Helge Bonesmo
Head of Department
(+47) 917 93 100
helge.bonesmo@nibio.no
Place
Trondheim
Visiting address
Klæbuveien 153, bygg C 1.etasje, 7031 Trondheim
Authors
Erlend Hustad Honningdalsnes Helge Bonesmo Erik Stensrud Marstein Steve Völler Heine Nygard RiiseAbstract
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Authors
Erlend Hustad Honningdalsnes Helge Bonesmo Erik Stensrud Marstein Steve Völler Heine Nygard RiiseAbstract
No abstract has been registered
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Commissioned by the Norwegian Environment Agency, this report presents methodologies for estimating annual numbers of animals and enteric methane emissions for pigs. The methodologies are designed for the Norwegian national inventory of GHG emissions (NIR) and are dynamic, reflecting the effects of progress in genetics and management of the pork production. The data sources for the proposed methodologies are the register for deliveries of carcasses to Norwegian slaughterhouses available from Statistics Norway, and the Norwegian litter recording system (Ingris) of the Norwegian meat and poultry research centre (Animalia).
Authors
Stine Samsonstuen Helge Bonesmo Bente Aspeholen Åby Eli Gjerlaug-Enger Erland Kjesbu Magne Bergfjord Rune Okstad Svein Skøien Tony BarmanAbstract
Through the joint project Climate Smart Agriculture, the agricultural sector in Norway have successfully implemented the whole-farm models HolosNor models as farm advisory tools for milk, beef, pig, sheep, poultry, and crop production. The HolosNor modes are empirical models based on the methodology of the Intergovernmental Panel on Climate Change with modifications to Norwegian conditions. The models estimate direct emissions of methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) from on-farm livestock production and includes indirect emissions of N2O and CO2 associated with inputs used on the farm in addition to including soil carbon balance through the ICBM model. The digital GHG Calculator automatically collects data from sources the farmer already uses for farm management, such as herd recording systems, manure planning systems, farm accounts, concentrate invoice, dairy, slaughterhouse, in addition to site-specific soil and weather data. Based on the collected data, both total emissions from the production and emission intensities for the different products are estimated. The emission intensities are shown by source relative to a reference group consisting of farms with the same type of production and production volume. Using the GHG Calculator, the farmers have the unique opportunity to have tailor-made mitigation plans to reduce emissions from the farm trough certified climate advisors. Participation and results from the GHG Calculator will be presented in addition to experiences from implementation of a GHG model as a farm advisory tool for commercial farms.
Authors
Puchun Niu Angela Dagmar Schwarm Helge Bonesmo Alemayehu Kidane Bente Aspeholen Åby Tonje Marie Storlien Michael Kreuzer Maria Clementina Alvarez Flores Jon Kristian Sommerseth Egil PrestløkkenAbstract
Simple Summary: Many techniques exist to quantify enteric methane (CH4) emissions from dairy cows. Since measurement on the entire national cow populations is not possible, it is necessary to use estimates for national inventory reporting. This study aimed to develop (1) a basic equation of enteric CH4 emissions from individual animals based on feed intake and nutrient contents of the diet, and (2) to update the operational way of calculation used in the Norwegian National Inventory Report based on milk yield and concentrate share of the diet. An international database containing recently published data was used for this updating process. By this the accuracy of the CH4 production estimates included in the national inventory was improved. Abstract: The aim of this study was to develop a basic model to predict enteric methane emission from dairy cows and to update operational calculations for the national inventory in Norway. Development of basic models utilized information that is available only from feeding experiments. Basic models were developed using a database with 63 treatment means from 19 studies and were evaluated against an external database (n = 36, from 10 studies) along with other extant models. In total, the basic model database included 99 treatment means from 29 studies with records for enteric CH4 production (MJ/day), dry matter intake (DMI) and dietary nutrient composition. When evaluated by low root mean square prediction errors and high concordance correlation coefficients, the developed basic models that included DMI, dietary concentrations of fatty acids and neutral detergent fiber performed slightly better in predicting CH4 emissions than extant models. In order to propose country-specific values for the CH4 conversion factor Ym (% of gross energy intake partitioned into CH4 ) and thus to be able to carry out the national inventory for Norway, the existing operational model was updated for the prediction of Ym over a wide range of feeding situations. A simulated operational database containing CH4 production (predicted by the basic model), feed intake and composition, Ym and gross energy intake (GEI), in addition to the predictor variables energy corrected milk yield and dietary concentrate share were used to develop an operational model. Input values of Ym were updated based on the results from the basic models. The predicted Ym ranged from 6.22 to 6.72%. In conclusion, the prediction accuracy of CH4 production from dairy cows was improved with the help of newly published data, which enabled an update of the operational model for calculating the national inventory of CH4 in Norway.
