Reducing emissions from agriculture – the role of new farm technologies

3.0 Methodology

3.1 Identifying the candidate approaches

3.1.1 Technologies at the farm level

Our current farm systems are composed of livestock, cropping, horticultural and mixed farms. These range from highly extensive enterprises - exemplified in upland sheep farming systems - to highly intensive fruit production. Current efficiency levels vary within these farm types and, whilst productivity is growing, those farms at the frontier are adopting improved varieties, or using genetic selection indices to promote herd productivity.

The major food producing components of Scottish farming is comprised of several functions, e.g., land management, livestock management etc. To provide a schema for categorising new technologies we identified a set of sub-functions that the technology would aim to target for the farm. These are shown in the schema below.

Figure 4. Schema for understanding farm functions and potential routes for technology improvement. The figure shows the main functions which all support food production that could be improved to reduce greenhouse gas emissions.

• Decision Support
  • Risk Management
  • Expert Systems Design
  Land management

One of our assumptions is that farms and their functional structure will remain the same over the next twenty years. Whilst there may be disruptive technologies that reform our current farm structures over this period, the institutional frameworks, e.g., land tenure, and proposed policy support schemes would, we argue, have a greater influence on disruptive change. Hence, any reconfiguration of farming functions is beyond the realm of this report.

3.2. Description of the Long List

Given the above criteria we explored a number of routes to identify new technologies. Expert knowledge and engagement with the commercial sector within the research group identified new technology that has been introduced elsewhere or is in development with major commercial producers. The patent literature was also explored using the schema of Figure 4 as a guide, e.g. ‘livestock improvement technologies’, with a view to identifying viable patents that infer applied technologies. This was achieved through patent searches using International Patent Office (IPO)/Ipsum, European Patent Office (EPO)/Espacenet, United States Patent and Trademark Office (USPTO) and GooglePatents. Finally, for several areas, we conducted reviews of the literature to assess the current state of these technologies, e.g., single cell proteins. Accordingly, whilst the search was not strictly systematic the expertise in the group covered these functions which were supported by literature and patents that define new technologies in these areas. What follows are brief descriptions of the long list. This is presented against the schema above in terms of the farm’s functions and functional areas by which the technology is targeted.

3.2.1. Decision Support

Risk management

• Distributed Ledgers (D1): Blockchain and distributed ledger technology (DLT) have the potential to increase efficiency, transparency, and trust throughout agricultural supply chains. Blockchain for food supply chain can empower all market players by building relationships of trust. Will offer oppourtunities for transparency for crop/livestock insurance. This will increase stability of incomes and therefore planning.
• Weather Tracking(D2): Brings in the oppourtunity to link real time data and forecasting for crop yield. Forecasting is based on a variety of machine learning or AI methods to improve robustness. This will improve planning for yield stability. This will lead to technological advancements such as online services and connected apps (in vehicles etc.)

Expert systems

• Low-cost infrastructure (D3): Functional tech to link up sensors and data analytics etc. Low cost and low power, wireless mesh network, this makes it attractive for application. Currently used in smart homes but has potential to field based sensor systems. Products would include a low-cost, low-power, wireless mesh network.
• Digital twinning (D4): Use of artificial intelligence coupled with sensor input to mimic a farm set up. Supports decision making to optimise solutions for resource use under uncertainty. Part of a ‘SMART farm’ assemblage.

3.2.2. Land Management

Soil Monitoring

• Moisture Sensors (L1): Wireless sensors networked to detect moisture/warn of changes in moisture. Not adopted in Scottish agriculture but may be more relevant under increasing drought in the East.
• Underground Sensors (L2): Soil sensor system distributed under turf. This is currently applied in golf and sports course care but has the potential for applying under land for intensive grazing.

Water Management

• Substrate Sensors (L3): Analysis of soil and moisture within a single sensor. Currently applied in greenhouse conditions.

Nutrient Management

• Biochar (L4): Carbon rich, pyrolysis of organic waste - found to mitigate carbon and support productivity - mostly applied in developing countries with uncertain application in temperate countries.
• Rock dust (L5): Application of crushed, reactive silicate rocks (such as basalt) found to offset fossil fuel CO₂ emissions. Only tested in tropical agriculture but has potential for temperate soils.
• Combined application of nitrification inhibitors with urea and urease inhibitors with deep placement (L6): Combines nitrification with urea inhibitors. Deep placement in soil has been found in experiments to reduce N²O and ammonia.
• Engineered soil microbes (L7): Advanced synthetic biology coupled with large data sets. This aims to deliver biological traits which target nitrogen fixation and thus reduce N²O emissions.

Carbon Sequestration

• Methane oxidation in soil (L8): Opportunity to feed flue gases from barns into underground pipes that allow oxidation of methane by soil microbes. Biological treatment methods are already common in the pig farming industry and can be adapted to methane removal if the issues around N²O generation can be resolved.

