With global demand for food projected to increase by ca. 50% by mid-century, the agri-food sector must produce this food in an environmentally sustainable manner. This is particularly challenging for an industry that is highly dependent on fossil fuels and where total system losses (waste and inefficiencies) are ca. 50%. It is against this background that Digital Agriculture has a pivotal role to play in enhancing overall operational efficiency. The agri-food sector comprises several stages, a continuum, from on-farm production, through processing, retail, the consumer (citizen) and beyond into an agri-food bioeconomy. Digital Agriculture offers the opportunity to enhance overall operational efficiency of the full agri-food chain, through: (i) gathering data of the quality and scale needed to understand, manage and monitor system performance; (ii) implementing data-driven systems that are designed to optimise (or near optimise) operational efficiency (and hence resource use efficiency); (iii) providing continuous system monitoring, enabling iterative feedback-based system improvement; (iv) providing regulatory overview (monitoring) of compliance with product assurance, environmental impact regulations; (v) providing the consumer (citizen) with credible high quality, verifiable information on the provenance, quality and safety of their food; (vi) providing verification of the provenance and quality of feedstock for a sustainable agri-food based bioeconomy. While digital agriculture has the potential to integrate these features, the critical factor in delivering effective operational systems is the data - its quality, quantity, time-sensitivity, scale and availability (including cost). System variability varies from stage to stage along the agri-food continuum (pre- and post-farm gate), with pre-farm gate (viz. on-farm production) being the most variable as operations are subject to weather, geo-spatial, bio- and market (demand and price) variability. This paper discusses the range of data sources required to achieve enhanced system efficiency, their availability, capture (i.e. sensing technology, scale, costs) and integration into suitable decision support and control systems. It concludes that while several new and existing sensing technologies offer a kaleidoscope of data sources, the real challenges are addressing data quality, variability, scale; and the analyses and integration of these data to deliver effective commercially implementable models delivering enhanced operational performance, monitoring and feedback.

Due to complex feature abstraction and learning power, CNNs have been the most successful machine learning algorithms for image classification tasks. The objective of this work was to evaluate the potential of convolutional neural networks (CNNs) for extracting underlying complex features and recognize these patterns towards the task of detecting healthy and diseased crop plants. The generalization of these algorithms was assessed on different situations of training and testing scenarios using images from controlled lab conditions and real field environments. Results have shown that when presented with sufficient data variability in training, englobing images with similar conditions faced in testing, the deep learning architectures delivered accurate results of over 90%. In contrast, the same architectures were not able to generalize the accuracy of training towards the detection of new unseen images that were not extracted in the same settings as the ones from the training set, delivering, in this case, a general accuracy of around 50%. The deployment of practical automated support systems for disease detection depends on the provision of robust datasets for training CNNs which contemplate the spectral variability conditions found in numerous crop cultivation environments encountered in diverse field sites across the globe.

Continuous monitoring of food loss and waste (FLW) is crucial for improving food security and mitigating climate change. By measuring quality parameters such as temperature and humidity, real-time sensors are technologies that can continuously monitor the quality of food and thereby help reduce FLW. While there is enough literature on sensors, there is still a lack of understanding on how, where and to what extent these sensors have been applied to monitor FLW. In this paper, a systematic review of 59 published studies focused on sensor technologies to reduce food waste in food supply chains was performed with a view to synthesising the experience and lessons learnt. This review examines two aspects of the field, namely, the type of IoT technologies applied and the characteristics of the supply chains in which it has been deployed. Supply chain characteristics according to the type of product, supply chain stage, and region were examined, while sensor technology explores the monitored parameters, communication protocols, data storage, and application layers. This article shows that, while due to their high perishability and short shelf lives, monitoring fruit and vegetables using a combination of temperature and humidity sensors is the most recurring goal of the research, there are many other applications and technologies being explored in the research space for the reduction of food waste. In addition, it was demonstrated that there is huge potential in the field, and that IoT technologies should be continually explored and applied to improve food production, management, transportation, and storage to support the cause of reducing FLW.

PurposeSince the end of the latest rice-pledging scheme, Thai rice farmers have had more freedom in selecting marketing channels. Understanding the determinants of farmers' decision-making associated with these channels is of particular interest to multiple stakeholders in the rice value chain. This study aims to examine how economic, relational and psychological factors concurrently underpin Thai rice farmers' decision-making and influence their marketing channel choice.Design/methodology/approachDrawing on the theory of reasoned action and utility maximization of farmers’ decision making, this study used structural equation modeling to examine data collected from a nationwide sample of Thai rice farmers (n = 637), focusing on their past and intentional use of the three major marketing channels for paddy rice.FindingsThe determinants identified include four direct independent variables: attitude, subjective norm (social referents), transaction conditions and economic goals, and two indirect independent variables: past behavior and trust. Multi-group analysis suggests that rice co-operative users were more empowered to consider economic goals and attitude toward the channel, whilst rice miller and local collector users were more likely to be influenced by their social referents and the transaction conditions offered by the channel.Practical implicationsThe findings highlight the need for policy to address trust and transparency issues with intermediaries and to empower farmers through the improvement of market access.Originality/valueThe study makes a unique and substantive contribution to the knowledge of farmers' decision-making about marketing channel choice in Thailand and theoretically contributes to the indirect role of past behavior in predicting prospective intention.

