OBJECTIVES

Improving on-farm grass growth and utilisation is imperative to building a resilient and sustainable dairy production sector in Northern Ireland. Building on a significant body existing work, this project will seek to examine the variability in grass growth potential across Northern Ireland and provide regional grass growth forecast to support grassland management on farm. Continual sward assessment and grass growth monitoring are also key aspects of improving grass utilisation. This project will examine the potential of novel, labour saving precision technologies such as reflectance imaging and laser sensing to assess both grass yield and quality. These technologies will be employed in swards under both grazing and conservation management. In addition, this project will investigate the potential use of these novel technologies on commercial farms, identifying potential barriers to uptake and appropriate use of data. Over the three year lifetime of this study, the project will also construct and test decision support tools to assist farmers in managing dairy cow grazing throughout the season.

Key Objectives

  1. Provide current and forecast information on grass growing conditions, growth rates and grass quality throughout the grazing season for regions across Northern Ireland.
  2. Evaluate the ability of precision technologies to reliably measure grass yield and quality in both grazing and silage systems.
  3. Investigate the application of precision grass technologies on a range of commercial farms in Northern Ireland and better understand the support needs of farmers adopting precision technologies.
  4. Development of decision support systems to aid grass management practices on commercial farms through the delivery of local grass growth forecasts and quality data and ‘potential and actual’ milk from grass calculators.

Lead Partner: AgriSearch

Industry Partner: AFBI

Lead Scientist: Debbie McConnell

Co-Funding: DAERA Research Challenge Fund

Start Date: January 2018

End Date: December 2020

 

BACKGROUND

Knowledge Gaps

The specific knowledge gaps identified and to be addressed in this proposal are:

  • Understanding of the variability in grass growth potential across N.I. and the potential for region specific forecasting of grass growth
  • The ability of a range of precision technologies to accurately and reliably measure yield and quality of perennial ryegrass managed under grazing and silage regimes
  • The impact of climatic (cloud cover, solar radiation), geographical (aspect, slope) and sward (species composition, ploidy, nutrient application rate, defoliation regime) factors on the ability of precision technologies to measure grass growth and quality, and in particular conditions reflective of N.I.
  • The challenges and benefits in the adoption of grass-based precision technologies on commercial farms
  • Understanding of the support mechanisms required to assist farmers in the uptake of novel technologies and data management tasks.

 

Need

Grassland agriculture underpins the ruminant livestock sectors in Northern Ireland (N.I.) and the potential for high levels of grass production and utilisation remains a key competitive advantage over other dairy production regions across the globe. In addition, a strong focus on grass based production systems within the N.I. dairy sector provides the opportunity to increase the economic and environmental sustainability of N.I. food production through a reduction in the amount of imported feedstuffs.

This project, by providing core research data on grass growth and utilisation potential across N.I., and through investigating new mechanism to measure and record grass growth and quality, will assist farmers in improving grassland management efficiency. This will assist farmers in improving resource use efficiency and consequently improve the competitiveness and sustainability of dairy production systems. In turn, the monitoring of grass growth and utilisation on commercial farms, assists in the development of an evidence-base to support the claim of being an economic and environmentally sustainable food region.

At the core of the project, evaluation of a range of new technologies for measuring grass yield and quality on both research and commercial farms will provide a robust understanding of the potential value of these technologies to N.I. livestock farmers. The use of both laser devices and reflectance maps to provide grass yield and quality data will assist farmers in improving grass growth and utilisation and in turn impact on farm profitability.

Within this study, the use of precision technologies on commercial farms on a regular basis will provide vital information on the potential challenges facing farmers and industry in adopting new technologies. This will assist in our understanding of the support mechanisms required to assist farmers in adopting technology and associated data management practices, and maximising return on investment.

 The evaluation of different technologies to assess within and between field variation in herbage mass and quality provides an excellent foundation for the use variable rate nutrient application strategies in grassland, both in silage and grazing production. At present variable rate nutrient application maps are primarily generated on soil nutrient content alone. Hence, the addition of above ground yield maps (produced via the grass measurement technologies outlined in this proposal) to the development of variable rate maps would provide opportunities to further target application of nutrients within fields, to meet crop demand. This in turn would support a reduction in the total amount of nitrogen, phosphorus and potassium applied to crops, potentially reducing phosphorus surpluses and associated negative impacts on water quality.

