Environmental impacts of agriculture cannot be always assessed by using direct measurements. Since the 1990s, numerous agri-environmental indicators were developed to assess the adverse effects of cropping and farming systems in the environment, such as water pollution, soil erosion, and emission of greenhouse gases. Here we present the different types of indicators developed during the last decade and review the progress of the methods used for their development. The application of different groups of indicators is discussed and illustrated by examples in the fields of nitrogen losses and pesticide risk: (1) indicators based on a single or a combination of variables related to farmer practices, (2) indicators derived from operational or more complex simulation models assessing emissions of pollutants, and (3) measured indicators linked directly to environmental impacts. The nitrogen indicator (IN) of the INDIGO method and the MERLIN indicator will be presented and used to illustrate the methodological discussion. We show that a good identification of the end-users, of the practical objectives of the indicator, and of the spatial and temporal scales is essential and should be done at a preliminary step before designing the indicator itself. The possibilities of deriving an indicator from a model and of setting a reference value are discussed. Several methods are also presented to study the sensitivity and the validity of agri-environmental indicators. Finally, several practical recommendations are made. As only few data are usually available at the regional level, several simple indicators should be used for assessing a given impact at this level. When more detailed information is available, indicators based on operational models can be useful to analyse the effects of several factors related to soil, climate, and cropping system on an environmental impact. In experimental studies, we suggest using both measured indicators and model-based indicators

Research has delivered convincing findings on the effect of biodiversity on ecosystem functioning and humankind. Indeed, ecosystems provide provisioning, regulating, supporting, and cultural services. The global value of annual ecosystem services of grasslands and rangelands is about US$ 232 ha −1 year −1 . Nevertheless, the precise evaluation of biodiversity benefits remains challenging. This issue is due to valuation methods, subjective assumptions, and complexity of drivers of plant community dynamics. Here, we review the primary factors that influence plant diversity of permanent grasslands, and we describe underlying processes. These factors must indeed be identified to focus policies meant to preserve and restore plant diversity and to advise farmers about efficient decision rules. We show that plant dynamics of permanent grasslands cannot be explained simply by agricultural management rules, e.g., grazing, fertilization, and mowing, implemented at the field scale. The configuration of the surrounding landscape, e.g., landscape heterogeneity, habitat fragmentation, and connectivity, acts as a species filter that defines the regional species pool and controls seed flow. The regional species pool often contains higher species richness in a heterogeneous landscape, because of a higher diversity of suitable habitats. This regional pool could be a major species sources for permanent grasslands according to the seed flow. We discuss the need to consider all of these factors to understand plant species composition of permanent grasslands and the necessity to study plant communities using both taxonomic and functional approaches. In order to report this integrative approach, we propose a conceptual model based on three ecological challengesdispersal, establishment, and persistence-that are considered to act as filters on plant diversity, and a graphical representation of the complexity of such studies due to the interaction effects between plant dispersal abilities, forage productivity, disturbances induced by farming practices, and landscape heterogeneity on plant diversity. Last, we discuss the ability of farmers to manage each factor and the necessity of such study in the improvement of the current agro-environment schemes efficiency for farmland biodiversity restoration or preservation.

Functional trait databases are powerful tools in ecology, though most of them contain large amounts of missing values. The goal of this study was to test the effect of imputation methods on the evaluation of trait values at species level and on the subsequent calculation of functional diversity indices at community level using functional trait databases. Two simple imputation methods (average and median), two methods based on ecological hypotheses, and one multiple imputation method were tested using a large plant trait database, together with the influence of the percentage of missing data and differences between functional traits. At community level, the complete-case approach and three functional diversity indices calculated from grassland plant communities were included. At the species level, one of the methods based on ecological hypothesis was for all traits more accurate than imputation with average or median values, but the multiple imputation method was superior for most of the traits. The method based on functional proximity between species was the best method for traits with an unbalanced distribution, while the method based on the existence of relationships between traits was the best for traits with a balanced distribution. The ranking of the grassland communities for their functional diversity indices was not robust with the complete-case approach, even for low percentages of missing data. With the imputation methods based on ecological hypotheses, functional diversity indices could be computed with a maximum of 30% of missing data, without affecting the ranking between grassland communities. The multiple imputation method performed well, but not better than single imputation based on ecological hypothesis and adapted to the distribution of the trait values for the functional identity and range of the communities. Ecological studies using functional trait databases have to deal with missing data using imputation methods corresponding to their specific needs and making the most out of the information available in the databases. Within this framework, this study indicates the possibilities and limits of single imputation methods based on ecological hypothesis and concludes that they could be useful when studying the ranking of communities for their functional diversity indices.