Barley Can Utilise Algal Fertiliser to Maintain Yield and Malt Quality Compared to Mineral Fertiliser

Barley Can Utilise Algal Fertiliser to Maintain Yield and Malt Quality Compared to Mineral Fertiliser | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Barley Can Utilise Algal Fertiliser to Maintain Yield and Malt Quality Compared to Mineral Fertiliser View ORCID Profile David James Ashworth , Stefan Masson , Tom Mulholland , View ORCID Profile Davide Bulgarelli , View ORCID Profile Kelly Houston doi: https://doi.org/10.1101/2025.08.12.667670 David James Ashworth 1 The James Hutton Institute, Cell and Molecular Sciences , Invergowrie, United Kingdom 2 University of Dundee, Division of Plant Sciences , Dundee, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David James Ashworth Stefan Masson 3 Chivas Brothers – Pernod Ricard , Dumbarton, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tom Mulholland 3 Chivas Brothers – Pernod Ricard , Dumbarton, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Davide Bulgarelli 2 University of Dundee, Division of Plant Sciences , Dundee, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Davide Bulgarelli Kelly Houston 1 The James Hutton Institute, Cell and Molecular Sciences , Invergowrie, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kelly Houston For correspondence: Kelly.houston{at}hutton.ac.uk Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Use of fertiliser is essential for global crop productivity; however, their use is unsustainable through high energy costs from sourcing, nitrous oxide emissions during application, and the potential to cause eutrophication if used in excess. An alternative proposition is use of microalgal biomass for fertilisation, which adds carbon to the soil, is as effective as mineral fertiliser when comparing the vegetative grown of wheat and barley, and has the advantage of accumulating nutrients present in wastewater if used as a growth media, with 80-90% removal efficiencies for N and P. This means algae can both be used as a fertiliser and wastewater treatment, which supports the possibility of recycling nutrients within the barley to malt to whisky value chain, as nutrients from pot ale and spent lees could be reclaimed and used on the subsequent barley crop. This study is the first step in realising that circular economy. We sought to quantify the phenology, reflectance indices, yield components (including yield itself) and malt quality of barley cv. Laureate grown with either Chlorella vulgaris powder or mineral fertiliser, with both treatments providing equal amounts of nitrogen at the appropriate level for malting spring barley, 118kg(N)/ha accounting for soil mineral nitrogen. We performed three glasshouse experiments to assess the repeatability of our findings and performed a field trial from which we malted the grain. Both lines of inquiry showed that plants grown with algae and mineral fertiliser do not significantly differ in any yield component, yield or malt quality, with the exception of total malt nitrogen. This study is an important proof of concept for enabling further, larger algal fertiliser trials, such as increasing the number of field sites under different environmental conditions to assess the widespread viability. 1 Introduction Mineral fertiliser has become ubiquitous in non-organic farming, owing to its ability to increase yield by an estimated 50% ( 1 ). However, the macronutrients found within mineral fertiliser, nitrogen (N), phosphorus (P) and potassium (K), have problems stemming from both sourcing and application. The Haber Process, responsible for nitrogenous fertiliser, is estimated to consume between 1-3% of total global energy and contribute 1-3% of total greenhouse gas emissions ( 2 ). P and K also contribute to ecological and climatic problems, as they are mined as rock phosphate and potash, respectively ( 3 , 4 ). This both damages the environment through mining and generates large volumes of CO 2 through energy and fuel consumption for extraction and processing. In addition to their production, the application of mineral fertilisers has further drawbacks. Applying nitrogenous mineral fertiliser further contributes to climate change as it decomposes to N 2 O, which accounts for 4-5% of global total greenhouse gas emissions ( 1 ). Furthermore, N and P applied in excess of what plants can take up may be dissolved in rainwater or water provided by irrigation and subsequently be washed off into local watercourses. Increased nutrients in the watercourses can then cause eutrophication, which is harmful to the local ecosystems and to humans should drinking water be contaminated or sources of food, especially fish, be affected ( 5 – 7 ). Mineral fertiliser must be replaced, or its use greatly reduced, if humanity is to mitigate its detrimental impacts on the planet while maintaining or increasing crop yields. One proposed replacement for mineral fertiliser are products derived from eukaryotic microalgae or cyanobacteria (hereafter collectively referred to as ‘algae’). Two such classes of algae derived products fall into microbial biofertilisers (MBFs), and the closely related microbial biostimulants (MBSs). There is some inconsistency about the definition of both MBFs and MBSs. The consensus is that MBFs are living algae (or other microbes) that can improve the availability of macro- and perhaps micronutrients in soil , while MBSs are whole algae or extracts from algae which can increase plant nutrient uptake, growth or quality through non-nutrient compounds affecting the plant’s physiology ( 8 – 12 ). These two classes of algae derived products ignore a third class of product, those which use algae, living or dead, to directly provide the macro- and micronutrients necessary for plant growth from their own biomass . These have no formal name, though are commonly put under the blanket term ‘organic fertiliser’ or ‘algal nutrients’ in papers that test these products ( 13 – 15 ). We will refer to these products, the focus of this study, as algal fertilisers. All three classes are proven to be effective at increasing one or more aspects of plant health or agronomy, see Kapoore et al, 2021 ( 12 ) for a detailed review on MBSs and Ronga et al , 2019 for a review on both MBSs, MBFs and algal fertilisers ( 11 ). There are advantages to using algal fertilisers beyond the increase in plant performance. Starting with their production, they do not rely on the Haber process or mining to provide their N, P and K, instead algae can be grown in controlled media, or nutrient rich wastewater. Multiple different species of algae, both pro- and eukaryotic, have been grown on different sources of wastewater including, but not limited to, synthetic wastewater as a proof of concept ( 16 ), municipal wastewater ( 17 – 22 ), animal slurry (and its anaerobic digestate) ( 21 ), waste aquaculture water and industrial food processing waste ( 21 , 23 , 24 ), with algal waste water treatment being reviewed in depth by Mohsenpour et al (2021) and Nguyen et al (2022) ( 25 , 26 ). Among the algae tested for treatment potential, species of Chlorella (including one putatively reclassified into the genus Chromochloris ( 27 )) are widely used, showing very high N, P and micronutrient removal efficiencies, with N removal efficiencies ranging from 40% (for nitrate specifically) to 100% (for ammonium specifically) and most reported total N removal efficiencies >90%, and P removal efficiencies ranging from 33% to 98% with most being above 80% in batch cultivation ( 16 , 18 – 21 , 23 , 24 ). In addition to efficient recovery of macro- and micronutrients, algal cultivation releases less greenhouse gas than traditional wastewater treatment ( 26 ). This is in partially because approximately 50% of the energy spent on wastewater treatment is oxygen delivery, which microalgae does not require ( 25 ). Furthermore, algae can absorb nitrogen compounds before they