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November 19, 2020   •   Events

07/12/2020 - 07/12/2020

Online event at 14:00 CET

Register Here

November 18, 2020   •   News

Wageningen University & Research (WUR) is recruiting a full professor to lead the Chair group of Cell Biology and Immunology (CBI).

As head of the CBI group, you will lead a vibrant  team of researchers and lecturers. You will support the group’s research ambitions by further strengthening its position in national and international networks, by consolidating established research lines as well as developing new lines of research. You will actively contribute to the excellent reputation and position of WUR in higher education through your active role in their current education programme and in the ongoing development of courses at BSc, MSc and PhD level.

The position will be based in Wageningen

For more information, including how to apply please click here

Applications close 21 December 2020

November 18, 2020   •   News

Indirect nitrous oxide (N2O) emissions from ammonia volatilization, nitrate leaching and run-off were first included in the Australian National Greenhouse Gas Inventory (NGGI) methodology in 2005.

The first implementation assumed that the fraction of synthetic fertiliser N volatilised as ammonia (NH3-N) and nitrogen oxide (NOx-N) (FracGASF) was based on the IPCC default emission factor (EF) of 0.1 Gg N/Gg applied i.e. 10% of all N fertiliser applied was deemed to be lost as ammonia.

This default method also assumed that 100% of this ammonia was then deposited on agricultural land, using the IPCC default emission factor for N2O from N fertiliser of 0.01 (Gg N2O-N/Gg N) i.e. assuming that 1% of this deposited nitrogen was then lost as N2O.

Following two sucesssive national N2O research programs in Australia, where emissions of nitrous oxide from varying soils, climates, agricultural systems and nitrogen inputs were quantified, a series of industry-specific Tier 2 emission factors were published and adopted into the NGGI.

These revised EFs (Gg N2O-N/ Gg N) were: Irrigated pasture 0.004; Irrigated crop 0.0085; Non-irrigated pasture 0.002; Non-irrigated crop 0.002; Sugar cane 0.0199; Cotton 0.0055; Horticulture 0.0085.

The Australian Agriculture Inventory Expert Advisory Panel then discussed the logic of assuming that ammonia emitted and then re-deposited into these systems would have an EF of 0.01, while fertiliser N entering the same soil as ammonia would have the industry-specific EF as listed above.

As the highest ammonia deposition rates are found within a few hundred meters of the emission source, the EFs applied for ammonia deposition were therefore considered to be related to the source of the N.

The NGGI was therefore updated so that the EFs applied for atmospheric deposition are the same as those applied for direct N2O emissions from N fertiliser applied to that system, as listed above.

https://www.industry.gov.au/sites/default/files/2020-05/nga-national-inventory-report-2018-volume-1.pdf

November 18, 2020   •   News

The agricultural sector and land use of agricultural soils in the LULUCF sector are responsible for 11 % of total German greenhouse gas emissions. Drained peatlands are a major source of CO2, which accounts for approximately one third of these emissions, from only 6 % of the agricultural land area. However, CO2 emissions from mineral soils currently have high uncertainty in the German LULUCF inventory and the impact of measures aiming at carbon sequestration by improved agricultural management practices cannot currently be reported.

The Thünen Institute[1] uses a Tier-2 approach to estimate annual SOC stock changes in mineral soils for the German LULUCF inventory. In order to better represent mitigation and carbon sequestration efforts, the long-term roadmap aims to switch to a Tier-3 modelling approach.  A representative SOC stock baseline is necessary as a first step and therefore, after completion of the second German forest soil inventory[2], the Federal Ministry of Food and Agriculture tasked the Thünen Institute to conduct the first comprehensive inventory of agricultural soils[3]. From 2009 to 2018, cropland and grassland soils at more than 3000 representative locations (8×8 km grid) were sampled to a depth of 100 cm. A unique feature of the German agricultural Soil Inventory is that the preceding ten years of management data were surveyed and evaluated for all sampling sites. This includes information on crop rotations, fertilizer inputs, yields and tillage for croplands as well as stocking density, fertilization and cutting frequency for grasslands.

On mineral soils, croplands (n=2204) had an average SOC stock of 61±25 (0-30 cm) and 96±48 Mg C ha-1 (0-100 cm), while grasslands (n=704) stored on average 88±32 (0 – 30 cm) and 135±70 (0-100 cm) Mg C ha-1. The 146 sampled organic soil profiles had an average SOC stock of 528±201 Mg C ha-1 (0-100 cm). A machine learning approach was used to predict the most important drivers of SOC stock on the national scale. Neither climate nor management variables are among the most important predictors, which does however not imply that management is irrelevant for SOC dynamics – the opposite is the case. However, on a national scale it is mainly soil properties that explain the variability in SOC stocks. The most important soil properties are C:N ratio, clay content and groundwater level; topsoil SOC stocks are also driven by land use (cropland vs. grassland). The reason for the high importance of soil C:N ratio is the abundance of so called “black sands” in the North West of Germany, which are mainly podzols with a peat or heathland history, low pH values and a large proportion of undecomposed, recalcitrant plant material.