3.2.3. Livestock Management and Production

Livestock Improvement

• Genetic profiling/Genomic testing in breeding programme (Liv1): Genetic tests can be used to manage groups of cattle. Tests can give producers a better idea of how animals will perform in specific situations. These tests enable producers to sort animals into particular management groups. This is commonly referred to as marker assisted management (MAM).
• Genetic profiling/Genomic testing in breeding programme (Liv2): Marker-Assisted Backcrossing. The goal of backcrossing in commercial agriculture applications is to move a single trait of interest—such as drought tolerance, high productivity, or disease resistance—from a donor parent to progeny. Marker-assisted backcrossing using Illumina microarrays or Next generation sequencing (NGS) enables researchers to monitor the transmission of the trait gene via a genetically linked marker that can be easily screened. This process significantly accelerates backcrossing programs and reduces the time to release of commercially viable plant lines.
• Tail Mounted Sensors (Liv3): Measures tail raising in animals, as accelerometers, fed to monitors for fertility measurement. Can be coupled with machine learning to forecast fertility, predict calving and detect potential health issues.
• Use of milk spectral data to predict new and existing phenotypes routinely (Liv4): Mid-infrared spectroscopy is based on crossing matter by electromagnetic radiation. MIRS (Mid infrared spectroscopy) has been tested to predict new milk phenotypes of economic relevance such as fatty acid and protein composition, coagulation properties, acidity, mineral composition, ketone bodies, body energy status, and methane emissions.
• Use of digital images to predict new phenotypes (Liv5): Sequencing and other omics technology facilitate deep phenotyping of livestock at the molecular level. Combines 'big data' collection with algorithms for high throughput.

Pest and disease management

• Automated heat detection system (Liv6): The rumen bolus measures direct values with the highest accuracy inside cattle, in the reticulum. The boluses are administered once and are completely maintenance-free.
• Connected animal mounted sensors (Liv7): Animal mounted sensors (pedometers, ear-tags, collars) record activity, feeding times, temperature, rumination - deviation of which can indicate ill-health.
• Visual Movement sensing (Liv8): Movement mapping to identify lameness and raise productivity - use of digital images to identify differences in behaviour.
• Digital pathology and computational image analysis (Liv9): Using computer images of samples, combined with large-scale data analysis to predict incidence of disease.

Feed management

Feed Supplements (aimed at reducing methane)

• Seaweed. The last decade studies explored the potential. Though only recently experienced commercial development (liv10)
• Agolin® Ruminant (Agolin SA, Bière, Switzerland) is a commercially available pre-mixture of flavourings. The main active compounds of this product are food grade and chemically defined plant extracts including coriander (Coriandrum sativum) seed oil (up to 10%), eugenol (up to 7%), geranyl acetate (up to 7%) and geraniol (up to 6%) along with some preservatives such as fumaric acid. (liv11)
• Mootral (garlic-oil based methanogenesis inhibitor) (liv12)
• Standardized combinations of active substances naturally occurring in aromatic plants and spices, selected for their proven in-vivo effects (liv13)
• Yucca extract; Quillaya extract; Tea seed extract; Sapindus extract (liv14)
• Unsaturated veg oils; Coconut oil (liv15)
• Monensin – commercially available product(liv16)
• Condensed & Hydrolysable tannins (liv17)
• Disodium fumarate & malate (liv19)
• Pyruvate carboxylase inhibitors (liv20)
• Calcium nitrate (liv22)
• Biochar as a feed additive (liv23)
• Glycerol monoester of lauric acid (liv24)
• Chinese red yeast rice (statins) (liv27)
• Solid-state fermentation of straw and other crop residues (liv28)
• N-alkyl-pABA derivatives (liv29)
• Peptide from L.lactis fermentation (liv30)
• ssential oils (cashew nut oil) (limonene)(peppermint oil) (liv31)
• Mustard oil; Sandalwood oil(liv32)
• Rhubarb (9,10-anthraquinone) (liv33)
• Bile acids are bio-surfactants and assist intestinal digestion and absorption of lipids and fatty soluble vitamins such as Vitamin A, D, E and K, and improve nutrient utilisation (liv34)
• MetaSmart^® anester of the analogue of methionine, is a unique feed ingredient and patented molecule from Adisseo. (liv35)
• Use of cultures to produce fermented feed substances (liv36)
• Monitoring of feed intake, and automated weigh crates combined with 3D cameras is an important management tool - less efficient animals generally producing more methane. (liv37)

Alternative proteins

Microbial proteins

• Yeast (liv38)
• Microalgae-based feed(liv39)
• Bacteria-extracted feed(liv40)
• Fermentation-based feed(liv41)

On-farm Insect production

• Currently produced at a small scale; there seem to be few barriers for production at farm level aside from cost of production. Zero waste Scotland produce guidelines of production. (liv42)

3D feed printing - algae for protein

• Algae can be used to derive balanced feeds targeting methane etc. Also, early-stage bioplastics for silage bales (liv43)

Further Methane management

• Fluoride and tannin additive to manure: The additive consists of the two naturally occurring substances fluoride and tannins, has the potential to drastically reduce emissions of various gases from manure, while at the same time reducing odours by 50% (liv44)
• Methane mask for cattle: Mask placed on cattle to capture methane expelled. This methane is oxidised (liv45).
• On-farm fertiliser production from manure with plasma technology: Using electricity atmospheric N2 is converted to nitrate and mixed with manure to make ammonium-nitrate, preventing ammonia losses and enhancing the fertiliser value of the manure (liv46).
• Methane Vaccine: Aims to introduce antibodies into a cow's saliva which then pass to the animal's rumen or stomach and bind with the methanogens which convert hydrogen to methane. There is no conclusive evidence of its efficacy but could be a future technology. Focus so far has been on dairy cows(liv47).