decompose to the greenhouse gas nitrous oxide, the generation of which is a concern in traditional wastewater treatment systems ( 25 ). These energy and nitrous oxide emission savings could therefore greatly reduce overall greenhouse gas emissions when using algae as a fertiliser if also linked to a reduction in the Haber process and P/K mining. When looking to application on farm, algal fertilisers are considered slow-release for both N and P, with Coppens et al (2016) reporting plant available N at 31% after 95 days (in line with commercial slow release fertilisers), plant available P at roughly 60% for the duration of the experiment, and phosphate P at 7% ( 28 ). Lower concentrations of soluble nutrients reduce runoff and so could result in less eutrophication. Additionally, there is evidence that use of algal fertilisers can significantly increase total soil carbon by up to 17%, as reported by Alobwede et al (2019) ( 14 ), meaning there is potential for both replenishment of degraded soils and nourishment of otherwise healthy soils ( 8 , 9 ). Unfortunately, algae derived products are not currently economically viable enough to outcompete mineral fertiliser despite having several benefits. However, charging a premium for algae grown produce, as is already done for produce grown with other organic fertilisers, or using algae as wastewater treatment, reducing the cost through turning a waste product into a saleable co-product, could be solutions to this problem ( 11 , 28 ). Recently, there was a world fertiliser shortage due to geopolitical events increasing the cost of energy and increasing difficulties in shipping ( 29 ). Short-term mineral fertiliser shortages and long-term sustainability goals could incentivise both farmers and the biotechnology sector into adopting more sustainable solutions, such as algae derived products. Cultivated barley ( Hordeum vulgare L. ssp. vulgare ) is a globally important cereal crop, being the 4 th most cultivated cereal behind maize, rice and wheat ( 30 ). In the UK, the 9 th largest producer globally and 7 th in Europe ( 30 ), barley is the second most produced crop by mass, behind wheat and ahead of rapeseed, contributing £1.16 billion to the economy ( 31 ). Barley grown in the UK has two main markets. The first is the premium market of brewing and distilling, which uses roughly 25% of the grain, and adds £5.5 billion to the economy from Scotch Whisky ( 31 , 32 ). The remaining 75% of barley goes almost entirely to animal feed. The use of barley in brewing and distilling is of particularly interest to this study, as both processes generate a large volume of wastewater, which must be treated by both UK and Scottish law ( 33 ). General distillery and brewery wastewater is currently treated by a range of processes including physical, chemical, physicochemical, and biological ( 34 – 41 ). The biological treatment methods can consist of anaerobic digestion, which often yields methane, aerobic digestion, algal growth, or plant growth through simulated wetlands. Chlorella species have been successfully cultivated on distillery wastewater, after it has been through a round of anaerobic digestion, to yield treated water which can be safely released ( 34 , 39 ). As discussed, different species of algae have been used successfully to treat wastewater, and experiments have used algal fertilisers to match growth seen under mineral fertiliser or even increase it and there have been a few attempts to link these two uses of algae. Renuka et al (2016) reported increased soil nutrients, nutrient uptake and increased growth and yield in wheat when using multiple species of algae of algae grown on wastewater together ( 42 ). Silva et al (2024) reported increased speed of growth of barley before 100 days old when grown with Arthrospira sp. (a cyanobacteria) grown on brewery wastewater as an algal fertiliser ( 43 ). This study seeks to be the first step in comprehensively investigating the suitability of algae as a fertiliser for barley for brewing and distilling use. We will quantify the effects of using algal fertiliser on the phenology, yield components, grain quality, and malt quality of spring barley, in both glasshouse and field trials, extending the work done previously on wheat and barley that has not reached maturity. We hypothesize that there will be no significant differences between yield components, grain quality or malt quality between plants fertilised with algae or mineral fertiliser, confirming what has been seen in previous work and adding to it by looking beyond the vegetative growth to the quality of the grain and malt. 2 Methods and Materials 2.1 1 st Algae Response Experiment 2.1.1 Plant Growth Conditions Four treatments were prepared using Bullionfield soil taken from a field (56.460253, - 3.071053) local to the James Hutton Institute, Dundee. Soil used for these experiments were analysed by Yara UK using their SA10 (soil mineral nitrogen) and BSE SOL (broad spectrum soil health) testing suites ( Table S1 ). Soil was passed through a 10mm sieve prior to weighing and mixing. The first treatment was soil with no additions to function as a negative control, designated No Additions. The second was Chlorella vulgaris B. (here after referred to as C. vulgaris ) powder that provided an addition of 94.3 kg(N)/ha, designated Algae Only. The third was C. vulgaris powder that provided an addition of 94.3 kg(N)/ha, with additional K and Ca, designated Algae Supplement. K was provided as KCl and Ca was provided as CaCl 2 .2H 2 O, with the chloride salts picked to not affect the concentrations of other plant essential nutrients. K and Ca were added to bring the concentration of these nutrients up to the level seen in the final treatment, which was addition of mineral salts providing 94.3 kg(N)/ha, following the molar ratios found in a modified Hoagland solution ( 44 ) recipe ( Table S2 ) to function as a positive control, designated Mineral. The algae chosen to use in these experiments was C. vulgaris , as it has a long history of safe use in food and nutraceuticals and is not classed as a ‘novel food’ under Regulation (EU) 2015/2283 ( 45 ). Additionally, it accumulates high concentrations of N, P and other macro and micronutrients ( 14 ), and has a wide body of literature proving its efficacy in other plants. For this experiment, we used C. vulgaris batch 1, which was purchased from Raw Living (Raw Living Ltd, UK), a nutraceutical company. Its elemental composition was analysed by a CN analyser and ICP-MS ( Table S3 and Figure S1 ). Each treatment was assigned to 12 “6 inch rose pots” (top diameter 15.24 cm, depth 20cm) with each pot containing 2.9kg of dry soil. One seven-day old seedling of barley cv. Laureate, a two-row spring barley, was planted into each pot. We chose this cultivar because it is the current dominant malting variety in Scotland ( 46 ). Two pots of each treatment were paired to give six pairs per treatment. Each pair was laid out in a complete block design with six blocks, one pair per treatment per block. Pots were individually placed in plastic dishes to ensure any excess water would pool and be reabsorbed, so no nutrients would be lost. The seeds were germinated 03/02/2023 and were transferred to soil 10/02/2023. Plants were grown in a glasshouse at the James Hutton Institute, Dundee, set to 18°C day, 14°C night, 16h light, 8h dark with supplemental lighting as necessary to maintain a minimum of 200 µmol photons m -2 s -1 . Each plant pot was watered to 80% field capacity with tap water three times a week for the first three weeks. From the fourth week onward, plants were watered to 80% field capacity twice a week, with one day a week being replaced with 72mL of 1x Micronutrient solution ( Table S2 ) plus 178mL tap water for mineral treatment plants, as the mineral treatment provided no additional trace micronutrients as solid salts, or 250mL tap water for no additions, algae only or algae supplement plants. A picture of the experiment in early growth can be found in the supplementary material ( Picture S1 ). 