Role of input self-sufficiency in the economic and environmental sustainability of specialised dairy farms.

Increasing input self-sufficiency is often viewed as a target to improve sustainability of dairy farms. However, few studies have specifically analysed input self-sufficiency, by including several technical inputs and without only focussing on animal feeding, in order to explore its impact on farm sustainability. To address this gap, our work has three objectives as follows: (1) identifying the structural characteristics required by specialised dairy farms located in the grassland area to be self-sufficient; (2) analysing the relationships between input self-sufficiency, environmental and economic sustainability; and (3) studying how the farms react to a decrease in milk price according to their self-sufficiency degree. Based on farm accounting databases, we categorised 335 Walloon specialised conventional dairy farms into four classes according to their level of input self-sufficiency. To this end, we used as proxy the indicator of economic autonomy - that is, the ratio between costs of inputs related to animal production, crop production and energy use and the total gross product. Classes were then compared using multiple comparison tests and canonical discriminant analysis. A total of 30 organic farms - among which 63% had a high level of economic autonomy - were considered separately and compared with the most autonomous class. We showed that a high degree of economic autonomy is associated, in conventional farms, with a high proportion of permanent grassland in the agricultural area. The most autonomous farms used less input - especially animal feeding - for a same output level, and therefore combined good environmental and economic performances. Our results also underlined that, in a situation of decrease in milk price, the least autonomous farms had more latitude to decrease their input-related costs without decreasing milk production. Their incomes per work unit were, therefore, less impacted by falling prices, but remained lower than those of more autonomous farms. In such a situation, organic farms kept stable incomes, because of a slighter decrease in organic milk price. Our results pave the way to study the role of increasing input self-sufficiency in the transition of dairy farming systems towards sustainability. Further research is required to study a wide range of systems and agro-ecological contexts, as well as to consider the evolution of farm sustainability in the long term.

Integrating geo-referenced multiscale and multidisciplinary data for the management of biodiversity in livestock genetic resources.

In livestock genetic resource conservation, decision making about conservation priorities is based on the simultaneous analysis of several different criteria that may contribute to long-term sustainable breeding conditions, such as genetic and demographic characteristics, environmental conditions, and role of the breed in the local or regional economy. Here we address methods to integrate different data sets and highlight problems related to interdisciplinary comparisons. Data integration is based on the use of geographic coordinates and Geographic Information Systems (GIS). In addition to technical problems related to projection systems, GIS have to face the challenging issue of the non homogeneous scale of their data sets. We give examples of the successful use of GIS for data integration and examine the risk of obtaining biased results when integrating datasets that have been captured at different scales.

Heavy metal transporters in Hemiascomycete yeasts.

We have compiled all known heavy metal transporters of the yeast Saccharomyces cerevisiae and identified their orthologs in four other species spanning the entire Hemiascomycete phylum. The 213 transporters belong to 27 distinct phylogenetic families distributed within the three classes: channels, secondary porters (permeases) and transport ATPases. They are present in all cellular membranes: plasma membranes, vacuoles, mitochondria, endoplasmic reticulum, nucleus, Golgi and various cytoplasmic vesicles. The major physiological heavy metals transported are: iron, manganese, zinc, copper, arsenite and cadmium. The major subfamilies that comprise the highest number of transporters are Siderophore-Iron Transporters (SIT) and CT2 (conjugated ABC transporters). They transport heavy metals (iron or cadmium, respectively) conjugated to organic chelators such as siderophores or glutathione. Both subfamilies are considerably amplified in the yeast Yarrowia lipolytica. The pattern of expansion and restriction of the subfamilies during the evolution of the different species is highly variable. The phylogenetic trees of the major transporters subfamilies distinguish homogenous clusters of transporters suggesting that possible different physiological or mechanistic functions evolved independently. We also validated the use of the Hemiascomycetes heavy metal transporters for identification of orthologs transporters in the pathogenic Basidiomycetes Cryptococcus neoformans.

Non-parametric interval mapping in half-sib designs: use of midranks to account for ties.

In QTL analysis of non-normally distributed phenotypes, non-parametric approaches have been proposed as an alternative to the use of parametric tests on mathematically transformed data. The non-parametric interval mapping test uses random ranking to deal with ties. Another approach is to assign to each tied individual the average of the tied ranks (midranks). This approach is implemented and compared to the random ranking approach in terms of statistical power and accuracy of the QTL position. Non-normal phenotypes such as bacteria counts showing high numbers of zeros are simulated (0-80% zeros). We show that, for low proportions of zeros, the power estimates are similar but, for high proportions of zeros, the midrank approach is superior to the random ranking approach. For example, with a QTL accounting for 8% of the total phenotypic variance, a gain from 8% to 11% of power can be obtained. Furthermore, the accuracy of the estimated QTL location is increased when using midranks. Therefore, if non-parametric interval mapping is chosen, the midrank approach should be preferred. This test might be especially relevant for the analysis of disease resistance phenotypes such as those observed when mapping QTLs for resistance to infectious diseases.

