Photo by Valerie O’Sullivan
Dairy farms in Ireland are currently challenged with finding implementable solutions to improve sustainability. The Farm Ambassador Programme, an initiative located in the Dingle Peninsula, aims to investigate the feasibility, application and impact of digital technology in the farming business.
Dairy farms in Ireland are currently challenged with finding implementable solutions to improve sustainability, in terms of environmental issues, labour demand and availability, herd management and welfare, while also improving profitability. Digital technology presents a strategy with potential to address these challenges. The Farm Ambassador Programme located in the Dingle Peninsula that involves the Corca Dhuibhne Creativity and Innovation Hub, Teagasc, Net Feasa, Kerry Agribusiness, and the local farming community.
This programme aims to investigate the feasibility, application and impact of digital technology in the farming business. The overall vision is to integrate smart technologies into pasture based farming systems, be they dairy, beef or sheep production with the objective of improving farm management, and ultimately have a positive impact on environment, profitability and lifestyle for the farmer. This is based on the premise that better and more precise management decisions can be made with the availability of ‘real time’ data from these smart technologies. But this data has to be captured, managed and used appropriately.
L-R: Deirdre de Bhailís, Corca Dhuibhne Creativity and Innovation Hub, Denis Galvin Lispole, Ronán Ó Siochrú Burnham, Micheál Céilleachair, Ventry
Six dairy farmers signed up to this Farm Ambassador Pilot Programme within the peninsular catchment area of Dingle, and a network of sensor technologies was put in place on the farms. This allowed ‘real-time’ automated monitoring of various aspects of the farm, e.g. grass and milk production, weather and soil. The sensors were installed using LoRaWAN communication technology. The sensors were chosen based on the importance of their respective and combined data for decision-making on farms, and cost effectiveness.
Data captured included milk volume data collected from Jan to Dec 2020 (Figure 1), i.e. bulk tank readings on an hourly basis together with tanker collection times. The milk volume readings can be validated on e.g. 3 occasions/week by matching the bulk tank readings with the collection tanker reading (Figure 2). Thus, morning, evening, daily and weekly milk production is available for the farm. Farmers were asked to do a farm walk weekly, record grass cover and upload this data to the ‘PastureBase’ decision support tool. A weather station which recorded rainfall and air temperature were deployed on each farm, together with sensors to determine soil moisture and to record slurry volume in the farm storage tank. Finally, a nutrient management plan was developed for each farm based on soil analysis.
Figure 2. Example of agreement between milk volume calculated for bulk tank and collection tanker
In order to add value to the data and increase its utility, the captured data from each sensor should be examined individually and also in association with linked data. The individual farm milk production level may be examined on a weekly, daily or morning/ evening basis at a specific time point or on a continuous basis over a defined timeframe. It can also be used to benchmark the farm against other farm groups of similar size and management criteria or comparison with appropriate targets. The milk volume data may also be used in conjunction with the measures obtained for the grass parameters, e.g. herbage mass (HM) and grass growth rate (Figure 3).
Figure 3. Grassland management parameters
The interaction of these data can be used to optimise and ensure more precise decision-making regarding grass allocation (Figure 4). Furthermore, the grass data may be considered in conjunction with the weather data.
Figure 4. A ‘wedge’ developed within the PastureBase decision support tool
This milk sensor represents an inexpensive milk meter, whose output data may be associated with paddock data and with weather data, with a view to observing association between these parameters and using them to predict what options should be taken in terms of management, e.g. allocation of grass. Herbage mass/ha is a valuable parameter that may be used to advise on recommended time for fertilizer/ slurry application and to match grass growth rates to weather. Soil moisture can also be related to weather. Slurry tank volume data can indicate slurry production rate and days of storage remaining in the tank. This data, together with weather data and soil moisture may be used to advise on application/ spreading dates. Finally, the NMP developed for each farm details the fertility index of the soil and indicates the varying requirements for lime, N, P, K in the different paddocks, which in turn, facilitates nutrient management planning in terms of targeted application rates, thus minimizing wastage and potential environmental issues.
So this project is basically about getting maximum value from real time data on relevant parameters, captured using appropriate measurement systems, and modelled to generate decision support systems to ensure improved decision-making and precision management.
A further aim of this work is that the smart farming technology would provide evidence-based attributes for the farming system, such as traceability, sustainability and low environmental impact, and that, in turn, would support a high market value for the farmers produce and facilitate a brand for farm products from the Dingle Peninsula.
Lessons learned from the work include: (a) the technologies must be relevant, appropriate and cost effective, and operate as required; (b) data management is a significant part of the work incorporating building of databases, storage, data flows, automated checking and appropriate data combining for analysis; and (c) a necessity for support and training of farmers in the interpretation and use of the decision support tool output.
This work is continuing with respect to data analysis and obtaining maximum benefit from the data captured. Plans are now in place for progressing this work further and scaling up to 30 additional farms. This is made possible through partnership in a recently funded EU project (PLOUTOS), which IFA are associated with as well. Decision making criteria will be developed through a co-creation process with farmers, technology providers and data analysts. These will be embedded in an online dashboard tool giving the farmer real-time access to information to support decision making. A further key aspect of this phase will involve looking to commercial models – exactly how can the solution be packaged, delivered and implemented at scale. Net Feasa will work with all actors to ensure that this will be done taking ease of use, cost effectiveness and sustainability into consideration.
This article was written by Bernadette O’Brien1 and Deirdre de Bhailís2
1Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark and 2Corca Dhuibhne Creativity and Innovation Hub, Dingle