Crop Protection Between Sciences, Ethics and Societies: From Quick-Fix Ideal to Multiple Partial Solutions

Crop protection has a very long history during which new methods have been developed whilst, at the same time, the older ones have retained their usefulness in certain conditions. The diversity of agricultural land and production has meant that it was futile to search for a unique and definitive approach or technical solution and, instead, the central concept has always been one of integration, during all the period of pre-Green Revolution and again today within what we call a sustainable agriculture. On a global level, it would seem that the current situation does not fundamentally contradict this idea. Nevertheless, in recent years (since the Second World War), two important advances, presented as the definitive solutions to problems and potentially exceeding previously less effective ones, have led to this integrative approach being questioned. These are agrochemistry and agro-genetics. We will detail, here, the agro-environmental limits of these two “miracle solutions,” followed by a review from an ethical and an epistemological point of view. This enables us to demonstrate the relevance of integrated approaches in agriculture and leads to a definition of crop protection that forms part of a strong approach in sustainable development. By changing the semantics, the epistemic position and our vision of production, we arrive at the proposal of sustainable agriculture.     

Prenatal diagnosis of neurofibromatosis type 1: sonographic and MRI findings

Prenatal ultrasound and magnetic resonance imaging (MRI) demonstrated a large oropharyngeal tumor, and cardiac and cranial abnormalities consistent with neurofibromatosis type 1 (NF1) in a third-trimester fetus, which were confirmed on postmortem examination. Sonographic features of NF1 are generally nonspecific; MR examination provided significant additional information, facilitating prenatal diagnosis. Copyright © 2006 John Wiley & Sons, Ltd.     

Streamside Forest Buffer Width Needed to Protect Stream Water Quality,Habitat, and Organisms: A Literature Review

This literature review addresses how wide a streamside forest buffer needs to be to protect water quality, habitat, and biota for small streams (≤~100 km² or ~5th order watershed) with a focus on eight functions: (1) subsurface nitrate removal varied inversely with subsurface water flux and for sites with water flux >50 l/m/day (~40% avg base flow to Chesapeake Bay) median removal efficiency was 55% (26‐64%) for buffers <40 m wide and 89% (27‐99%) for buffers >40 m wide; (2) sediment trapping was ~65 and ~85% for a 10‐ and 30‐m buffer, respectively, based on streamside field or experimentally loaded sites; (3) stream channel width was significantly wider when bordered by ~25‐m buffer (relative to no forest) with no additional widening for buffers ≥25 m; (4) channel meandering and bank erosion were lower in forest but more studies are needed to determine the effect of buffer width; (5) temperature remained within 2°C of levels in a fully forested watershed with a buffer ≥20 m but full protection against thermal change requires buffers ≥30 m; (6) large woody debris (LWD) has been poorly studied but we infer a buffer width equal to the height of mature streamside trees (~30 m) can provide natural input levels; (7, 8) macroinvertebrate and fish communities, and their instream habitat, remain near a natural or semi‐natural state when buffered by ≥30 m of forest. Overall, buffers ≥30 m wide are needed to protect the physical, chemical, and biological integrity of small streams.     

Air pollution below WHO levels decreases by 40 % the links of terrestrial microbial networks

Air pollution has a deleterious impact on public health and the environment. There is few knowledge on the effect of air pollution on terrestrial microbial communities, despite the major role of microbes in ecosystems. Here, we designed an in situ trial ecosystem to assess the impact of moderate atmospheric pollution, below World Health Organization (WHO) thresholds, on an indigenous microbial communities, including bacteria, fungi, ciliates, algae, cyanobacteria, testate amoebae, rotifers and nematodes, extracted from terrestrial bryophytes. These micro-ecosystems were placed at a rural, an urban and an industrial site in France and were thus exposed to various levels of nitrogen dioxide (NO₂), from 6.6–67.9 μg·m^?3, and particulate matter, from 0.7–7.9 μg·m^?3. Microbial analysis was performed by microscopy. We determined atmospheric temperature, relative humidity and particulate matter with diameter lower than 10 µm (PM₁₀), Cu, Cr, Fe, Ni, Pb, Zn in PM₁₀, and (NO₂). Results show a significant impact of chronic moderate exposure to NO₂ and copper Cu-associated particulate matter on the global microbial network complexity. This is evidenced by a loss of about 40 % of microbial co-occurrence links during incubation. Most lost microbial links are ecologically positive links. Moreover, most changes in community co-occurrence networks are related to testate amoebae, a major top predator of microbes. Overall, our findings demonstrate that air pollution can have strong deleterious effects on microbial interactions, even at levels below WHO thresholds.     