2.1.2 Measurements of Development and Morphology Three times a week, plant development was quantified using the Zadok Scale ( 47 ), and the number of tillers were counted. At 38 days after sowing (DAS), when plants were at ZS22-25 (tillering), one plant per pair was selected to be harvested. The transmission spectrum from the middle of the 3 rd leaf on the main tiller was recorded, using the PolyPen RP410 UVIS (Photon Systems Instruments, Czechia), after which we removed the 3 rd leaf from the plant. The transmission spectrum was used to calculate the Normalised Difference Vegetation Index (NDVI) as per Rouse et al (1974) ( 48 ). We measured the length of the 3 rd leaf by hand, the area of the 3 rd using ImageJ ( 49 ) and collected the aboveground biomass from each plant then dried it at 60°C until no further decrease in mass was seen, after which it was weighed it to determine aboveground dry biomass. The aboveground biomass was harvested from the remaining plants at maturity. Ears were separated from the vegetative tissues and the fresh mass of the ears and stems + leaves were recorded. The ears and stems + leaves were dried at 60°C and weighed to determine the aboveground dry biomass. Ear length was measured using ImageJ. We counted the grains per ear and calculated the proportion of fertile and semi-sterile ears per plant. Ears were then threshed and, after removing any broken grain, we passed grain from each plant through a MARViN ProLine (MARViTECH GmbH, Germany) to determine mean grain length, width and area, screenings and thousand grain weight (TGW). 2.2 2 nd Algae Response Experiment This experiment was designed and carried out as described in section 2.1.1 , with the following exceptions. A different soil (designated Quarryfield 4, 56.453737, -3.076683) from near the James Hutton Institute, Dundee, ( Table S1 ) was used. The algae and mineral treatments provided 82.6 kg(N)/ha to reach a target soil mineral nitrogen of 118 kg(N)/ha, and the algae supplement treatment had additional K, Ca, Mg, S and Fe to match the concentrations found in the mineral treatment, in the form of KCl, CaCl 2 .2H 2 O, CaSO 4 .2H 2 O, MgSO 4 and FeNaEDTA, with the sulphate and chloride salts of Ca used to ensure the molar ratios of Ca and S were unchanged with regard to the other nutrients. 118kg(N)/ha was chosen as the target soil N as it is the average optimum N rate in AHDB’s malting barley field trials ( 50 ). Each treatment was assigned to 16 plants (eight pairs of pots). Seeds were germinated on 02/05/2023 and were transferred to soil on 10/05/2023. When over half of the plants were at ZS22/had two tillers, we harvested one member of each pair. This was in place of harvesting at a fixed day after sowing, to account for differences in speed of development between experiments. 2.3 3 rd Algae Response Experiment This experiment was designed as in section 2.2 with the following exceptions. A different batch of soil (Quarryfield 5) taken from the same field near the James Hutton Institute, Dundee, was used ( Table S1 ). The algae and mineral treatments provided an additional 38.96kg(N)/ha, to make up the 79.04kg(N)/ha already in the soil to 118kg(N)/ha. Batch 2 of algae powder was used for this experiment ( Table S3, Figure S1 ). 16 seedlings per treatment were planted and grouped into eight pairs. Seeds were germinated on 11/07/2023 and were transferred to soil on 18/07/2023. 2.4 Spring 2024 Field Trial 2.4.1 Planting We performed a field trail comparing the phenology and yield components of cv. Laureate when grown with no additional nutrients, Chlorella vulgaris pellets (algae pellets) or 24-4-14 NPK granules. This experiment was sown at Balruddery Farm, Angus, located close to the James Hutton Institute, Dundee (Hutchens field, 56.485530, -3.110772, Table S1 ). The plots were sown on 26/04/2024. The no additions and NPK plots were sown in one pass, while the plots receiving algae had seed sown first, then had a second pass to sow the algae pellets. Plots were 6m long and 1.5m wide with seeds and fertiliser sown in 8 parallel lines per plot. Treatments were assigned in three blocks, each a 3×3 Latin square. The experimental plots were separated by guard plots of cv. LG Diablo. Pellets were chosen as opposed to use of powder for ease of handling by farm machinery. 27 packets were filled with 185g of cv. Laureate, to achieve a seed rate of 360seeds/m 2 . Nine packets received no additions. Nine packets received 224g NPK granules ( Table S4 , Figure S2 ). The remaining nine packets were paired with separate packets containing 563g of Chlorella vulgaris pellets ( Table S4 , Figure S2 ), as the pellets and seeds would not all fit in one packet. The algae pellets and NPK granules both provided an additional 61.6kg(N)/ha to bring the 56.4kg(N)/ha in the top 90cm of soil up to 118kg(N)/ha. All plots received two standard fungicide and herbicide applications. An aerial image of the trial can be found in the supplementary material ( Picture S2 ). 2.4.2 Development and Harvest Measurements At 26DAS, we scored the plots for percent emergence of the plants. At 26, 35, 40 and 56DAS, the NDVI of each plot was recorded using the PolyPen RP410 UVIS (Photon Systems Instruments, Czechia). Ten plants per plot were chosen at random, their third leaf on the main tiller selected for measurement, and their values averaged to get the mean plot NDVI. At 33, 40 and 54DAS, we recorded the median number of tillers and Zadok scale for each plot. We also determined flag leaf emergence date, when approximately half of the plot had a horizontal flag leaf, and heading date, when approximately half of the plot had visible ears. At maturity, prior to harvest, we collected grab samples of the plants in the middle 20cm of the middle two rows of each plot. Grains per ear and ear length of three random ears per grab sample were recorded. We threshed the whole grab sample, performed tiller counts on the straw, dried the totality of the threshed grain (150-175 seeds per plot) at 60°C, then measured TGW and grain dimensions on the MARViN ProLine (MARViTECH GmbH, Germany). Grain from grab samples were used for dimension measurement as it had not been passed over a sieve, giving a more accurate-to-field size distribution. The plots were harvested and threshed by combine harvester, which also measured mass of grain per plot. Grain from each plot was kept separate and dried briefly to prevent growth of mould. Approximately 1kg of grain from each plot was cleaned by passing the grain over a series of sieves, removing debris and leaving grain >2.5mm. The protein content and moisture of the cleaned sample was measured using an Infratec (Foss, Denmark). Protein content was converted to nitrogen by dividing by 5.75, the conversion factor recommended by AHDB ( 51 ). Using the mass of grain per plot and the moisture content, we calculated yield of dry grain per plot. 2.4.3 Malt Quality Grain from the combined (not grab) samples was malted, after it had been stored at 15°C for approximately five months. Each plot had 50g samples of grain malted in quadruplicate, to give four malting reps. Malting reps were distributed during malting as a random complete block design, where each tank acted as a block. Each sample was weighted into a water permeable tin, four of which could fit in one half of a drum, with four drums per malt tank, in two malt tanks running simultaneously (Curio Malting, formerly Custom Laboratory Products Ltd, UK). Samples were malted using the following regime, with all water and air rests at 15°C: 8hr steep, 10hr air rest, 6hr steep, 8hr air rest, 4hr steep, 2hr air rest. The steeping phase was followed by germination for 24hrs at 20°C, 24hrs at 18°C, 24hrs at 17°C and 24hrs at 16°C for a total malting time of 132hrs and 39minutes. Drums were rotated throughout the