3.2.4. Crop Management and production

Crop improvement

• Mini-chromosomes (Crop1): A mini-chromosome is a tiny structure containing a cell that can add dozens of traits to a plant without altering its original chromosomes. This could result, for example, in drought tolerance and nitrogen use efficiency. It is not considered genetic engineering and therefore quicker regulatory approval and acceptance would be expected.
• Cloud-based bioinformatics (Crop2): Cloud platforms to link genomic discoveries to plant breeding decisions, programme design and monitoring.
• Short Stature Corn (Crop3): Hybrid crop that grows shorter and therefore reduced loss from wind damage (lodging)
• Drone seed spreaders (Crop4): Mostly US based applications. Currently small niche but reduces compaction of soil.
• infrared reflectance (NIR) (Crop5): Non-invasive technique that measures the reflection of different wave light lengths to monitor the composition of grain as it ripens (moisture, starch, protein, and oil content)
• Genetic profiling/Genomic testing in breeding programme (Crop6):A form of marker assisted management (MAM). Genetic tests can give producers a better idea of how crops will perform in specific situations. These tests enable producers to sort crops into particular management groups.
• Genetic profiling/Genomic testing in breeding programme (Crop7): Marker-Assisted Backcrossing (MAB). The goal of backcrossing in commercial agriculture applications is to move a single trait of interest—such as drought tolerance, high productivity, or disease resistance—from a donor parent to progeny. This process significantly accelerates backcrossing programs and reduces the time to release of commercially viable plant lines.

Pest and disease management

• Automated expert system for field crops (crop8): Fusing IPM with Automated Decision Support Tools leads to early identification of pest/diseases. Current focus is on weed and pest detection.
• Crop walk UAVs (crop9): UAVs which monitor disease through infrared (same as NIR (Crop5) but applied to crop health using different algorithms.
• Nano-TiO₂ Photo Semiconductors (crop10): The nano-TiO2 applications of degrading pesticides, plant germination and growth, crop disease control, water purification, pesticide residue detection.

Targeted nutrient management

• Nano fertilizers (crop11):A nano-fertilizer refers to a product in nanometer regime that delivers nutrients to crops. It will improve nitrogen use efficiency.
• Biological nitrification inhibitors (crop12): BNI is the natural ability of certain plant species to release nitrification inhibitors from their roots that suppress nitrifier activity.

3.2.5. Buildings and Energy Management

Crop storage

• Radio wave grain drying (B1): Radio wave grain drying is an emerging technology that uses radio waves instead of heat to remove water from the inside out of individual grains. Reduces grain drying energy costs but also claims to improve grain quality by reducing over- and under-drying.
• Real time monitoring of stored grain/potatoes (B2): Linked robotic drones to check moisture content in grain stores and potatoes. Autonomously digs through grain store and offers real time monitoring against set levels. Also, turns over grain to support drying function.

Energy management

• Smart buildings (B3): Increased monitoring and regulation of heating/cooling/ventilation etc. with increased control through apps. Similar to smart homes concept only applied to sheds and stores. Applied to glasshouses/smaller kit could be developed for polytunnels.
• Smart Cattle sheds(B4): Monitoring system based on LoRa, a low-power, long-range wireless communication technology. The proposed system wirelessly collects real-time stable information from sensors installed in the cattle shed, and the collected data are analysed by the integrated management system, delivered to the user, and controlled by the application.
• Greenhouse automation(B5): Using sensors and connectivity reporting on greenhouse condition data, smart weather stations can also use predefined settings (and machine learning) to automatically adjust the environmental conditions to match the given parameters.

Capturing emissions from buildings

• Thermal-catalytic oxidation(B6): Catalyst-based oxidation by the passage of hot ambient air (approx. 400°C) over cheap catalysts like Hopcalite (Cu-Mn Oxides). Could run through animal sheds to capture methane.
• Photocatalytic oxidation(B7): Ultraviolet light is used to split an oxygen molecule into two free radicals. In photocatalytic reactors, a catalyst such as zinc oxide or titanium dioxide is used to increase the generation of free radicals, and thus the methane reaction rate. This is very attractive as ultraviolet light in sunlight can be used, for example on the roofs of cattle barns.
• Zeolite or membrane extraction(B8): Methane extraction by gas separation membranes is a potentially feasible method of methane removal through sheds. These methods rely on techniques that are already in widespread industrial use that could likely be readily upscaled in a way that a more experimental technology could not, which is a significant advantage when dealing with the immediate need for decarbonisation.
• Replace farm buildings with sustainable materials(B9): Concrete is a significant contributor to CO₂ replaced by sustainable wood products (e.g., hybrid sandwich wall (half concrete/half wood); or Hemp (with warming properties)

Tractors

• Autonomous and semi-autonomous tractors (V1): Self-driving, more accurate than human driver; much lighter and driven by electrical motors hence reduces soil compaction

Equipment

• Autonomous sprayers (V2): Combining robots and drones to direct spraying on grass (Autospray). John Deere autonomous spray system with adjustable heights for row cropping
• Continuously variable transmission (V3): Continuously variable transmission with an electro-mechanical power split- produces power which links up tractor to e.g., slurry sprayer - gives lighter load and more power, so less compaction and claims to be carbon neutral.

Fuel Management

• Electrification (V4): Electrical drives to replace engines and hydraulics. Electric motors have huge torque at low speeds, they are more efficient, more reliable, and lighter.
• Hydrogen powered vehicles (V5): Currently mixes hydrogen with diesel as a hybrid engine - introduced recently in the Netherlands. Claim of reducing CO₂ by 40%
• Methane powered vehicles (V10): Methane powered production tractor. Use of biomethane (from animal or agricultural waste) which powers the tractor. Alternatively, there may be possibilities in the future for refilling directly from the gas grid network or at specific biomethane station.

Smart Monitoring

• Soil and Water Sensors (V6): Sensors allow examination of the soil’s moisture and nitrogen level. Thus, farmers know which parts of their arable feed need watering or fertilizing.
• Central command of vehicles and equipment (V7): A central control booth with joystick control, display, and networking. Allows the farmer to control all vehicles with support from AI algorithms from a single point, e.g., integrating real-time weather data and syncing activities. This therefore provides a time and labour-saving solution.
• Automated harvesting equipment (V8): 3D imaging for automatic sorting in field to reduce waste. Seems to be tested on a traditional tractor set up but could be coupled to advanced equipment. Uses 3D laser scanners, robotics, image processing and deep learning software.
• Machine Synchronisation(V9): This technology supports the unloading of the combine on the trailer to avoid spilling of grain. Supports multiple machines in the field at the same time.

