The Sustainable Nitrogen Application Project (SNAP) will develop techniques for precision application of nitrogen in commercial apple orchards. Standard industry practice is to apply a uniform rate of fertiliser across an orchard or vineyard at set times in the season. However, different trees and different regions in the orchard will have different requirements for fertiliser depending on the size, vigour, and amount of crop on the trees. By varying the rate of nitrogen applied across the orchard, it can be concentrated on trees that need it and are able to use it readily. Weaker areas of the orchard can be strengthened, and overall homogeneity of the growing block can be improved, which leads to higher yields for the grower at harvest. SNAP will use recent developments in crop remote sensing and precision application hardware to help growers optimise and reduce their use of nitrogen fertilisers, whislt driving the global fruit industry's adoption of precision farming techniques. This will deliver benefits to growers through increased yields, and to the wider environment through reduced environmental damage, increased food security and improved climate risk mitigation.

With Innovate-UK support, Messium Limited, in collaboration with H L Hutchinson Limited, UK Agri-Tech Centre, and The Allerton Project, will develop the first dedicated optimal nitrogen recommendation platform that combines first-of-a-kind hyperspectral imagery with rapid AI/ML crop modelling tools to provide tailored nitrogen application analytics and insights, in doing so reducing resultant financial pressures on farmers/agronomists/growers with significant positive environmental impacts related to nitrogen leaching, runoff and emissions (to be assessed during project). The solution has been designed to address key agricultural challenges identified through substantial end-user input with information to be presented in an easy to understand format aimed at all target users regardless of ethnicity/culture, age, gender and level of technological literacy. Grant funding accelerates development and exploitation within _global precision agriculture market_ ($9.7Bn in 2023 projected to grow to $21.9Bn by 2031) and supports future application to other crops and application to grazing management.

**Overview**: This project focuses on developing a technology-verified carbon insetting scheme for Cranswick to 1) reduce carbon emissions associated with their agricultural supply chain and 2) quantify biodiversity changes. The new scheme will provide a financial incentive for Cranswick's producer base by planting wildflowers and/or flower-rich grasses, creating new forms of income for farmers at a time of financial uncertainty. Verification of positive environmental changes in response to land use changes will come from AgriSound (biodiversity monitoring using bioacoustics) and Hutchinson (soil carbon mapping). The carbon insetting scheme will be co-designed with farmers following a double diamond methodology. **Innovation**: Whilst Cranswick has made significant progress in reducing carbon emissions, there remains pressure to do more to meet net zero commitments. Carbon insetting (using nature-based solutions within a supply chain) is an attractive alternative to purchasing carbon credits from offsetting schemes, but there is a major challenge around verifying the quality of the insetting work due to current reliance on manual assessments to quantify levels of improvement. Efforts have been made by Cranswick to identify suitable technology providers and internal trials have validated key technologies (AgriSound, Hutchinson/Omnia) with the ability to provide partial verification methods. However, more work is required to a) expand the monitoring capabilities (particularly for biodiversity monitoring) and b) integrate technologies into a single platform with the ability to manage the resulting scheme and provide the necessary level of confidence by all stakeholders. The project will deliver multiple new bioacoustic algorithms for key indicator species (honeybees, bumblebees, hoverflies, butterflies, moths) as well as re-parameterise open-access birdsong algorithms to operate in a real-time manner. APIs for data transfer from AgriSound into the Omnia platform will be developed, as well as new farmer and corporate dashboards for scheme management. The project also has a strong focus on commercial innovation, with Cranswick working closely with their farming base to develop the scheme, balancing the level of financial incentives required with the need for rapid, large-scale adoption. The blueprint for the Cranswick Carbon Insetting Scheme (CCIS) will be made available for use by the wider agri-food industry, creating a legacy that will be critical in driving environmental gains and a significant commercial opportunity for the technology providers.

Apple scab and downy mildew are devastating diseases of apple orchards and vineyards, respectively. They are spread by airborne spores and, if left untreated, cause significant losses to growers. Currently, growers control these diseases by frequent applications of fungicides (up to 15 treatments annually in apples and 10 in vineyards), which are costly to the grower, can adversely impact the environment and are increasingly unacceptable to many consumers. Growers and agronomists use weather-based disease risk forecasting tools to identify when the crop is at risk of infection, though these tools give frequent false positive and negative signals, resulting in both under and over use of synthetic pesticides. The outcome of which are unnecessary costs for the grower when sprays are unneeded (false positives) and disease emergence/increase when they are not used (false negatives). The risk forecasting tools do not currently take into account the presence of spores which spread disease. This project will demonstrate the feasibility of using spore measurements to indicate to growers when the disease is moving into their crops. The SporeSentry (Optisense) instrument is a prototype battery-powered, automated spore sampling and testing device that comprises a multi-functional microfluidic cartridge that separates spores from the air, breaks up the spores to release the DNA and performs DNA based tests for the specific disease targeted. The instrument is connected to the mobile network to transfer data in real-time. The project will test the feasibility of using real-time measurement of spores moving in a crop to predict disease. In addition we will value engineer the consumable cartridge to make it as affordable as possible for uptake in the agri-tech sector. Linking information on spores to risk forecasting tools that predict when conditions are conducive for infection and indicating this to the growers/agronomists in real-time is a significant innovation which could massively change our use of pesticides. This will lead to both a decrease in unnecessary spraying when risks are low and improvement in precision application and fungicide choice when the risk of disease is high.

