2nd European Agroforestry Conference. European Agroforestry Federation, Cottbus, Germany, pp. 257

* Ripoche, A., Celette, F., Cinna, J.P., Gary, C., 2010. Design of intercrop management plans to fulfil production and environmental objectives in vineyards. Eur J Agron 32, 30-39

* Schuller, A., Gosme, M., Talbot, G. and Dupraz, C., 2015. A model-based assessment of the adaptation of Mediterranean agroforestry systems to climate change (Poster). In: Cirad-Inra (Editor), Climate-Smart Agriculture, Montpellier

* Stamps, W.T., Linit, M.J., 1999. The problem of experimental design in temperate agroforestry. Agroforestry Systems 44, 187-196.

* Talbot, G., 2011. L'intégration spatiale et temporelle des compétitions pour l'eau et la lumière dans un système agroforestiers noyers-céréales permet-elle d'en comprendre la productivité?, Université de Montpellier 2, 281 pp.
# Design of Agroforestry systems with coffee is facilitated by the description of relationships between Ecosystem Services provided

Martin Notaro *¹,², Aurélie Metay ², Sandrine Fréguin-Gresh ³, Jean-François Le Coq ⁴, Pablo Siles ⁵ & Bruno Rapidel ¹,⁶

1 CIRAD, UMR System, 2 Place Viala, Montpellier, France

2 Montpellier SupAgro, UMR System, 2 place Pierre Viala, 34060 Montpellier Cedex 2, France

3 CIRAD, UMR ART-Dev, Universidad Centroamericana UCA, Campus Managua, Managua, Nicaragua

4 CIRAD, UMR ART-Dev, Université Montpellier 3, Site Saint-Charles, rue Henri Serre, 34090 Montpellier, France

5 CIAT, Sub-regional Office for Central America, Managua, Nicaragua

6 CATIE, Programa de Agroforesteria y Agricultura Sostenible, 7170, Turrialba, Costa Rica

## 1 Introduction

Ecosystem services (ES) have been defined as “conditions and process whereby the ecosystem sustains the primary human needs” (Daily, 1997). Cropping system do not provide only agricultural products, but also a range of services and disservices to the society. A way to achieve agroecological intensification of cropping systems could be reached by optimizing the ES they provide (Doré, 2011). Our study focused on coffee agroforestry systems that are supposed to provide a bigger ES panel in comparison with monospecific cropping systems (De Beenhouwer, 2013). The specific and structural complexity of these systems makes this optimization a methodological challenge. Our aim was to propose pathways for an agroecological intensification of these cropping systems by studying the determinants of the provision of ES and the relationships between them. Four ES have been considered, coffee production, tree biodiversity, carbon sequestration into the aboveground biomass and quality of output water from coffee agroecosystems. We bore a specific interest to coffee production because economically is the most important service for growers, the 3 other services being more environmental and thus important for the cropping system sustainability but not for producer livelihoods. Several steps were necessary in order to reach the objectives: (i) understanding and quantifying ES determinants (ii) assessing the links between ES (independence, facilitation or trade-off) (iii) identifying cropping systems and innovating cropping practices that optimized the delivery of these four ES.

## 2 Materials and Methods

The study was done in Nicaragua, municipality of Tuma-La Dalia, 40 km north of the regional capital Matagalpa. A first survey was carried on 82 coffee producers selected by snowball sampling from April to June 2014. It enabled the determination of services related to water quality and coffee production at farm scale (by interview) and to tree biodiversity and carbon sequestration measured in a 20x50 m² plot in a representative coffee plantation. To better assess the determinant of coffee production, a second survey was led with 27 farmers, part of the 82 initial sample, from July to October 2014: we measured the main state variables of the system in 3 repetitions in the 20x50 m² plot and led thorough interviews about cropping practices with the producers.

The service of Water Quality - WQ - (score without unity) has been constructed based on doses of active ingredients of pesticides applied, and from the active molecule properties of the pesticides (IUPAC, the Pesticide Properties DataBase - PPDB - 2013). Tree biodiversity - Sh - (without unity) and carbon sequestration - C seq - (t of C) services have been applied for the shade trees of the agroforestry systems and calculated respectively with the index of Shannon (1948) and with an allometric equation from Chave et al. (2005). The service of coffee yield (kg.ha⁻¹) has been first picked up from the interview (data for 2013) and then field estimated (2014).

## 3 Results – Discussion

There was no connection between tree biodiversity and carbon sequestration (Fig. 1). Carbon sequestration was much more strongly related to tree diameter -and, to a lesser extent, to wood density- than to the number of trees. We found weak but significant correlation between coffee yield and water quality (negative, p-value = 0.015) (Fig. 2). The more pesticides are applied, the higher is coffee yield. But yield was not correlated with tree biodiversity nor with carbon sequestration (Fig. 2). Agronomic diagnosis enabled to know that coffee yield was highly and negatively correlated to shade density, to fungal disease and to weed pressure and positively correlated to soil pH.

Based on the quantification of the four ES, we separated by cluster analysis two types: one, smaller -9 coffee plantation among the 27- where high quantities of ES were provided, and the other one -the 18 coffee plantation remaining where provision was lower (Table 1). The mean values obtained for the 9 selected growers can be used as goals to reach for agroecological intensification. Disease, soil pH and nitrogen are statistically different, and also shade and weeds of the agroforestry coffee based system. To reduce shade, more time for pruning shade trees will be necessary. This would allow a faster weed development, and thus to mitigate it, producers will have to increase density of coffee plantation or

<footnote>* Speaker</footnote>

<footnote>± Corresponding author:</footnote>
spend more time for mechanical weeding. To minimize weeds and fungal disease, the use of environmental friendly and efficient pesticide (identified during our study) could be a solution. Soil nitrogen and pH are important for coffee production and are as well correlated with the density of leguminous tree. Many Fabaceae trees contribute to maintaining a good level of soil Nitrogen but at the same time acidify the soil (Moura, 2015). Therefore to optimize the soil parameters it is necessary to find a good leguminous density, around 50 trees.ha⁻¹. Besides biodiversity seems to have a role to obtain this high joint provision of ES, except water quality service, and a more regular renewal of coffee plantation should allow better production.

<figure><img src="image_247.png" /><figcaption>Fig. 1. Example of C seq to understand the determinants for the provision of this service. Ø means tree diameter.</figcaption></figure>

<figure><img src="image_248.png" /><figcaption>Fig. 2. Feasible relations between ES with the correlation coefficient (R-squared) and the p-value indicating the curve significance. Green ellipses shows where we should find trade-offs or win-win situations.</figcaption></figure>

<table><caption>Table 1. Comparisons of ES values, state variables of agroforestry systems and managing practices means between a group of 9 producers with high ES provision and the others (group of 18 producers). Student test was running to bring out the significant differences. Code signification, p-value: <0.05 "***", <0.15 "*"</caption><thead><tr><th>Comparison element</th><th>Means of the 9 producers selected</th><th>Means of the other 18 producers</th><th>T-test</th></tr></thead><tbody><tr><td rowspan="4">ES</td><td>Coffee yield(kg.ha<sup>-1</sup>)</td><td>2318 ±602</td><td>774 ±412</td><td>*</td></tr><tr><td>WQ (index)</td><td>45.4 ±4.7</td><td>39.5 ±11.3</td><td>*</td></tr><tr><td>C seq (t.ha<sup>-1</sup>)</td><td>40.4 ±45.6</td><td>27.8 ±19.1</td><td></td></tr><tr><td>Sh (index)</td><td>1.47 ±0.69</td><td>1.22 ±0.47</td><td></td></tr><tr><td rowspan="5">State variables of agroforestry systems</td><td>Disease (% affected leaves)</td><td>0.08 ±0.13</td><td>0.30 ±0.34</td><td>***</td></tr><tr><td>Shade (% plot covered by tree shade)</td><td>41.4 ±15.2</td><td>52 ±11.7</td><td>*</td></tr><tr><td>Weeds (% ground plot covered)</td><td>21 ±14.6</td><td>30.4 ±16.2</td><td>*</td></tr><tr><td>pH</td><td>6.23 ±0.44</td><td>5.79 ±0.25</td><td>***</td></tr><tr><td>N soil (mg/L of nitrogen in the soil)</td><td>0.31 ±0.06</td><td>0.22 ±0.09</td><td>***</td></tr><tr><td rowspan="6">Managing practices and shade tree choices</td><td>Organic matter (% soil)</td><td>4.74 ±0.55</td><td>4.53 ±0.77</td><td></td></tr><tr><td>Nitrogen fertilization (kg.ha<sup>-1</sup>)</td><td>44.4 ±48.1</td><td>26.9 ±40.1</td><td></td></tr><tr><td>Pruning time of shade trees (days.ha<sup>-1</sup>)</td><td>4.1 ±3.1</td><td>5.1 ±3.8</td><td></td></tr><tr><td>Number of tree species (/1000m<sup>2</sup>)</td><td>9.5 ±6.1</td><td>5.9 ±2.7</td><td>*</td></tr><tr><td>Number of fruit trees (/ha)</td><td>260 ±210</td><td>150 ±160</td><td></td></tr><tr><td>Number of firewood/timber trees (/ha)</td><td>70 ±25</td><td>50 ±70</td><td></td></tr><tr><td></td><td>Number of Fabaceae trees (/ha)</td><td>50 ±70</td><td>140 ±90</td><td>***</td></tr><tr><td></td><td>Coffee age</td><td>8.4 ±5.1</td><td>14.6 ±13.6</td><td>*</td></tr><tr><td></td><td>Density of coffee plantation</td><td>4940 ±1425</td><td>4720 ±1233</td><td></td></tr></tbody></table>

## 4 Conclusion

This method permit to provide some pathways for designing agroforestry system in accordance with agroecological intensification trying to optimize the provision of several ES. We identified the action leverage, in other words state variables and managing practices to change in the coffee based agroforestry. However, we did not assess the feasibility for the producer to implement those modifications that would probably take more time than conventional management.

## References

* Chave J. et al. (2005). "Tree allometry and improved estimation of carbon stocks and balance in tropical forests" Oecologia 145: 87-99.

* Daily, G. C. (Ed.). 1997. *Nature's services. Societal dependence on natural ecosystems*. Island Press, Washington, DC. 392 pp. ISBN 1-55963-475-8

* De Beenhouwer M. et al. (2013). "A global meta-analysis of the biodiversity and ecosystem service benefits of coffee and cacao agroforestry" Agriculture Ecosystems & Environment 175: 1-7.

* Doré T. et al. (2011). "Facing up the paradigm of ecological intensification in agronomy: Revisiting methods, concepts and knowledge" Europ.J.Agronomy 34: 197-210.

* Jose S. (2009). "Agroforestry for ecosystem services and environmental benefits: an overview". Agroforest Syst 76: 1-10.

* Moura E. et al. (2015). "Contribution of legume tree residues and macrofauna to the improvement of abiotic soil properties in the eastern Amazon" Applied Soil Ecology 86: 91-99.

* Shannon, C. E. and Weaver W. (1948) A mathematical theory of communication. The Bell System Technical Journal, 27, 379-423 and 623-656.
# What is the multifunctionality of the mango orchards in Senegal?

Hubert de Bon *3, Paterne Diatta 1, Lamine Diame 1,2, Cheikh Amet Bassirou Sané 1,2, Jean-Yves Rey 1,3,
Karamoko Diarra 2 & Isabelle Grechi 4

## 1 Introduction

Since forty years, the Senegal mango production has been developed to supply both the national and international markets. Around 100,000 tons are yearly produced from quite diverse orchards. Some functions of the mango orchards dedicated to the fruit production marketing, are analyzed. The subject is the mango orchard rooted in a territory (Rapey *et al.*, 2004). The functions are defined with respect to user expectations. Various users exist: farmers, breeders, walkers, inhabitants, local representatives, territory administrator. The functions that are defined at the orchard level, involve different stakeholders. They go beyond the fruit production functions. The objective is to compare the functions of four types of orchards, resulting from a survey on orchards design and management.

## 2 Materials and Methods

**The choice of the functions.** A typology based on agronomic data (42 variables) classified 64 mango-based orchards in Niayes area. Four categories were obtained: (1) No-input mango diversified orchards ;(2) Low-input mango orchards; (3) Medium-input citrus predominant orchards; (4) Medium-input large mango or citrus predominant orchards (Grechi *et al.*, 2013).

The functions identified from literature and expert opinion are in three types:

- Productive functions: creating income from fruit production, maintaining employment in the area, land markers, increasing assets, other economic production within the perimeter of the orchard (hedges, grazing, market gardening).

- Social functions: wooded savannah landscape, tourism, maintaining employment on farms on a human scale, use of local variety.

- Environmental functions: storage of carbon in the soil (soil cover by trees, hedge, fertilization), plant biodiversity and fauna, cultivated biodiversity (other crops, plant diversity in hedges), pesticides pollution of water and soil, water depletion, creation of humid zones.

These functions can be negative or positive in relation to the stakeholders and the duration that are concerned.

**The selected indicators.** For each of the functions, we have selected some variables coming from the survey already done. They are used as indicators of the different functions and sub-functions. 13 indicators have been chosen, based on the author's experiences.

**Productive functions:** There was no yield data in that survey. We assumed that all the orchards have a fruit production. Four indicators characterize four sub-functions of productions other than fruits in the orchards: "pasture" that means that the orchard is used for pasture during some part of the year; "other crop": there are some crops grown between the line of the fruit trees; "hedge": the trees and shrubs in the hedges produced some fruits, leaves for medicinal uses and food, as well as wood for fire or building. An indicator "concrete block wall" is related to the land property, and consequently to the financial land valorization.