Results of soil analyses and profile descriptions are made available on an open data repository[4]. The agricultural soil inventory will be repeated in 2023-2028 at the same representative locations to enable assessing the temporal developments of SOC stocks. Average baseline SOC stocks are already integrated into LULUCF reporting. As a next step, it is planned to use stratified SOC stocks for more reliable estimates of land use change effects. This follows the logic that the average SOC stock difference between croplands and grasslands cannot be fully ascribed to land use, but is also driven by soil properties. Finally, machine learning is currently also applied for SOC stock regionalization to derive a reliable, high resolution SOC map of Germany that is needed for higher Tier approaches. 

Figure 1: Soil sampling with interested audience.

Figure 2: Spatial distribution of topsoil (0-30 cm) organic carbon stocks (adapted from Poeplau et al. 20205).


[1] https://www.thuenen.de/en/

[2] https://www.thuenen.de/en/wo/projects/soil-protection-and-forest-health/projekte-bodenzustandserhebung/national-forest-soil-inventory/

[3] https://www.thuenen.de/en/ak/projects/agricultural-soil-inventory-bze-lw/

[4] https://www.openagrar.de/receive/openagrar_mods_00054877

[5] Poeplau, C., Jacobs, A., Don, A., Vos, C., Schneider, F., Wittnebel, M., Tiemeyer, B., Heidkamp, A., Prietz, R., Flessa, H., 2021. Stocks of organic carbon in German agricultural soils—Key results of the first comprehensive inventory.  Journal of Plant Nutrition and Soil Science.

November 16, 2020   •   News

Summary

As part of CEDERS (Capturing Effects of Diet on Emissions from Ruminant Systems), a project funded through FACCE ERA-GAS, we investigated how well existing on-farm GHG accounting models for dairy cattle systems could capture the effect of dietary strategies for GHG abatement. In general, the better a model can simulate rumen function, the greater the opportunity to capture dietary mitigation strategies. All models can be refined to better capture dietary mitigation strategies, but the value of doing so should be a careful balance between gains in accuracy, the need for additional input and activity data, and the need for consistency with other approaches.

Background

Simulation models have an important role to play in estimating greenhouse gas (GHG) emissions from ruminant systems, and assessing the farm system impacts of potential GHG mitigation strategies such as animal feed choices and dietary management. We reviewed existing on-farm GHG accounting models for dairy cattle systems to explore their ability to capture the effect of dietary strategies in GHG abatement. The focus of the review was on methane (CH4) emissions from enteric and manure management sources and nitrous oxide (N2O) emissions from excreta and manure management sources.

Results

We identified three generic types of on-farm GHG models (Figure 1), based on the degree at which diet-related characteristics are captured: from ‘none’ (Type 1) to ‘some’ by combining key diet parameters with emission factors (EF) (Type 2) to ‘many’ by using process-based modelling (Type 3). Most of the models we reviewed have adopted a hybrid approach between Type 2 and Type 3 models. Type 2 models can use many key diet characteristics (e.g., dry matter intake DMI, organic matter digestibility OMD, and energy, protein and carbohydrate content) and then use a CH4 EF and a N2O EF to estimate GHG emissions. Some models only use different CH4 EF for different animal species while others further refine EFs for different diets or dietary ingredients. Empirical models based on commonly measured dietary inputs can predict CH4 and N2O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal fermentation and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological and comprehensive way.

Figure 1: Overview of three generic approaches used by models to estimate on-farm greenhouse gas emissions from dairy systems.

Conclusions

In general, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to common variables such as DMI and OMD, and thus the greater the opportunity to capture dietary mitigation strategies. However, capturing the effect of combinations of different feed additives and the potential interactions with diet composition will be an ongoing challenge. There are opportunities for all models to improve their ability to capture dietary mitigation strategies. The value of doing so, however, should be carefully balanced against gains in accuracy, the need for additional input and activity data, the variability encountered on-farm, and the need for consistency between different approaches used for different purposes (e.g. on-farm accounting vs. national inventory, vs. carbon footprinting).