The agricultural industry is undergoing a transformative revolution, leveraging new technologies and knowledge to increase yields while dramatically reducing environmental impacts, lowering production costs, and reducing waste. Plants have evolved many capabilities to detect and respond to stress long before visible symptoms appear, yet current agronomic methods rely predominantly on skilled humans detecting these issues once they become visible. A tool that can tap into early plant stress response mechanisms will provide an invaluable source of agronomic knowledge informing growers, agronomists, or automated systems to apply interventions earlier, thereby minimising yield losses and maximising efficiency. Plant electrophysiology is a unique approach to capturing plant-scale stress-related information. It has demonstrated promise in controlled environment agriculture, but its applicability to outdoor farming requires investigation. To make this technology widely adoptable, current electrophysiology sensors need adaptation to field conditions, including: miniaturisation to increase spatial finesse and crop attachment, ergonomic design to aid farmer acceptance, radio-frequency data communication and solar charging capacities for independence, and integration into digital agronomy systems to deliver precision control and value for the growers. To develop the next generation of plant electrophysiological sensors (NGES), we have assembled a multidisciplinary and collaborative team in the UK. The project will be led by Benchmark Control, an electrical engineering and product development firm that has expertise in sensor fabrication; Adrian Scripps is a premier grower and distributor of UK fruit with a forward-thinking approach to sustainability that will provide orchard infrastructure for NGES testing; Hutchinsons is a leading agronomic consulting firm at the forefront of developing digital farming platforms that will integrate NGES outputs into a user-friendly strategic plan; and NIAB, the largest UK research institute conducting applied research in horticulture will conduct the NGES orchard experiments and provide project management support. Our research and development approach enables multiple rounds of NGES design and refinement, performance comparison to currently existing sensors, controlled trials of stress detection in orchard trees, and integration into existing digital farming platforms. After successful completion of these tasks, our NGES will be directly marketable to growers of high-value perennial crops, such as apples, grapes, nuts, and citrus. In the UK, adoption of NGES will reduce costs by avoiding the application of unnecessary crop protection agents or nutritional supplements and increase profits by maximising yield potentials. Ultimately, our radical approach to capturing plant-based information will help transform the agricultural sector into the sustainable version our society and environment needs.

Current tree fruit production applies crop management products uniformly across each orchard, however, orchards exhibit substantial variation across them and between trees. Even neighbouring trees have very different growth and crop loads. Treating all trees uniformly regardless of their size, density, crop load, or health limits yield, is inefficient, and detrimental to the orchard productivity and the environment. This project will develop a Precision Variable Rate Spray (PVRS) machine, control software system, and new systems for measuring and assessing each individual trees' status. These components will be combined into products and services that will transform the tree fruit industry and deliver new levels of environmentally sustainable crop production, increasing efficiency and yield whilst lowering costs and environmental impacts. Integral to this approach is that every individual tree in the orchard will be assessed, its requirements calculated, and then treated with a tailored quantity of crop management products. This will reduce wastage and improve yields. Led by one of the UK's leading and forward-thinking agronomy companies, this project includes a range of high quality growers, a large top fruit marketing organisation, a software engineering company specialising in global positioning systems, a top fruit digital agronomy company, a crop phenotyping specialist, a robotics company, a horticultural engineering company, the UK's agricultural chemical regulation organisation, and three academic institutions specialising in agricultural engineering and robotics, computer science, economics, and horticultural agronomy. Working closely across the tree fruit industry's production chain ensures that the products and services developed during the project are designed for the grower and meet their requirements. The consortium's network allows us to engage with the wider fruit industry. The project will showcase the products and services to the horticulture sector with a range of knowledge exchange activities and field demonstrations. During the project we will assess the new spray system's benefits relative to conventional spraying, and report on the economic and environmental advantages of investing in Precision Variable Rate Spraying. At the end of the project, UK growers will have access to the most advanced and efficient tree fruit crop management system available, and understand the environmental and economic benefits of using the system.

