Influence of grassland and feeding management on technical and economic results of dairy farms

A field study of 38 dairy farms was set up to determine the relationships between feeding management, grassland management and feed costs/100 kg milk, 305-day milk yield and nitrogen surplus/ha. Data of the farms were on management (based on questionnaires), grassland calendar, milk yield and economic data for May 1996 to May 1997. Partial Least Squares (PLS) was used to analyse data, because of the large number of variables relative to the number of farms. The R2 of the models varied between 0.32 (nitrogen surplus model) and 0.60 (feed costs model). The nitrogen surplus model did not have predictive relevance. The PLS-model for feed costs resulted in the hypotheses that (1) a high percentage of pasture that cannot be grazed by the cows results in an increase in feed costs, (2) a high percentage of grazings lasting >4 days increases feed costs, (3) mistakes in set-up of the paddocks cannot be compensated for by exact planning, and (4) farmers who have not organized their grazing management well, also tend to have worse results as to their silage management. The milk yield model showed that a high milk yield/cow is realized on farms with too low a number of growing days for cutting

Evaluation d'émissions de gaza effet de serre au cours du cycle de vie de trois systémes espagnols de production ovine

The livestock sector increasingly competes for scarce resources and has a severe impact on air, water and soil So far, no study exists that compares the enviran mental impact of different sheep production systems. We used Life Cycle Assessment (LCA) to evaluate greenhouse gas (GHG) emissions of three contrasting meat-sheep farming systems in Spain, which differ regarding their degree of intensification (reproduction rate, Jand use and grazing management) The GHG emissions of these systems varied from 19 5 to 28 4 kg C02 eq per kg Jive-animal or 38.9 to 56.7 kg C02 eq per kg Jamb-meat Highest values refer to the pasture-based Jivestock system. which however also provide severa! ecosystems services that need to be considered when assessing its environmental impactLe secteur élevage est de plus en plus en compétition pour des ressources límitées et a un ímpact important .sur l'air, /'ea u et le sol Ju.squ a maintenant il n'exíste pas d'études quí comparen! rímpact envíronnemental des différents systeme.s de production avine Nous avons utilisé le "Life Cycle As.sessment" (LCA, Evaluation du Cycle de Vie) pour évaluer les émissíons de gaza effet de serre de troís systéme.s de production de víande o vine en Espagne, qui dífférent .selon feur degré d intensificatíon (taux de reproduction utilisation des terres, gestion du ptiturage) Les émission.s de gaz a effet de serre de ces sy.stémes varient de 19,5 ¿ 28,4 kg de C02 eq par kg d'animaf vivant, c'est-a-dire de 38,9 a 56.7 kg de C02 par kg de viande d agneau Les plus hautes va/eur.s corresponden! au .systéme d'élevage en páturage, toutefois ce systéme remplit p/usieurs fonctions au sein des écosystémes qui devront étre prises en compte /or:s de /'évaluation de son impact environnementa

Vergelijking klimaateffecten van de gangbare vs. de biologische landbouw

Deze studie vergelijkt de prestaties van gangbare en biologische landbouw- en veehouderijsystemen in Nederland op klimaatgebied. Er is een meta-analyse uitgevoerd naar verschillende bestaande vergelijkende studies en verschillen zijn geduid door onderscheidende factoren te noemen---This study compares the environmental performance of conventional and organic agricultural and livestock farming systems in the Netherlands concerning greenhouse gas emissions. A meta-analysis has been carried out into several existing comparative studies and differences have been identified by mentioning differentiating factor

Automated assessment of COVID-19 reporting and data system and chest CT severity scores in patients suspected of having COVID-19 using artificial intelligence

Background: The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed.Purpose: To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems.Materials and Methods: The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted kappa values, and classification accuracy.Results: A total of 105 patients (mean age, 62 years +/- 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years +/- 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted k values of 0.60 +/- 0.01 for CO-RADS scores and 0.54 +/- 0.01 for CT severity scores.Conclusion: With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. (C) RSNA, 2020Cardiovascular Aspects of Radiolog

Sixty years of Dutch nitrogen fertiliser experiments, an overview of the effects of soil type, fertiliser input, management and of developments in time