**Social functions:** three indicators are identified in relation with three sub-functions of the orchards; the landscape indicator is the ratio "hedges/acreage": the higher is the ratio, the higher is the percentage of hedges in the landscape with esthetic and walk values; "acreage" of the orchards in relation with the fact that the orchards should remain at a human scale; percentage of local mango varieties in the orchard "local varieties" in contrast to export varieties.

**Agro-environmental functions:** Six indicators have been chosen in relation with three sub-functions: the capacities of the orchards to maintain biodiversity, the soil protection and the water protection. The diversities in the "hedges species" around the orchard and the diversity of the fruit trees "species" in the orchards will provide some trends for the biodiversity. The soil protection is characterized by "fertilization" based on the hypothesis that fertilization will improve the storage of carbon in the soil. No difference has been done in this indicator between mineral fertilizers and animal manure. The second indicator is the "cover" of the soil by the canopy. The higher is the "cover", the higher is

<footnote n="1">1 Centre de Développement Horticole, ISRA, Dakar, Sénégal</footnote>

<footnote n="2">2 Faculté des Sciences et Technologies, Université Cheikh Anta Diop, Dakar, Sénégal</footnote>

<footnote n="3">3 UPR HortSys, CIRAD, Montpellier, France</footnote>

<footnote n="4">4 UPR HortSys, CIRAD, La Réunion, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Hubert.de_bon@cirad.fr</footnote>
the protection of the soil. The relations with the water protection will be estimated by two indicators: the “irrigation” describing mainly the level of the irrigation in the orchards and the “pesticides” indicating also the level of pesticides use in the orchards. High irrigation and high pesticides application cause a low protection of the water.

The individual values from the previous survey are transformed by arithmetic operations and then added together to give “synthetic indicators” of same order.

## 3 Results – Discussion

Synthetic indicators of the productive, social and agro-environmental functions are gathered for the four types of orchards in the table 1.

<table><caption>**Table 1.** Synthetic indicators of functions for the 4 types of orchards in Niayes areas</caption><thead><tr><td>Functions</td><td>Productive</td><td>Social</td><td>Agro-environmental</td></tr></thead><tbody><tr><td>(1)No-input mango diversified orchards</td><td>9,4</td><td>6,1</td><td>15,1</td></tr><tr><td>(2)Low-input mangoorchards</td><td>11,0</td><td>5,9</td><td>13,0</td></tr><tr><td>(3)Medium-input citrus-predominant orchards</td><td>13,0</td><td>7,8</td><td>12,1</td></tr><tr><td>(4)Medium-input large mango or citrus predominant orchards</td><td>10,2</td><td>5,3</td><td>8,4</td></tr></tbody></table>

The table 1 puts in evidence that the functions of the different types of orchards seem intuitively in accordance with the expected results for social and agro-environmental functions, although a low number of indicators are used compared to the 42 indicators of the IDEA method (Villain *et al.* 2008). Agro-environmental functions are lower in the large orchards mainly focus on export production. But the type (3) orchard provides more productive and social functions that the types (1), (2), (4). The results of the productive functions raise the questions about the measurements of the yields and other productive sub-functions in spite of the lack of yields.

## 4 Conclusions

It seems possible to re-analyze an agronomic survey in a multifunctional approach which allows to better characterize the asset of orchards in a rural area.

*Acknowledgements.* We sincerely thank all the farmers who have answer patiently to all our questions.

## References

* Grechi, I., Sane, C.A.B., Diame, L., de Bon, H., Benneveau, A., Michels, T., Huguenin, V., Malézieux, E., Diarra, K., Rey, J.Y. (2013). Mango-based orchards in Senegal: diversity of design and management patterns. Fruits, 68, 447-466.

* Rapey, H., Lardon, S., Josien, E., Servière, G., Fiorelli, C., Klingelshmidt, F., Matter E. (2004). Multifonctionnalité de l'espace agricole d'un territoire : premières conclusions issues d'une démarche de recherche. Les cahiers de la multifonctionnalité, n°5, Cemagref editions, 71-85.

* Villain, L., Boisset, K., Girardin, P., Guillaumin, A., Mouchet, C., Viaux, P., Zahm, F. (2008). La méthode IDEA. Indicateurs de durabilité des exploitations agricoles. Guide d'utilisation 3ème édition, Educagri, Dijon.
# Mapping spatial distribution of Cocoa Swollen Shoot Disease for effective rehabilitation strategies in infected areas

Zokou Franck Oro<sup>*±1,2</sup> Lucien Diby<sup>2</sup>, Noel Dougba Dago<sup>1</sup>, Marie-Paul N'Guessan<sup>2</sup>, Christophe Kouame<sup>2</sup>, Hypolite Diby<sup>4</sup> & Christian Cilas<sup>5</sup>

## 1 Introduction

Cocoa Swollen Shoot Virus (CSSV) is a semi-persistent virus transmitted to cocoa plants by mealybugs (Fig.1B) (Dufour, 1988; Lot et al., 1991). CSSV disease evolves slowly and gradually through the plot (Oro et al., 2012; Castel et al., 1980; Partiot et al., 1978). The symptoms of most virulent isolates are characterized by intense red coloration along the secondary veins and limb on young leaves (Fig. 1A), discoloration on adult leaves, swelling of stems and branches (Fig.1C) and stunted pods. This disease is endemic in major cocoa production regions of Cote d'Ivoire where it represents the main threats for cocoa production in the country (Kouakou et al., 2012).

<figure><img src="image_249.png" /><figcaption>**Fig. 1.** Symptoms and vector of Swollen shoot Disease: (A): intense red coloration along the secondary veins and limb on young leaves, (B): mealybugs colony on mature pod, (C): swelling of stems.</figcaption></figure>

In 2009, more than 12% of cocoa areas in Côte d'Ivoire were affected by disease (Anonym, 2009), but this figure seems to have increased given the damage observed in major production areas such as Soubré. CSSV disease is currently a major challenge for developing cocoa farms regeneration strategies, because there is no information system capable to update producers on disease prevalence. Hence, the implementation of the spatial distribution of CSSV at different scales is necessary for efficient monitoring of the disease and development of rehabilitation strategies. However, little data exists locally to accurately inform stakeholders on the prevalence the disease. Therefore our investigations aimed at supplying the stakeholders with evidence-based information on the prevalence of CSSV in main cocoa production areas in Côte d'Ivoire; the Nawa region.

## 2 Materials and Methods

### Data collection

This study was conducted in the Nawa region located in the South-West of Côte d'Ivoire. This region is recognized as the leading cocoa producing area. Data were collected in two sentinel sites (Petit-Bondoukou and Koda), based on LDSF method (Land Degradation Monitoring Framework). Next, Field Survey Units have been defined as blocks of 10 x 10 km i.e. 10'000 ha and each of them has been installed in two sites of the Nawa region. 160 sample plots; each covering a circle of 50 m radius (7850 m<fnref n="2" />) were randomly designed in each site. Data recorded include: Presence or Absence of CSSV outbreaks, Number of outbreaks, size of outbreaks and spectra data of outbreaks, which delimit the CSSV focus. The geographical coordinates of each plot were recorded to identify the spatial distribution. The number of cocoa trees was also counted for planting density estimation.

### Spatial distribution and mapping

Data relating to presence or absence of CSSV disease at each plot were analyzed with statistical logiciel "R". Data were represented in an orthonormal with abscissa X, longitude and ordinate Y, latitude to produce spatial distribution maps for Petit Bondoukou and Koda. These maps have observed a large spatial variability of CSSV disease between the two sites. To better understand this spatial variability, we studied the correlation between "Short cocoa tree density» or "Great cocoa tree density" for both sites and areas affected by CSSV. The results were represented as boxplot. The chi-square test was applied to areas affected by CSSV and cocoa trees density to determine if there is a significant difference firstly between infected areas and short cacao tree density and secondly between infected areas and great cocoa trees. That as well for Petit Bondoukou site and for Koda site.

<footnote n="1"><sup>1</sup> Unité de Formation et de Recherche (UFR) des Sciences Biologiques, Université Pôleforo GON COULIBALY de Korhogo</footnote>

<footnote n="2"><sup>2</sup> Word Agroforestry Centre (ICRAF), Côte d'Ivoire.</footnote>

<footnote n="3"><sup>3</sup> Unité de Formation et de Recherche (UFR) des Sciences Biologiques, Université Pôleforo GON COULIBALY de Korhogo</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: franckoro@yahoo.fr</footnote>
## 3 Results – Discussions

### Disease spatial distribution maps

<figure><img src="image_250.png" /><figcaption>**Fig. 2.** CSSV distribution maps in both A: Petit Bondoukou (PBON) and B: Koda with red dots: presence of CSSV and black dots: no CSSV infection.</figcaption></figure>

There is large variability of CSSV spatial distribution between Koda and Petit Bondoukou. This variability is expressed by 63% of disease presence in Petit Bondoukou plots (Fig. 2A) against 3% in Koda plots (Fig. 2B). CSSV infection seems to increase with both Longitude and Latitude coordinates. Heterogeneous data distributions have been observed between both Cocoa tree density and CSSV occupied area analyzed parameters (Fig. 3). However cocoa density trees parameter is normally distributed after data normalization around 0.5).

### Rate in cocoa short and great tree evaluating CSSV propagation

<figure><img src="image_251.png" /><figcaption>**Fig. 3.** Boxplot shows the tree distribution (short and great trees) between both Petit Bondoukou and KODA sites.</figcaption></figure>

Boxplot of short and great tree distribution in both Koda and Petit Bondoukou sites showed a significant difference between these two analyzed parameters. Furthermore, in Koda a consistent number of short tree have been observed with respect to Petit Bondoukou (Fig. 3). In this case the heterogeneity of the cocoa trees within the two considered sites is strongly evidenced by the statistical Chisq test from R software (p-value < 2.2e-16). This statistical analysis suggests that the high presence of short cocoa tree in the KODA with respect to those of Petit Bondoukou, could explain the difference in CSSV infection between the two under-analyzed sites. The Chisq test suggest that CSSV presence and/or absent in both Koda and Petit Bondoukou site, depend on the rate of short cocoa trees. Knowledge on the spatial variability can help design rehabilitation strategies in infected areas. Indeed, the use of full replantation technique and barrier trees are recommended in highly infected areas while grafting techniques on mature orchards are recommended in low disease prevalence areas.

## 4 Conclusions

The results presented in this work are partial and give but gives good prospects for understanding the spatial variability of the disease between Koda and Petit Bondoukou sites. This variability is related to the strong presence of short cocoa trees at Koda unlike Petit Bondoukou where there is only great cocoa trees.

*Acknowledgements:* The work is funded by MARS Inc. through the Vision for Change Project. The technicians H. N'Guettia and H.C. N'Guessan and anonymous casual workers contribute actively to the field surveys.

## References

* Oro, F Z. et al. (2012). Spatiotemporal pattern analysis of Cocoa swollen shoot virus in experimental plot in Togo, Plant Pathology, **61**, 1043-1051

* Kouakou, K. et al. (2011). Impact de la maladie virale du Swollen shoot du cacaoyer, Journal of Applied Biosciences, **43**, 2947-2957

* Anonyme. (2009). Personnel communication, CNRA (Côte d'Ivoire). International meeting on Cocoa swollen shoot, Bassam, 22-25 December, 2013

* Dufour, B. (1991). Place et importance des différentes espèces d'insectes dans l'écologie du CSSV (Cocoa swollen shoot Virus) au Togo. Thé Café Cacao, **35(3)**, 197-203.

* R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.
# SYSTEMIC ANALYSIS OF A TEMPERATE FOREST GARDEN: A CONTRIBUTION TO COMPLEX AGROSYSTEMS STUDY

Charlotte Pasquier*<fnref n="1" />, Alain Canet<fnref n="2" /> & Jacques Wéry±<fnref n="1" />

## 1 Introduction

Forest gardens often described as permaculture systems are agrosystems that intimately mix a large number of trees and annual plants species on a small surface. Mainly studied in the tropics and almost unknown in temperate climates [1], they are today proposed as models of a sustainable agriculture although there is few quantitative data about their functioning, efficiency and sustainability (Torquebiau, 1992; Kumar & Nair 2004). The potentialities and principles of these gardens could be used as inspiration to design and improve agroforestry systems in farm conditions. Thus, questions arise about the biophysical functioning of such agrosystems and as a first step about the proper methods that should be used to study them. The objective of this paper is to present the systemic analysis of a temperate forest garden and discuss the interest of this method to study such complex agrosystems.

## 2 Materials and Methods

The studied garden is located in Belgium and has been cultivated on 2000m<fnref n="2" /> for 40 years. This agrosystem is cultivated as subsistence and pedagogic farming with extreme diversity and density. A close garden with similar area (6000 m<fnref n="2" />) and age (28 years) was used as a control to compare the measurements. It is cultivated with more classical techniques with lower plant density and diversity and in the objective of producing food.

The first step in our analysis was to define the conceptual model of the agrosystem in order to select process and variables to be further analyzed. We used the conceptualisation protocol of Lamanda *et al.* (2012) which allowed to identify the key sub-systems and their interactions. The active environment included physical input and labor as well as climate constraints. The passive environment included vegetable, fruit, wood and wild plant production, efficiency indicators and ecological services. This conceptual model then helped to analyse emergent properties of the system with five types of variables: the agrosystem performance, the structure of the plant stand, the animal biodiversity, the tree root distribution and soil characteristics driving key biophysical processes of the agrosystem (Altieri, 1999; Fernandest & Nair, 1986).

