eKhSACIR інституційному репозитарії Харківської державної академії культури
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Mixtures effect of antibiotics on soil nitrification and denitrification - an experimental and modelling approach
No full textMixtures effect of antibiotics on soil nitrification and denitrification - an experimental and modelling approach. 1ère conférence internationale d’Ecotoxicologie Microbienne, EcotoxicoMi
Measurements of VOC fluxes by Eddy-covariance with a PTR-Qi-TOF-MS over a mature wheat crop near Paris: Evaluation of data quality and uncertainties
No full textInternational audienc
Effect of subsidies on technical efficiency excluding or including environmental outputs: An illustration with a sample of farms in the European Union
No full textÉvénement(s) lié(s) : - 5. IAAE inter-conference symposium, "Agricultural Productivity, Climate Change and Sustainable Resource Management"; Talca (CHL) - (2017-10-17 - 2017-10-20) Événement(s) lié(s) : - 5. IAAE inter-conference symposium, "Agricultural Productivity, Climate Change and Sustainable Resource Management"; Talca (CHL) - (2017-10-17 - 2017-10-20)With a sample of farms in the European Union (EU) and Farm Accountancy Data Network (FADN) data completed by additionally collected data, we illustrate how the effect of farm subsidies on technical efficiency changes when environmental outputs are incorporated in the calculation of technical efficiency. Results indicate that the effect of the Common Agricultural Policy (CAP) operational subsidies on farm technical efficiency changes when environmental outputs (namely greenhouse gas emissions, nitrogen balance and ecological focus areas) are taken into account in the efficiency calculation: some effects change significance, and more importantly, some effects change sign
Intake prediction and energy requirements for lactating dairy small ruminants: Comparison of systems
No full textFeeding systems use similar feed evaluation approaches for all dairy ruminants, whereas feed intake prediction and requirement assessment are species-specific. This review compares the most recent models currently used to predict dry matter intake (DMI) and energy requirements in lactating dairy small ruminants (DSR), i.e., the Small Ruminant Nutrition System (SRNS, 2004 and 2010, updated in the Ruminant Nutrition System, 2016), the NRC(2007), and the new INRA-Systali (2017) systems, to highlight differences in approach, inputs required and flexibility used. Because DSR are normally group fed, accurate prediction of feed intake is necessary to adjust diet composition according to their requirements. However, when predicting DMI for ewes and goats, the approaches used, variables considered and predicted values vary considerably. All the models used are empirical and species-specific, and use body weight and milk yield as main predictors. Variables associatedwith diet composition and/or type of feeds are also considered, but vary among models, while dietary particle size is not considered.The SRNS and NRC do not have specific models to predict intake on pasture, while INRA uses the Fill Unit system (i.e., based on a forage of reference) to predict DMI considering both housing and grazing conditions. For energy requirements, the SNRS uses the same model for both species, while NRC and INRA use species-specific models. The INRA (2017) largely updated these predictions for all productive functions. For maintenance energy requirements, besides some variability in the basal metabolic rate and energy units used (i.e., ME or NEL), most differences regard the additional factors affecting these requirements (e.g., age, gender, body condition, growth, previous nutrition, movement, cold and heat stress, dietary nitrogen unbalance). Differently, energy requirements for milk production and pregnancy are estimated using similar approaches and values. Overall, the feeding systems for dairy sheep and goats have markedly evolved in the last decade, by improving their ability to account for the great diversity of production systems in which they are raised worldwid
Implementation of an untargeted liquid chromatography ion mobility-mass spectrometry-based metabolomics method for inherited metabolic diseases investigation
No full textInternational audienc
Exploring QTLxE in a network of fields: identification of QTLs associated with scenarios of heat and drought for predicting yields in future climates
No full textGenetic analysis of tolerance to climate change is hamperedby climatic variability that generates GxE interactions dependingon the timing and intensity of water or heat stresses. Wehave performed a genome-wide analysis of yield in 29 fieldsover Europe, with irrigated and rainfed treatments in each location.Six scenarios of heat and drought were first identified byclustering observations over 60 European sites x 35 years. Adetailed environmental characterisation was carried out in allexperiments, which were ascribed to environmental scenarios.A panel of 244 maize hybrids genotyped with 832K polymorphicSNPs was analysed in these experiments, together with acharacterization of phenology in a phenotyping platform. Wehave first identified by GWAS genomic regions (QTLs) associatedwith grain yield. Nearly all QTLs had scenario-dependentallelic effects, with positive, negative or null effects dependingon heat/drought scenarios. Second, by combining informationcoming from the field and the platform, we have estimated theresponses of yield components to environmental conditions viaregression analysis over the whole dataset. We have identifiedin this way massive effects of temperature and water deficit atflowering time, and of intercepted light during the vegetativestage. The slopes of these responses largely differed betweenhybrids and were dissected into QTLs. A model combining environmentalconditions with QTL of responses accounted for72% of the variance of yield, including in eight new experiments.This work, therefore, allows prediction of genotypes tocope with climate changes. (Millet et al 2016, Plant Physiology72:1)