The effect of non-uniform depth on the baroclinic response to wind in elongated basins

We examined the effect of along-thalweg depth variability on the baroclinic response to wind in elongated narrow basins with a sharp thermocline. The effect of depth variability was examined by deriving a modal-based forced model with two density layers and applying the model to a symmetric curved-bottom basin (CB), an asymmetric wedge-shaped basin (with a sloping bottom towards a vertical wall, WB), and a flat-bottom basin (FB). The baroclinic responses of CB, WB, and FB to uniform wind were found to differ in time-scale, number and energy of excited modes, and temporal pattern and along-thalweg structure of baroclinic flow and thermocline deflection. For all bottom profiles that were examined, the fundamental mode was found to dominate the response to spatially-uniform wind. Compared to FB, the asymmetric depth variability in WB increased the number and energy of excited higher modes and localized the interface shear, while the symmetric deviation from flat bottom in CB caused the opposite effects. Linear deviation from uniform wind was found to feed energy into higher baroclinic modes for the symmetric CB, but was found to reduce the energy of higher baroclinic modes for WB when the deviation from uniform wind is comparable to the spatial-average magnitude. Our results can explain the observation of the second baroclinic mode and irregular wave patterns in some lakes and reservoirs. Further, our results suggest that one-dimensional vertical mixed-layer models provide better results for shear entrainment in curved-bottom basins than in wedge-shaped basins.     

Prevalence-adjusted optimisation of fuzzy models for species distribution

Most performance criteria which have been applied to train ecological models focus on the accuracy of the model predictions. However, these criteria depend on the prevalence of the training set and often do not take into account ecological issues such as the distinction between omission and commission errors. Moreover, a previous study indicated that model training based on different performance criteria results in different optimised models. Therefore, model developers should train models based on different performance criteria and select the most appropriate model depending on the modelling objective. This paper presents a new approach to train fuzzy models based on an adjustable performance criterion, called the adjusted average deviation (aAD). This criterion was applied to develop a species distribution model for spawning grayling in the Aare River near Thun, Switzerland. To analyse the strengths and weaknesses of this approach, it was compared to model training based on other performance criteria. The results suggest that model training based on accuracy-based performance criteria may produce unrealistic models at extreme prevalences of the training set, whereas the aAD allows for the identification of more accurate and more reliable models. Moreover, the adjustable parameter in this criterion enables modellers to situate the optimised models in the search space and thus provides an indication of the ecological model relevance. Consequently, it may support modellers and river managers in the decision making process by improving model reliability and insight into the modelling process. Due to the universality and the flexibility of the approach, it could be applied to any other ecosystem or species, and may therefore be valuable to ecological modelling and ecosystem management in general.     

Coregionalization analysis with a drift for multi-scale assessment of spatial relationships between ecological variables 1. Estimation of drift and random components

In two articles, we present ‘coregionalization analysis with a drift’ (CRAD), a method to assess the multi-scale variability of and relationships between ecological variables from a multivariate spatial data set. In phase I of CRAD (the first article), a deterministic drift component representing the large-scale pattern and a random component modeled as a second-order stationary process are estimated for each variable separately. In phase II (this article), a linear model of coregionalization (LMC) is fitted by estimated generalized least squares to the direct and cross experimental variograms of residuals (i.e., after the removal of estimated drifts). Structural correlations and coefficients of determination at smaller scales are then computed from the estimated coregionalization matrices, while the estimated drifts are used to calculate pseudo coefficients at large scale. The performance of five procedures in estimating correlations and coefficients of determination was compared using a Monte Carlo study. In four CRAD procedures, drift estimation was based on local polynomials of order 0, 1, 2 (L₀, L₁, L₂) or a global polynomial with forward selection of the basis functions; the fifth procedure was coregionalization analysis (CRA), in which large-scale patterns were modeled as a supplemental component in the LMC. In bivariate and multivariate analyses, the uncertainty in the estimation of correlations and coefficients of determination could be related to the interference between spatial components within a bounded sampling domain. In the bivariate case, most procedures provided acceptable estimates of correlations. In regionalized redundancy analysis, uncertainty was highest for CRA, while L₁ provided the best results overall. In a forest ecology example, the identification of scale-specific correlations between plant species diversity and soil and topographical variables illustrated the potential of CRAD to provide unique insight into the functioning of complex ecosystems.