The agrosystems performances were measured with efficiency indicators. The list of inputs and outputs was first established through interviews of the gardeners and observation of the garden. We took into account any exchange of the agrosystem with its environment. Then the quantity of inputs and outputs was evaluated when possible.

The structure of the garden was described with the biological composition and the organization of the three dimensions: horizontal, vertical and chronological. Shannon and Simpson indexes were calculated to evaluate structural complexity (Ngo Bieng, 2007). We determined the tree density and defined the interface index as the length of the imaginary contact line between trees and vegetables divided by the surface cultivated with vegetables (Pasquier, 2014).

Animal biodiversity was studied with three sampling methods. Aerian arthropods were caught with a yellow trap, and crawling arthropods with a Barber trap. Traps contents were collected every five days. We also used a earthworms catching protocol. We repeated this sampling procedure three times in the forest garden as well as in a control garden and a nearby grassland. An analysis of variance was done to compare abundance and diversity of the samples.

An auger was used to sample tree roots in different soil layers and at different distances from the fruit tree hedge in the forest garden and in the control garden (Fernandest & Nair, 1986). An analysis of variance was used to study the influence of tree density, depth and distance from the fruit tree hedge on the biomass of roots.

Soil characteristics driving biophysical functioning of the agrosystem were also measured in the first soil layer (0-30cm): soil texture, organic matter content, bulk density, water infiltration rate and biological activity through soil respiration.

## 3 Results and discussion

A complete quantification of inputs and outputs appeared impossible in such an agrosystem but we found low levels of inputs for a significant level of production. The study of N cycle in the forest garden revealed a diversity of small N inputs including atmospheric deposition, human urine, neighboring lawn cuttings and chickens feeding. Specific

<footnote n="1">¹ Montpellier Supagro, UMR System, 2 place Pierre Viala, 34000 Montpellier, France.</footnote>

<footnote n="2">² Arbre et Paysage 32, 10 avenue de la Marne, 32000 Auch, France.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: jacques.wery@supagro.fr</footnote>
practices were highlighted that are not documented in the scientific literature such as tree disease management by ground deposition of infected tree shouts. Summer pruning of the fruit trees was a key practice in the agrosystem management influencing light catchment by tree strata, organic matter cycling and tree disease management.
Indicators of species diversity as well as of structural complexity like the Shannon and Simpson indexes (Table 1) showed results similar to tropical forest gardens.

<table><caption>Table 1. Different structure indexes</caption><thead><tr><th rowspan="2"></th><th colspan="2">Species diversity</th><th colspan="3">Structural complexity</th></tr><tr><th>Forest garden</th><th>Control garden</th><th>Forest garden</th><th>Control garden</th></tr></thead><tbody><tr><td>Tree species</td><td>71 (1237*)</td><td>33</td><td>Shannon tree</td><td>3,252</td><td>2,598</td></tr><tr><td>Vegetable species</td><td>57</td><td>43 (149*)</td><td>Simpson tree</td><td>0,701</td><td>0,115</td></tr><tr><td>Wild plants</td><td>53 (28 edible)</td><td>25 (+24 ornamental plant species)</td><td>Tree density</td><td>1,14</td><td>0,64</td></tr><tr><td>Total species</td><td>181</td><td>125</td><td>Interface index (m.m<sup>-2</sup>)</td><td>0,88</td><td>0,35</td></tr></tbody></table>

The vertical organization showed five strata. The dwarfing rootstocks enabled the creation of an intermediate strata between tall trees and shrubs. The garden was organized following a north-south axis. Vegetables were associated to dwarf trees and shrubs in the southern part of the garden and tall trees protected the garden from the wind in the northern part. Wild plants were harvested with a low level of management. Insect diversity was higher in the two gardens than in the grassland (Table 2). In the forest garden we found much more earth worms and we trapped isopods and carabidaes which were absent in the control garden.

<table><caption>Table 2. Animal biodiversity indexes</caption><thead><tr><th></th><th>Forest garden</th><th>Control garden</th><th>Grassland</th></tr></thead><tbody><tr><td>Mean of insect morphotypes number</td><td>15,7</td><td>17</td><td>10,7 *</td></tr><tr><td>Total isopods trapped</td><td>119 *</td><td>1</td><td>0</td></tr><tr><td>Total carabidaes trapped</td><td>10 *</td><td>1</td><td>1</td></tr><tr><td>Mean trapped earthworms</td><td>44,7 *</td><td>6,3</td><td>-</td></tr></tbody></table>

The tree root distribution study showed a high root biomass in the soil of the forest garden (not shown). The pattern of root biomass distribution from the tree hedge, was different in the two gardens. This would deserve further research and suggest the role of rootstock vigor and shoot pruning in the management of root competition between trees and vegetables. The forest garden had a high content of organic matter which was linked to a low bulk density and a high water content (table 3). These findings are consistent with our knowledge of the benefits of agroforestry and no tillage practices [2,6].

<table><caption>Table 3. Soil characteristics</caption><thead><tr><th></th><th>Forest garden</th><th>Control garden</th></tr></thead><tbody><tr><td>Organic matter content (%)</td><td>15,6</td><td>5,5</td></tr><tr><td>Bulk density (g.cm<sup>-3</sup>)</td><td>0,84</td><td>1,14</td></tr><tr><td>Water content (g.g<sup>-1</sup>)</td><td>0,52</td><td>0,23</td></tr></tbody></table>

## 4 Conclusions

Putting back the above results in the conceptual model allowed to analyze the emergent properties of the system related to productivity, efficiency and resilience, in the perspective of designing commercial cropping systems which combine fruit trees and vegetables. The efficiency of forest gardens remains to be evaluated using methods of systematic measurement of inputs and outputs. Despite this lack of quantitative data, many research hypothesis were highlighted such as favorable forest garden micro-climate, the effects of plant nutrition, microorganisms and summer pruning on plant health and production as well as the nutritional quality of shaded vegetables. Structural indicators like the interface index will be useful to define agrosystem simplification thresholds concerning biodiversity and structure when extending the principles of forest gardens to design cropping systems in farm conditions.

*Acknowledgements.* We thank the gardeners in Mouscron and Aalbeke.

## References

* Crawford, M. (2010). Creating a forest garden, working with nature to grow edible crops. Green Books Publisher, Cambridge.

* Torquebiau, E. (1992). Are tropical agroforestry home gardens sustainable ?, *Agriculture, ecosystems and environment*, **41**, 189-207.

* Kumar, B.M & Nair, P. K. R. (2004). The enigma of tropical homegardens. *Agroforestry Systems*, **61**, 135-152.

* Lamanda, N., Roux, S., Delmotte, S., Merot, A., Rapidel, B., Adam, M., & Wery, J. (2012). A protocol for the conceptualisation of an agro-ecosystem to guide data acquisition and analysis and expert knowledge integration. *European Journal of Agronomy*, **38**, 104-116.

* Altieri, M. (1999). The ecological role of biodiversity in agroecosystems, *Agriculture, Ecosystems and Environment* **74**, 19-31

* Fernandest, E. C. M., & Nair, P. K. R. (1986). An Evaluation of the Structure and Function of Tropical Homegardens. *Agricultural Systems*, **21**, 279-310

* Ngo Bieng, M.-A. (2007). Construction de modeles de structure spatiale permettant de simuler des peuplements virtuels realistes. Application aux peuplements melanges chene sessile - pin sylvestre de la region centre. Sciences of the Universe. ENGREF (AgroParisTech)

* Pasquier, C (2014). Agroforesterie et permaculture en climat tempéré, quels enseignements tirés de l'analyses systémique du jardin-forêt des Fraternités Ouvrières de Mouscron? Master thesis in agronomy, Supagro, Montpellier

<footnote>* number of varieties</footnote>

<footnote>(* indicates a significant difference from the numbers in the same line)</footnote>
# "Cropping the roots" of agroforestry systems: applying moderate water stress and water competition at plantation to increase tree root biomass

Oswaldo Forey *±1*, Jacques Wery 2 & Aurélie Metay 2

## 1 Introduction

Agroforestry systems performances and stability rely on reduced competition for soil resources between trees and the intercrop. A management strategy at plantation could hence be designed to shape root systems, so that crop roots forage the top soil horizon and tree roots forage deeper horizons. We hypothesize that root growth can be favored by applying a moderate water stress that limits tree shoot growth while maintaining net carbon assimilation (Li *et al.*, 1989; Pellegrino *et al.*, 2006) in order to allocate more carbon to the roots. We therefore hypothesized that such type of soil water deficit should increase root biomass and if combined with a shallow-rooted intercrop stimulate this additional tree root growth in deeper horizons. Therefore, our study aims to investigate the effects of a moderate water stress and intercrop competition on net photosynthesis and carbon allocation to root and shoot compartments, of peach trees intercropped with a grass cover (continuous under the trees) during the first growing season after plantation.

## 2 Materials and Methods

A 2000m<fnref n="2" /> drip-irrigated peach tree orchard with 475 one-year old trees was planted in January 2014 on a clay-loam soil in Southern France with three water treatment replicated three times in a Latin square design: (i) a well irrigated with a canvas soil-cover control treatment (T1), a moderately stressed with a canvas soil-cover treatment (T2) and (ii) a moderately stressed treatment intercropped with a grass cover crop (T3). Soil water potential within the tree root zone was monitored every two days with tensiometers (3 replicates per treatment) and readings at 40cm depth were used to keep soil water status in T1 between 0 and -200hPa, and between -400 and -600hPa in T2 and T3, which is for T2 and T3 sufficient to limit shoot growth without impacting net photosynthesis (Pellegrino *et al.*, 2005). Net photosynthesis was monitored three times with a portable Licor 6200, and pre-dawn leaf water potential twice during the growing season with a pressure chamber. Total root and shoot biomass were weighed at the end of the root growth season on three trees per treatment. Effect of water treatment on these variables was tested with one way Anova.

## 3 Results – Discussion

Soil water potential (SWP) from the beginning of the fast-growing shoot phase (2014/06/01) to the stopping of irrigation (2014/09/05) indicates that in T1, water status was on average kept at -241± 263 hPa, in T2 at -405± 342 and in T3 at -372± 288 hPa (mean values± standard deviation). This corresponds to our target range only for T2 due to soil heterogeneity (in T3 two tensiometers mean values out of three fall within the range, in T1 one out of three) and some unavoidable variability of SWP in such type of irrigation management in field conditions, as can be seen in Fig. 1, A.

<figure><img src="image_252.png" /><figcaption>Figure 1: A: soil water potential at 40cm depth (hPa) mean values (n=3), B: pre-dawn water potential (Pa) mean values (n=12) ± standard deviation and C: Net photosynthesis (µmol CO₂·m⁻<fnref n="2" />·s⁻<fnref n="1" />) mean values (n=6) ± standard deviation per treatment. Figures followed by different letters are significantly different at P<0.05 according to Tukey test. Verticals bars delineate the fast-growing shoot phase and black arrow is the date of T2 soil-cover removal.</figcaption></figure>

<footnote n="1">¹ INRA, UMR System, Bât. 27, 2 place Viala, 34090 Montpellier Cedex</footnote>

<footnote n="2">² Montpellier Supagro, UMR System, Bât. 27, 2 place Viala, 34090 Montpellier Cedex</footnote>

<footnote>* Speaker</footnote>

<footnote>‡ Corresponding author: (oswaldoforey@gmail.com)</footnote>
Indeed, pre-dawn water potential (Fig. 1, B) show that only T3 was stressed at the beginning of the fast-growing shoot phase (2014/06/05), due to the weed control canvas sheet used on T2 which prevented soil evaporation. Soil was then uncovered in T2 leading SWP to decrease and reach the target level of stress after 12 days (vertical arrow on figure 1, C). On the other hand at the end of the summer (early September), T3 was above the target SWP due to an excessive supply of water. The target levels of soil water deficit was therefore hard to maintain for each treatment during all the crop cycle but were achieved for most of the period and/or in most parts of the orchard.

By the end of June, net photosynthesis in T2 and T3 decreased in comparison with the control (T1), showing that water stress was sub-optimal in T2 and T3 (Fig. 1, C). At the end of July, i.e. at the maximum of the fast-growing shoot phase, net photosynthesis was lower in T2 than in T3. One explanation could be that trees in T2 having been stressed later than trees in T3, they had time to develop a higher leaf area (data not shown) and therefore a higher transpiration making it more difficult to maintain SWP in the target range. By the end of the growing season and after the stopping of irrigation, net photosynthesis was identical for all treatments.

Final tree biomass (Root+Shoot biomass) was significantly lower in T3 compared to T1 and T2 (Fig. 2) which is consistent with an earlier soil water deficit reducing plant photosynthesis. T3 also had a significantly higher Root/Shoot ratio than T1 and T2, whereas T2 was not significantly different from T1. Our first hypothesis was that water stress would increase Root biomass without decreasing net photosynthesis and hence total biomass. We therefore expected both T2 and T3 to have a higher Root/Shoot ratio that T1 but with an equal total biomass. This is the case for T3, but not T2, certainly due to an inappropriate timing and level of water stress. T2 was stressed later than T3, which allowed trees to produce more biomass before net photosynthesis decreased, but certainly too hard at a time where its water demand was high due to its high leaf area. Our second hypothesis was that water stress and competition with a herbaceous plant would force tree roots to growth at depth. We expected T3 roots to be hende found at depth, which is not the case (figure 3). The reason is that T3 was stressed too early and too hard, which hindered leaf development and hence carbon fixation, leading to dwarf trees with not enough carbon resource to allow sufficient root and shoot growth.