End-of-life apple orchards are currently managed using the environmentally unsustainable practice of grubbing and burning. In this project we will investigate the use of pyrolysis as an alternative more sustainable approach. Pyrolysis converts biomass to biochar in a clean, heat-generating process. This stabilises the carbon effectively removes CO2, avoiding the emissions associated with burning. The resulting biochar will be investigated as a soil improver for increased orchard yields and productivity. The potential of carbon credits as a new revenue stream opportunity for growers, and for carbon auditing within supply chains (off-setting and in-setting) will further improve the economic resilience of the apple growing sector. This will support the transition of the commercial apple growing sector towards Net Zero

Feeding 9.8 billion people in 2050 in a climate change context will depend on our skills to keep soils alive. Food production is directly correlated with soil health. To manage and improve soil health, farmers need reliable information about the chemical, physical and biological properties of their soils. There are methods available to assay soil nutrients and determine the physical properties of soils. Only respiration-based methods are currently available to farmers to measure the microbial contributions to soil health, but these give no information on the microbiota present and are affected by other sources of CO2 in the soil. Next-generation sequencing has potential as a biological indicator of soil health, but the costs are high, the tests take hours to conduct, and the data obtained requires experts in order to interpret it. Our solution is to tap into the wealth of information contained in the volatile organic compounds (VOCs) released by soil biota. These have been demonstrated to be excellent indicators of soil biota activity, but their detection and analysis currently requires laboratory-based instrumentation and skilled personnel. In preliminary work we developed a sensor that can detect soil VOCs and demonstrated that its responses can be correlated with soil health. In this project we will determine the responses of such sensors to a wide range of different soils and cropping systems. These will be correlated with conventional soil health indicators and next-generation sequencing data. Machine learning will be used to process the data obtained to provide a cloud-based database that can be accessed directly by sensors in the field. Use of robots to deploy the sensors with associated GPS data will be investigated to provide farmers with comprehensive and fine-scale data of soil health on their farms so that they can assess the impact of farming practices on soil health and adapt these to increase soil health and productivity. Testing every square meter of land data would be unfeasibly expensive with current testing methods (£60/sample) as the average UK farm size is 930,000 sq. m.. The project will be led by P.E.S. Technologies, a start-up company that developed a plastic electronic sensor for soil VOCs, in collaboration with Hutchinsons, UK agronomy specialists, and the Small Robot Company. Academic partners will be NIAB-EMR, the leading UK horticultural research organisation, the Natural Resources Institute with long experience in VOC profiling, and the University of Essex with expertise in machine learning.

"Today, the majority of farmers spray fungicides prophylactically on crops to minimise risk and insure against disease ingress. Most farmers, or their consultants, spend hours inspecting crops but can't easily predict what incubating (invisible) infection is already in the crop or what may start to develop as a result of increasing pathogen presence in the environment. Weather-based disease forecasting methods have been introduced to predict when to spray crops but often have unreliable results, especially against sporadic diseases. The market opportunity for **SpraySaver** is to transform today's '_**spray-and-pray**_' practices by offering a more reliable and precise scientific method of determining when to spray -- using locally gathered disease pathogen data and risk prediction/decision support models to assess crop disease risk. The added value to farmers is a big reduction in crop spray costs, safeguarded or better crop yields/productivity and greater effectiveness when sprays are applied. **SpraySaver** is the world's first automated field analyser system specifically designed for early detection of crop disease pathogens within crop growing environments. One analyser can monitor a wide geographic area of around 100 Ha (dependant on local environmental conditions) and can be configured to detect multiple crop disease pathogens. Each in-field analyser transmits 4G mobile data for analysis in the cloud. Local pathogen data is analysed alongside local weather data within a disease risk model to determine risks of crop disease infection. Pathogens that will be detected include _Sclerotinia_, which affects oilseed rape and carrots, yellow and brown rust of wheat, _Fusarium_ _graminearum_ of cereals, potato late blight, beet rust, and onion down mildew, thereby covering sporadic diseases of a wide range of crops typically grown near to each other under crop rotation. Analysis outputs at a local, regional or even national level can be viewed on multiple display devices with automatic alerts set at predetermined levels. This ambitious project will develop a better DNA quantification method, develop new assays for specific diseases of onion and wheat, and integrate detection with infection-condition models and economic models to make recomendations for spray regimes. The system will ultimately eliminate today's '_**spray-and-pray**_' practices by offering a more reliable scientific method of determining when to spray -- using locally gathered disease pathogen data and risk prediction/decision support models to assess crop disease risk. Integration of the system as a network will add robustness and reliability to the decision-making process."

