Estimation of abomasum strongyle nematode infections in sheep at necropsy: tentative proposals for a simplified technique.

Several necropsy techniques are available for estimating the abundance of gastro-intestinal nematodes in abomasum of ruminants. Standardization of techniques is needed to allow accurate comparisons between laboratories. Here we propose a standardized technique for estimating the abundance of worms. We intend to compare the worms' number estimations in lambs and ewes based on contents and washings, to determine the uniformity of worm counts in aliquots, and to estimate the total worm number from washings. The digesta (or "contents") and the washings of the abomasum are treated separately. The worms of each subsample are diluted with water and the total number of worms is estimated on a small volume (aliquots) of these subsamples. The use of aliquots assumes that the worms are uniformly distributed in the whole volume of each subsample. We first confirmed that the use of aliquots is appropriate in most cases. We then show that the use of the washings alone allows a faster and a suitable estimation of the total worm burden for all strongyle species of the abomasum in both ewes and lambs. The evaluation of our necropsy procedure is a first step to a standardized technique which should be improved by validation in other laboratories.

Modelling non-stationary spatial covariance structure from space-time monitoring data.

Accurate interpolation of soil and climate variables at fine spatial scales is necessary for precise field management. Interpolation is needed to produce the input variables necessary for crop modelling. It is also important when deciding on regulations to limit environmental impacts from processes such as nitrate leaching. Non-stationarity may arise due to many factors, including differences in soil type, or heterogeneity in chemical concentrations. Many geostatistical methods make stationarity assumptions. Substantial improvements in interpolation or in the estimation of standard errors may be obtained by using non-stationary models of spatial covariances. This paper presents recent methodological developments for an approach to modelling non-stationary spatial covariance structure through deformations of the geographic coordinate system. This approach was first introduced by Sampson & Guttorp, although the estimation approach is updated in more recent papers. They compute a deformation of the geographic plane so that the spatial covariance structure can be considered stationary in terms of a new spatial coordinate system. This provides a non-stationary model for the spatial covariances between sampled locations and prediction locations. In this paper, we present a cross-validation procedure to avoid over-fitting of the sample dispersions. Results concerning the variability of the spatial covariance estimates are also presented. An example of the modelling of the spatial correlation field of rainfall at small regional scale is presented. Other directions in methodological development, including modelling temporally varying spatial correlation, and approaches to model temporal and spatial correlation are mentioned. Future directions for methodological development are indicated, including the modelling of multivariate processes and the use of external spatially dense covariables. Such covariates are frequently available in precision agriculture.

Geostatistics for spatial genetic structures: study of wild populations of perennial ryegrass.

Methods based on geostatistics were applied to quantitative traits of agricultural interest measured on a collection of 547 wild populations of perennial ryegrass in France. The mathematical background of these methods, which resembles spatial autocorrelation analysis, is briefly described. When a single variable is studied, the spatial structure analysis is similar to spatial autocorrelation analysis, and a spatial prediction method, called "kriging", gives a filtered map of the spatial pattern over all the sampled area. When complex interactions of agronomic traits with different evaluation sites define a multivariate structure for the spatial analysis, geostatistical methods allow the spatial variations to be broken down into two main spatial structures with ranges of 120 km and 300 km, respectively. The predicted maps that corresponded to each range were interpreted as a result of the isolation-by-distance model and as a consequence of selection by environmental factors. Practical collecting methodology for breeders may be derived from such spatial structures.

Hierarchical clustering of perennial ryegrass populations with geographic contiguity constraint.

An algorithm of automatic classification is proposed and applied to a large collection of perennial ryegrass wild populations from France. This method is based on an ascendant hierarchical clustering using the Euclidian distance from the principal components extracted from the variance-covariance matrix between 28 agronomic traits. A contiguity constraint is imposed: only those pairs of populations which are defined as contiguous are grouped together into a cluster. The definition of contiguity is based on a geostatistical parameter: the range of the variogramme, i.e. the largest distance above which the variance between pairs of population no longer increases. This method yields clusters that are generally more compact than those obtained without constraint. In most cases the contours of these clusters fit well with known ecogeographic regions, namely, for macroclimatic homogeneous conditions. This suggests that selective factors exert a major influence in the genetic differentiation of ryegrass populations for quantitatively inherited adaptive traits. It is proposed that such a method could provide useful genetic and ecogeographic bases for sampling a core collection in widespread wild species such as forage grasses.

Comparison of short- and long-feed transmission of the cauliflower mosaic virus Cabb-S strain and S delta II hybrid by two species of aphid: Myzus persicae (Sulzer) and Brevicoryne brassicae (L.).

The cauliflower mosaic virus (CaMV) hybrid S delta II, partially deleted in ORFII, loses its transmissibility by the aphid Myzus persicae on 5-min acquisition feed. We have also shown that it is not transmitted after 8-h acquisition feed. The same occurs with Brevicoryne brassicae. Therefore, the aphid transmission factor (ATF) is involved in both means of transmission and in both aphid species. M. persicae can acquire CaMV Cabb-S strain in less than 20 s. M. persicae is a more efficient vector during a short feed than during a long feed, contrary to B. brassicae which transmits better during a long feed.

An experimental test of a nitrogen uptake and partitioning model for young trees.

Simulation models of nitrate uptake and total nitrogen partitioning during the exponential growth phase of one-year-old peach trees (Prunus persica (L.) Batsch.) were tested in an experiment with 88 plants grown in soil-filled containers. Plants were fertilized with (15)N-NO(3) (-) and nitrate uptake estimated by periodic destructive analysis of plants for excess (15)N. Partitioning of N within the trees was followed by the analysis of plant parts for total N and (15)N. The nitrate uptake model, which provides one of the main inputs to the partitioning model, is based on a simplified form of the Michaelis-Menten equation adapted to describe uptake by roots growing in soil layers. The nitrogen partitioning model considers each plant part (e.g., roots, trunk, shoots, leaves) as either a sink or a source for nitrogen. The model uses a flow equation, which is the same for all plant parts, to model the dynamics of nitrogen partitioning in the tree using increases in dry matter of various plant parts as driving force variables. The experiment demonstrated an error in the compartment organization of the partitioning model as a result of which the model failed to simulate changes in root N. A modification of the partitioning model structure to take account of the importance of trunk nitrogen reserves for root growth at the beginning of the growing season, which was indicated by the (15)N data, greatly improved prediction of root N. This modification is discussed in relation to the modeling approach.