<figure><img src="image_253.png" /><figcaption>Figure 2: Root/Shoot ratio vs total biomass mean value (n=3) ± standard deviation per treatment at the end of the root growing season.</figcaption></figure>

<figure><img src="image_254.png" /><figcaption>Figure 3: Root biomass mean value (n=3) ± standard deviation per soil horizon and per treatment at the end of the root growing season.</figcaption></figure>

## 4 Conclusions

Our results show that photosynthesis, a physiological process thought to be the last to be impacted by water stress (Pellegrino et al., 2006), is actually impacted by a moderate stress within the range of tensiometers readings. This means that tensiometers are appropriate tools to apply such a moderate stress, even though it is still difficult to steer water stress with them in field conditions. It also means that in order to test our hypothesis that root growth can be favored by applying a moderate water stress, we need to lower our water stress target range, in order not to decrease net photosynthesis. We also need to apply this water stress later during the growing season so that trees can develop a sufficient leaf area to assimilate enough carbon to support differential root growth.

*Acknowledgements:* The authors wish to thank "la Fondation de France" for financially supporting this project.

## References

Li, B. S., Huguet, J. J., Schochj, P. G., & Orland, P. Y. (1989). Response of peach tree growth and cropping to soil water deficit at various phenological stages of fruit development, *64*, 541-552.

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. *European Journal of Agronomy*, *25*(1), 49-59. doi:10.1016/j.eja.2006.03.003

Pellegrino, A., Lebon, E., Simonneau, T., & Wery, J. (2005). Towards a simple indicator of water stress in grapevine (Vitis vinifera L.) based on the differential sensitivities of vegetative growth components. *Australian Journal of Grape and Wine Research*, *11*, 306-315.
# New agro-ecologic paradigm for little farming exploitations to obtain alimentary sovereignty

Serge Valet *±1 & Mikael Motelica-Heino 2

## 1 Introduction

To face to the failure of intensive monocultural systems, because of soil degradation and pollution and (micro) biodiversity destruction, in tropical to sahelian zones, we propose a new agro-forestry ecologic paradigm. This concept refers to traditional practices and techniques that mimic the most natural functions. These new systems are based on the ecological intensification provided by the innovative rational multi-stratified multi-specific croppings according to climate change, pedoclimatic conditions, and socio-economic variations. For these systems the ecological performance results from the free Eco-Systemic Services. The approach down-top-down-top was taken with farmers to understand and to improve them by some innovations. The multi-species and multi-stratified systems designed to support productivity in the long term have been widely used in traditional agricultures in the entire world.

## 2 Materials and Methods

The study of these agro-ecologic systems began in the sixties by surveys among peasants and simultaneously y agronomical trials. The typology of different traditional systems and numerous trials were made in different pedoclimatic conditions. The combinations can be described in many seven main types: decay, relay, sequential, mixed cropping, row intercropping, strip intercropping, and mosaic intercropping. From European to Tropical pedo-climates the Density Equivalent Ratio (1) varies from 1 to 9 which improves the Land Equivalent Ratio (2) from 1 to 3 without trees. The concept of Eco-Systemic Services (ESS), a process whereby agricultural ecosystems produce benefits for society, is introduced by the Millenium Ecosystem Assessment (MEA-2005). These different services supply some agro-forestry ecologic conditions which contribute to increase yields.

## 3 Results – Discussion

### 3.1. Surveys

In the Sudano-Sahelian zone, the BIOSOL project initiated au Burkina Faso in 2012, in tree pedoclimatic conditions, has verified that in the Sudanese zone (Orodara) there remains about 43% of mixed cropping whereas in the Sudano-Sahelian zone (Baraniand Sampéri) this fraction has diminished a lot. Bi (39%) and three (61%) species (cereals-cereals or cereal-leguminous) mixed croppings still remain surrounded or not by hedges and some tree parks have been maintained. 56 types of cultural successions in mono and associated cropping have been monitored.

Tests and trials on cultural associations were implemented in peasant fields in Sudan-Sahelian zones. These trials were carried out on cultural density, fertilization and the struggle against weeds, pests and diseases.

In the Equatorial zone, in West Cameroon, it appeared that the peasants vary qualitatively (species and varieties) and quantitatively (density) the associations according to the eco-systemic potential, social traditions, family food preferences, the distance from local markets and both national and global economic variations.

### 3.2. Trials

The different studied services are Supporting, Regulating, Provisioning and Socio-Cultural.

#### 3.2.1. Supporting services

Cultural associations were found to favor the fertilization and the conservation/transfer of fertility thus resulting in the economy of fertilisers. The increase of N supply is the result of N leaching reduction. Coffee-Erythrina association reduces N leaching from 14 to 20 NO3-N mg/NL in comparison to conventional monoculture. Cassava-maize association reduces nutrients leaching in the order of 20-30 units of NPK due to greater association efficiency. This was due to the good Root Equivalent Ratio (RER>1)(3). In the tropical zone, in Cameroon, the Maize-Colocasia-Xanthosoma intercropping system with 50 NPK units brings a LER (2) median de 1.76 (0.76 to 2.44) for trials in 5 sites with very different pedoclimatic conditions. In peasants and research fields, the calculations give a NPK Efficiency (Yield/NPK Unity) Equivalent Ratio (4) more large. These NPK efficiency Equivalent Ratio reached 5 (NP) to 4 (K) with the yield maximum for each systems (50 U for intercropping and 100 U for pure crops). For the intercropping

<footnote n="1">1. PASSERELLES, 9, rue du Bât d'Argent, 69001, Lyon</footnote>

<footnote n="2">2. ISTO, UMR 7327, CNRS-Université d'Orléans</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: valet.serge2@wanadoo.fr</footnote>
system, the NPK reduction was for maize 50% of N and 25% for each tubercle, 40% of P and 30% for each tubercle and 30% of K and 35.5% for each tubercle respectively.

For 2 plants, the NEfER was 3.53 (Maize-Sojabean) and 2.36 (Maize-Bean) whereas PEfER was 1.45 (Maize-Sojabean) and 1.93 (Maize-Bean). NPK application of imbalanced or excessive application or because of bad climate led to declining nutrients-use efficiency making fertilizer consumption uneconomical and producing adverse effects on both atmosphere and groundwater.

An economy of water was also found in the Sahelo-Sudanese zone (Senegal at Louga and Bambey), as for the millet-Cowpea intercropping the grains-LER = 1.20 was obtained (5 years of drought with 4randomised replications). The calculated Water Use Efficiency (Yield/AET) Equivalent Ratio (4) equals 2.

### 3.2.2. Regulating services:

Protection against runoff, erosion and crusting is improved together with pest and diseases control. Inter/mixed cropping control disease and parasite and striga weed because of the pull-push effect, vertical/horizontal barrier, micro environment modification, and favor niches for parasite predators. Pisumsativum and Camelia sativa intercropping brings LER from 1.43 (2003) to 1.98 (2004) because the suppression of adventices. Intercropping increases bacterial and fungal biomass more than monocropping and thus maintains Arbuscular Mycorhyse. Weed stifting is also favored.

### 3.2.3. Provisioning services:

- Production of food

LERs vary with the number (2 to 12), DER (0.98 to 9) and the associated species from 0.98 (DER<1) to 3 (LER from 1.5 to 9). The important production of straw and tree leaves allow to maintain breeding whereas the culture's residues produce compost and hedges can be used to produce Ramial Chipped Wood.

### 3.2.4. Socio-cultural and ecologic services, healthy food:

Because of the increasing potentially valuable of biomass intercropping allows better than monoculture to maintain or reintroduce breeding. The healthy soil delivers healthy food richer in proteins and vitamins to improve human health.

Intercroppings produce more and require more staff thus curbing the exodus from the countryside:

## 4 Conclusions

The combination of both logical and strategic farmer knowledge and analytic and predictive researcher's knowledge offers a practical framework for the integration of farming practices in a perspective of sustainable improvement. This system supplies some results which are belonging to different socio and agro-forestry-ecological domains. These intercropping systems are implanted traditionally with trees and quick hedges. However to stop erosion due to hard deforestation and ploughing it is also necessary to implement quick hedges, zaï and stone diguettes too.

This new agro-forestry-ecological paradigm which associates much of different plants (trees, shrub, pasture, food/industrial plants...) and animals could ensure the peasants food security and sovereignty.

## NB

1) Density Equivalent Ratio (DER) = (Density intercrop 1 / Density monocrop 1) + (Density intercrop 2 / Density monocrop 2) + (Density intercrop 3/Density monocrop 3) + ....

2) Land Equivalent Ratio (LER) = (Yield intercrop 1 / Yield monocrop 1) + (Yield intercrop 2 / Yield monocrop 2) + (Yield intercrop 3/ Yield monocrop 3) + ....

3) Root Equivalent Ratio (RER) = (Root Yield intercrop 1 / Root Yield monocrop 1) + (Root Yield intercrop 2 / Root Yield monocrop 2) + (Root Yield intercrop 3/ Root Yield monocrop 3) + ....

4) NPK/Wu Efficiency Equivalent Ratio (NPK/Wu-EfER) = (NPK/Wu intercrop 1 / NPK/Wu monocrop 1) + (NPK/Wu intercrop 2 / NPK/Wu monocrop 2) + (NPK/Wu intercrop 3/ NPK/Wu monocrop 3) + ....
# W6. Pathways for sustainable intensification of African agriculture?

Chair: Ken Giller, WUR

Co-chairs: Philippe Lecomte, CIRAD

Liang Weili, Hebei Agricultural University
# INTEGRATING THE WOMEN'S LABOR INVESTMENT INTO THE PERFORMANCE ASSESSMENT OF OX-DRAWN COTTON PRODUCTION IN CÔTE D'IVOIRE

Michel Fok *±1, Siaka Koné 2 & Faridath Aboudou 3

## 1 Introduction

Cotton production in Francophone African countries has been considered as one of the few success stories (Gabre-Madhin and Haggblade 2003) lying greatly on the development of ox-drawn mechanization (Campagne and Raymond 1994) which remains somehow labor intensive. Women do contribute to the labor mobilized in cotton production in Africa, but their role was only coped with in a few qualitative analyses, having highlighted that cotton development had implied more women's involvement in cotton fields (Peltre-Wurtz and Steck 1991), up to reduce their capacity to conduct their own fields (Bassett 1988).

This communication is a first study attempting to quantitatively assess the impact of women in cotton production performance. It explicitly takes into account the extent of polygamy of farm heads –for its direct implication on women's availability and that of their offspring for field works.

## 2 Materials and Methods

This communication is based on a study conducted in 2013 in Northern Côte d'Ivoire through a one-way survey along a sample of 223 farmers in a major cotton producing province ("Département de Korhogo") represented by four of its counties ("Arrondissement").

Given the fact that most farmers were illiterate and no written records were kept at the farmers' level, the survey was conducted by enumerators calling upon farmers' memories. It was not realistic to ambition capturing perfectly the absolute values of time allocation, but the gap calculated for two labor investments (e.g. in distinct types of fields or in distinct periods) could be quite liable.

In each sampled farm, farm heads (males) were interviewed to capture the farmers' characteristics (age, education level), the farm features (size, ox-drawn equipment...) as well as production and costs. When dealing with the composition of families, the involvement of family members in field activities and their possible responsibility in managing plots for their own account were recorded. Women were interviewed in addition to capture their own assets (notably mobile phones and bicycles) and to address factors that could potentially impact their labor contribution in the fields of their husbands: size of their cultivated lands, time spent daily in their fields and in those of their husbands, and the number of days in ten-day period they were allowed to work in their own fields. Women were also asked whether their husband should keep on growing cotton, their opinion was interpreted as reluctance to cotton production in case of negative or absence of answers.

In data processing, descriptive analysis was complemented by multivariate regressions encompassing independent variables related distinctly to characteristics of farm heads, farms and women.

## 3 Results and discussion

There were more polygamous farm heads than monogamous, although only one fourth of them had more than two wives. The more farm heads had wives, bigger were their families, higher was the number of family members in fields, greater was their frequency to have at least two complete sets of ox-drawn equipment as well as were the cultivated and cotton areas. In terms of production performance indicators, either technical (farm cotton production, yield) or financial ones (gross income or surplus), superiority was found only for farms having more than two wives, while no difference was observed between farms having one and two wives, respectively. These results confirm and are more precise about the positive relationship observed formerly between cotton development and women, as above mentioned.

With regard to women, they kept on having land cultivated for their own. Furthermore, lower was the number of wives within a farm holding, bigger was the size of land women could manage for their own, and higher was the frequency they had a bicycle if not a cell phone. However, regardless of the number of women within a farm, the time women had to dedicate daily to the plots of their husbands far exceeded that allocated to their own plots. Hence, women lacked more time than land to produce for their own.

In multivariate regressions, the significant influence was only found for a relatively small number of variables either for

<footnote n="1">¹ CIRAD UPR AIDA, France</footnote>

<footnote n="2">² INP-HB, ESA, Côte d'Ivoire</footnote>

<footnote n="3">³ LARES, Bénin</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: michel.fok@cirad.fr</footnote>
technical performance indicators or financial ones, likely because of the limited size of the study sample.

The influence of the number of wives was confirmed for technical performance indicators. The absence of effect of input intensification could be related to the little variation between farms in this intensification. With regard to the financial performance indicators, the influence of the number of wives vanished if not reversed somehow. The women's reluctance to see their husbands engaged in cotton production could be a reason. Allowing more days to women to go and care for their plots -for short duration- seemed to be positive.