3.3. Short listing Approach

Once compiled and agreed by Scottish Government the long list was then assessed in terms a number of criteria. These are highlighted below.

• System Fit: how the technologies will fit the current farming system or would it require a change, e.g., more mixed farming; access to the power grid or nearby facilities to enable its application. Running from 1(fits now) to 5(requires a major system change). Full scale: 1=fits now; 2=requires a small change in the farm, 3= requires change in an enterprise, 4 =requires a change in more than one enterprise; 5=major system change.
• Level of technology readiness: the lead-in time and market rollout of a technology. This reflects the current state of the technology, i.e., has it been introduced elsewhere and needs to be modified or is it at the conceptual stage. Runs from 1(near market) to 5(not ready within 20yrs). Full scale: 1=near market,2=development stage, 3= applied research stage, 4= strategic research stage, 5= not ready within 20 yrs.
• Cost of Implementing for the farm: the amount of on-farm investment for implementing the technology to a cost-effective level. These include capital and running costs mostly but also training costs if it involves new equipment that is radically different. Runs from 1(no change) to 5(high level of investment). Full scale: 1=no change, 2=minimal cost, 3=small cost, 4= higher level of investment, 5= significant level of investment.
• Impact on the Market: This accounts for any productivity effects on adoption - e.g., would adoption lead to a loss in efficiency despite the GHG gains, but also brings in savings as well. Hence the scale reflects the level of market disruption that the technology would cause from either an increase or a decrease in prices. Running from 1:(no effect on price) to 5(high effect on price). Full scale: 1=no effect on market price, 2=small change in market price, 3=some change in market price, 4=higher level of change in market price, 5=significantly high level of change in market price.
• Level of market acceptance : the level of acceptance of the approach within current supply chains. How will this fit the way the supply chain operates? Would it require modifying materials/methods/machinery etc. Runs from 1(feasible) to 5(not feasible). Full scale: 1= feasible within the current supply, 2= small effect on supply chain; 3=some effect on supply chain; 4=large effect on supply chain; 5= not feasible within the current supply chain.
• Social Acceptance: the level of acceptance for both farming communities and society as a whole. Are there any production processes that would be seen as controversial for society as a whole. Full Scale: 1(total acceptance) to 5(no acceptance). 1= total acceptance, 2=few concerns; 3= some concerns; 4= major concerns; 5= no acceptance.
• GHG Savings: The expected impact on GHGs if the technology were adopted within Scotland level. This considers the current structure of Scottish farming and the potential for GHG savings. Full scale: 1(no impact) to 5(high impact). Full scale: 1=no impact, 2=low level impact, 3=medium impact, 4=moderately high impact, 5=high impact.

The technologies were ranked individually against these criteria by the 6 researchers. Each criteria is was weighted equally to give an implementation scale to reflect the consensus. This was then ranked in terms of the lowest score, which identifies the most feasible of the long list of technologies that could implemented.

Secondly, the GHG score was also ranked by the researchers in terms of the predicted GHG savings that each technology could offer. This was an estimate based on the potential population that the technology could be applicable to, e.g., all livestock, or specific species or systems. Then the average of the implementation ranking and the GHG ranking were used to estimate the final rank. This means that the GHG savings were equally ranked with the implementation score^[7],^[8].

3.4. An assessment of the performance of the candidate technologies

The candidate technologies were explored in-depth to provide a fuller description of the technology, the GHG saving potential, an estimation of the costs for adoption at the farm level, their applicability towards the sector and any interaction effects. These are detailed within the Appendix. A summary table (table 3) is provided below of the potential GHG saving and the uncertainties around these estimates.

Significant gaps emerge in some of the technologies through lack of a systematic evidence base or, more fundamentally, a paucity of trials which reflect Scottish conditions and contexts. In summary, the table indicates a significant GHG saving potential for most of the technologies at the most optimistic end, but a number have shown no effect given the context and applications.

Measures Applicable to tillage and grassland`

Key

L5

Main Farm Functions

Land management

Functional Area

Rock dust

GHG Saving

Abatement potential with agricultural soils in the UK has been estimated between 0.2 and 0.8 tonnes of CO₂ per tonne of basic rock

Uncertainty

Low uncertainty: sequestration potential will vary depending on the chemical composition of the rock material. Ultrabasic rocks with especially high magnesium and calcium contents can sequester > 1 tonne of CO₂ per tonne of rock applied (Kantola, 2017). There is some evidence that rock minerals may also contribute to the reduction in methane and nitrous oxide emissions from soils, whilst boosting the productivity of arable soils (Blanc-Betes et al. 2021; Das et al. 2019).

Key

L4

Main Farm Functions

Land management

Functional Area

Biochar

GHG Saving

UK studies estimate an abatement potential of 0.7-1.4 t CO₂eq/ oven dry tonne

Uncertainty

Medium uncertainty: total GHG abatement will vary depending on organic feedstock, production technology, and predicted effects on crop yields.

Key

L2

Main Farm Functions

Land management

Functional Area

Underground Sensors

GHG Saving

Unknown

Uncertainty

High uncertainty: No study on underground sensors and GHG savings exist but a number promote the idea that this would have benefits. Savings would be on nutrient application, and reduced crop failure may have positive, if marginal, effects on CO₂ above ground sequestration.