programme. The programme was run twice, each with two of the four malting reps. These were then kilned (Curio Malting, UK) in two batches immediately following malting, using the following regime: 6hrs at 50°C fan speed 70, 6hrs 55°C fan speed 70, 60°C until 7% air humidity fan speed 30, 65°C to 5% air humidity fan speed 30. After the malt was kilned, we dressed it by removing the shoots and roots by rubbing it in cloth and passing it over a 2.5mm sieve to remove the debris. Malt was stored in sealed plastic bags at 15°C until analysis was carried out at Glen Keith Technical Centre (Chivas Brothers, UK) where the following analyses were performed. Each malting replicate was kept separate. The entirety of each sample (∼46g) was ground to flour using a Bühler DLFU disc mill (Bühler, Switzerland) set to 0.2mm. 5.0000g ± 0.01g of flour was weighed into a metal dish, recording the exact mass. Each dish was heated in a Thermo Scientific Heratherm oven (Thermo Fischer Scientific Inc, USA) at 105°C for 3 hours, after which they were left to cool in a desiccator for 20 minutes, then weighed again to determine the moisture content of the malt. 25.00g of flour (not dried) from each sample was weighed into a mashing tin. Each tin had a 4.5cm magnetic stir bar added and was placed in a Rototherm CT4 Mashing Bath (Rototherm Canongate Technology, UK). Between 20 and 23 flour samples were mashed per run of the mashing bath, for a total of five mashes. Each tin was filled with 360 ± 10mL of 65°C demineralised water, then a watch glass placed on top to prevent evaporation. Flour samples were kept at this temperature and stirred for one hour, after which they were cooled to 20°C by replacement of the warm water surrounding the tins with cold water. The contents of each tin were added to separate brewer’s flasks (a 515mL volumetric flask) and the volume made up to 515mL with demineralised water. The contents of each brewer’s flask were filtered through Whatman V2 filter paper (Cytiva, USA) for 30 minutes. The resulting filtrate is the fine hot water extract, hereafter referred to as ‘extract’. Approximately 50mL of each extract was decanted into a vial and the specific gravity was measured on a DMA 5000M density meter (Anton Paar GmbH, Austria), which the associated software automatically converted to strength in Litre degrees/kg. During analysis we doubled this value, as the method as written uses 50g of flour. Doubling was validated as accurate by measuring the strength of the extract of 50g and 25g of the same malt. 1mL of each extract, noting the exact mass, was decanted into separate Elemental Microanalysis Evaporating Tin Foil Cups (Elemental Microanalysis Ltd, UK), and heated on a SB300 hot plate (Cadmus Products Ltd, UK) with the cup holding attachment at setting 3.5 until a dry, brittle crust formed. The nitrogen content of the extract (soluble nitrogen) was measured using a LECO FP828 (LECO Corporation, USA), corrected to 0% grain moisture then doubled to account for the 25g sample in a 50g method. Also using the LECO FP828, 1.4-1.6g of flour from each sample was weighed, noting the exact mass, into a LECO tin foil cup (LECO Corporation, USA), and the total nitrogen of the malt, corrected to 0% grain moisture, was measured. From the extract and total nitrogen, we calculated the predicted spirit yield (PSY) using Chivas Brothers’ proprietary equation for the 2024 harvest. Using the moisture content, we calculated the PSY on a dry basis. 2.5 Statistical Analysis For all analyses and models fitted, diagnostic plots were examined for homoscedasticity and normality of residuals. If satisfactory, the appropriate parametric tests were performed, either ANOVA followed by Tukey’s HSD for cases where only the treatment was significant, or mixed model ANOVA followed by mixed model contrasts where a factor other than the treatment was significant. All analyses of data collected from separate experiments used linear mixed models, in which treatment was modelled as a fixed effect, with block nested within experiment as a random effect. Generalised linear model (poisson family) contrasts were used to analyse field trial tiller count data. If residual variance was found to increase with increasing fitted values, a log transform was applied to the response variable, diagnostic plots re-examined, and the appropriate parametric tests conducted. Where a log transform has been applied, a log axis is also provided on the graph and the p-value reported is from the log transformed model. If the response could not be transformed to give residuals suitable for parametric testing, the Kruskal-Wallis test followed by Dunn test with Benjamini-Hochberg correction for multiple testing was performed. All analysis and graphing was conducted using R version 4.5.1 and R studio version 2025.05.1+513 ( 52 , 53 ). We used the packages “car” ( 54 ), “FSA” ( 55 ), “ggplot2” ( 56 ), “ggpubr” ( 57 ), “ggbeeswarm” ( 58 ), “ggthemes” ( 59 ), “lme4” ( 60 ), “afex” ( 61 ), “fmsb” ( 62 ), “tidyr” ( 63 ), “stringr” ( 64 ), “extrafonts” ( 65 ), “patchwork” ( 66 ) and all their dependencies. All scripts can be found at https://github.com/DJAshworth1015/Barley-Can-Utilise-Algal-Feriliser-To-Maintain-Yield-And-Malt-Quality . 3 Results 3.1 Responses to Algal Fertiliser in Glasshouse Trials 3.1.1 Phenology In the first algae response experiment, the algae only treated plants produced tillers significantly faster than those with the algae supplement treatment at 26DAS (days after sowing, Table 1 , Figure S3 (A) ), and faster than those with no additions at 38DAS ( Table 1 , Figure S3 (B) ). The measuring points either side of the reported days showed no significant differences in Zadok Score between treatments. View this table: View inline View popup Download powerpoint Table 1. Significance of comparisons for the glasshouse experiments in supplemental material. Graphs for each trait listed can be found in the supplemental material, with figure numbers listed in Sections 3.1.1 and 3.1.2. For methods of obtaining p-values, KW = Kruskal-Wallis followed by Dunn Test, MMA = Mixed model ANOVA followed by mixed model contrasts. Different letters represent significance groups, with multiple letters representing non-significant intermediates ( i.e. ab is not significantly different from a or b, which are significantly different from one another). The “^” sign represents the highest value in the comparison ( e.g. Algae Only has a higher Zadok Score (ZS) than Algae Supplement at 26DAS). Measurements were taken at ZS22 unless otherwise stated. At 42DAS, plants grown with the algae supplement treatment entered stem elongation (node formation) earlier than plants grown with no additions. The higher number of nodes compared to the plants grown with no additions was maintained across 45DAS and 46DAS, with the algae supplement treated plants also developing nodes significantly faster than the mineral treatment at 46DAS ( Table 1 , Figure S4 (A), (B), and (C) ). The difference between the algae supplement treated plants and the other treatments becomes non-significant at the next timepoint (48DAS). The only other timepoint at which there was a significant difference between treatments was at 66DAS, where the ears of the algae supplement treated plants started to emerge from the flag leaf sheath faster than those of the plants grown with no additions ( Table 1 , Figure S4 (D) ). In both the second and third algae response experiments, there were no significant differences in Zadok Score at any point during development. 3.1.2 Morphometrics and Reflectance in Early Tillering There were no significant differences between plants grown with no additions, algae powder alone, algae powder with supplement, or mineral salts in the third leaf length, third leaf area or aboveground dry biomass of cv. Laureate in early tillering ( Table 1 , Figure S5 (A), (B) and (C) ). Likewise, there are no significant differences in NDVI between any of the treatments at this point in time ( Table 1 , Figure S5 (D) ). The lack of a significant difference between the negative control (no additions) and the positive control (mineral fertiliser) suggests that, at this point in development, the available nutrients are not a limiting factor for growth, nor is the ability to absorb photosynthetically active red light. 