Abstract
The environmental sustainability of food production systems, including net greenhouse gas (GHG) emissions, is of increasing importance. In Norwegian pork production, animal performance is high in terms of reproduction, growth, and health. The development and use of an IPCC methodology-based model for estimating GHG emissions from pork production could be helpful in identifying the effects of progress in genetics and management. The objective was to investigate whether an IPCC methodology-based model was able to reflect the effects of the progress in genetics and management in pork production on the GHG emissions per kg carcass weight (CW). It is hypothesized that this progress has led to low GHG emissions intensities in Norwegian pork compared to global levels and that expected improvements will give a lasting reduction in GHG emissions intensities. A model ‘HolosNorPork’ for estimating net farm gate GHG emissions intensities was developed, including allocation procedures, at the pig production unit level. The model was run with pig production data from in average 632 farms from 2014 to 2019. The estimates include emissions of enteric and manure storage methane, manure storage nitrous oxide emissions, as well as GHG emissions from production and transportation of purchased feeds, and direct and indirect GHG emissions caused by energy use in pig-barns. The model was able to estimate the effects on net GHG emissions intensities from pork production on the basis of production characteristics. The estimated net GHG emissions intensity was found to have decreased from on average 2.49 to 2.34 kg CO2 eq. kg−1 CW over the investigated period. For 2019 the net GHG emission for the one-third lower performing farms was estimated to 2.56 kg CO2 eq. kg−1 CW, whereas for the one-third medium and one-third best performing farms the estimates were 2.36 and 2.16 kg CO2 eq. kg−1 CW, respectively. The net GHG emissions intensity for pork carcasses from boars was estimated to be 2.07 kg CO2 eq. kg−1 CW. For the health regimes investigated, Conventional and Specific-Pathogen Free (SPF), the estimated GHG emissions intensities for 2019 were 2.37 and 2.24 kg CO2 eq. kg−1 CW, respectively. The effects on net GHG emissions intensities of breeding and management measures were estimated to be profound, and this progress in pig production systems contributes to an on-going strengthening of pork as a sustainable source for human food supply.
Authors
Stine Samsonstuen Bente Aspeholen Åby Paul Crosson Karen A. Beauchemin Marit Smith Wetlesen Helge Bonesmo Laila AassAbstract
Emission intensities from beef production vary both among production systems (countries) and farms within a country depending upon use of natural resources and management practices. A whole-farm model developed for Norwegian suckler cow herds, HolosNorBeef, was used to estimate GHG emissions from 27 commercial beef farms in Norway with Angus, Hereford, and Charolais cattle. HolosNorBeef considers direct emissions of methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) from on-farm livestock production and indirect N2O and CO2 emissions associated with inputs used on the farm. The corresponding soil carbon (C) emissions are estimated using the Introductory Carbon Balance Model (ICBM). The farms were distributed across Norway with varying climate and natural resource bases. The estimated emission intensities ranged from 22.5 to 45.2 kg CO2 equivalents (eq) (kg carcass)−1. Enteric CH4 was the largest source, accounting for 44% of the total GHG emissions on average, dependent on dry matter intake (DMI). Soil C was the largest source of variation between individual farms and accounted for 6% of the emissions on average. Variation in GHG intensity among farms was reduced and farms within region East, Mid and North re-ranked in terms of emission intensities when soil C was excluded. Ignoring soil C, estimated emission intensities ranged from 21.5 to 34.1 kg CO2 eq (kg carcass)−1. High C loss from farms with high initial soil organic carbon (SOC) content warrants further examination of the C balance of permanent grasslands as a potential mitigation option for beef production systems.