Key

Crop2

Main Farm Functions

Crop Management and Production

Functional Area

Cloud-based bioinformatics

GHG Saving

Unknown

Uncertainty

High uncertainty: Would improve crop production and soil health through targeted solutions. Note benefits may be offset by the carbon emissions generated from large scale computing arrays.

Key

Crop12

Main Farm Functions

Crop Management and Production

Functional Area

Biological nitrification inhibitors

GHG Saving

Tropical grasslands: reduce N₂O emissions in the field by up to 90%. New Zealand: reduce nitrous oxide emissions by 50% for the use of plantain within species rich swards.

Uncertainty

High uncertainty: The mechanism of this effect is not entirely clear, as it could result from the direct effects of plant exudates on soil nitrification rates but could also result from digested forages having an impact on nitrification rates in the urine deposited by grazing livestock.

Measures Applicable to livestock

Key

Liv10

Main Farm Functions

Livestock Management and Production

Functional Area

Feed Supplements (Seaweed)

GHG Saving

Beef -56% CH₄; Dairy -22% CH₄; -53% CH₄ Sheep

Uncertainty

High uncertainty: Only several trials conducted within the US and Australia

Key

Liv15

Main Farm Functions

Livestock Management and Production

Functional Area

Feed Supplements

GHG Saving

Several technologies show reductions in methane yield of 5-15%.

Uncertainty

Medium uncertainty: reflecting differences between supplements and the amount of data available. Though there is a higher uncertainty concerning interactions between different supplements (e.g., will there be synergies or trade-offs).

Key

Liv38

Main Farm Functions

Livestock Management and Production

Functional Area

Microbial proteins

GHG Saving

Unknown

Uncertainty

High uncertainty: Considered as a replacement for soya-based meal and is therefore carbon off-setting. Though dependant on the mixtures of proteins with other feeds.

Key

Liv1

Main Farm Functions

Livestock Management and Production

Functional Area

Genetic profiling/Genomic testing in breeding programme

GHG Saving

Genetic gain per generation would be equivalent to an up to 8% reduction in methane emissions per year / cumulatively up to 50% in 10 years.

Uncertainty

Low uncertainty: Based on research conducted at SRUC Beef Research Centre and therefore reflective of Scottish conditions.

Key

Liv44

Main Farm Functions

Livestock Management and Production

Functional Area

Fluoride and tannin additive to manure

GHG Saving

In pigs: 95% reduction in ammonia emissions, 99% reduction in methane emissions, and 50% reduction in odour (Dalby, 2020b).

Uncertainty

High uncertainty: Studies at lower dosages have not identified any emission reductions (Dalby, 2021). In addition to direct emission reductions, tannic acid with fluoride(TA-NaF) will reduce nitrogen losses from manures, improving crop productivity and potentially reducing emissions related to synthetic fertiliser use.

Key

Liv47

Main Farm Functions

Livestock Management and Production

Functional Area

Methane Vaccine

GHG Saving

Efficacy ranged from 7.7% to 69% methane reduction

Uncertainty

High uncertainty: Multiple studies were unsuccessful in vivo.

Key

B4

Main Farm Functions

Buildings and Energy Management

Functional Area

Smart Cattle sheds

GHG Saving

Estimate of around 14 to 25% reduction in to CO²e for beef finishing

Uncertainty

Medium uncertainty: Savings relate to the SRUC GreenShed project and therefore reflective of Scottish conditions. However, this is a single trial.

Key

Liv37

Main Farm Functions

Livestock Management and Production

Functional Area

Connected animal mounted sensors

GHG Saving

Beef: Modelled scenarios for PLF introduction reduced both total farm emissions (2.4 - 7.4%) and emission intensities (1.5 - 11.9%); Dairy: showed reductions in whole farm emissions (0.4 - 0.9%) and all scenarios reduced emissions intensities (3.0 - 9.0%).

Uncertainty

Low uncertainty: use of Agrecalc (a carbon foot printing tool based on Scottish conditions) to model impacts of PLF introduction on average Scottish beef and dairy farms.

3.5. Implementation pathways of candidate approaches

The previous section identified the GHG saving potential of these technologies. The purpose of this section is to examine the candidate technologies in terms of their current stage of development, barriers to market entry and the potential intervention approaches that could be used to engender faster uptake of these technologies. We classify our technologies against the following criteria:

• Current stage of development: These range from a theoretical proposition, a single trial, at the testing stage, near-market, niche/roll-out elsewhere.
• Potential timescale: An estimate of the time to market of these technologies, given the barriers and issues around the candidate technologies.
• Potential barriers: the main barriers that inhibit the further development of the technology, or which could be addressed to lead to more rapid entry to the market.
• Intervention logic: To understand the rationale for intervention, based on the characteristics of the technology and whether this is purely private enterprise, or whether the public sector has a role.
• Type of intervention: Given the barriers to the technology, what interventions are available which may lead to more rapid development. Most of this will be directed at the sectors developing the technology, e.g., the agrochemical sectors, but other interventions would be focused on the farm level, e.g. to generate demand and support behavioural change to make product development more desirable.
• Farm Types: The most likely farm types that would benefit from the product, to give an indication of the potential market size if adopted.

This will offer some insight into how near the candidate approaches are to market and help inform where interventions could be focused. Thus, we consider some of the implementation pathways for developing frameworks for support, regulatory baselines and stimulus for private sector investment. These are presented below.

3.5.1. Seaweed (Asparagopsis)

Intervention Logic: Feed Additives are mostly developed through the commercial sector. Some patents are held by public research institutes and university which implies some public good argument for intervention, mostly around mitigation of methane and in the ruminant sector this is a significant portion of overall GHGs.