3.1.3 Morphometrics and Grain Traits at Maturity Overall, when combining the morphometric and grain traits, plants grown with algae are not significantly different from those grown with mineral fertiliser in every measured trait. Furthermore, plants grown with algae have significantly greater earless dry biomass, number of tillers, filled grain, TGW, grain width and grain area than those grown with no additions. At maturity, plants grown with the algae only, algae supplement, or mineral treatment did not have a significantly different earless dry biomass from one another, and plants grown in all three treatments had a significantly higher earless dry biomass than plants grown with no additions (Mixed model ANOVA, p < 0.001, Figure 1 (A) ). Plants grown with either algae or mineral fertiliser did not have a significantly different number of tillers, and the number of tillers for plants grown with all three treatments significantly exceeded those grown with no additions (Mixed model ANOVA, p < 0.001, Figure 1 (B) ). Total filled grain per plant followed the same pattern as earless dry biomass, with the two algae treatments and the mineral treatment not significantly differing, and all three filling significantly more grain than plants grown with no additions (Mixed model ANOVA, p = 0.007, Figure 1 (C) ). Filled grain per ear did not differ significantly between treatments (Mixed model ANOVA, p = 0.969, Figure 1 (D) ), indicating that the increases in filled grain in the algae and mineral treatments were driven by an increased number of tillers only. Download figure Open in new tab Figure 1. Morphometrics of cv. Laureate at maturity when grown with four different nutrient sources across three glasshouse experiments. (A) Earless dry mass. (B) Number of tillers. (C) Total filled grain per plant. (D) Mean filled grain per ear per plant. For all boxplots, the thick horizontal line is the median, thin horizontal lines are the 1st and 3rd quartile. Whiskers extend to the range, excluding outliers (>1.5x the interquartile range below quartile 1 or above quartile 3). Overall significance determined by mixed model ANOVA, p-value reported in the main text. Pairwise significance determined by mixed model contrasts. For (B) , the model fitted used the natural log of the tiller number, but untransformed tiller number is presented here for ease of interpretation. N = 21 for no additions, 21 for algae only, 20 for algae supplement, 22 for mineral. Each rep is one point. When analysing the grain traits, there were no significant differences between plants grown with either algae treatment and mineral fertiliser in thousand grain weight (TGW), and all three treatments produced plants with significantly greater TGWs than plants grown with no additions (Mixed model ANOVA, p = 0.008, Figure 2 (A) ). Grain from plants grown with algae only or mineral fertiliser did not differ significantly in width and had significantly wider grain than plants grown with no additions, with the algae supplement treatment being a non-significant intermediate (Mixed model ANOVA, p = 0.029, Figure 2 (B) ). There were no significant differences in grain length between any of the treatments (Mixed model ANOVA, p = 0.197, Figure 2 (C) ). Grain area was significantly increased for the algae only treatment relative to the no additions treatment, with the algae supplement and mineral treatment being non-significant intermediates (Mixed model ANOVA, p = 0.018, Figure 2 (D) ). Download figure Open in new tab Figure 2. Grain traits of cv. Laureate when grown with four different nutrient sources across three glasshouse experiments. (A) Thousand grain weight (TGW). (B) Mean grain width per plant. (C) Mean grain length per plant. (D) Mean grain area per plant. For all boxplots, the thick horizontal line is the median, thin horizontal lines are the 1st and 3rd quartile. Whiskers extend to the range, excluding outliers (>1.5x the interquartile range below quartile 1 or above quartile 3). Overall significance determined by mixed model ANOVA, p-value reported in the main text. Pairwise significance determined by mixed model contrasts. N = 21 for no additions, 21 for algae only, 20 for algae supplement, 22 for mineral. Each rep is one point. 3.2 Spring 2024 Field Trial 3.2.1 Phenology At 26DAS, all plots were at 99% or 100% emergence, which were therefore considered to be fully germinated. Plants grown with algae reached flag leaf unfurling significantly sooner (median 59DAS) than those grown with no additions (median 61DAS), with plants grown on mineral fertiliser (median 59DAS) being a non-significant intermediate ( Table 2 , Figure S6 (A) ). For days to heading, there is no significant difference between plants grown with algae (median 64DAS) or mineral fertiliser (median 63DAS), with both reaching this stage significantly sooner than plants grown with no additions (median 65DAS, Table 2 , Figure S6 (B) ). View this table: View inline View popup Download powerpoint Table 2. Significance of comparisons for the field trial in supplemental material. Graphs for each trait listed can be found in the supplemental material, with figure numbers listed in the sections 3.2.1 and 3.2.2. For methods of obtaining p-values, KW = Kruskal-Wallis followed by Dunn Test, A = ANOVA followed by Tukey’s HSD. Different letters represent significance groups, with multiple letters representing non-significant intermediates ( i.e. ab is not significantly different from a or b, which are significantly different from one another). The “^” sign represents the highest value in the comparison ( e.g. No Additions has a higher Days to Flag Leaf than Algae), the “,” sign represents the lowest value in the comparison, only used when there are more than two significance groups. 3.2.2 Reflectance Indices Across the Growing Season At 26DAS, NDVI is not significantly different between plots grown with algae or mineral fertiliser, and both treatments show significantly higher NDVIs than plots grown with no additions ( Table 2 , Figure S7 (A) ), suggesting more leaf greenness and a better ability to absorb photosynthetically active red light. At 35DAS, NDVI has increased for the algae and mineral treated plots, while staying similar for the no additions plots, keeping the same significant differences but increasing the level of significance ( Table 2 , Figure S7 (B) ). At 40DAS, NDVI is starting to decrease across all treatments, which may be due to plants beginning to remobilise nutrients from the lower leaves. Nevertheless, the pattern of significance remains the same ( Table 2 , Figure S7 (C) ). At 56DAS, all treatments have significantly different NDVIs from one another, with the no additions plots being the lowest, mineral being the highest and algae being a significantly different intermediate ( Table 2 , Figure S7 (D) ). 