<table><caption>**Table 1.** Farm characteristics and performance according to the number of wives of the farm heads (the presentation of a few variables is omitted for lack of place)</caption><thead><tr><th rowspan="2"></th><th colspan="3">Number of wives of the farm head</th><th rowspan="2">Total</th><th rowspan="2">p value</th></tr><tr><th>1</th><th>2</th><th>> 2</th></tr></thead><tbody><tr><td>Number of farms</td><td>90</td><td>96</td><td>37</td><td>223</td><td></td></tr><tr><td>Age of farm heads</td><td>39,4</td><td>41,2</td><td>41,9</td><td>40,6</td><td>0,242</td></tr><tr><td>% of illiterate farm heads</td><td>84,3%</td><td>73,7%</td><td>88,2%</td><td>80,2%</td><td>0,070</td></tr><tr><td>Number of family members</td><td>6,5 c</td><td>10,0 b</td><td>13,8 a</td><td>9,2</td><td>&lt; 0,0001</td></tr><tr><td>Number of family members in fields</td><td>5,3 c</td><td>7,0 b</td><td>8,1 a</td><td>6,5</td><td>&lt; 0,0001</td></tr><tr><td>Degree of implementation of ox-drawn agriculture</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>% of farms with one complete equipment set<sup>1</sup></td><td>15,6%</td><td>18,8%</td><td>5,4%</td><td>15,2%</td><td>0,158</td></tr><tr><td>% of farms with at least two complete equipment sets<sup>2</sup></td><td>10,0%</td><td>19,8%</td><td>37,8%</td><td>18,8%</td><td>0,001</td></tr><tr><td>Total cultivated area, ha</td><td>18,9 b</td><td>20,6 b</td><td>31,9 a</td><td>21,8</td><td>0,000</td></tr><tr><td>Cotton area, ha</td><td>3,8 c</td><td>5,5 b</td><td>8,5 c</td><td>5,3</td><td>&lt; 0,0001</td></tr><tr><td>Few characteristics of women in farm</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Area of women's land, ha</td><td>3,4 a</td><td>3,2 a</td><td>2,1 b</td><td>3,1</td><td>0,018</td></tr><tr><td>Hours per day in husband's field</td><td>5,8</td><td>5,8</td><td>6,0</td><td>5,8</td><td>0,619</td></tr><tr><td>Hours per day in her fields</td><td>1,1</td><td>0,9</td><td>0,7</td><td>0,9</td><td>0,285</td></tr><tr><td>% women with cell phones</td><td>42,2%</td><td>31,3%</td><td>37,8%</td><td>36,8%</td><td>0,297</td></tr><tr><td>% women with bicycles</td><td>44,4%</td><td>29,2%</td><td>16,2%</td><td>33,2%</td><td>0,005</td></tr><tr><td>Cotton performance</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Seedcotton yield, kgha<sup>-1</sup></td><td>1118 b</td><td>1200 b</td><td>1873 a</td><td>1 285</td><td>0,010</td></tr><tr><td>Gross income after payment of inputs, FCFAha<sup>-1</sup></td><td>157835 b</td><td>160612 b</td><td>339614 a</td><td>190 775</td><td>0,017</td></tr><tr><td>Non-input cash expenses, FCFA ha<sup>-1</sup></td><td>16988 b</td><td>20361 ab</td><td>25137 a</td><td>19 820</td><td>0,050</td></tr><tr><td>cost of occasional labour, FCFA ha<sup>-1</sup></td><td>9069 a</td><td>12645 a</td><td>15247 a</td><td>11 653</td><td>0,041</td></tr><tr><td>Gross surplus, FCFA ha<sup>-1</sup></td><td>140638 b</td><td>139444 b</td><td>314477 a</td><td>170 513</td><td>0,027</td></tr></tbody></table>

<table><caption>**Table 2.** Multivariate regressions of technical and financial performance indicators (the presentation of integrated independent but non-significant variables is omitted for lack of place)</caption><thead><tr><th rowspan="3">Independent variables</th><th colspan="2">Farm cotton production, kg</th><th colspan="2">Yield, kg.ha<sup>-1</sup></th><th colspan="2">Gross margin, CFA per ha</th><th colspan="2">Gross margin, CFA per ha and family labour</th></tr><tr><th>Coef.</th><th>p value</th><th>Coef.</th><th>p value</th><th>Coef.</th><th>p value</th><th>Coef.</th><th>p value</th></tr></thead><tbody><tr><td>Zonal effect relatively to Mankono county</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Boundiali county</td><td>-0,085</td><td>0,469</td><td>-0,018</td><td>0,886</td><td>0,031</td><td>0,022</td><td>-0,199</td><td>0,032</td></tr><tr><td>Ferke county</td><td>-0,079</td><td>0,436</td><td>-0,031</td><td>0,772</td><td>0,043</td><td>0,000</td><td>-0,240</td><td>0,003</td></tr><tr><td>Korhogo county</td><td>-0,067</td><td>0,564</td><td>-0,015</td><td>0,905</td><td>0,030</td><td>0,024</td><td>-0,201</td><td>0,030</td></tr><tr><td>Effects related to the farm and its head</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Head's education<sup>1</sup></td><td>0,078</td><td>0,285</td><td>0,033</td><td>0,671</td><td>-0,019</td><td>0,036</td><td>-0,065</td><td>0,302</td></tr><tr><td>Number of ox-drawn ploughs</td><td>0,281</td><td>0,000</td><td>0,119</td><td>0,149</td><td>-0,003</td><td>0,738</td><td>0,118</td><td>0,081</td></tr><tr><td>Effect of the number of wives relatively to more than two</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Only one wife</td><td>-0,349</td><td>0,001</td><td>-0,280</td><td>0,016</td><td>0,025</td><td>0,068</td><td>0,318</td><td>0,001</td></tr><tr><td>Two wives</td><td>-0,319</td><td>0,003</td><td>-0,272</td><td>0,016</td><td>0,006</td><td>0,653</td><td>0,095</td><td>0,299</td></tr><tr><td>Effects related to women's features in farms</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Having enough days in their fields<sup>3</sup></td><td>-0,011</td><td>0,872</td><td>0,009</td><td>0,902</td><td>0,019</td><td>0,030</td><td>0,239</td><td>0,000</td></tr><tr><td>Reluctance to their husband's cotton<sup>4</sup></td><td>-0,051</td><td>0,581</td><td>-0,063</td><td>0,526</td><td>-0,041</td><td>&lt; 0,0001</td><td>-0,043</td><td>0,551</td></tr><tr><td>Performance and costs</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Yield, kg/ha</td><td></td><td></td><td></td><td></td><td>0,994</td><td>&lt; 0,0001</td><td>0,446</td><td>&lt; 0,0001</td></tr><tr><td>Probability > F value</td><td colspan="2">0,000</td><td colspan="2">0,391</td><td colspan="2">&lt; 0,0001</td><td colspan="2">&lt; 0,0001</td></tr></tbody></table>

## 4 Conclusions

Women's labor contribution is real and its influence to cotton production could be quantitatively appraised even through a one-way survey. The technical performance is under influence of the number of wives within a farm holding but the financial performance depends more on factors of good will from women which could nevertheless be captured.

*Acknowledgements.* The paper is based on a study conducted in the framework of the project "AFINE" (Activités agricoles des femmes dans leur intégration à l'exploitation et la sécurité alimentaire des zones cotonnières) funded by the West African Economic and Monetary Union (UEMOA)

## References

Bassett, T J. 1988. "Breaking up the Bottlenecks in the Food Crop and Cotton Cultivation in Northern Côte d'Ivoire." *Africa* 58 (2): 147-73.

Campagne, P, and G Raymond. 1994. "Le Coton En Afrique de l'Ouest: Une Modernisation Réussie ?" In *Economie Des Politiques Agricoles Dans Les Pays En Développement. Tome III: Les Fondements Microéconomiques*, edited by Benoît-Cattin, 3:11-60. Paris: Revue Française d'économie.

Gabre-Madhin, Eleni Z, and Steven Haggblade. 2003. *Successes in African Agriculture: Results of an Expert Survey*. Edited by Market and strucutural studies Division. MSSD Discussion Paper. Vol. No. 53. Washington: IFPRI.

Peltre-Wurtz, J, and B Steck. 1991. *Les Charrues de La Bagoué. Gestion Paysanne D'une Opération Cotonnière En Côte d'Ivoire*. Bondy: ORSTOM.

<footnote n="1">¹At least two oxen and a plough; ² At least four oxen and two ploughs. Different letters are attached to means when different</footnote>

<footnote n="1">¹Having been to school ² Having cattle of at least 15 heads ³ Women declaring having at least 5 days out of ten to work in their own fields</footnote>

<footnote n="4">⁴ Reluctance interpreted through their absence of answer to the question whether their husband should grow cotton</footnote>
# The risk of declines in soil fertility and crop productivity due to decreased livestock presence in agropastoral zones of West Africa

Jonathan Vayssières *±<fnref n="1" />, Mélanie Blanchard <fnref n="1" />, Mathieu Vigne <fnref n="1" />, Dominique Masse <fnref n="2" />, Alain Albrecht <fnref n="2" />, EricVall <fnref n="1" />, René Poccard-Chapuis <fnref n="1" />, Christian Corniaux <fnref n="1" /> & Philippe Lecomte <fnref n="1" />

## 1 Introduction

In West Africa, food security and living standards improvement are still major issues in rural areas. Until the 20th century, the main feature of agricultural systems in semi-arid and sub-humid areas was livestock, crop and tree integration. At landscape level, livestock traditionally drove nutrient transfers from rangelands to croplands (Fig. 1). These transfers were essentials for making crop productions sustainable (Dugué, 1998; Manlay et al., 2004; Schlecht et al., 2004). Sincemid-1900's the traditional crop-livestock systems are impacted by climate change, population growth and resulting land use change. Cropland extension was done at the expense of range lands leading to a decrease in biomass available for livestock (Lericollais, 1999). Livestock are consequently relegated to areas with low population densities and to sylvo-pastoral regions, i.e. arid areas were climatic constraints are too limiting for crop activities.

<figure><img src="image_255.png" /><figcaption>**Fig. 1.** Land-use change and dynamic of livestock-based nutrient transfers in village territories in semi-arid and sub-humid areas of West Africa during the 20th century</figcaption></figure>

These dynamics emphasized tensions around biomasses (crop residues, manure) and led to the decline in wooded parkland and soil fertility. Increase in food demand, from cities in particular, emphasized the exports of agricultural products from rural areas to cities (cereals, hay, meat, etc.). While mineral fertilizers are poorly used, these dynamics are resulting in a negative nutrient balance that questions the sustainability of the resulting farming systems (Smaling et al., 1997; Sanchez, 2002). The growing demand in livestock products in West Africa cities also represents an attractive market that may support the development of intensive livestock systems not any more based on free grazing but where animals are kept in-barns and fed with byproducts of the local agroindustry as feed concentrates. This study examines whether the reintroduction of livestock in landscapes affects the functioning and performances of agricultural territories.

<footnote n="1">¹ Selmet Unit, CIRAD</footnote>

<footnote n="2">² Eco&Sols Unit, IRD</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: jonathan.vayssieres@cirad.fr</footnote>
## 2 Materials and Methods

The study focused on two villages within the groundnut basin of Senegal that adopted contrasting agricultural strategies; the first one kept a relatively traditional system based on livestock free-grazing (Diohine), while the second developed a system based on livestock fattening (Barry Sine).

Partial nitrogen (N) balances at plot, household and village levels were the indicators used to assess the sustainability of observed agricultural systems. The balances were built on a systematic inventory of biomass flows based on household survey: 44 households and 420 plots in Diohine and 74 households and 620 plots in Barry Sine.

## 3 Results – Discussion

Household scale nitrogen balances (13 kgN.ha⁻¹ for Diohine, 25 kgN.ha⁻¹ for Barry Sine) and village scale's ones (9 kgN.ha⁻¹ for Diohine, 25 kgN.ha⁻¹ for Barry Sine) demonstrate that Barry Sine village is more sustainable in terms of soil fertility maintenance (Table 1). Its higher nitrogen balances are mainly due to a larger use of manure (on average 1.83 kgN.ha⁻¹ in Diohine, 2.86 kgN.ha⁻¹ in Barry Sine). The introduction of the livestock fattening activity in farming systems improves animal presence at landscape level (0.96 TLU.ha⁻¹ in Diohine, 2.31 TLU.ha⁻¹ in Barry Sine), and provides an additional nitrogen input in the agro-ecosystem through imported concentrate feeds (3.14 kgN.ha⁻¹ in Diohine, 17.6 kgN.ha⁻¹ in Barry Sine). The cash-flow generated by the selling of fattened animals gives farmers better access to mineral fertilizers. An equivalent of 1 kgN.ha⁻¹ is used on average in Diohine, versus 6 kgN.ha⁻¹ in Barry Sine.