Type of intervention(s) to enable technology: Main vehicles of support may be regulatory recognition, namely authorities could change the way seaweed as a methane mitigator is regulated in the EU (i.e., requiring approval as a zootechnical feed additive). Moreover, this is a non-native species and this would need a source of sustainable seaweed to avoid negative consequences.

Current stage of development

near-market

Potential timescale

5-10 years

Potential barriers

Patents jointly held by CSIRO and James Cook University, Australia. ‘Future Feed pty’ is a company established to licence this intellectual property for the development of supply chains of Asparagopsis for the feed industry. Authorities could change the way seaweed as a methane mitigator is regulated in the EU (i.e., requiring approval as a zootechnical feed additive). This would either prevent such products reaching the market or require several years for relevant dossiers to be compiled and reviewed.

Availability of seaweed; Lack of Infrastructure; Regulatory compliance on Iodine in foods

Limits on supply of safe feed may be a barrier to implementation.

Asparagopsis can contain a high level of iodine and high levels are potentially toxic, and transfer into milk. Current EU (and UK) maximum concentrations of iodine in dairy and beef diets (5 and 10 mg/kg complete feedstuff, respectively) may limit the use of Asparagopsis as a feed material. Implementation may also be constrained by production capacity (wild harvesting, at sea cultivation or tank-based cultivation on land).

Farm Types Suitable

Beef; Dairy; Sheep; Pigs; Poultry

3.5.2. Feed Supplements (as methane mitigators)

Intervention Logic: Feed supplements targeting methane address one of the major externalities in Scottish livestock production. Many feed supplements have been evaluated as methane mitigators but few have found practical application.

Type of intervention(s) to enable technology: Incentives (market or regulatory) are needed to drive use of supplements that reduce methane but which do not deliver improved animal performance that farmers can currently monetise, e.g. increased efficiency. Different additives, working via different mechanisms, may be complementary hence interventions may be needed to encourage collaboration between suppliers of different supplements (who otherwise behave as competitors).

Current stage of development

near-market

Potential timescale

1-15 years

Potential barriers

Feed supplements can be simply but usefully classified by mode of action: 1. Direct inhibitors of methanogenesis, 2. Alternative hydrogen sinks, 3. Suppressors of hydrogen formation (fermentation). Each class contains supplements close to market (e.g., 3-NOP, nitrate), supplements that have been extensively researched but not applied, for various reasons (e.g., statins, monensin, saponins) and novel approaches currently undergoing varying degrees of active research (e.g., plant extract screening programmes, probiotics). Thus, the potential timescale for development and implementation is large.

Many potential obstacles between initial discovery and practical adoption. Even when these are overcome, adoption will require incentives (financial or regulatory).

There may be insufficient proof of efficacy, lack of data on interactions between supplements and the environment in which they will be used; lack of vested interest to fund product development (e.g., lack of IP ownership); lack of viable supply chain; practical challenge of reaching target animals (e.g., extensive grazing systems); challenges in the regulatory pathway; lack of financial reward to end-users and/or supplement manufacturers.

Farm Types Suitable

Beef; Dairy; Sheep

3.5.3. Rock dust

Intervention Logic: Rock dust is commercially available and applied by a small number of niche farmers. Main issue is measuring GHG potential and wider environmental impacts on food production through transfer of heavy metals.

Type of intervention(s) to enable technology: This limited evidence base requires further testing to prove efficacy to other farmers but also to identify the key sources of rock dust which minimise exposure to potentially toxic elements.

Current stage of development

at the testing stage

Potential timescales (years to market)

3 years

Potential barriers

There is already application of rock dust to agricultural soils small-scale in tropical climates. More widespread use depends upon rigorous testing and analysis of the greenhouse gas mitigation potential, alongside work to ensure that the technology can be applied without any adverse environmental safety consequences.

Cost of product; Regulatory Compliance

The availability of rock dust varies across the country, and transport costs can be high. There are also concerns that some materials may contain heavy metals for potentially toxic elements that would preclude their use from agricultural soils.

Farm Types

Cereals; General Cropping; Horticulture

3.5.4. Biochar

Intervention Logic: Biochar is commercially available (e.g., SoilFixer; Pyreg) but there are limited production facilities and competition for reliable organic inputs for production.

Type of intervention(s) to enable technology: High Capital costs could be reduced through targeted supported for business. More support for testing of spreading equipment; improved clarity on the role of biochar and waste management licensing.

Current stage of development

niche/roll-out elsewhere

Potential timescales (years to market)

1-5 years

Potential barriers

Biochar is a mature technology; it is already produced across the world for diverse uses. A recent report estimated the time to implementation for biochar in Scotland at 1-5+ years (Hazeldine, 2019). Notably, a longer time frame and higher costs will be required to link biochar production with bioenergy and carbon capture and storage, though this would achieve higher greenhouse gas abatement in the long term.

Availability of feed stock; Regulatory; Efficacy; Capital Costs

One of the largest barriers to implementation of biochar as a mitigation technology is uncertainty in the longevity of soil carbon storage. At the farm level, further barriers include logistic challenges in spreading low density material on large areas of farmland, unclear impacts on crop productivity, and high costs. At the production level, barriers include high capital costs for new production facilities, competition for organic feedstocks with other technologies, and a lack of clarity in waste management licensing for Biochar (Shackley & Sohi, 2010).

Farm Types

Cereals; General Cropping; Horticulture

3.5.5. Microbial Proteins

Intervention Logic: Commercially developed products exist but focus has been on high value sectors, e.g., poultry, pigs, fish. There is little work in cattle or sheep sectors.

