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The development of sustainable cropping systems is a key priority for agronomists and crop scientists. A first step involves understanding the relationship between cropping system performance and farmers' practices. To complete this step, a methodological framework entitled Regional Agronomic Diagnosis (RAD) has been developed. During the last ten years, the scope of the regional agronomic diagnosis has been enlarged to include several factors describing crop quality and the environmental impact of cropping systems. Regional agronomic diagnosis has led to several major advances such as (1) the assessment of the effect of preceding crop and soil structure on malting barley quality in France and (2) the assessment of the effects of ploughing, nematicide use and fertilisers on soil properties in intensive banana plantations in the West Indies. Improvements have also been gained in methodology, particularly by the selection of indicators for assessing the effects of crop management, soil and weather conditions, and data analysis. Finally, regional agronomic diagnosis has been integrated into more general approaches of agricultural development. We review here this methodological progress. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Crop yield can be decreased by many limiting factors such as water stress, nitrogen stress and disease. The agronomic diagnosis method was developed by agronomists to understand the origin of crop yield variability, identify important limiting factors, and define new cropping systems. The rigorous implementation of this method requires the determination of boundary lines giving the maximum value of a yield component in relation to the value of another yield component; for example, grain weight versus grain number per square metre. Such boundary lines are used by agronomists to adjust cropping practices to environmental characteristics and, thus, to reduce the risk of pollution due to agricultural activities. We describe here a new method based on quantile regression to estimate boundary line parameters from experimental data. First, quantile values were computed from models describing the effect of limiting factors on yield components and accounting for measurement errors. Then, boundary line parameters were estimated by quantile regression, with observations weighted according to the quantile values. This approach was applied to two case studies. The quality of the parameter estimator derived by quantile regression was analysed in relation to the size of the dataset and the practical consequences of a misspecification of the quantile value was studied. Our findings show that quantile regression gives more accurate parameter estimators than the methods currently used by agronomists. Nonetheless, the bias and variances of these estimators highly depend on the chosen quantile value. The use of quantile regression should thus help agronomists to analyse crop yield variability from yield component measurements. [ABSTRACT FROM AUTHOR]