<table><caption>Table 1. Main village characteristics and performances for the 2012-2013 campaign</caption><thead><tr><th>Village</th><th>Human population density (inhabitants.km<sup>-2</sup>)</th><th>Livestock stocking rate (UBT.ha<sup>-1</sup>)</th><th>Crop grain productivity (kgDM.ha<sup>-1</sup>)</th><th>Crop residues productivity (kgDM.ha<sup>-1</sup>)</th><th>Livestock productivity (kgLW.ha<sup>-1</sup>)</th><th>Nitrogen Balance (kgN.ha<sup>-1</sup>)</th><th>Nitrogen use efficiency (dmnl)</th></tr></thead><tbody><tr><td>Diohine</td><td>180</td><td>0.96</td><td>400</td><td>2070</td><td>25</td><td>8.5</td><td>0.15</td></tr><tr><td>Bary Sine</td><td>320</td><td>2.31</td><td>510</td><td>3150</td><td>213</td><td>24.9</td><td>0.64</td></tr></tbody></table>

Livestock reintroduction in villages improve N use efficiency at both household and village scales (0.15 in Diohine, 0.64 in Barry Sine). However similar plot scale nitrogen balances (-20 kgN.ha⁻¹ in Diohine, -23 kgN.ha⁻¹ in Barry Sine) point out that Barry Sine's livestock fattening manure management is not optimal. There is still room for progress in N use efficiency through the improvement of manure management (urine collection via straw bedding, better manure transport facilities, covered manure heaps, in-soil manure incorporation), especially in Barry Sine where most of the manure is managed contrary to Diohine where most of the manure is directly deposited on-fields (Audouin, 2014).

## 4 Conclusions

Livestock was a strong driver of nutrient transfers from rangelands to croplands at landscape level and an essential component of sustainable soil fertility management in traditional crop-livestock systems based on free grazing. Due to growing population in rural areas, land use change results in a significant reduction of rangelands to the profit of croplands. The gain of such an expansion in terms of staple crop production is not evident because livestock stocking rate in villages is negatively affected resulting in a reduction of manure available for crop fertilization. To counter the resulting unbalance in nutrients, this study shows that livestock can be reintroduced in crop-livestock landscapes via the promotion of fattening livestock systems. Livestock intensification is also the opportunity to indirectly intensify cropping systems and then feed more people in rural areas. This new livestock system also revolutionizes the manure management system. The "animal driven" system is replaced by a "human driven" system including manure collection, storage and spreading. Farmers are requiring new knowledge and new equipment to entirely benefit from the larger manure available. New manure management systems aiming nutrient conservation along the biomass recycling have also to be designed to improve the nutrient use efficiency and the productivity of the new farming systems generated.

*Acknowledgements.* This study was allowed by financial supports from the European Union and the French National Research Agency within the KBBE Animal Change and ANR Cerao projects.

## References

Audouin, E. (2014). Terroirs comparison in terms of biomass flows and nitrogen balance - Study case of Diohine and Barry Sine in the former groundnut basin of Senegal. MSC Thesis, Norwegian University of Life Sciences, Aas, 92 p.

Dugué, P. (1998). Les transferts de fertilité dus à l'élevage en zone de savane. *Agriculture et développement* **18**, 99-107.

Lericollais, A. (1999). Paysans sereer: dynamiques agraires et mobilités au Sénégal. Editions d'IRD (ex-Orstom), Paris, 668 p.

Manlay, R.J., Ickowicz, A., Masse, D., Feller, C. & Richard, D. (2004). Spatial carbon, nitrogen and phosphorus budget in a village of the West African savanna--II. Element flows and functioning of a mixed-farming system. *Agricultural Systems* **79**, 83-107.

Sanchez, P.A. (2002). Soil fertility and hunger in Africa. *Science* **295**, 2019-2020.

Schlecht, E., Hiernaux, P., Achard, F. & Turner, M.D. (2004). Livestock related nutrient budgets within village territories in western Niger. *Nutrient Cycling in Agroecosystems* **68**, 199-211.

Smaling, E.M.A., Nandwa, S.M. & Janssen, B.H. (1997). Soil Fertility in Africa Is at Stake. In: Buresh, R.J., Sanchez, P.A., Calhoun, F. (Eds.), Replenishing Soil Fertility in Africa. Soil Science Society of America and American Society of Agronomy, pp. 47-61.
# Soil nutrient balance, economic performance and scenarios for closing nutrient gaps in heterogeneous smallholder farm systems in south-western Burkina Faso

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

## 1 Introduction

Cobo et al. (2010) reviewed nutrient balance studies in Sub-Saharan Africa, which showed widespread nutrient mining. Most of these studies' findings revealed large negative balances, raising the issue of the sustainability of land management practices in Sub-Saharan African farming systems. It is hard to find studies that focused on the soil sub-component nutrient balance for different farming systems. Furthermore, the relationship between soil nutrient balance and a farm's economic performance needs to be investigated. This may help improve the efficiency of policy intervention, as well as contribute to the body of knowledge for farm design. This study's main objectives were to analyse the soil nutrient balances of different farm types and their linkage with farm economic performances and to evaluate scenarios for replenishing soil nutrients in smallholder farms.

## 2 Materials and Methods

The study was conducted in Ioba Province in the southwest region of Burkina Faso, where Thiombiano and Le (submitted) identified five main types of agricultural livelihood systems (hereafter referred to as farm types): i) farm type I - better-off, cotton-and livestock-based farms; ii) farm type II - better-off, non-farm activities preference farms, iii) farm type III - pro-poor, labourless and landless farms; iv) farm type IV - medium-income, labour-rich, marketable food crop-oriented and educated farms and v) farm type V - poor, insecure land-tenure, livestock-based farms. By using the Nutrient Monitoring (NUTMON) Framework (De Jager et al., 1998), 15 farms representing the five farm types (three replications per type) were monitored during a full year for cropping, livestock and off-farm activities and related nutrient flows. Soil nutrient (nitrogen, phosphorus and potassium [N, P and K]) balances were calculated for the whole farm system and the soil subsystem. The relational soil nutrient balance-economic performance was investigated for the five farm types by using two dimensional diagrams. We evaluated three management scenarios for replenishing soil nutrients. The business-as-usual (BAU) scenario represents the actual practices. The intensification of mineral fertiliser use (IMF) scenario involves the increasing use of mineral fertilisers for replenishing soil nutrients. The recycling crop residues (RCR) scenario replenishes soil nutrients through enhancing the use of crop residues for fertilising crops.

## 3 Results and Discussion

Nutrient balance analyses revealed heterogeneity across farm types (Fig. 1). The whole farm's full nutrient balance showed positive N and P balances for all farm types, with the lowest values for the pro-poor farm type (III). The K balance was negative for the better-off farm types (I and II) and the middle-class farm type (IV). The soil subsystem nutrient balance showed large, negative N and K balances, mainly for better-off farms. These results imply that better-off farms draw their wealth from farm nutrient mining, as observed by Van Der Pol (1992) in northern Mali. Crop and crop residues removal and erosion were the main sources of nutrient depletion. Crop and crop residues removal, and erosion were the main sources of nutrient depletion. Potassium appears to be limiting for wealthy farmings systems which exhibit negative whole farm full nutrient balance for that nutrient only. The large negative soil subsystem nutrient balances with positive farm full nutrient balances indicates inefficient nutrient resources management within the farming system resulting in nutrient accumulation in livestock production subsystem (unused manure).

The results showed that the middle-class farm type (IV) had the highest crop gross margin per cultivated land unit, while the poor farm type (V) had the lowest crop revenue per land unit. The analysis of the relational soil nutrient balance and crop gross margin per land unit revealed two main cases (Fig. 2).

In the first case, farms with a negative soil nutrient balance and a low margin comprised pro-poor, poor and off-farm preference farm types (III, V and II, respectively). They invested less in soil nutrients, due mainly to insufficient resources for poor and pro-poor farms and to the livelihood strategy of off-farm preference farms. Pro-poor and poor farms were likely in a poverty-soil nutrient depletion trap. Low productivity drives poverty, which in return, aggravates nutrient mining. Farms need to recycle locally available resources (crop residues and animal manure) and use soil conservation techniques, as well as N-fixing crops. The owners of the off-farm preference farm type may need training on sustainable soil nutrient management to leverage their incentive to invest in soil fertility.

Farms with a negative soil nutrient balance and a better margin, consisting of better-off cotton-based and middle-class

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

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

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

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

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: boundia@gmail.com</footnote>
farm types (I and IV), were found in the second case. These farms invested better in soil nutrients but still insufficiently. They would face depleted soil nutrient stock in the future, lose their profitability and become problematic. These farms need to combine mineral fertiliser use and organic fertiliser, which reinforce soil organic matter and fertility. They should also invest in soil conservation practices, as well as in livestock-agriculture integration.

<figure><img src="image_256.png" /></figure>

<figure><img src="image_257.png" /><figcaption>b-Whole farm full nutrient balancesa-Farm soil subsystem nutrient balances
Fig. 1. Farm nutrient balances</figcaption></figure>

<figure><img src="image_258.png" /><figcaption>Fig. 2. Farm soil nutrient balance versus crop gross margin</figcaption></figure>

The scenario analysis showed that farms could hardly afford reversing the trend of the nutrient depletion observed in the BAU scenario. In the IMF scenario, farmers should reinvest 72% of the crop gross margin per unit of cultivated land. This seems unaffordable, given that nearly 44% of households in the region live under the poverty line at US$217/person/year (Institut National de la Statistique et de la Démographie [INSD], 2010). Fully recycling crop residues under the RCR scenario improves nutrient balance by 40–90%. However, farmers have to face the labour constraints observed for many farm types and make trade-offs between competing uses of crop residues. Livestock-agriculture integration seems the best option for farmers to maintain productive and sustainable farms.

## 4 Conclusions

The study confirmed the findings of past research that drew attention to the alarming soil depletion in Sub-Saharan Africa. By investigating soil nutrient balance and farm economic performance, the study showed the nutrient mining-poverty trap in smallholder farms. The scenario analysis indicated that removing the observed nutrient gaps by increasing the use of chemical fertilizers would be costly and inefficient for farmers. Policy interventions and farm design should focus on the subsidiary linkages between livestock and crop production.

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

## References

Cobo, J. G., Dercon, G. & Cadisch, G. (2010). Nutrient balances in African land use systems across different spatial scales: A review of approaches, challenges and progress. Agriculture, Ecosystems & Environment 136(1-2): 1-15.

De Jager, A., Nandwa, S. M. & Okoth, P. F. (1998). Monitoring nutrient flows and economic performance in African farming systems (NUTMON) I. Concepts and methodologies. Agriculture, Ecosystems and Environment 71: 37-48.

INSD (2010). Analyse de quelques résultats des données de la phase principale de l'enquête intégrale sur les conditions de vie des ménages, EICVM 2009 Ouagadougou, Institut National de la Statistique et de la Démographie.

Thiombiano, A. B. & Le, Q. B. (Submitted). Social-ecological heterogeneity in shaping smallholder farms’ nutrient management and efficiency: The case of Ioba province in Burkina Faso. Agriculture, Ecosystems and Environment.

Van der Pol, F. (1992). Soil mining. An unseen contributor to farm income in southern Mali. Amsterdam, Netherlands, Royal Tropical Institute (KIT).
# Pathways for the sustainable intensification of agriculture

D. Rodriguez *±<fnref n="1" />*, A. Bekele <fnref n="2" />, P. deVoil <fnref n="1" />, M. Herrero <fnref n="3" />, B. Power <fnref n="3" />, M. Rufino <fnref n="4" /> & M.T. van Wijk <fnref n="5" />

## 1 Introduction

Sustainable development is at the top of the most challenging tasks facing humanity, and agriculture, being the largest business in the world, has a key role to play. The largest potential for the sustainable development of agriculture exists in parts of the world where most of the population is involved in small scale farming (up to 90%), and where the demand for food and levels of poverty are the largest i.e. Sub Saharan Africa. However, given the large diversity of agro-ecological, socio-economic and market conditions smarter approaches are required to guide investments and interventions that bridge the gap between present and achievable levels of farm production. Here we combined data from a household survey (n=672) and a dynamic and functional whole farm model (APSFarm-LivSim) to describe likely pathways of agriculture intensification across four agro-ecologies in Ethiopia. Intensification pathways were defined in terms of the effects of increasing on-farm investments on the reduction of down side risk for food security i.e. the likelihood of not attaining the household energy requirements; as a function of the household potential for intensification i.e. difference between present and achievable levels of production of maize, sorghum, beans, teff and livestock products (Fig. 1).

## 2 Materials and Methods

<figure><img src="image_259.png" /></figure>

**Fig. 1.** Hypothesised framework for the analysis of pathways for the intensification of agriculture for households having contrasting intensification trajectories i.e. households that after intensification are likely to remain food insecure; households exposed to a high risk pathway to reduce food production gaps; and households exposed to low risks as they reduce production gaps and move towards the "confort" zone i.e. low chance of being food insecure after the intensification potential has been significantly reduced. The baseline situation, before intensification, is represented in the graph by the red circles.

We used data from an extensive and homogeneous household survey collected by the SIMLESA program (http://aciar.gov.au/page/simlesa-program), across four contrasting agro-ecologies in Ethiopia. The survey data was used to parameterise a whole farm simulation model i.e. APSFarm-LivSim. The model was used to describe the existing diversity in pathways for intensification (Fig. 1) by simulating all the farms in the survey (n=672). The LivSim model (Rufino et al., 2009) was linked to the APSFarm model (Rodriguez et al., 2011), and; used to study the relationship between the chance of not meeting household energy requirements as levels of productivity of maize, sorghum, teff, beans and livestock increases. Each of the 672 households were simulated using climate records from the MarkSim V2 model (http://www.ccafs-climate.org/pattern-scaling/) i.e. 99 years, baseline 2013, for nine sites associated to the four agro-ecological regions present in the survey i.e. Adami Tulu, Meskan, Bako Tibe, Mesrak Nadawacho, Dugda, Pawe, Gubuesyo, Shalla, and Hawasa Zurya.