This study takes a systems perspective to evaluate the potential of bioenergy to enable the transition to a net zero energy system in Ireland. There has been extensive research carried out on the potential socio-technical-economic options to achieve national renewable targets but there has been limited research to date on assessing the role that bioenergy could play to support this policy objective. This review addresses this gap in knowledge by providing a contextual view of the role of bioenergy across a range of potential decarbonisation pathways. The study develops an energy system model and uses illustrative scenarios to assess the potential roles that bioenergy could play in the pathways to a net zero carbon energy system that will complement the other enablers of decarbonisation (electrification, hydrogen and carbon capture and storage). The study finds that bioenergy can play an integral role in a zero carbon energy system potentially generating between 20% - 30% of the Irish energy system’s primary energy needs. To enable this opportunity Irish policymakers should; 1) enable the utilisation of existing organic agricultural and food waste to generate biogas; 2) create a predictable and stable bioenergy demand for indigenous forestry residues; 3) develop a framework of policy measures to enable a portfolio of renewable energy technologies that is adaptable enough to manage the uncertainty created by emerging technologies and new markets

Food waste is a global challenge that has significant environmental, social and economic implications. Food retailers are in a powerful position to influence food waste reduction by producers, manufacturers and consumers. There is a paucity of worldwide understanding regarding the scope and scale of operations by retailers in minimising/managing food waste. The aim of this research was to develop a systematic understanding of how food retailers deal with food waste both internally and externally, within a five-tier 'food waste hierarchy' framework. This study is based on a qualitative synthesis of 460 articles systematically gathered from nine bibliographic databases and eight grey literature sources and published in English between 1998 and 2019. The review suggests a growing research/reporting interest in retail food waste management. The review identified 199 named and unnamed retailers from 27 countries that have reported some form of the 35 types of food waste management activities. There is evidence of retailers following the food waste hierarchy in reporting their practices with more focus on reducing food waste and redistribution of surplus food for human consumption, and less on recycling and energy recovery by incineration. The wide range of practices adopted by food retailers to mitigate food waste were mapped in a sustainable value framework which showed a typology of five approaches: repositioning, reallocating, reacting, re-engineering and relating. This demonstrates that economic, social and environmental benefits can be realised by retailers' food waste management, but not in a homogeneous way. Further empirical work should be undertaken to see how different retail business models aligns with the different approaches in the sustainable value framework.

PurposeThis research aims to explore retail managers' views on how food waste (FW) management activities contribute to sustainable value creation and how the customer value proposition (CVP) for a given food retailer interacts with their approaches to FW management.Design/methodology/approachA three-stage exploratory qualitative approach to data collection and analysis was adopted, involving in-depth interviews with retail managers, documentary analysis of multiple years of relevant corporate reports and email validation by seven major UK grocery retailers. Thematic content analysis supplemented by word similarity cluster analysis, two-step cluster analysis and crisp-set qualitative comparative analysis was undertaken.FindingsFW management practices have been seen by retail managers to contribute to all forms of sustainable value creation, as waste reduction minimises environmental impact, saves costs and/or serves social needs, whilst economic value creation lies at the heart of retail FW management. However, retail operations are also framed by CVP and size of a retailer that enable or inhibit the adoption of certain FW management practices. Low-price retailers were more likely to adopt practices enabling them to save costs. Complicated cost-incurring solutions to FW were more likely to be adopted by retailers associated with larger size, high quality and a range of services.Originality/valueThis study is the first of its kind to empirically explore retail managers' perception of sustainable value creation through FW management activities and to provide empirical evidence of the linkages between retail CVP and sustainable value creation in the context of retail FW management.

