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Livestock production is important for food security, nutrition, and landscape maintenance, but it is associated with several environmental impacts. To assess the risk and benefits arising from livestock production, transparent and robust indicators are required, such as those offered by life cycle assessment. A central question in such approaches is how environmental burden is allocated to livestock products and to manure that is re-used for agricultural production. To incentivize sustainable use of manure, it should be considered as a co-product as long as it is not disposed of, or wasted, or applied in excess of crop nutrient needs, in which case it should be treated as a waste. This paper proposes a theoretical approach to define nutrient requirements based on nutrient response curves to economic and physical optima and a pragmatic approach based on crop nutrient yield adjusted for nutrient losses to atmosphere and water. Allocation of environmental burden to manure and other livestock products is then based on the nutrient value from manure for crop production using the price of fertilizer nutrients. We illustrate and discuss the proposed method with two case studies., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In life cycle assessment (LCA), simple models are currently used to estimate cropping system nitrogen (N) emissions on farms. At large spatial scales (e.g., countries), these models are valid. At a smaller spatial scale (e.g., territories), these models may be less accurate, since they completely or partially ignore local conditions such as management practices, soil or climate. The purpose of this study was to consider the variability of those factors when estimating N emissions in LCA at the watershed scale. To this end, Syst'N, decision-support software based on a simulation model of crop and soil N dynamics at field and crop-rotation scales, was applied to predict N emissions from cropping systems in a coastal watershed (Lieue de Grève, France). Syst'N predictions were compared to N emissions estimated by AGRIBALYSE, a static site-dependent method at field and single-crop scales. Syst'N was more sensitive to site-specific soil properties than AGRIBALYSE. Estimates of N emissions that include spatial variability in soil and climate therefore become possible in LCA when a simulation model such as Syst'N is used in the inventory phase.

The need for better conciliation between food production and environmental protection calls for new conceptual approaches in agronomy. Ecological intensification (EI) is one of the most encouraging and successful conceptual frameworks for designing more sustainable agricultural systems, though relying upon semantic ambivalences and epistemic tensions. This article discusses abilities and limits of the EI framework in the context of strong social and environmental pressure for agricultural transition. The purpose is thus to put EI at stake in the light of the results of an interdisciplinary and participatory research project that explicitly adopted EI goals in livestock semi-industrialized farming systems. Is it possible to maintain livestock production systems that are simultaneously productive, sustainable, and viable and have low nitrate emissions in vulnerable coastal areas? If so, how do local stakeholders use these approaches? The main steps of the innovation process are described. The effects of political and social dynamics on the continuity of the transition process are analyzed, with a reflexive approach. This experiment invites one to consider that making EI operational in a context of socio-technical transition toward agroecology represents system innovation, requiring on-going dialogue, reflexivity, and long-term involvement by researchers.

The major decrease in grassland surfaces associated with changes in their management that has been observed in many regions of the earth during the last half century has major impacts on environmental and socio-economic systems. This study focuses on the identification of grassland management practices in an intensive agricultural watershed located in Brittany, France, by analyzing the intra-annual dynamics of the surface condition of vegetation using remotely sensed and field data. We studied the relationship between one vegetation index (NDVI) and two biophysical variables (LAI and fCOVER) derived from a series of three SPOT images on one hand and measurements collected during field campaigns achieved on 120 grasslands on the other. The results show that the LAI appears as the best predictor for monitoring grassland mowing and grazing. Indeed, because of its ability to characterize vegetation status, LAI estimated from remote sensing data is a relevant variable to identify these practices. LAI values derived from the SPOT images were then classified based on the K-Nearest Neighbor (KNN) supervised algorithm. The results points out that the distribution of grassland management practices such as grazing and mowing can be mapped very accurately (Kappa index = 0.82) at a field scale over large agricultural areas using a series of satellite images.