Quantitative trait loci for resistance against Yellow rust in two wheat-derived recombinant inbred line populations.

Yellow rust, which is a major disease in areas where cool temperatures prevail, can strongly influence grain yield. To control this disease, breeders have extensively used major specific resistance genes. Unfortunately this kind of resistance is rapidly lost due to pathogen adaptation. More-durable resistance against yellow rust can be achieved using quantitative resistance derived from cultivars with well-established durable resistance. The winter wheat Camp Remy has maintained a high level of resistance for over 20 years. In order to map quantitative trait loci (QTLs) for durable yellow rust resistance, we analysed a set of 98 F(8) recombinant inbred (RI) lines derived from the cross Camp RemyxMichigan Amber. We also mapped QTLs for adult resistance to yellow rust using the International Triticae Mapping Initiative RI population (114 lines derived from the cross Opata85xsynthetic hexaploid). Two and five QTLs, respectively, were identified from these two populations. This work has highlighted the importance of the centromeric region of chromosome 2B and the telomeric regions of chromosomes 2AL and 7DS in durable yellow rust resistance. The same chromosomal regions are also implicated in resistance to other pathogens.

Statistical power of QTL mapping methods applied to bacteria counts.

Most QTL mapping methods assume that phenotypes follow a normal distribution, but many phenotypes of interest are not normally distributed, e.g. bacteria counts (or colony-forming units, CFU). Such data are extremely skewed to the right and can present a high amount of zero values, which are ties from a statistical point of view. Our objective is therefore to assess the efficiency of four QTL mapping methods applied to bacteria counts: (1) least-squares (LS) analysis, (2) maximum-likelihood (ML) analysis, (3) non-parametric (NP) mapping and (4) nested ANOVA (AN). A transformation based on quantiles is used to mimic observed distributions of bacteria counts. Single positions (1 marker, 1 QTL) as well as chromosome scans (11 markers, 1 QTL) are simulated. When compared with the analysis of a normally distributed phenotype, the analysis of raw bacteria counts leads to a strong decrease in power for parametric methods, but no decrease is observed for NP. However, when a mathematical transformation (MT) is applied to bacteria counts prior to analysis, parametric methods have the same power as NP. Furthermore, parametric methods, when coupled with MT, outperform NP when bacteria counts have a very high proportion of zeros (70.8%). Our results show that the loss of power is mainly explained by the asymmetry of the phenotypic distribution, for parametric methods, and by the existence of ties, for the non-parametric method. Therefore, mapping of QTL for bacterial diseases, as well as for other diseases assessed by a counting process, should focus on the occurrence of ties in phenotypes before choosing the appropriate QTL mapping method.

On the use of linear regression and maximum likelihood for QTL mapping in half-sib designs.

Methods of identification of quantitative trait loci (QTL) using a half-sib design are generally based on least-squares or maximum likelihood approaches. These methods differ in the genetical model considered and in the information used. Despite these differences, the power of the two methods in a daughter design in very similar. Using an analogy with a one-way analysis of variance, we propose an equation connecting the two test-statistics (F ratio for regression and likelihood ratio test in the case of the maximum likelihood). The robustness of this relationship is tested by simulation for different single QTL models. In general, the correspondence between the two statistics is good under both the null hypothesis and the alternative hypothesis of a single QTL segregating. Practical implications are discussed with particular emphasis on the theoretical distribution of the likelihood ratio test.

Selection for increased longevity in Drosophila melanogaster: reflections on new data.

In recent papers, new data were presented on the late-age reproduction experiment initiated by Luckinbill and Clare in 1981: when early- and late-reproduced lines were compared simultaneously 10 years after the end of the original experiment, differences in the mean life span are observed between the lines. Yet the conditions in which these measurements were done are highly questionable. More fundamentally, using these data, the analysis of the selection process is impossible and conclusions about the determinism of life span are debatable.

Realized heritability of longevity in Drosophila melanogaster.

From the data of an experiment of selection for increased longevity, a realized heritability of longevity was calculated. The low value of this heritability (3.4%) was very close to values observed in other experiments concerning Drosophila melanogaster wild strains. The error variance of the heritability estimator was calculated through the use of orthogonal contrasts. In this way, it was possible to calculate the confidence interval of the realized heritability. The amplitude of this interval was wide although the size of the sample was large. This shows the difficulty of estimating with precision the heritability of longevity from data obtained in selection experiments.