3.2.3 Yield, Morphometrics and Grain Traits at Harvest At harvest, grain moisture was between 17% and 18.9%. Grain yield, corrected to 0% moisture, was not significantly different between plots grown with algae or mineral fertiliser, and both yielded significantly more grain than plots grown with no additions (ANOVA, p < 0.0001, Figure 3 (A) ). This was driven in part by an increase in tiller number, which was not significantly different between algae and mineral treatments and was significantly higher for both of those than for plots grown with no additions ( Figure 3 (B) ). Plants grown with algae (median 21) had a statistically significantly higher number of grains per ear than those grown with mineral fertiliser (median 20), with the plants grown with no additions (median 20) being a non-significant intermediate (ANOVA, p = 0.023, Figure 3 (C) ). The algae grown plants also have significantly longer ears than both those grown with no additions or mineral fertiliser, which are not significantly different to each other (Mixed model ANOVA, p = 0.008, Figure 3 (D) ). Download figure Open in new tab Figure 3. Harvest and grab sample traits of cv. Laureate when grown in a field trial under three different fertiliser regimes. (A) Dry grain yield per plot. (B) Tiller number for every plant in each grab sample. (C) Grain per ear for three random ears in each grab sample. (D) Ear length for three random ears in each grab sample. For all boxplots, the thick horizontal line is the median, thin horizontal lines are the 1st and 3rd quartile. Whiskers extend to the range, excluding outliers (>1.5x the interquartile range below quartile 1 or above quartile 3). For the violin plot, width of the violin represents density of counts. The point is the mean. For (A) and (C), overall significance determined by ANOVA, p-value reported in main text, and pairwise significance determined by Tukey’s HSD. For (B) , pairwise significance determined by generalised linear model contrasts with no overall p-value determined due to the type of model fitted. For (D) , the block effect was significant, so a mixed model was used. Overall significance determined by mixed model ANOVA, p-value reported in main text, and pairwise significance determined by mixed model contrasts. For (A) , N = 9 for all treatments, each rep is one point. For (B) , N = 345 for no additions, 335 for algae and 366 for mineral. For C and D , N = 27 for all treatments, each ear is one point. The other driver of increased yield for plots grown with algae or mineral fertiliser is an increase in thousand grain weight (TGW). Plots grown with algae or mineral fertiliser do not significantly differ in their TGW, and both have significantly higher TGWs than plants grown with no additions (ANOVA, p = 0.00036, Figure 4 (A) ). There is no significant difference in grain width between any treatment ( Figure 4 (B) ). Plots grown with mineral fertiliser have significantly longer grain than those grown with no additions, with the algae treatment being a non-significant intermediate (Kruskal-Wallis, p = 0.0013, Figure 4 (C) ). This same pattern is seen for overall grain area, mineral plots having grains with significantly more area than no additions plots, with algae fertilised plots being a non-significant intermediate. (ANOVA, p = 0.007, Figure 4 (D) ). Download figure Open in new tab Figure 4. Grain mass and dimensions of cv. Laureate when grown in a field trial under three different fertiliser regimes. (A) Thousand grain weight on a dry basis per plot. (B) Mean grain width per plot. (C) Mean grain length per plot. (D) Mean grain area per plot. For all boxplots, the thick horizontal line is the median, thin horizontal lines are the 1st and 3rd quartile. Whiskers extend to the range, excluding outliers (>1.5x the interquartile range below quartile 1 or above quartile 3). For (A) and (D), overall significance determined by ANOVA, p-value reported in main text, and pairwise significance determined by Tukey’s HSD. For (B) , no p-value was determined. For (C) , overall significance determined by Kruskal-Wallis, p-value reported in main text. Pairwise significance determined by Dunn test with Benjamini-Hochberg correction for multiple testing. N = 9 for all treatments. Each rep is one point. 3.2.4 Grain Quality and Malting Grain nitrogen on a dry basis was not significantly different between plants grown with algae or no additions, while plants grown with mineral fertiliser had significantly higher grain nitrogen than both other treatments (Mixed model Anova, p = 0.004, Figure 5 (A) ). When malted, all three treatments have significantly different total malt nitrogen, with the no additions malt having the lowest, mineral having the highest and malt grown with algae being a significant intermediate (Mixed model ANOVA, p < 0.001, Figure 5 (B) ). For soluble nitrogen, there is no significant difference between malt grown with algae or mineral fertiliser, and both fertilisers give malt with higher soluble nitrogen than malt grown with no additions (Mixed Model ANOVA, p < 0.001, Figure 5 (C) ). There are no significant differences in soluble nitrogen as a percentage of total nitrogen (ANOVA, p = 0.24, Figure 5 (D) ). Download figure Open in new tab Figure 5. Grain and malt nitrogen of cv. Laureate when grown in a field trial under three different fertiliser regimes. (A) Grain %Nitrogen by dry mass. (B) Malt total %Nitrogen by dry mass. (C) Malt soluble %Nitrogen by dry mass. (D) Malt soluble nitrogen as a percent of malt total nitrogen. For all boxplots, the thick horizontal line is the median, thin horizontal lines are the 1st and 3rd quartile. Whiskers extend to the range, excluding outliers (>1.5x the interquartile range below quartile 1 or above quartile 3). For (A) , (B) and (C), overall significance determined by mixed model ANOVA, p-value reported in main text, and pairwise significance determined by mixed model contrasts. For (D) , significance was determined by ANOVA, p-value reported in main text. For (A) , N = 9 for all treatments, each point is one plot. For (B) , N = 36. For (C) and (D) , N = 36 for no additions, 35 for algae and 34 for mineral. Each point is one replicate. Malt moisture ranged between 4.86% to 8.52%. Fine hot water extract strength (HWE), not moisture corrected, did not significantly differ between the algae or mineral fertiliser treatments, and both had higher HWEs than malt grown with no additions (Mixed model Anova, p < 0.001, Figure 6 (A) ). This pattern is maintained when HWE is corrected for moisture, now with increased significance due to range shrinkage (ANOVA, p < 0.0001, Figure 6 (B) ). There is no significant difference in predicted spirit yield (PSY) before it has been moisture corrected, as this value is highly affected by the variable malt moisture (Mixed model ANOVA, p = 0.061, Figure 6 (C) ). After correcting for moisture content, there is no significant difference in PSY between malt grown with algae or mineral fertiliser, and both malts have significantly greater PSY than malt grown with no additions (ANOVA, p = 0.002, Figure 6 (D) ). Download figure Open in new tab Figure 6. Fine hot water extract strength (HWE) and predicted spirit yield (PSY) of cv. Laureate when grown in a field trial under three different fertiliser regimes. (A) HWE not corrected for grain moisture. (B) HWE corrected to 0% grain moisture. (C) PSY not corrected for grain moisture. (D) PSY corrected to 0% grain moisture. For all boxplots, the thick horizontal line is the median, thin horizontal lines are the 1st and 3rd quartile. Whiskers extend to the range, excluding outliers (>1.5x the interquartile range below quartile 1 or above quartile 3). For (A) and (C), overall significance determined by mixed model ANOVA, p-values reported in main text, and pairwise significance determined mixed model contrasts. For (B) and (D) , significance was determined by ANOVA, p-values reported in main text, followed by Tukey’s HSD. N = 35 for no additions, 31 ( (A) and (C) )/30 ( (B) and (D) ) for algae and 33 for mineral. Each point is one replicate. 