Nicolas Urruty
a,b,*, Hervé Guyomard
a, Delphine Tailliez-Lefebvre
c, Christian Huyghe
a(a) INRA, CODIR Agriculture, 147 rue de l’Université, F-75338 Paris, France
(b) University of Poitiers, PRES France Centre Atlantique Université, 15 rue de l’Hôtel Dieu, 86073 Poitiers, France
(c) AgroSolutions, 81 avenue de la Grande Armée, Paris, France
Abstract
Characterization and quantification of wheat exposure to abiotic perturbations is of high importance to assess the robustness of agricultural systems to future climatic conditions as well as the identification of adequate measures to adapt to unpredictable changes. In this paper, we propose an original approach to characterize and quantify abiotic perturbations on wheat yield and we apply this framework to a large network of 145 French wheat-growing farms. First, abiotic perturbations were characterized at farm level through the use of 15 indicators related to different weather limiting factors and different growth stages. Next, these indicators were used to define a limited number of wheat-producing environments. Finally, the STICS crop model was used to simulate wheat yields under variable weather conditions over the period 1983-2014 and develop an index measuring the aggregated impact of abiotic perturbations on wheat yield. Our results highlight remarkable spatial differences in both the nature and intensity of abiotic perturbations. Results of temporal variability show that the intensity of abiotic perturbations and their inter-annual variability have increased in several specific climatic zones, demonstrating the growing context of unpredictability for wheat producers. Our study contributes to a better understanding of the impact of climate variability on wheat yields and allows the use of this index for the assessment of yield robustness.
Key words: Bread wheat yield; abiotic perturbations; weather varibility, crop modeling;
stress index.
I. Introduction
Wheat is one of the major food crops in the world. Worldwide production reached 730 million tons in 2015 (FAO, 2016). However, due to unpredictable perturbations such as weather variability or pest outbreaks, its yield can vary considerably at both farm and regional level and from one year to another (Olesen et al., 2000). By the end of this century, climate change is expected to increase weather variability, pest dynamics and extreme events such as droughts and storms (IPCC, 2014). Consequently, developing a better understanding of the relationships between weather variability and crop production is crucial to effectively address
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how climate change may affect food security and propose region-specific adaptation and mitigation strategies (National Research Council, 2010).
Recently, various concepts have been developed to discuss the sustainability of agricultural systems in a context of global changes (Urruty et al., 2016). Among them, the concept of robustness can be defined as the ability of agricultural systems to maintain its performances under changing weather or economic conditions. Despite some applications in animal science (ten Napel et al., 2011; Ollion, 2015), the empirical assessment of robustness remains a challenge, notably in arable cropping systems. For its implementation, it is necessary to define precisely the boundaries of the system under consideration (robustness of what?), the type of perturbations (robustness to what?) and the performance studied (robustness for
what?). In the specific case of wheat production, an urgent need is thus to develop indexes to
assess local-level exposure to a perturbation and measure the capacity of agricultural systems to resist and adapt to that perturbation.
Many studies have focused on the influence of meteorological drivers and weather variability on wheat yield (Bray et al., 2000; Porter and Semenov, 2005; Lobell and Burke, 2010; Ceglar
et al., 2016). In recent years, the number of studies published on the subject has grown, at
the same time as climate change concerns have become increasingly important. However, the quantification of local-levels of exposure to abiotic perturbations has been usually restricted to specific factors such as drought (Luers et al., 2003; Simelton et al., 2009) or heat waves (Barnabas et al., 2008), and/or at particular growth stages, such as during grain filling (Liu et
al., 2014). Few studies have attempted to construct aggregated indexes of local-level
exposure taking into account the combination of abiotic perturbations over the different growth stages of wheat (Holzkämper et al., 2013, Caubel et al., 2015; Thiry et al., 2016). These approaches are usually based on expert-assessment and, consequently, involve subjective judgment for the aggregation of the indicators. In particular, aggregation weights and normalization functions can vary considerably according to the expert assessment and her (his) specific area of knowledge (Caubel et al., 2015). Other approaches are based on mechanistic (dynamic) crop modeling (van der Velde et al., 2012) but very few have been applied to large-scale implementations and real-farm conditions.
The aim of this paper is thus to develop an index of abiotic perturbations at farm level which summarizes the negative impacts of weather variability on wheat growth and yield. We provide an original approach based on both agro-climatic indicators and the use of a crop model, and we apply this framework to a large network of 145 French wheat-growing farms. This approach was divided into four steps (Figure 12): 1) the main abiotic limiting factors of wheat were characterized at farm level through the use of 15 agro-climatic indicators related to different limiting factors and different growth stages; 2) a classification of the main wheat-producing environments was established on the basis of the average intensity of abiotic limiting factors; 3) the impacts of weather variability on wheat yield were assessed through crop modelling; and finally 4) an index of abiotic perturbations was calculated.
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Figure 12 : The four steps of the method and materials used to develop the index of abiotic
perturbations
II. Materials and methods
II.1. Data
Our approach was performed using data from a sample of 145 farms involved in the French governmental program Ecophyto, intended to significantly reduce agricultural and non-agricultural pesticide use (Ecophyto Plan, 2008). The dataset include wheat yields, soil characteristics and numerous agronomic practices of a total of 2,327 wheat parcels over the period 2011 to 2014. The dataset was provided by the major French agricultural cooperative group InVivo and its member cooperatives.
Daily meteorological data over the period 1983-2014 were provided by Météo-France for each farm location. They originate from the SAFRAN/France database which combines weather observations and model simulations at the scale of 8x8 km spatial meshes. This database has been validated over a wide range of environmental conditions in France (Quintana-Seguí et