Type of intervention(s) to enable technology: Regulatory or tax related interventions could be considered, e.g., for replacing soya-based meal, may encourage innovation and adoption to overcome technical barriers.

Current stage of development

near-market

Potential timescales (years to market)

3-5 years

Potential barriers

Profloc and Feedkind Terra are examples of commercial products though focused in the US or the fish/poultry/pig sector

Technical Barriers

The small number of studies may reflect a decreasing marginal effect, e.g. when replacing soya based meal with single cell proteins (Hombegowda et al 2021). A technical barrier also exists when scaling up the technology, e.g., where the process of photosynthesis is used by these organisms to capture CO₂ from the air as a carbon source, ensuring enough light reaches all algal cells as their density increases over time requires innovative tank design and/or lighting systems.

Farm Types

Poultry, pigs, cattle, sheep

Current stage of development

near-market

Potential timescales (years to market)

3-5 years

Potential barriers

Profloc and Feedkind Terra are examples of commercial products though focused in the US or the fish/poultry/pig sector

Technical Barriers

The small number of studies may reflect a decreasing marginal effect, e.g. when replacing soya based meal with single cell proteins (Hombegowda et al 2021). A technical barrier also exists when scaling up the technology, e.g., where the process of photosynthesis is used by these organisms to capture CO₂ from the air as a carbon source, ensuring enough light reaches all algal cells as their density increases over time requires innovative tank design and/or lighting systems.

Farm Types

Poultry, pigs, cattle, sheep

Current stage of development

near-market

Potential timescales (years to market)

3-5 years

Potential barriers

Profloc and Feedkind Terra are examples of commercial products though focused in the US or the fish/poultry/pig sector

Technical Barriers

The small number of studies may reflect a decreasing marginal effect, e.g. when replacing soya based meal with single cell proteins (Hombegowda et al 2021). A technical barrier also exists when scaling up the technology, e.g., where the process of photosynthesis is used by these organisms to capture CO₂ from the air as a carbon source, ensuring enough light reaches all algal cells as their density increases over time requires innovative tank design and/or lighting systems.

Farm Types

Poultry, pigs, cattle, sheep

Current stage of development

near-market

Potential timescales (years to market)

3-5 years

Potential barriers

Profloc and Feedkind Terra are examples of commercial products though focused in the US or the fish/poultry/pig sector

Technical Barriers

The small number of studies may reflect a decreasing marginal effect, e.g. when replacing soya based meal with single cell proteins (Hombegowda et al 2021). A technical barrier also exists when scaling up the technology, e.g., where the process of photosynthesis is used by these organisms to capture CO₂ from the air as a carbon source, ensuring enough light reaches all algal cells as their density increases over time requires innovative tank design and/or lighting systems.

Farm Types

Poultry, pigs, cattle, sheep

3.5.6. Underground soil sensors

Intervention Logic: Commercially developed but the main factor is the high cost of equipment plus training for their optimal usage. This has a diffuse path to GHG saving - through the saving of input resources - which makes it attractive from a private productivity perspective. Though there are concerns around tying farmers into long term contracts for analysis. This may argue for some public intervention to support translation of metrics from the sensors.

Type of intervention(s) to enable technology: Lower cost alternatives could be offered through development and support for innovation. Additionally training of farmers - in metrics and their use - may encourage more adoption. Public grants for developing open-source platforms for analysing data would also address the issues around tying farmers into lengthy contracts.

Current stage of development

near-market

Potential timescales (years to market)

1 to 5 years

Potential barriers

Already available from suppliers but no evidence of uptake beyond trial farms outside of Scotland. No testing so would expect innovation leaders to try the technology. May need support to translate this to in-field operations in cereals, general cropping, and intensive grassland.

High capital cost seems main constraint

A product is commercially available and used in sports turf management. The cost of product seems to be the most prohibitive factor and may only apply to high value crops to ensure a return. The kit is sold with infrastructure support, e.g., echo station to boosts signals across the farm. Distance between underground sensors may be an issue in terms of connectivity and the topography of Scottish fields. No compliance or regulatory issues

Farm Types

General Cropping; Horticulture

3.5.7. Cloud based bioinformatics

Intervention Logic: Some initiatives are developing out of public-private sector initiatives. There is a potential link with the UK funded Agri-metrics innovation centre.

Type of intervention(s) to enable technology: Encouragement of skills and training for software and data analysts in metrics and offering training/degrees with an application in agricultural data and decision-making may support development.

Current stage of development

near-market

Potential timescales (years to market)

1-5 years

Potential barriers

Would only expect mid-to long term level of adoption if tech start-ups focus on Scottish agriculture.

Lack of Infrastructure

Main barrier seems to be lack of infrastructure, namely tailoring services to regional aspects of Scotland would require testing and trailing.

Farm Types

Cereals, Horticulture, General Cropping

3.5.8. Biological nitrification inhibitors

Intervention Logic: Key issue seems to be testing to generate robust evidence of impact. This technology has potential for high savings in GHGs but is relatively untested. Accordingly, there may be justification to support testing, development and trialling to monitor impacts within publicly funded research programmes.

Type of intervention(s) to enable technology: Through public funded research to trial and measure impacts in field conditions in Scotland.

Current stage of development

a single trial

Potential timescales (years to market)

5-10 years

Potential barriers

Given the need to generate new evidence, it would be anticipated that the time taken to implement BNIs would be between 5 to 10 years. The anticipated lack of any negative impacts would accelerate potential uptake of this technology, given probable ancillary benefits.