4 Discussion In this study, we have demonstrated that, in both glasshouse and field trials, there is no metric related to NDVI or yield components in which barley cv. Laureate fertilised with Chlorella vulgaris is significantly worse than barley grown with mineral fertiliser, except NDVI at 56DAS for field plots, where NDVI for the algae treated plots is significantly lower than that of mineral treated plots. However, this does not seem to impact the malt quality of the grain, as there is no significant difference in fine hot water extract strength or predicted spirit yield between plants grown with algae or mineral fertiliser. In three glasshouse trials, barley cv. Laureate grown with algae accumulates as much biomass, as many tillers, fills as much grain and has a thousand grain weight that is not significantly different from plants grown with mineral fertiliser. Additionally, both algae and mineral treated plants significantly exceed the no additions negative control in these aspects. Combining the data across three experiments that have been grown at different times of year, in different soils and with different amounts of algae and mineral fertiliser added (though up to a common N rate) still gives a consistent effect, showing that algal fertiliser is as suitable as mineral fertiliser in the range of conditions tested. The minor differences in speed of development between treatments, in the first experiment only, and between experiments do not have an impact on the plant’s ability to use one type of fertiliser or the other, as evidenced by the lack of significant differences between the algae and mineral fertiliser. Concerns about algae not releasing nutrients as quickly as mineral fertiliser, and therefore starving the plant in early development, are not supported by these experiments, as in early tillering there is no difference in leaf dimensions, aboveground dry biomass, or NDVI between any of the treatments. The results in the glasshouse are replicated in the field trial. Taking the grab sample results, algae and mineral fertiliser do not significantly differ in tiller number, nor TGW, mirroring what was seen in the glasshouse experiments. When looking at yield for the field trials, and the yield components of total filled grain and TGW for the glasshouse experiments, algae and mineral fertiliser are not significantly different and give significantly greater values than plants grown with no additions in both experimental settings. There are some differences between the results of our field trial and glasshouse experiments when it comes to ear and grain dimensions. In our glasshouse experiments, there is no significant difference in filled grain per ear between any treatment, whereas in our field trial, algae treated plots fill significantly more grain per ear than the mineral fertiliser treated plots, though the medians only differ by one grain, while the no additions treatment is a non-significant intermediate. Looking at grain length, width and area, in our glasshouse experiments, there is no difference in grain length, and some differences between no additions and the other treatments for grain width and area. In contrast, in our field trial, there is no difference in grain width between any treatment, but mineral treated plots have significantly longer grains with more area than no additions plots and algae treated plots are a non-significant intermediate. The NDVI also differs between glasshouse and field experiments. In the glasshouse, at Zadok Stage 22 there is no significant difference in NDVI between any treatments, while the field trial, NDVI for algae and mineral treated plots is higher than that of no additions plots for all measured time points, with no significant difference between algae plots and mineral plots until 56 days after sowing, where the algae treatment becomes significantly lower than mineral. A more granular time series of NDVI across the growing season is needed to establish whether there is a difference in the ability of algae to support the plant’s ability to absorb photosynthetically active light later in development, relative to mineral fertiliser. Despite the observed difference in NDVI, the yield at the end of the trial was not significantly different between algae and mineral fertiliser, so the drop in NDVI is not a cause for concern. Silva et al (2025) ( 43 ) reported increased speed of growth of barley (experimental setting and cultivar not stated) between 40-100 days after sowing, using aerial length, with increasing concentrations of Arthrospira sp., a cyanobacterium, which is also commonly used as a protein rich health food like C. vulgaris . This roughly agrees with the increase in node development we saw in our first experiment between 42-46DAS, but they report a much longer period of time where the algae increases growth rate, whereas we saw only a few days where the algae treated plants were ahead, and only in one experiment, with the other two glasshouse experiments showing no difference in phenology at any time. The results of the morphometrics from our glasshouse experiments agree with the work of Schreiber et al (2018) ( 13 ) where they found that, in glasshouse trials using wheat cv. Scirocco growing in the soil-like substrate “Null Erde”, plant size (from projected leaf area) and shoot dry mass were not significantly different when growing with Chlorella vulgaris , the same species we used here, or mineral fertiliser, and that both give values significantly higher than plants grown with no additions. Our work extends beyond the work of Schreiber et al , who terminated their experiment before their wheat reached maturity. However, Renuka et al (2016) ( 42 ) grew their wheat var. HD2967 to maturity in growth chambers, using consortia of algae made up of multiple taxa, one of which included species of Chlorella among other genera, to partially replace mineral fertiliser in a compost mix. They found that dry mass at maturity was not significantly different to, or increased over, the full NPK control using their algae treatments, which agrees with our and Schreiber’s work, that there is no penalty to use of algae rather than mineral fertiliser. Renuka et al (2016) also found that thousand grain weight significantly increased over the full NPK control and far exceeded the TGW of the 75%N negative control when using their consortia of algae, a result beyond what we found, which only shows algal fertiliser matching the mineral fertiliser positive control. In contrast, the work of Mückschel et al (2023) ( 67 ) disagrees with Renuka’s and our findings. They found that when using consortia of algae as fertiliser, wheat cv. KWS Starlight growing in a glasshouse in soil still gives significantly higher shoot dry masses and grain yield per plant than unfertilised controls but had significantly lower values than plants grown with mineral fertiliser, which they attribute to lower availability of the forms of nitrogen in algae compared to mineral forms. The translatability between wheat and barley is positive, though not entirely unexpected, as far more distantly related crops have shown similar responses to algae, such as tomato ( 68 ) or lettuce ( 69 ). Additionally, this also shows that a range of formulation methods can be employed to give similar results, though with some exceptions. The current study applied algae and mineral fertiliser to provide equivalent nitrogen, whereas Schreiber et al (2018) added algae and fertiliser to provide equivalent phosphorus. Renuka et al (2016) added algae based on green mass (µg chlorophyll per gram of carrier), which resulted in a not statistically different amount of nitrogen being present in the positive control and algae supplemented treatments. In contrast, Mückschel et al (2023) added N, P and K salts to match the amounts of these elements found in their algae consortia. This implies that, if no nutrient is lacking in a certain formulation, algae can be used to wholly or partially substitute for either N or P, and likely any other nutrient, provided that the nutrients in that algae can be made available to the plant. The importance of understanding the nutrient content of algal fertiliser and applying it at suitable rates, comparable to what one would use for mineral fertiliser, is demonstrated by the work of Alobwede et al (2019) ( 14 ). They found that when adding Chlorella sp. based on mass of algae per area, not mass of individual nutrient per area, to provide 24.28kg(N)/ha to field plots planted with wheat cv. Tybalt, there was no significant