Variability of rumen acidosis and intake behavior of dairy goats submitted to a dietary acidogenic challenge
No full textEight rumen cannulated dairy goats in early lactation (3.7 ± 0.2 kg/d) from the INRA-AgroParisTech experimental farm (Thiverval-Grignon, FR), were provided with wireless bolus sensors (KB1001 Kahne, Auckland, NZ) of pH and temperature to study the relationship between intake and subclinical acidosis for 35 d. After adapting to a TMR control diet (CO, 20% concentrate) for 12 d, goats were brusquely changed to an acidogenic diet (AC, 50% concentrate) for 23 d. Diets were fed ad libitum twice daily (a.m. 1/3, p.m. 2/3) according to milking intervals. Rumen pH and temperature data were captured every 15 min and intake measured every 2 min by weighing scales. Rumen samples were collected (h 0, 1, 2, 4 and 6 post feeding) to measure pH by pH-meter before (d 8 and 11) and after (d 13, 14, 15, 16, 20, 26 and 34) the change. One sensor failed and was discarded, the rest of data being modeled by logistic regression with Solver of Microsoft Excel. Data were analyzed by MIXED for repeated measurements and GLM procedures of SAS. Values of pH-meter vs. sensors correlated (r2 = 0.86; P < 0.01) and were used for sensor recalibration. Mean rumen pH varied markedly by hour and diet; on average, it was higher in CO vs. AC (6.34 ± 0.06 vs. 6.10 ± 0.03; P < 0.001). Despite the high concentrate percentage of AC diet, rumen pH was shortly under pH 6.0. Feed intake reached plateaus during the day when pH was closer to the a.m. or p.m. nadirs, and correlated negatively (r2 = 0.77 to 0.87; P < 0.01) during the periods in which pH dropped. Correlations between rumen temperature and pH were very poor (r2 < 0.1), except for the nightly resting period (r2 = 0.93; P < 0.001), the rumen being slightly colder in AC goats (CO vs. AC, 39.73 ± 0.09 vs. 39.61 ± 0.09°C; P < 0.001). Temperature and pH data from sensors fit logistic models (r2 = 0.97 to 0.99; P < 0.001). Pattern of pH logistic models and time spent under pH 6.0, allowed us to classify the goats as sensitive (3/7, 43%) or tolerant (4/7, 57%) to acidosis, which was related to individual feeding behavior. In conclusion, daily intake measurement and wireless sensors proved to be useful for monitoring rumen function, which allow for an individual separation of sensitive and tolerant goats to rumen acidosis
Comparisons of temporal changes in carabid communities inhabiting five different crops
No full textAgricultural areas are currently expanding in the world. It resulted in the decline of biodiversity, affectingecosystem functioning and the provision of ecosystem services associated. To decrease the pressure ofagricultural activities, we should better manage the ecosystem services delivered by natural enemies, suchas carabids. Carabids are usually considered as providers of ecosystem functions, such as pest regulation.The objective of this study was to investigate the effect of five different crops (Wheat, Sugar beet, Oilseedrape, Pea and Barley) under non-inversion tillage on carabid communities’ dynamics during spring. Wequantified three diversity indices (abundance-activity, number of species and equitability index) and usedfunctional-trait based approach, the communities’ diet profiles, as an evaluation of the ecosystem servicesdelivered by carabid’s community. The study was conducted every year from 2009 to 2012 in 5 sites locatedin the Seine et Marne region. The carabid communities were recorded from 67 fields cropped with: oilseedrape (9 fields), winter wheat (27 fields), sugar beet (12 fields), spring barley (11 fields) and spring pea (8fields). Carabids were sampled using three replicate pitfall traps per field, each fulfilled with a saturated saltsolution containing a small amount of strong detergent as a preservative. The carabids were sampled oncetime every month during four months, from March to June. 33 621 individuals have been trapped in 4 yearsand 11 carabid species represented each at least 2% of the total abundance-activity. In all crop, totalabundance-activities significantly increased during the year as the number of species. Contrary to the indicesrelated to taxonomic diversity, the equitability indices did not significantly change during the year in allsampled crops. Crops were characterised by significantly different composition of carabid species in April,May and June but not in March. Concerning the diet profile, the expression of phytophagous significantlyincreased while the expression of zoophagous significantly decreased with time, whatever the cropconsidered. Despite great differences between crops, ecosystem services delivered by carabid’s communitychanged similarly during spring. According to the crop studied, agricultural practices were probably notexplaining changes in carabid communities and demonstrated strong influences of resources availability. Thelatter seemed to be the main cause of changes in diet characteristics of carabid communities. To maximiseecosystem services related to pest regulation, it seems necessary to better understand the influences ofresources used by carabid in order to control their diet expression