Authors
Seyda Ozkan Bouda Vosough Ahmadi Helge Bonesmo Olav Østerås Alistair Stott Odd Magne HarstadAbstract
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The dataset comprises detailed mappings of two communities of interacting populations of white clover (Trifolium repens L.) and grass species under differing experimental treatments over 4-5 years. Information fromdigital photographs acquired two times per season has been processed into gridded data and documents the temporal and spatial dynamics of the species that followed from a wide range of spatial configurations that arose during the study period. The data contribute a unique basis for validation and further development of previously published models for the dynamics and population oscillations in grass-white clover swards. They will be well suited for estimating parameters in spatially explicit versions of these models, like neighborhood based models that incorporate both the dispersal and the local nature of plant-plant interactions.
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Seyda Ozkan Bouda Vosough Ahmadi Helge Bonesmo Olav Østerås Alistair Stott Odd Magne HarstadAbstract
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Seyda Ozkan Bouda Vosough Ahmadi Helge Bonesmo Olav Østerås Alistair Stott Odd Magne HarstadAbstract
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Audun Korsæth Anne Zimmer Jacobsen Anne-Grete Roer Hjelkrem Trond Henriksen U. Sonesson Arne Oddvar Skjelvåg Helge Bonesmo Anders Hammer StrømmanAbstract
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Authors
Helge Bonesmo Shannan Little Odd Magne Harstad Karen A. Beauchemin Arne Oddvar Skjelvåg Otto SjelmoAbstract
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The model FROSTOL simulates course of frost tolerance in winter wheat on a daily basis from sowing on as affected by soil temperature (2 cm), snow cover, phenological development, and a genotypic maximum level of frost tolerance (LT 50). A series of cultivar trials in Finland was used to evaluate the model's ability to estimate plant survival in natural field environments during winters with differing weather conditions. Recorded survival was compared with number of intersections between the curves of simulated LT50 and the soil temperature curve for each field. A cumulative stress level (CSL) was calculated based both on number of intersections and FROSTOL simulated stress levels. The correlation between CSL and field recordings was quite low. While the field trials characterize a general ability to stand various types of winter stress, FROSTOL estimates damage caused by the soil temperature regime only. However, FROSTOL simulations seemed to correspond reasonably well to field observations when low temperature was the eventual cause of damage.
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A Canadian model that simulates the course of frost tolerance in winter wheat under continental climatic conditions was adopted and further developed for use in an oceanic climate. Experiments with two cultivars were conducted during two winters in Central Norway. All plants were hardened at the same location. After hardening, in mid November, they were distributed to three locations with contrasting winter climates. Plants were sampled several times during autumn and winter and tested for frost tolerance, expressed as LT50 (the temperature at which 50% of the plants were killed). Results from the experiment were used in parameterization and cross validation of the new model, called FROSTOL, which simulates LT50 on a daily basis from sowing onwards. Frost tolerance increases by hardening and decreases by dehardening and stress, the latter caused by either low temperatures, or by conditions where the soil is largely unfrozen and simultaneously covered with snow. The functional relationships of the model are all driven by soil temperature at 2 cut depth. One of them is in addition affected by snow cover depth, and two of them are conditioned by stage of vernalization. Altogether five coefficients allotted to four of the functional relationships produced a good agreement (R-2 = 0.84) between measured and modelled values of LT50. A cross validation of the model indicated that the parameters were satisfactorily insensitive to variation in winter weather. (c) 2007 Elsevier B.V. All rights reserved.
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