Lack of Evidence; Recognition in climate inventory

The major barrier to implementation would be the lack of evidence supporting the efficacy of BNIs. Following a demonstration of the nitrous oxide production mitigation potential in field-based experimentation it would be necessary to implement appropriate representation of this mitigation option in climate inventory reporting for it to be recognised as a mitigation option.

Farm Types

Cereals, Horticulture, General Cropping

3.5.9. Genetic profiling/Genomic testing in breeding programme

Intervention Logic: Public-Private work is ongoing towards this. There are private gains in high value sectors, in lower value sectors there may be less intent to support the technology.

Type of intervention(s) to enable technology. A key factor is the collection of enough data for testing. Hence testing stations, or something similar to the Beef Efficiency scheme, could support generation of more samples and data for increasing accuracy.

Current stage of development

niche/roll-out elsewhere

Potential timescales

5-10 years

Potential barriers

It is expected that microbiome-driven breeding is implemented by some breeding organisation within the next 5 year.

Lack of Infrastructure; Costs of sampling and storage

The present barrier for implementation of microbiome-driven breeding is mainly the logistics, as well as additional costs, involved in taking rumen samples, its storage and analysis to determine the rumen microbiome composition.

Farm Types

Cattle; Sheep.

Current stage of development

niche/roll-out elsewhere

Potential timescales

5-10 years

Potential barriers

It is expected that microbiome-driven breeding is implemented by some breeding organisation within the next 5 year.

Lack of Infrastructure; Costs of sampling and storage

The present barrier for implementation of microbiome-driven breeding is mainly the logistics, as well as additional costs, involved in taking rumen samples, its storage and analysis to determine the rumen microbiome composition.

Farm Types

Cattle; Sheep.

3.5.10 Fluoride and tannin additive to manure

Intervention Logic: Currently developed through public R&D funding in Denmark. Intention would be to commercialise this through public-private sector networks.

Type of intervention(s) to enable technology: Seems to be lack of testing outside Denmark, so it would need support for demonstration within the Scottish context. Sourcing low cost tannins is also an issue from the supply chain which may lead to higher price of final product.

Current stage of development

single trial

Potential timescales

10-15 years

Potential barriers

Development is currently centred in Denmark at Aarhus University and the University of Southern Denmark through government funded research. The researchers intend to develop a granulate which can be marketed to farmers, but timelines are unclear as further trials are needed.

Cost of Product: Regulatory; Low evidence base

Major barriers include the lack of marketed or patented products, lack of effective dosage information, and the small evidence base which relies heavily on a small number of experiments in Denmark (Dalby, 2021). High costs of tannic acid and regulatory processes will provide an additional barrier to adoption as the technology matures.

Farm Types

Cattle

3.5.11. Methane Vaccine

Intervention Logic: Developed through Fonterra - a levy funded research body in New Zealand and this may argue for public-private sector initiatives, e.g. through levy boards, to develop these technologies.

Type of intervention(s) to enable technology: Requires testing but also focus on breeds and efficacy to reflect Scottish conditions. Testing and trials could be set up after regulatory approvals to confirm efficacy.

Current stage of development

a single trial

Potential timescales

10 years

Potential barriers

Still in development stage – estimated to be commercially available in 7-10 years after prototype (Reisinger et al. 2021)

Low confidence in results; regulatory and compliance related

Product still in testing but has quite variable results. If successfully passed trial, then regulatory compliance and testing would be expected.

Farm Types

Cattle; Sheep

3.5.12. Smart Cattle Sheds

Intervention Logic: All work on this has emerged from the public research institutes who are experimenting with agricultural design challenges. However, components of the shed, namely air filters, monitors etc. would seem to be the province of the private sector. Also, the payback, in terms of energy saved and associated productivity, may support more private investments.

Type of intervention(s) to enable technology: This would equate to improving or, more likely, replacing a farm building. There is a history of support for capital payments, e.g., for buildings and drainage in the 1980s. However, the cost of establishing smart cattle sheds would be prohibitively expensive for the Government.

Current stage of development

at the testing stage

Potential timescales (years to market)

5-10 years

Notes

Still at trial stage, early versions retrofitting sheds may be in next 5 years.

Potential barriers

Cost of Product; Lack of Infrastructure

Notes

Cost of the product will be high, also a lack of infrastructure in terms requirements for building, materials, networking and servicing.

Farm Types

Housed, e.g. finishing cattle

3.5.13. Connected animal mounted sensors

Intervention Logic: Mostly the domain of the private sector both in the development of sensors but also the software to monitor the output of these sensors. However, public-private partnerships to support testing and trailing these as a system has occurred. Note though, this may require locking in farmers into contracts for analysis services.

Type of intervention(s) to enable technology: The type of technology is available, but the costs of the product is prohibitive. Nevertheless, companies argue that a payoff over time will occur, hence training farmers and providing support for investment decision making may be a route to support uptake. A further constraint is patchy rural broadband for transfer and relay of data in real time. The general progression of improving rural broadband from the Scottish Government may overcome this barrier.

Current stage of development

niche/roll-out elsewhere

Potential timescales (years to market)

On market

Notes

Many systems are already available for commercial use; however, it should be noted that these commercial systems are often not validated for their intended use they are marketed for.

Potential barriers

Cost of product; Lack of infrastructure - rural broadband.

Notes

Recent work found the cost of systems was identified as the main barrier to uptake (Bowen, forthcoming); however, this is likely to be coupled with lack of understanding on return of investment. Other limitations/barriers included internet connectivity requirements in regions often lacking in access to sufficient coverage, and not understanding the benefits of the system or how to fully use the system. These limitations and barriers are also applicable to dairying systems.

Farm Types

Cattle; Sheep.