Chicken manure is an agricultural by-product that is a problematic feedstock for anaerobic digestion due to its high nitrogen content inhibiting methane yields. This research examines a novel pilot-scale method of ammonia stripping, the nitrogen recovery process (NRP) developed by Alchemy Utilities Ltd. The NRP was designed to remove and recover nitrogen from chicken manure and two different operating conditions were examined. Both operating conditions demonstrated successful nitrogen removal and recovery. The biochemical methane potential assays were used to compare the digestibility of the NRP-treated chicken manures to that of a fresh chicken manure control. Overall, the biochemical methane potential assays demonstrated that some NRP-treated chicken manure treatments produced significantly more methane compared to untreated manure, with no inhibition occurring in relation to ammonium. However, some of the NRP-treated chicken manures produced similar or lower methane yields compared to fresh chicken manure. The NRP requires further development to improve the efficiency of the pilot-scale unit for commercial-scale operation and longer-term continuous anaerobic digestion trials are required to determine longer-term methane yield and ammonium inhibition effects. However, these initial results clearly demonstrate the technology’s potential and novel application for decentralised, on-farm nitrogen recovery and subsequent anaerobic digestion of chicken manure.

The emergence of advanced technologies has helped in solving specific problems in agriculture. In many sectors, interoperability of new technologies has helped in solving much bigger problems on a large scale and has eventually led to widespread automation, but this automation wouldn't have been possible without openness in communication protocols. These standard protocols have evolved over time to cater to industry needs, from partial to fully automated manufacturing processes. The overall system obtained by the integration of these technologies for automated process control and management is commonly referred to as Industry 4.0. This paper discusses the protocols and technologies used in precision farming analogous to Industry 4.0. This paper also addresses the gaps to fill by exploring the technologies and standards and by proposing a unified architecture. It is hoped that addressing these gaps will help to create a solution for fully automated process control in an Agriculture 4.0 perspective and race towards more advanced Agriculture 5.0.

Standard in vitro and in vivo tests help demonstrate efficacy of hand hygiene products; however, there is no standard in vivo test method for viruses. We investigated the bactericidal and virucidal efficacy of povidone-iodine (PVP-I) 7.5% scalp and skin cleanser, chlorhexidine gluconate (CHG) 4% hand cleanser and the reference hand wash (soft soap) in 15 healthy volunteers following European Standard EN1499 (hygienic hand wash test method for bacteria), which was adapted for virucidal testing.

—Public and private organizations have been investing significant financial and human resources to develop crop varieties suitable for different commercial destinations, regional characteristics and agronomic factors. The high number of variables and consequent complex analysis are factors that make the task of selecting a specific crop variety, that best fulfill the particularities of a given farm, a challenging one. In this scenario, this work proposes a ranking/decision method to deal with the stochastic problem of select a winter wheat variety, taking into account the random factors that influence in the specific decision. The system evaluates the commercial destination, site-specific and agronomic importance of varieties treats, such as resistance to diseases and lodging, to output a list of best winter wheat varieties choices, for a particular situation. The system's accuracy has been verified by experts of crop science, where a number of random outcomes were tested against specialist opinion.

Continuing population growth and increasing consumption are driving global food demand, with agricultural activity expanding to keep pace. The modern agricultural system is wasteful, with Europe generating some 700 million tonnes of agrifood (agricultural and food) waste each year. The Agricultural Centre for Sustainable Energy Systems (ACSES) at Harper Adams University is involved in a major research and innovation project (AgroCycle) on the application of the ‘circular economy’ across the agri-food sector. In the context of the agrifood chain, the ‘circular economy’ aims to reduce waste while also making best use of the ‘wastes’ produced by using economically viable processes and procedures to increase their value . Led by University College Dublin, AgroCycle is a Horizon 2020 collaborative project with 26 partners. AgroCycle will address such opportunities directly by implementation of the ‘circular economy’ across the agri-food sector. The authors will present (a) a summary of the AgroCycle project and (b) the role played by Harper Adams in the project in evaluating the potential for small-scale anaerobic digestion (AD) technology that can be applied on farm to provide local heat, energy and nutrient recovery from mixed agricultural wastes.

Average bird weight is the primary measure of crop yield and is the basis for calculating payment for the grower by the wholesaler. Furthermore the profit per bird is very small. Thus very tight control of growing process that is essential to ensure average bird weight is maximised. The important factors (air temperature, air humidity, carbon dioxide concentration and ammonia concentration) that affect the intake of feed and water must be kept at their optimum during the progress of the growing cycle. These factors can be influenced by activating burners and opening the vents on walls of the growing house. It then follows that the burning and venting strategy will be influential on the average bird weight of the crop. Currently the burning and venting strategy is based on notional ideal levels and data from wall mounted sensors. This suffers from two fundamental problems: firstly the strategy is determined by ideals that may not be suitable for all growing houses and secondly the data are not measured from the chickens own airspace. Thus the management strategy is based on a model that may not reflect reality and on data that may not reflect reality The "BOSCA" project addresses these problems by placing wireless environmental sensors into the chickens own airspace. This provides for direct measurement of the air experienced by the chickens and reports the recorded data in near real-time to a cloud based data management system. The sensor data are merged with the data from the growing house weighing scales in the cloud repository so a predictive model of average bird weight from the measured environmental data can be calibrated and validated. Furthermore, a time shift can be applied to the environmental data during model calibration and validation so the average bird weight can be forward predicted by 72 h(R2up to 0.89 with neural networks). This gives the grower advance notice of a deviation from ideal feeding and watering conditions and the likely consequences of failing to take remedial action such as turning on the burners or venting the house.