Data of nitrogen fertilization experiments of 1934 - 1994 have been analysed, using models for N uptake and dry matter (DM) yield. Both models were affected by fertilizer level, soil type, soil organic matter content, grassland use, cutting frequency, grassland renovation, white clover content and the N content analysis (Crude Protein or total-N). Effects on Soil Nitrogen Supply (SNS), Apparent Nitrogen Recovery (ANR) and Nitrogen Use Efficiency (NUE) are discussed. Differences in SNS, ANR and NUE between sand and clay were small, SNS on poorly drained peat soil was 60 and 80 kg N per ha higher than on clay and sand, respectively, ANR on poorly drained peat soil was 7 and 10% lower. The NUE was similar on sand, clay and poorly drained peat. ANR was low at low N application levels, due to immobilization. ANR increased from 35% to 65% at application levels of 50 and 250 kg N per ha, respectively. At application levels of more than 250 kg N per ha, ANR decreased. NUE decreased from 45 to 29 kg DM per kg N with increasing N application levels of 0 and 550 kg per ha. It is suggested that for a good N utilization a minimum N application of 100 kg N per ha should be used. SNS increased by a mixed use of grazing and cutting with 27 and 40 kg N per ha for sand/clay and poorly drained peat respectively. ANR on sand decreased from 5 to 10% at applications of 200 and 500 kg N per ha and NUE decreased with 1-2 kg DM per kg N. The effect of grazing was stronger under pure grazing than with a mixed use of grazing and cutting. Increasing the cutting frequency from 3 to 8 cuts per year had no effect on SNS, increased ANR with 0-20% and decreased NUE with 4-7 kg DM per kg N. The positive effect of the higher ANR compensated the lower NUE at application levels of 400 kg N per ha. Changes in ANR over the last sixty years can be explained by changes in experimental conditions, experimental treatments and chemical analysis. Changes in NUE can be explained by a higher proportion of perennial ryegrass and genetic improvement

Effectiveness of climate change mitigation options considering the amount of meat produced in dairy systems

Many of the climate change mitigation options for dairy systems that aim at optimizing milk production imply a reduced output of meat from these systems. The objective of this study was to evaluate effectiveness of a number of mitigation strategies for dairy systems, taking into account compensation for changes in the amount of beef produced. Four commonly used mitigation strategies for dairy systems were evaluated using an LCA modelling approach: increasing the milk production per cow, extending the productive life span of cows, increasing the calving interval, and changing breed from Holstein Friesian to Jersey. The Dutch dairy system was taken as a case study. For each scenario, analyses were done in two steps. First, effects of the mitigation strategy on production of milk and carcass weight from the dairy system were calculated. Second, GHG emission intensities were calculated for three different functional units (FU): one kg of fat and protein corrected milk (FPCM), one kg of carcass weight (CW), and a fixed amount of milk and beef (i.e. 1 kg FPCM and 40 g CW). In the third FU, in case the amount of CW produced by the dairy system was lower than 40 g per kg FPCM, the remainder was compensated by CW produced in pure beef systems, assuming a GHG emission intensity of 30 kg CO 2 -eq. per kg CW for pure beef. Results showed a reduction in CW per kg FPCM from the dairy system in all four mitigation strategies. Considering GHG emissions per kg of FPCM only, the strategies reduced emissions by 0.2 to 18.1%. When considering emissions per kg of CW only, emissions were reduced by 12.5 to 48.9%. However, when we used a FU of 1 kg FPCM and 40 g CW, changes in emissions ranged from −0.2 to 3.8%. This was caused by the compensation of the lower CW production from dairy systems by CW from pure beef systems. Differences in emissions per kg FPCM and 40 g CW were smaller when the assumed emission intensity of pure beef was lower. We concluded that the mitigation strategies for dairy systems evaluated in this study were less effective for reduction of GHG emissions from production of milk and beef, when accounting for changes in the amount of beef produced. This study showed that the challenge of reducing GHG emissions of milk and beef production is interrelated. Hence, analyses of GHG emissions related to changes in production of milk and beef requires an integrated approach, beyond the system boundaries of the dairy farm