Laure Hossard

Lack of evidence for a decrease in synthetic pesticide use on the main arable crops in France

The frequent, widespread use of pesticides in agriculture adversely affects biodiversity, human health, and water quality. In 2008, the French government adopted an environmental policy plan, Ecophyto 2018, to halve pesticide use within 10years. Trends in synthetic pesticide sales and use in France were described, through three different indicators: the number of unit doses (NUD), the quantity of active ingredient (QAI), and the treatment frequency index (TFI). Changes in pesticide use on seven of the principal arable crops in France since the implementation of this policy plan were analyzed, together with the impact of changes in pesticide use on water quality. No evidence was found for a decrease in pesticide sales at national level between 2008 and 2013. In terms of the TFI values for individual crops, the only decrease in pesticide use observed since 2001 was for soft wheat. This decrease was very slight, and pesticide use did not decline more rapidly after 2006 than before. Changes in pesticide use differed between French regions and crops. Water pollution did not decrease during the period studied. Possible explanations for the lack of effectiveness of the French environmental plan are considered in the context of European legislation.

Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France

Abstract Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16xa0days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002–2012 showed that sowing in the Camargue was usually done on or around November 1st (±4xa0days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.

A web application to facilitate crop model comparison in ensemble studies

Crop models are reference tools that can be used to evaluate the performances of cropping systems under current and future agro-climatic scenarios. A recent trend is the adoption of multi-model ensembles, as crop model responses vary across pedoclimatic contexts. We present the web application MOBEDIS, aimed at investigating the causes of differences in crop models’ behaviour. MOBEDIS combines non-parametric statistical methods (Spearman correlation, Random Forest, Hierarchical clustering, Mantel statistics) to analyze and cluster crop models according to the relationship between final outputs (e.g., yield) and a set of intermediate outputs related to plant processes. We applied MOBEDIS in three case studies to (1) discuss its capability to facilitate the understanding of the behaviour of two crop models in a simulation experiment, and (2) prove its applicability for model ensemble studies. MOBEDIS is freely available and ready-to-use for understanding single model responses and identifying groups of crop models sharing similar behaviour.