Incidence of limiting factors on organic winter wheat performance

M. Casagrande, C. David, M. Valantin-Morison, and M. H. Jeuffroy

Abstract – French organic farmers are asked to increase Grain Protein Content (GPC) of organic winter wheat to guarantee the breadmaking quality required from millers. Facing with the recent depletion of organic products, farmers also need to stabilize and increase the yield performance. The aim of our study is to give the explanations of the yield and GPC variability generally observed in organic production. A diagnostic method will be implemented on a network of ca. 10 fields per year during two crop years added with previous field network (77 fields) and experiments (4 experiments on cultivars). We will understand yield and GPC performance from environmental and agronomic conditions  and relate the observed limiting factors with crop management, climatic conditions and characteristics of the cropping systems.

Introduction
The low organic wheat grain yields (4,4 t/ha on average in 2004, in Agence Bio, 2005) are essentially explained by several factors such as nitrogen (N) deficiency, water deficit, weeds, pests and diseases or compacted soil structure (Taylor et al., 2001). Organic wheat production is also characterized by variable yields (from 1t/ha to 6,5 t/ha in a single region, (David et al., 2004a) and low and variable GPC (from 7 to 13 g per 1000 g). Moreover, the balance between offer and demand on organic wheat leads to a price decline : 255 €/t for organic milling wheat in 2005 against 365 €/t in 2002 (Viaux, personal communication). Consequently, millers often disqualify or discount batches when GPC is too low facing breadmaking criteria.
It is now critical for organic farmers to increase and stabilize grain yield and GPC throughout a better control of limiting factors affecting winter wheat production.
As grain yield and GPC are strongly linked to N availability in soil, assessment of adequate spring organic fertilisation is a real stake.
Optimal strategies of N fertilization can be derived from the Azodyn-Org model (David et al., 2004b), as soon as the occurrence and effect of limiting factors on the field can be predicted (David et al., 2005). It is thus necessary to assess early indicators to forecast the occurrence of limiting factors and their effects on yield and GPC. The aim of this work is to understand and rank the limiting factors explaining both yield and GPC and then identify the characteristics of the cropping systems explaining these limiting factors. We will also generalize the results from the previous study conducted only on yield in the Southeast of France (David et al., 2005) by enlarging the studied sites 

Materials and methods
This research is based on a multi-year and multi-site fields’ network. The diagnosis method used (Doré et al., 1997) is divided in two steps: (i) analyse the yield build-up from environment and crop status on farmers’ fields and then (ii) identify the characteristics of cropping systems, climate and soil responsible for differences in environment and crop status.

Experimental design
Three field network taken from previous studies were chosen, in order to provide a wide range of conditions involving various limiting factors : 52 fields during 4 crop years in Southeast of France (David et al., 2005), 25 fields during 2 crop years in the Paris Basin (Champeil, 2004) and four experiments on cultivars during 5 crop years (Rolland et al., 2003). 
Moreover, a new farmers’ fields’ network will be designed for the next two years (2005-2006 and 2006-2007) including circa 10 organic winter wheat fields per crop year within Southeast of France in order to cover a large range of environmental conditions (particularly soil types and water availability) and cropping systems, leading a priori to various limiting factors (David et al, 2005). Cropping systems are characterised by crop rotation, previous crop, soil tillage, sowing date and density, N fertilization (type of fertilizer, date, amount), weeding and irrigation (date, amount). Some fields will include an irrigated treatment to avoid water stress. Moreover variation in weed pressure and N fertilization will be included within some fields, by applying various fertilization strategies or controlled treatments with (no weeding) or without (by hand weeding) weeds. Thus several treatments will be provided in some fields, varying in water availability, N availability and weed pressure.