"There is substantive tree-to-tree variability in tree structure (size, density) and crop load and quality in tree fruit orchards which are the major causes of less than optimal, often poor, overall yield and quality. Previous work in Innovate UK project 101405 showed that tree-to-tree variability in yield ranged from 2-3 fold in the six most productive and uniform apple orchards in the UK, with much greater variability in poorer orchards. Larger scale within-orchard variability and inter-annual bienniality also contribute to poor performance. In this project, we shall develop precision dosing orchard foliar spraying system to improve the uniformity of orchards and greatly increase their economic performance, using apple as an exemplar. Current practice is to spray whole orchards at the same dose regardless of tree structure or crop load. The equipment will apply precision doses according to need to optimise performance. The performance of poorer performing trees will be increased towards that of the best and the tendency for out of sync tree-to-tree biennial bearing minimised. IP will be protected and the system sold internationally, creating a new substantive UK business contributing to the UK economy. The technology will have application for the wide range of spray applications to tree fruits for crop protection and crop management worldwide. The precision orchard mapping technologies will have additional wide application for other methods of crop management and Agri Decision Support Systems (AgriDSS) worldwide. This new technology will have substantive impacts on the UK apple industry making a step change in productivity and competitiveness and allowing the UK industry to increase production. Keywords: Precision orchard spraying, tree fruit production, apple production, crop scanning and mapping, Decision Support System"

Phoma stem canker is a damaging disease of oilseed rape in the UK, leading to yield losses > £100M p.a. despite the use of fungicides. This disease is caused by two related pathogens: Leptosphaeria maculans and L. biglobosa. However, current control of the disease focuses only on L. maculans. Recent work showed that L. biglobosa can cause substantial yield losses and that it is less sensitive to triazole fungicides than L. maculans. L. biglobosa is a threat to oilseed rape production in the UK since no methods have been developed to control it. This project will investigate stem canker epidemics caused by L. biglobosa, determine the proportions of L. biglobosa and L. maculans in pathogen populations, screen cultivar resistance against L. biglobosa, determine efficacy of different fungicides for control of L. biglobosa as well as L. maculans. The new knowledge about the pathogens, host resistance and efficacy of fungicides will be used to develop new control strategies that ensure both pathogens are targeted.

Phoma stem canker, caused by the fungal pathogen Leptosphaeria maculans, is a damaging disease on oilseed rape in the UK, causing annual yield losses > £100M despite use of fungicides. With recent loss of the most effective fungicides through EU legislation and predicted global warming, potential yield losses will increase. Use of host resistance to control this disease is becoming ever more important. However, new sources of resistance are often rendered ineffective due to pathogen population changes. This project will monitor emergence of new virulent races of L. maculans and prevent them from spreading into new regions; investigate molecular mechanisms of mutation from avirulent to virulence in L. maculans populations; understand effects of environmental factors (e.g. temperature) on durability of host resistance. New knowledge will be used to develop new control strategies by optimising deployment of host resistance and targeted fungicide application. This project will bring together a consortium with breeders, distributor, farmer and scientists to ensure effective control of phoma stem canker by directly applying knowledge from research into farming practice.

To measure ‘sustainable intensification’ we must compare crop yield (intensification) & gross margin (economic sustainability) with relevant, quantifiable environmental impact indicators (environmental sustainability). The main environmental indicators farmers should consider are Water Management/Pollution; Greenhouse Gas Emissions & Biodiversity. We propose to develop a system to assimilate, calculate & display this environmental impact data alongside yield, quality & fiscal performance data to create a valuable representation of farm physical, financial & environmental performance on a field by field basis. This feasibility study will look at the potential of utilising data already stored within GateKeeper (a farm data software tool) and several other data sources, combined with new farm scale data in a series of models, fused in a single software system we call SIAMS. These models will help the farm manager & agronomist identify & modify their agronomic inputs avoiding wasteful & potentially harmful applications. Subject to feasibility study results we will then need to develop the data fusion platform & possibly two systems for capturing & analysing localised flora & fauna data. This technology could position UK agriculture at the forefront of precision farming & sustainable intensification.

There is a close link between the rotational intensity of OSR, reduced yield and the presence of novel pathogens. Results clearly demonstrate Koch’s postulates for both Olpidium brassicae and Pyrenochaeta sp, and the impact of Pyrenochaeta sp on growth of oilseed rape. This project brings together novel molecular pathogen detection methodology with industry led variety screening, agronomic research and knowledge transfer capability. Specific activities in the technical approach include a combination of sampling of commercial and, field trials (both variety and fungicide/biological control agent) with appropriate sampling, assessment and data collection, and lab based qPCR analysis of samples. This project builds on existing primary research but covers areas not previously investigated. Innovative aspects will include the assessment of varietal tolerance to novel soil pathogens to unlock yield improvements of OSR. The control work will provide detail of techniques to alleviate the problem.Recent development of qPCR detection methods will facilitate this project enabling direct comparisons of pathogen levels in roots. The project builds on existing expertise to develop novel plant breeding and agronomy based solutions (opening up new markets) to address the emerging issue. These routes are suitable for developing a new robust IPM based strategy for dealing with these novel pathogens and increasing oilseed rape yields.