<footnote n="1">¹ Queensland Alliance for Agriculture and Food Innovation (QAAFII), The University of Queensland, Australia</footnote>

<footnote n="2">² Ethiopian Institute for Agriculture Research (EIAR), Ethiopia</footnote>

<footnote n="3">³ CSIRO, Brisbane Australia</footnote>

<footnote n="4">⁴ CIFOR, Indonesia</footnote>

<footnote n="5">⁵ ILRI, Costa Rica</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: d.rodriguez@uq.edu.au</footnote>
## 3 Results - Discussion

Based on total on-farm energy production, intensification of agriculture is likely to meet the energy requirements of more than 60% of the surveyed farmers in Ethiopia (Fig. 2). Different success rates can be expected in different regions. The Sub-humid region appears to be the most challenging (only 49% food secured) while the Humid regions the most promising (68% food secured). Identifying low risk and high-risk pathways is also likely to help better target interventions in regions where the likelihood of farmers not meeting household requirements is high. Livestock keeping, and technology adoption were associated to the low risk pathway, while households remaining food insecure had larger proportion of the farm income from off-farm sources.

<figure><img src="image_260.png" /><figcaption>**Fig. 2.** Proportion of households across the four studied regions that fall within each of the proposed pathways for intensification in Figure 1, i.e. households that are likely to remain food insecure after the intensification took place; households that are exposed to high risk during the intensification process; and households that can be intensified at a low risk.</figcaption></figure>

## 4 Conclusions

Ignoring the contribution from off-farm income, significant sections of the population are likely to remain food insecure after intensification in the Central Rift Valley and Sub Humid regions. Low risk household usually have larger number of

livestock (tropical livestock units, TLU), and have adopted a larger number of improved technologies. Households that are likely to remain food insecure showed larger proportion of their income originating from off-farm sources.

<table><caption>**Summary of pathway groups characteristics**</caption><thead><tr><th></th><th>Remains food insecure</th><th>High risk</th><th>Low risk</th></tr></thead><tbody><tr><td><strong>Humid</strong></td><td></td><td></td><td></td></tr><tr><td>Farm size(ha)</td><td>2</td><td>2</td><td>3</td></tr><tr><td>TLUs</td><td>34</td><td>38</td><td>49</td></tr><tr><td>Manure (t/ha)</td><td>1432</td><td>1734</td><td>1013</td></tr><tr><td>Fertilisers (kg/ha)</td><td>96</td><td>85</td><td>86</td></tr><tr><td>Tecnology factor</td><td>26</td><td>27</td><td>29</td></tr><tr><td>Household size</td><td>5</td><td>6</td><td>7</td></tr><tr><td>Experience in maize</td><td>16</td><td>18</td><td>19</td></tr><tr><td>Distance to seed markets</td><td>35</td><td>38</td><td>35</td></tr><tr><td>Off farm income (%)</td><td>23</td><td>19</td><td>17</td></tr><tr><td>% produce sold</td><td>12</td><td>18</td><td>27</td></tr><tr><td><strong>CRV</strong></td><td></td><td></td><td></td></tr><tr><td>Farm size(ha)</td><td>1</td><td>2</td><td>2</td></tr><tr><td>TLUs</td><td>6</td><td>7</td><td>14</td></tr><tr><td>Manure (t/ha)</td><td>1561</td><td>496</td><td>565</td></tr><tr><td>Fertilisers (kg/ha)</td><td>23</td><td>29</td><td>34</td></tr><tr><td>Tecnology factor</td><td>25</td><td>28</td><td>31</td></tr><tr><td>Household size</td><td>7</td><td>7</td><td>7</td></tr><tr><td>Experience in maize</td><td>24</td><td>18</td><td>18</td></tr><tr><td>Distance to seed markets</td><td>42</td><td>53</td><td>65</td></tr><tr><td>Off farm income (%)</td><td>43</td><td>37</td><td>29</td></tr><tr><td>% produce sold</td><td>16</td><td>17</td><td>19</td></tr><tr><td><strong>Sub humid</strong></td><td></td><td></td><td></td></tr><tr><td>Farm size(ha)</td><td>3</td><td>3</td><td>3</td></tr><tr><td>TLUs</td><td>6</td><td>3</td><td>4</td></tr><tr><td>Manure (t/ha)</td><td>1804</td><td>2903</td><td>2376</td></tr><tr><td>Fertilisers (kg/ha)</td><td>35</td><td>22</td><td>22</td></tr><tr><td>Tecnology factor</td><td>32</td><td>30</td><td>32</td></tr><tr><td>Household size</td><td>5</td><td>4</td><td>4</td></tr><tr><td>Experience in maize</td><td>17</td><td>16</td><td>13</td></tr><tr><td>Distance to seed markets</td><td>57</td><td>78</td><td>65</td></tr><tr><td>Off farm income (%)</td><td>25</td><td>26</td><td>32</td></tr><tr><td>% produce sold</td><td>19</td><td>16</td><td>18</td></tr><tr><td><strong>Semi dry</strong></td><td></td><td></td><td></td></tr><tr><td>Farm size(ha)</td><td>2</td><td>2</td><td>2</td></tr><tr><td>TLUs</td><td>10</td><td>18</td><td>16</td></tr><tr><td>Manure (t/ha)</td><td>2097</td><td>1798</td><td>1692</td></tr><tr><td>Fertilisers (kg/ha)</td><td>64</td><td>71</td><td>66</td></tr><tr><td>Tecnology factor</td><td>36</td><td>36</td><td>37</td></tr><tr><td>Household size</td><td>8</td><td>7</td><td>7</td></tr><tr><td>Experience in maize</td><td>22</td><td>20</td><td>22</td></tr><tr><td>Distance to seed markets</td><td>58</td><td>57</td><td>65</td></tr><tr><td>Off farm income (%)</td><td>15</td><td>23</td><td>22</td></tr><tr><td>% produce sold</td><td>14</td><td>20</td><td>14</td></tr></tbody></table>

<caption>**Table 1.** Table of mean values of main characteristics of the households in the Humid, Sub-humid, Central Rift Valley, and Semi dry regions of Ethiopia, within each of the identified pathway paths.</caption>

*Acknowledgements.* This research is part of the Sustainable Intensification of Maize and Legume cropping Systems in Eastern and Southern Africa (SIMESA) funded by the Australian Centre for International Agriculture Research (ACIAR)

## References

Rodriguez D., deVoil P., Power B., Crimp S., Meinke H. (2011). The intrinsic plasticity of farm businesses and their resilience to change. Field Crops Research 124, 157-170

Rufino M.C., Herrero M., Van Wijk M. T., Hemerik L., De Ridder N., Giller K.E. (2009) Lifetime productivity of dairy cows in smallholder farming systems of the Central highlands of Kenya. Animal 3:7, 1044–1056.
# Tailoring cropping systems to variable climate, diverse farms and landscapes

Leonard Rusinamhodzi<sup>*±1</sup>, David Berre<sup>2</sup>, Santiago-Lopez Ridaura<sup>3</sup> & Marc Corbeels<sup>4</sup>

## 1 Introduction

Soil fertility depletion and climatic volatility are the major biophysical barriers confronting small scale farmers in Africa (e.g. Smaling et al., 1997; Challinor et al., 2007). On the other hand, African farming systems and landscapes are highly heterogeneous creating complex socio-ecological environments characterised by wide differences in farmers’ resource endowments and the use of such resources (van Wijk et al., 2009). Although a basket of possible technologies is available, poor resource endowments and seed and fertilizer costs limit the options available for farmers to improve their circumstances. Predictive crop modelling may help to explore the *ex ante* effects of an array of intensification options and answer most of the ‘what if’ questions (Jones et al., 2003). Participatory modelling with farmers provides for integrative analysis and development of alternatives for increased productivity and sustainability, at both the cropping and farming systems levels. Thus a comprehensive farm design has to take into account climatic variability and change, diverse farms (household priorities and production objectives), and a landscape consisting of fields of different soil fertility status. The objective of this study was to assess the potential of conventional tillage and conservation agriculture to offset the effects of climate variability and change on crop productivity across farms of different resource endowment. This interactive analysis at multi-scales allows the identification of cropping systems that are suitable to farmers’ conditions, increase productivity and offer possible adaptation strategies to climate variability and change.

## 2 Materials and Methods

The modelling approach used historical as well as generated future climatic data, farm and field typologies from a case study site in Monze, Zambia to explore the trajectories of current and alternative cropping systems. Climatic data was input into the Agricultural Production Systems Simulator (APSIM), version 7.6 with management scenarios derived from different farm typologies created by classifying farmers based on resource ownership and production orientation. The model was described in detail by Keating et al. (2003). APSIM is a process-based model developed to simulate biophysical processes in farming systems in response to management decisions as well as climatic perturbations (Keating et al., 2003). In this study the APSIM model was used to simulate the productivity of maize under conventional and conservation agriculture options with different scenarios of future climate change generated using global circulation models (GCMs). APSIM was calibrated and evaluated using data derived from a long-term agronomic experiment at Monze in Zambia. Future climate change met files were generated by an ensemble of 17 global circulation models (GCMs) using two extreme emission scenarios: (a) the low emission scenario - Representative Concentration Pathway (RCP2.6), and (b) the high emission scenario - Representative Concentration Pathway (RCP8.5). The weather files were generated and downscaled using MarkSim web version for IPCC AR5 data (CMIP5) (Jones & Thornton, 2000). To assess effects of climate change, the 2013/14 cropping season was taken as base and compared with the future season of 2049/50.

## 3 Results - Discussion

Future (2049/50) projected climate for Monze showed no significant change in solar radiation, but higher total season rainfall compared with current climatic conditions. There was an increase in both minimum (+1°C) and maximum (+1.5 °C) temperatures for the two emission scenarios. However, the ensemble of models showed high variability indicating an uncertainty in future climate prediction. Farmer classification revealed four broad farm types (F1-F4) which confirmed the existence of heterogeneity across farms with fertiliser use ranging from 0 kg ha⁻<fnref n="1" /> to 100 kg ha⁻<fnref n="1" /> per year. Livestock ownership and land cultivated also differed greatly across the farms (2.5 - 4.3 ha yr⁻<fnref n="1" />). Consequently these differences had a significant effect on crop productivity (Fig. 1). There was a direct relationship between resource ownership and crop productivity for both climate change scenarios. Farm type two (F2) who did not own cattle and did not afford fertiliser achieved the least yield that contrasted sharply with that from F4 farmers who owned the most livestock and applied the largest fertiliser of about 100 kg ha⁻<fnref n="1" />. Simulated crop yield results showed that the advantage of CA in the future will be for the low emission scenario only (Fig. 1). This is because of the projected increase in

<footnote n="1">¹ CIRAD-Annual Cropping Systems C/O CIMMYT, P.O. Box MP163, Mt Pleasant, Harare, Zimbabwe</footnote>

<footnote n="2">² CIMMYT, P.O. Box MP163, Mt Pleasant, Harare Zimbabwe</footnote>

<footnote n="3">³ CIMMYT, Apdo. Postal 6-641 06600 Mexico, D.F., Mexico</footnote>

<footnote n="4">⁴ CIRAD-Annual Cropping Systems C/O Embrapa-Cerrados, BR 020 - Rodovia Brasília/Fortaleza, Planaltina, DF, Brazil</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: leonard.rusinamhodzi@gmail.com</footnote>
rainfall in combination with the moisture conservation effects through crop residues retention that may lead to waterlogging (Araya and Stroosnijder, 2010).

<figure><img src="image_261.png" /><figcaption>Fig. 1. Maize yield probability distribution for conventional tillage (CT) and conservation agriculture (CA) with projected future climate (RCP2.6 and RCP8.5) for the four farm types (F1-F4).</figcaption></figure>

## 4 Conclusions

CA has potential to mitigate against moisture stress but may depress yields when moisture is abundant. There is need to understand better the impact of a combined increase in both temperature and rainfall as the impact of this two variables has been assessed when temperature is predicted to increase and rainfall to decrease. Historical data shows that cropping seasons are starting late which reduces significantly the planting window i.e. the option of staggering planting dates to deal with climatic uncertainties may not be viable in the future.

*Acknowledgements.* We thank Dr. Christain Thierfelder for providing data from the long-term trial at Monze, Zambia. Financial support from CCAFS through the CGIAR-CRP Twin Post-Doc Program is greatly appreciated.

## References

* Araya, A., Stroosnijder, L., 2010. Effects of tied ridges and mulch on barley (Hordeum vulgare) rainwater use efficiency and production in Northern Ethiopia. Agricultural Water Management 97, 841-847.

* Challinor, A., Wheeler, T., Garforth, C., Craufurd, P., Kassam, A., 2007. Assessing the vulnerability of food crop systems in Africa to climate change. Climatic Change 83, 381-399.

* Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., Ritchie, J.T., 2003. The DSSAT cropping system model. Eur. J. Agron. 18, 235-265.

* Jones, P.G., Thornton, P.K., 2000. MarkSim: Software to generate daily weather data for Latin America and Africa. Agron. J. 92, 445-453.

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

* Smaling, E.M.A., Nandwa, S.M., Janssen, B.H., 1997. Soil fertility in Africa is at stake. In: Buresh R. J, Sanchez, P.A. (Eds.), Replenishing Soil Retility in Africa. SSSA Special Publication SSSA, pp. 47-61.