Measurements
The physical and chemical composition of soil samples (10 per field) will be measured (texture, N, P, K and organic matter content). Daily weather data (minimum and maximum temperature, rainfall, radiation and evapotranspiration) will be recorded.
At the end of winter, an homogeneous zone (with regard to soil type, wheat density and stage and weed population) will be determined per field. Within this area, 7 sample plots (0,25 m²) per treatment will be collected at harvest to measure grain yield, thousand kernel weight, kernel number and GPC.
Several indicators of limiting factors will be measured. Water stress will be estimated from a dynamic water balance. High temperatures will be calculated. Weed pressure will be characterized at different stages through several variables : density, cover rate, Leaf Area Index, height ratio with wheat, aboveground biomass and N content of weeds and wheat height, biomass and N content at different stages. Aerial foliar diseases will be assessed on the upper two leaves of wheat after flowering, soil diseases will be assessed at stem elongation and lodging will be assessed at harvest. In each field, a soil profile pit will be examined after flowering in order to determine the soil compaction.
Farmers cropping systems and farmers techniques will be recorded by interviews.

Statistical analysis
The identification of limiting factors and the quantification of their effect on yield and GPC will be conducted using quantile regression (Cade and Noon, 2003).

Results and expected results
The effects of environment and cropping system on the yield-build up and GPC will be analysed in four stages : (i) identifying the yield and quality components that best explained variations in output, (ii) identifying the environmental conditions of the fields (iii) analysing the relationships between indicators of possible limiting factors and yield components and (iv) determining the relationships between cropping system characteristics and measured yield-limiting factors.
In a previous study (David et al., 2005) observed that the heat temperature during grain filling  limiting thousand grain weight. However, the lack of diversity within the field network considering water rainfall and stress did not allow to evaluate the effect of water balance on wheat performance. They also demonstrated that N nutrition and weeds mainly limited the kernel number/m² when soil compaction limited kernel weight. The diagnosis also highlighted that sowing date, crop rotation and previous crop mainly determined the level of the N nutrition and weeds.
With the new designed fields’ network including various treatments we expect a large range of environmental conditions and cropping systems : mixed farms producing cereals and fodder legumes with organic manure, more intensive mixed farms producing cereals, grain legumes and/or hoed crops with organic N fertilizer and extensive mixed or arable farms with poor fertilization. With this including various treatments we want to explain the GPC variability by the limiting. We also expect that the new network associated with previous databases will help to precise the ranking of the limiting factors especially and to identify some expected limiting factors such as water stress.
The characteristics of the cropping systems involved in the limiting factors will be determined, thus allowing us to imagine some recommendations to the farmers in order to reduce their yield and GPC variability and improve their performance.

Conclusion
This work will provide results on the effects of limiting factors on yield and GPC by using the diagnostic method. Then it will be necessary to find some early indicators of these limiting factors in order to use them to determine adequate fertilization or weeding strategies. One of the issue of this work is to build a tool used by farmers that allow to apply optimal fertilization strategies by observing early and accessible indicators on field.

References
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Cade, B.S., and B.R. Noon. (2003). Frontiers in Ecology and the Environment 1:412-420.

Champeil, A. (2004). Contribution à la compréhension des effets des systèmes de culture sur l'infection des cultures de blé tendre d'hiver par la fusariose et la contamination des grains par les mycotoxines associées. PhD Thesis. INA P-G.

David, C., P. Viaux, and J.-M. Meynard. (2004a). Le Courrier de l'Environnement de l'INRA 51:43-53.

David, C., M.-H. Jeuffroy, S. Recous, and F. Dorsainvil. (2004b). European Journal of Agronomy 21:249-266.

David, C., M.-H. Jeuffroy, J. Henning, and J.-M. Meynard. (2005). Agronomy for Sustainable Development 25:213-223.

Doré, T., M. Sebillotte, and J.-M. Meynard. (1997) Agricultural Systems 54:169-188.

Rolland, B., A. Bouguennec, X. Charrier, O. Gardet, A. Faye, M.A. Rifai, J.Y. Morlais, and F.X. Oury. (2003). In "Actes du Séminaire INRA-CIAB", Draveil, France.

Taylor, B.R., C.A. Watson, E.A. Stockdale, R.G. McKinlay, D. Younie, and S.A.A. Cranstoun. (2001). "Current practices and future prospects for organic cereal production : survey and literature review". (HGCA).