Acknowledgements

This review was funded by: the New Zealand Government, in support of the objectives of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases (GRA; S7-SOW16-ERAGAS-CEDERS); the Ministry of Agriculture, Nature and Food Quality, The Netherlands (PPS project AF-EU-18010) and The Netherlands Organisation for Scientific Research (ALW.GAS.2); Ministry of Agriculture and Forestry, Finland; The Secretary of State for Environment, Food and Rural Affairs, UK; French National Research Agency, France; Federal Ministry of Food and Agriculture, Germany; TEAGASC  and Department of Agriculture, Food and the Marine, Ireland; Innovation fund, Denmark; Research Council for Environment, Areal Industries and Community Development, Sweden.

Authors

Ronaldo Vibart, Cecile de Klein, Arjan Jonker, Tony van der Weerden, André Bannink, Ali R. Bayat, Les Crompton, Anais Durand, Maguy Eugène, Katja Klumpp, Björn Kuhla, Gary Lanigan Peter Lund, Mohammad Ramin, Francisco Salazar

November 12, 2020   •   News

Dr. Ayaka Kishimoto-Mo’s presentation on Carbon sequestration through biochar amendments in Japan Farmlands covering the history, potential and promoting schemes is linked above beginning at 45:18 minutes.

This event was hosted in collaboration between the Croplands Research Group and the Global Research Alliance in October 2020.

November 5, 2020   •   News

The brand new Transparency in agriculture and land use sectors network has just been launched- a supportive group of experts and practitioners working to prepare for and fulfil the requirements of the Enhanced Transparency Framework of the Paris Agreement. Network members receive regular updates and access to webinars, online courses and other learning opportunities. Membership is open to everyone.  

November 4, 2020   •   News

A Science Slam will be held for the third time on the occasion of the 13th GFFA on 21 January 2021. Subject and schedule:

The Science Slam is intended to illustrate the scientific spectrum of this year’s GFFA subject: “How to Feed the World in Times of Pandemics and Climate Change”. Therefore, the following questions, in particular, come into consideration:

  • How can food systems emerge strengthened from the COVID-19 pandemic?
  • How can the agricultural sector contribute to preventing further pandemics?
  • How can food systems become more climate-resilient?
  • How can food systems contribute to climate change mitigation better than before?

At this entertaining event, four Science Slams lasting 10 minutes at most that relate to the main subject “How to Feed the World in Times of Pandemics and Climate Change” will be presented after a short welcome by the Federal Ministry of Food and Agriculture (BMEL) and introduction by the facilitator. The slam presentations must be held in English. Simultaneous interpretation into German will be arranged. In line with the virtual format of the 2021 GFFA, the slammers, facilitator and the audience will be virtually interconnected and the audience will decide in the end in the form of an online vote to whom the BMEL prize will be awarded.

What is a Science Slam?

A science slam is a science flash talk competition where the speakers, within a specified time, present their research topics to a mixed audience. The focus here is on communicating scientific concepts to a non-expert audience. The audience will assess the slams in form of an online vote. Apart from the scientific contents, the comprehensibility and the entertainment value are also rated.

Complete the Submission Form and send it to the following e-mail address by 22 November 2020: [email protected]

November 3, 2020   •   News

On 28 October 2020, the GRA and CCAFS successfully hosted the final webinar of the 2020 CLIFF-GRADS Science Collaboration Series.

The final session was an open forum for discussion on potential research collaborations between CLIFF-GRADS Alumni and among wider networks. The session included a recap of all previous webinars in the 2020 Science Collaboration Series and information for CLIFF-GRADS Alumni on how to stay involved in the programme.

Special thanks to our guest panellists and chair who discussed their experience in international research collaboration, including how to make the most of international research opportunities:

  • Sinead Leahy, Senior Scientist, New Zealand Agricultural Greenhouse Gas Research Centre (NZAGRC)
  • Ngonidzashe Chirinda, Soil and Climate Change Scientist, Mohammed VI Polytechnic University, Morocco
  • Bjoern Ole Sander, Senior Scientist and Climate Change Specialist, International Rice Research Institute
  • Jacobo Arango, Environmental Biologist at the Tropical Forages Program at CIAT
  • Hayden Montgomery, Special Representative, Global Research Alliance on Agricultural Greenhouse Gases (Panel Chair).

Please listen to the recording for more!

November 2, 2020   •   Events

17/11/2020 - 17/11/2020

Online Event

Register here

October 29, 2020   •   News

Issue 6 of the Food and Agriculture Organization of the United Nations Livestock Environmental Assessment and Performance Partnership (FAO LEAP) Newsletter is now available!

This edition includes:

  • “Livestock, Climate and Environment”, a new Community of Action
  •  A new study on the livestock impact on global nitrogen flows and emissions
  • Launch of the new guidelines on feed additives
  • Call for experts to form a new Technical Advisory Group (TAG)

Read the newsletter here