* van Wijk, M.T., Tittonell, P., Rufino, M.C., Herrero, M., Pacini, C., Ridder, N.d., Giller, K.E., 2009. Identifying key entry-points for strategic management of smallholder farming systems in sub-Saharan Africa using the dynamic farm-scale simulation model NUANCES-FARMSIM. Agr. Syst. 102, 89-101.
# Socio-ecological conditions for food security in African drylands: A quantitative and spatially-explicit typology to facilitate learning

Diana Sietz *±<fnref n="1" />, Jenny Ordoñez <fnref n="1" />, Marcel Kok <fnref n="2" />, Peter Janssen <fnref n="2" />, Henk Hilderink <fnref n="2" /> & Han Van Dijk <fnref n="1" />

## 1 Introduction,

Food production is key to achieving food security in African drylands. Agricultural productivity is however limited due to low and variable water supply and soil fertility. In addition, climate variability, inequitable trade conditions and social exclusion frequently exacerbate the already pronounced constraints on agricultural productivity. Vulnerability is employed in this study as a concept to capture the relation between socio-ecological systems and perturbations impacting upon them. The diverse and heterogeneous conditions that shape a system's vulnerability in various locations challenge policy-making for improved food security given that decisions are usually taken at a higher than local level. To facilitate learning, the aim of this study is to identify quantitative and spatially-explicit patterns of vulnerability to global change considering farmers' and pastoralists' livelihoods in African drylands. The typology explicitly incorporates malnutrition as cause and consequence of vulnerability.

## 2 Materials and Methods

This study employed cluster-based pattern recognition relying on well-defined and formalised mechanisms that generate vulnerability (Sietz et al. 2010; Kok et al., 2015). We quantitatively indicated the most relevant environmental and socio-economic properties of the dryland systems at a sub-national resolution including water availability, soil erosion sensitivity, agropotential, child malnutrition, income, population density, urban population share, distance to markets and governance with reference to the early 2000s. After indicator normalisation, cluster analysis was performed in the nine-dimensional data space using a sequence of clust and k-means algorithms. Based on stochastic initialisation, we calculated the reproducibility of partitions for a pre-given number of clusters to determine stable partitions.

## 3 Results – Discussion

Clustering revealed nine vulnerability patterns with distinct and transparent indicator combinations (Fig. 1A; for their spatial distribution see Fig. 1B). Seven clusters are characterised by high levels of malnutrition while only two clusters show low malnutrition levels.

<figure><img src="image_262.png" /><figcaption>**Fig. 1.** Typology of vulnerability: Cluster profiles showing indicator combinations at cluster centres (1A) and spatial distribution (1B) (Note 1A: Box boundaries = 25th and 75th percentiles; whiskers = 5th and 95th percentiles; circle inside the box = mean value; bold line = median value; dots = outliers).</figcaption></figure>

<footnote n="1">1 Sociology of Development and Change, Wageningen University, The Netherlands</footnote>

<footnote n="2">2 Netherlands Environmental Assessment Agency, The Netherlands</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: diana.sietz@wur.nl</footnote>
The clusters in *extremely dry areas* (red, orange and yellow clusters) describe most resource-constrained situations, both in terms of natural resource quality and income, medium to very high malnutrition and low levels of governance. In clusters with *better agropotential* (dark blue, light blue and turquoise clusters), the somewhat better natural resource endowment and market access do not translate in improved wellbeing indicated by very low income and medium malnutrition. Comparable to rural areas, *semi-urban areas* are also characterised by very low income and high malnutrition, though better market access (grey cluster). In contrast, clusters with *better governance and higher income* (dark purple and light purple clusters) depict situations with lowest vulnerability and better food security.

To ground-truth our results, we used independent case studies to confirm cluster-specific mechanisms and their spatial distribution. For example, Northern Afar region in Ethiopia may serve as an example to illustrate specific processes indicated by the red cluster (Fig. 1). Recurrent droughts, food shortages and political conflicts are among the major causes of food insecurity in Northern Afar. However, pastoralists in this region have frequently been excluded from development programmes as compared to other pastoral groups in Ethiopia (Tesfay & Tafere, 2004). Government support in this remote and very sparsely populated region remains largely ineffective partly as a result of centralised, top-down approaches in project planning and implementation.

Overall, indicator combinations given by the clusters provide valuable insights into entry points for vulnerability reduction which should be reflected in policies and intervention efforts. For example, combined efforts to increase income and reduce malnutrition are required in the majority of clusters. But simultaneous enhancement of market access is most relevant in the orange cluster, governance improvement is essential in the turquoise cluster and tapping the potential of existing agropotential and better water availability is very important in the light blue cluster. Assuming that intervention options are transferable among similar socio-ecological systems, strategies such as food storage and livelihood diversification that were successful in one location are expected to reduce vulnerability and subsequently food insecurity in other locations categorised in the same cluster.

## 4 Conclusions

The cluster-based typology enables policy-makers to evaluate key inter-linkages between vulnerability and food security based on similarities among African drylands. It allows the essence of these inter-linkages to be grasped beyond individual cases, while at the same time representing spatial and functional heterogeneity at an aggregate level. Besides regional comparison, similarities support the identification of key entry points for managing transitions towards improved food security and may facilitate the transfer of successful strategies. Based on typical combinations and manageable number of key indicators, our findings enable new insights into the prioritisation of intervention efforts and related monitoring efforts.

## References

* Kok, M., Lüdeke, M.K.B., Lucas, P., Sterzel, T., Walther, C., Janssen, P., Sietz, D. & de Soysa, I. (2015). A new method for analysing socio-ecological patterns of vulnerability. *Regional Environmental Change*, Published online 1 January 2015.

* Sietz, D., Lüdeke, M.K.B. & Walther, C. (2011). Categorisation of typical vulnerability patterns in global drylands. *Global Environmental Change* 21(2), 431-440.

* Tesfay, Y. & Tafere, K. (2004). Indigenous rangeland resources and conflict management by the North Afar pastoral groups in Ethiopia. DCG Report 31, Drylands Coordination Group, Mekelle, Ethiopia.
# Improving the productive performance of family farms in Senegalese rural area

Assane Beye *±1 & Astou Diao Camara 2

## 1 Introduction

Despite the Millennium Development Goals, food security and poverty remain unavoidable challenges for the world in general and Africa in particular. However, a dynamic agricultural sector is expected to implicitly reduce poverty and food insecurity. In Senegal, growth in the agricultural sector is generally very beneficial to the poor in that it mobilizes the major endowments of underprivileged populations, and makes dynamic economy in rural areas. Moreover, given the growing nutritional needs of a population of over 13 million rising to over 19 million in 2030 and 26 million in 2050 according to the low population projections assumptions (AFD, 2013), increasing agricultural production sounds like an inescapable challenge.

Like other developing countries, Senegalese agriculture suffers mainly from low productivity. Indeed, less than half of the workforce, employed in industry and services, accounts for over 80% of GDP, while agriculture which occupies more than half of the labor force represents only 7.6% of GDP (ANSD, 2013). However, in recent years many initiatives have been taken by the government to make agriculture the engine of growth. In this context, why does the productive performance still low in Senegal? What are the drivers affecting this productive performance? Identifying the factors determining the productive performance instructs judgment and should allow to know the best farming system to improve farmer's productivity and reduce poverty and food insecurity. The overall objective of this study is to analyze the productive performance of farms in Senegalese rural area. It will first calculate the efficiency scores for each farm of the sample before identifying its determinants.

## 2 Materials and Methods

This study was conducted in the Senegal River Valley which is one of the six agro-ecological zones of Senegal with 240,000 ha of cultivable land (8% of total arable land of the country). Sampling conducted in this study has identified three zones: Matam Bakel and Kidira. The selected sampling plan was to choose three rural communities by zone, and then 40 households by rural community. At the end, 360 farms was interviewed for this study.

The productive performance of farms is analyzed through two stages. In the first stage, the efficiency scores will be calculated with the non-parametric approach Data Envelopment Analysis (DEA) for each farm. An advantage of DEA approach is that it allows the estimation of multi-input and multi-output models. Thus, the production of maize, sorghum, millet, groundnuts, cowpeas and rice are used as output. For inputs, the total area of the farm (in hectare), the labor force evaluated through the FAO weights, the capital which consists of amortization of agricultural machinery and equipment, the amount of seed used per hectare are used to calculate efficiency scores (Djimasra, 2009; Abatania et al., 2012; Fasasi, 2007).

In the second step, the proportional nature of the efficiency scores has led to the exploration of fractional response models (Papke and Wooldridge, 1976; Ramalho et al., 2011; Suhyeon, 2014), which include the generalized linear model and calls for the choice of a functional family for the distribution of errors, the binomial distribution in the study and a mathematical function for the transformation of the dependent variable called "link function", the logit distribution. The efficiency scores are thus regressed on a set of socio-economic variables assumed to explain the productive performance including age, sex, type of instruction, the first activity of the household head, the use of external labor, the household size, the existence of emigration in the household, the total number of fields, the location of the farm, irrigation, the existence of non-farm income, access to credit and herd size.

## 3 Discussions

The DEA model can simultaneously calculate total technical efficiency and its components that are pure and scale efficiencies (Coelli, 1998). A farm would be technically efficient if compared to other farms, it uses the least input for a given level of production when returns to scale are constant. The results show that with a more efficient utilization of production factors, a discount of 55.9% could be done on the inputs, while maintaining the production at the same level. The scale efficiency show that farms could reduce on average cultivated area by 20%, while maintaining the same level of production. The 12 family farms operating at optimum level have an average area of 1.25 ha. The pure efficiency reflects the best practices in terms of input management when it is assumed that the returns to scale are variable. Thus, in a short-term perspective, a reduction of 46.6% production factors is possible, while maintaining the same level of

<footnote n="1">¹Cheikh Anta Diop University of Dakar (UCAD)</footnote>

<footnote n="2">² Senegalese Agricultural Research Institute (ISRA)</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: beyassane@hotmail.com</footnote>
production. This hypothesis reflects the production conditions of our study area, thus pure efficiency was chosen for estimating the generalized linear model.

The estimation of generalized linear model shows that agriculture and livestock activities are relatively complementary as the productive performance increases with the size of the herd. This result seems logical in view of cultural practices in the area where the use of organic manure from the animals is very common. Also, animals are used for traction by more than 80% of farms (ISRA / BAME, 2013). In addition, Gueye and Dièye (2002) noted the importance of agro-pastoral systems in Senegalese agriculture due to agricultural origin of most of the animal and plant production. In the Upper and Middle Senegal River Valley, access to credit impacts strongly the productive performance of farms. Indeed, the more farms have access to credit, the more efficient they are. These credits, which may take the form of credit campaign, allow producers to adjust inputs problems especially seeds and fertilizer. It is also noted, unfortunately, that the percentage of farmers that have access to credit remains low. In our sample, only 20% of farmers have access to credit. Thus, credit access facilitation policies through public instruments should improve the productive performance of the Senegalese agriculture. Furthermore farms where farming is considered as the main activity are the best performing. Yet the possession of several fields has no impact on productive efficiency. This result calls for better targeting of agricultural promotion policies.

The results show also that farms where the household head has been at the French schools are more efficient. Indeed, the school helps develop cognitive abilities that can help them to better respect the technical procedures and integrate new technologies. Also, farms headed by men are more effective than those led by women. Similarly, the experience gained through the age acts positively on productive efficiency. However, non-farm income and revenues from transfers have no impact on the productive efficiency. Likewise, the household size does not impact the performance of farms because of the lack of interest generated by agriculture among youth. Thus, there is increasing substitution of family labor by external labor, which does not affect the productive performance. Also, during the raining season, because of the availability of water, irrigation is not decisive in achieving a high level of efficiency.

## 4 Conclusions

In a context of resource scarcity, efficiency in the allocation of inputs becomes necessary to improve the productivity of family farms in the middle and upper Senegal River Valley. Indeed, family farms could increase their productivity simply by making a better allocation of land, water, human and financial resources. To improve the performance of farms, the production system combining agriculture and livestock should be promoted by policy makers as the herd ownership positively affects the efficiency.

*Acknowledgements.* The research leading to this paper was implemented at Senegalese Agricultural Research Institute (ISRA) with financial support from Syracuse University. We are grateful for this support.

## References

Abatania L. N., Hailu A., Mugera A. W. (2012) "Analysis of Farm Household Technical Efficiency in Northern Ghana using Bootstrap DEA" 56th Conference of the agricultural and resource economics science.

Coelli, T. (1998): "A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer) Program", Centre for Efficiency and Productivity Analysis, Department of Econometrics, University of New England, Armidale, NSW 2351, Australia.

Dièye P. N. et Gueye M. (2002) "Les systèmes agriculture-élévage au Sénégal: Importance, Caractéristiques et contraintes" Reports from the workshop on Crop-Livestock

Djimasra (2009) "Efficacité technique, productivité et compétitivité des principaux pays producteurs de coton" *Thèse, Université d'Orléans*

Fasasi A. R. (2007) Technical Efficiency in Food Crop Production in Oyo State, Nigeria J. Hum. Ecol., 22(3): 245-249

Rapport Agence Française de Développement (2013) "Agriculture familiales du monde: Définitions, contributions et politiques publiques" Étude sur la contribution de l'agriculture familiale à la sécurité alimentaire

Rapport l'Isra/Bame (2013) Caractérisation des exploitations agricoles familiales de la haute et moyenne vallée du fleuve Sénégal

Papke, L. and J.M. Wooldridge (1996), "Econometric methods for fractional response variables with an application to 401(K) plan participation rates", *Journal of Applied Econometrics*, 11(6), 619-32

Ramalho, E.A., J.J.S. Ramalho and J.M.R. Murteira (2011), "Alternative estimating and testing empirical strategies for fractional regression models", *Journal of Economic Surveys*, 25(1), 19-68

Sunyeon N. (2014) "Multiple Fractional Response Variables with Continuous Endogenous Explanatory Variables" Department of Economics Michigan State University