difference in shoot dry mass or ear number relative to the no algae control. As hypothesised in their discussion, they likely used too low a mass of algae to find a significantly different response. In contrast, the current study added 61.6kg(N)/ha in our field trial and did find a significant increase in yield and yield components of barley. As barley is a key component of the high value markets of brewing and distilling, the examination of malt quality was a necessary step for this study. For grain nitrogen, our values for all treatments are well below a typical industry maximum of 1.60%, and also below the target range of 1.35-1.55%. Malt nitrogen is similar, with every value being below the specification range. The significant difference in grain and malt %N between algae and mineral fertiliser differs from the previous pattern of yield and yield components, possibly indicating that the plant will allocate nitrogen from each source differently. There is no difference in soluble nitrogen ratio (SNR) between any of the treatments, and it is below specification for most samples. SNR is a proxy for malt modification (the extent of mobilisation of the sugars and proteins within the endosperm during germination), with a higher value indicating more modification. The low values found for SNR in this study imply under-modification during the malting process, which in our case is likely due to poor germination, rather than insufficient germination time. Extract strength as-is and dry extract strength for algae and mineral fertiliser were not significantly different from each other, and both had significantly greater extract strength than malt grown with no additions. However, dry extract strength is again below the specification minimum. PSY as-is is not significantly different between treatments, which can be explained by the wide distribution of malt moistures. Accounting for moisture content, dry PSY again agrees with the earlier measurements, where algae and mineral fertiliser are not significantly different in PSY, and have significantly higher PSYs then malt grown with no additions. In this case, the dry PSY for all treatments meets the specification. Despite extract strength, which is proportional to PSY, being below specification, the low %N, which is inversely proportional to PSY, increases the PSY to an acceptable value. However, the PSY calculation may only be valid for %Ns in the range specified, and too low a %N may impact the ability of starch in the grain to be mobilised during malting and mashing due to lack of enzymes. The low values for grain and malt %N for all treatments may be due to a combination of two factors. Firstly, the entirety of the fertiliser was applied at the start of the growing period. On some farms, the timing of application of fertiliser has been reported to change the grain %N, with earlier applications decreasing grain %N, and later applications ( e.g . as a top dressing) increasing grain %N ( 50 ). Secondly, the trial area was at the top of a sloping field. May of 2024 had ∼20mm more rainfall than the 30 years average (70.8mm in 2024 compared to 51.2mm 1991-2020), meaning more of the residual soil N and applied N may have been washed away. This would decrease the nitrogen available to the plant and thus decrease the grain %N. Field trials in different locations over multiple years are needed to properly assess the viability of use of algae as a fertiliser for malting barley, but this study is a promising start given that algae and mineral fertiliser did not differ in terms of extract and PSY. In conclusion, this study demonstrates that barley cv. Laureate can use algal fertilisers just as effectively as mineral fertilisers, in both glasshouse and field settings. There is no penalty to use of algal fertiliser in any measured metric, and significant improvements over the unfertilised, negative control were seen for algal and mineral fertiliser when examining yield and yield components. Malting quality was below industry specification for all treatments, but within the experiment, malt grown with algae performed the same as malt grown with mineral fertiliser, except for malt total nitrogen. More field trials followed by malting are necessary to properly assess the use of algal fertilisers for the end use of brewing and distilling, but this study serves as an initial proof of efficacy of algal fertilisers on barley. Furthermore, additional trials using algae specifically grown on distillery co-products and waste should be performed to assess the practicality of nutrient recycling within the barley to malt to whisky value chain. Conflict of Interest During the period in which this work was conducted, Stefan Masson and Tom Mulholland (until end 2023) were employed by Chivas Brothers – Pernod Ricard. Chivas Brothers were an industrial partner on this project through the BARIToNE CTP (Collaborative Training Partnership) as part of their commitment to reducing scope 3 emissions. Chivas Brothers was kept informed of the progress of the work and their lab space and equipment was used for malt analysis, but they exerted no control over what data was produced, how it was produced or whether it could be reported. Author Contributions DJA: Conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing – original draft, writing – review & editing KH: Conceptualization, funding acquisition, supervision, writing – review & editing DB: Resources, supervision, writing – review & editing TM: Funding acquisition, writing – review & editing SM: Resources, methodology, writing – review & editing Funding DJA and this study was supported by a PhD studentship funded by BBSRC through the BARIToNE CTP, grant number BB/X511687/1, awarded to the James Hutton Institute, the University of Dundee, and Chivas Brothers – Pernod Ricard. KH would like to acknowledge the support of the Rural and Environmental Science and Analytical Services division of the Scottish Government. Data availability statement The datasets generated for this study can be found at Zenodo.org under ‘Datasets supporting the paper “Barley Can Utilise Algal Fertiliser to Maintain Yield and Malt Quality Compared to Mineral Fertiliser”’ at https://doi.org/10.5281/zenodo.16810830 . Acknowledgements We would like to acknowledge the assistance of Richard Keith, Christopher Warden and Derek Matthews for their assistance with organising, planting, treating, and harvesting the field trial. We would also like to thank Jim Wilde and Alison Dobson as glasshouse technicians, Niki McCallum as lab manager, and Cameron McCarthy at Chivas Brothers, Glen Keith Technical Centre for overseeing David Ashworth’s malt analysis competency training. 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Share Barley Can Utilise Algal Fertiliser to Maintain Yield and Malt Quality Compared to Mineral Fertiliser David James Ashworth , Stefan Masson , Tom Mulholland , Davide Bulgarelli , Kelly Houston bioRxiv 2025.08.12.667670; doi: https://doi.org/10.1101/2025.08.12.667670 Share This Article: Copy Citation Tools Barley Can Utilise Algal Fertiliser to Maintain Yield and Malt Quality Compared to Mineral Fertiliser David James Ashworth , Stefan Masson , Tom Mulholland , Davide Bulgarelli , Kelly Houston bioRxiv 2025.08.12.667670; doi: https://doi.org/10.1101/2025.08.12.667670 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Plant Biology Subject Areas All Articles Animal Behavior and Cognition (7624) Biochemistry (17651) Bioengineering (13871) Bioinformatics (41882) Biophysics (21424) Cancer Biology (18566) Cell Biology (25461) Clinical Trials (138) Developmental Biology (13365) Ecology (19867) Epidemiology (2067) Evolutionary Biology (24290) Genetics (15590) Genomics (22476) Immunology (17714) Microbiology (40331) Molecular Biology (17148) Neuroscience (88483) Paleontology (666) Pathology (2828) Pharmacology and Toxicology (4817) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9815) Zoology (2268)