A milled peat production system, known as precision peat production (PPP), is described that incorporates precision agriculture technology. Milled peat production involves the harvesting of a dry layer of peat, in crumb form, from the surface of the peat field. This crumb (milled) layer is very shallow (ca. 10-20 mm) and is produced by scarifying (milling) the bog surface with a specially designed milling machine. The crop so produced takes 3 to 4 d to dry, and is harrowed (inverted) two to three times during this period. Typically, 12 such harvests are taken per year, but this is dependent upon peat type and weather conditions. The technologies incorporated in PPP include, inter alia, GPS, GIS, numerical weather prediction (NWP) model, peat type maps (analogous to yield maps), load sensors and an integrated decision support system (DSS). The output of the DSS effects control over machinery operations, including the depth of operation of the miller and the operating frequency of the harvesters. It is estimated that the implementation of PPP would increase total peat production by at least 7%. In addition, it enables more uniform moisture content to be achieved in the end product thereby improving the profitability of the industry and minimizing its environmental impact.

Increases in fuel and feed prices are placing a significant burden on the poultry industry in Ireland and worldwide. For producers to meet their financial targets, increased performance and output is a key issue, now more than ever. To optimise performance in broiler production houses, the effect of environmental and air quality parameters on bird performance and energy consumption must be known to allow farmers make informed management decisions. This paper concentrates on the application precision livestock farming sensors to develop recommendations for improved bird performance and energy consumption in broiler production farms in Ireland. Air temperature, relative humidity, light, air speed and air quality (in particular CO2 and NH3 concentrations) are identified as important parameters for improving bird performance and energy consumption in broiler production houses. Several of these parameters (temperature, relative humidity, CO2 and NH3) were monitored on two farms during the study over the initial 2 weeks of the production cycle. Air quality was often overlooked during the production process, as farmers struggled to limit high heating and feed costs. However, elevated levels of CO2 (>3000 ppm) did not appear to affect broiler growth rates. Additionally, a strong correlation was observed between relative humidity and NH3 (R 2 = 0.86 0.92). Producers tend to use relative humidity as an indication for NH3 levels and the research shown in this study confirms the close relationship between the two parameters. It is recommended that further data should be gathered from producing units and novel performance technologies should also be investigated.

Increases in production input costs are driving innovation in the poultry industry in Ireland and worldwide. Integration of so called ‘Precision Livestock Farming’ techniques into the poultry industry supply chain will help producers to optimize management systems. This manuscript provides an overview of monitoring and performance sensor technologies within poultry production. It outlines traditional sensing methods and looks at the potential of novel performance related systems that could be incorporated into production facilities. Critical environmental parameters which are relevant to poultry production include inter alia air temperature, relative humidity, light, air speed and air quality (in particular CO2 and NH3 concentrations). Current industry practice with regard to the measurement of these parameters in addition of the effect of these parameters on bird welfare is reviewed, and improvements underpinned by novel technologies and processes are also investigated. Finally, the integration of such systems is also discussed.

The purpose of this study was to evaluate differential global positioning system (DGPS) positional accuracy on Irish forest roads with typical peripheral canopies. The peripheral canopy obstruction at 20 forest road sites in Roundwood State Forest, was determined using a hand-held clinometer and magnetic compass. This simple field technique permitted quantification of the canopy obstruction using graphical means and resulted in a graphical skyplot of each site. The equipment, one Trimble ProXRS DGPS unit and two Trimble 4000SSi units permitted determination of the DGPS accuracy (average of 2.9 m) and precision (average of 2.1 m) with a range of peripheral canopies. DGPS performance was quantified in terms of the average absolute error in positional dilution of precision (PDOP) (DPDOP = 1.6). The relationship between DPDOP and percentage of open sky was found to be statistically significant (r = 0.706, r = 0.001). Statistical analysis also indicated a strong relationship between relative precision and DPDOP (r = 0.796, r = 0.000). Satellite constellation in the measurement period was not the sole factor affecting DGPS useability. Three distinct classes of peripheral obstruction at road sites were defined (Class I: 100-66 %; Class II: 65-33 %; Class III: 32-0 % obstruction) and it was found that both DGPS accuracy (3.70 m, 3.23 m, 1.91 m, respectively) and precision (4.10 m, 2.43 m, 0.83 m, respectively) improved with decreasing peripheral obstruction. These classes may be used as a means of predicting signal attenuation which might be expected under particular forest canopy conditions elsewhere.