  

Additional linkages

This research projects builds on an existing research programme ‘GrassCheck’ previously funded by DAERA and AgriSearch, which has provided long term seasonal grass growth and quality monitoring. At the same time as assisting farmers in making core grass management decisions, this long term dataset has also proved highly valuable from a policy perspective, providing scientific evidence for the £5.4 million Weather Aid payments in 2002, and the £1 million Forage Aid in 2013.  In 2017, this project has recruited an extensive network of farmer co-researchers to assist with grass growth and quality monitoring across the project. The research proposal enclosed here aims to build on this existing wealth of data to provide an in-depth understanding of regional variation in grass growth, and create and test farmer focused decision support tools to provide individual farm benchmarking of milk production from grazed grass. Collaborative discussions are currently underway with both AgriNet and PastureBase Ireland, examining how grass support software can be further advanced to assist in the management of cows at pasture.

This project also builds heavily on AFBI’s new Precision Grassland Platform initiative, funded through the Centre for Innovation and Excellence in Livestock (CIEL). Funding supplied through CIEL has enabled the purchase of precision technologies for monitoring grass growth, grass quality and detailed weather parameters both at the AFBI research platform and on-farm. It is anticipated these technologies will be deployed in this proposed project, reducing additional equipment costs in this study.

 

Existing Research

Well-managed forages remain among the most cost-effective feedstuffs for UK dairy cows (Kingshay, 2015). With increased global demand for, and fluctuations in both the availability and cost of imported feedstuffs, efficient forage utilisation is and will continue to be a key driver of profitability on UK dairy farms. In N.I. grazed or ensiled grass remains the dominant forage source, occupying an estimated 93% of the total farmed area. However, the current performance of managed grasslands in N.I. remain sub-optimal with an estimated 7.5 tonne of grass dry matter utilised per hectare on dairy farms, significantly behind levels achievable by modern day grass varieties (>12 t DM/ha; Mayne & Bailey, 2016).

Most recently the Sustainable Agricultural Land Management Strategy for Northern Ireland (2016) has called for an increase in the uptake of ‘sward assessment and grass utilisation measurement and recording’ on grassland farms as one mechanism by which improvements in grass utilisation can be achieved.

 

Grass growth monitoring and forecasting

Within the UK, centralised data on grass growth and quality remains extremely limited, with few data sources and limited research has been conducted to understand the variable in grassland productivity across regions. This continues to be a significant concern given the important of grazed and ensiled grass in U.K. livestock production systems. During the last 19 years GrassCheck has provided livestock farmers in Northern Ireland with grass growth and quality information on a weekly basis during the main grazing season.  The addition of the growth prediction element in 2004 was developed by AFBI (Barrett &Laidlaw, 2005), providing farmers with an estimate of grass growth rates for up to two weeks ahead. Additional sub-models (e.g. CloverCheck; Laidlaw et al. 2007) have also been developed.

Whilst the previous GrassCheck projects provide an excellent evidence base of seasonal differences in grass growth and quality, significant variability in soil types and climatic conditions exist across Northern Ireland, potentially impacting heavily on actual and potential grass dry matter production (Cruikshank, 1997). Consequently further information is required on the extent of regional variation in grass growth conditions.

Rising plate meter, cut and weigh methodology, electric capacitance probes and sward sticks have all been employed for assessing grass yield on commercial farms. However the use of these tools is often thought to be constrained by lower prediction accuracy associated with calibration errors and slow sampling speeds (Quinn, 2016).

 

Grass measurement

Advances in multispectral and hyperspectral imaging to sense across various wavelengths (visible, near infrared and thermal), has huge potential for providing information on biomass, leaf area index, disease and water stress (Higgins et al. 2017). These images and grass measurement techniques could be used to inform decision-making in management, yield forecasting and environmental protection (Zhang and Kovacs, 2012). To date, these imaging techniques have been successfully employed in the derivation of vegetation indices to explain canopy condition, such as chlorophyll content and stress level, and its variability in space and time. The ‘red edge inflexion point’ from sensors mounted on UAV (drone) platforms, has been found to be correlated with nitrogen status, and so could potentially be used as an indicator of soil nitrogen supply and uptake across fields (Higgins et al. 2017).

Whilst  the foundations of this technology lies within arable crop monitoring, early research highlights significant potential of reflectance and laser technologies to measure grass dry matter yield in pastures managed under a grazing defoliation regime (e.g. Pullanagari et al. 2015, Pullanagari et al. 2012, Schut and Ketelaars 2003,  Trott et al. 2010).

 

Grass quality and nutrient use

Airborne hyperspectral imaging has shown significant promise for estimating herbage quality. Pullanagari et al. (2016) has identified positive correlations between aerial reflectance and ground truth measurement for crude protein (R2 = 0.77) and metabolisable energy (R2 = 0.79) in upland areas. Further work is required to assess the potential for these technologies to assess grass quality in intensively managed grazed or silage swards.

Between 2012  & 2014 a DARDNI E&I project (12/1/04), was carried out to review nutrient use efficiency in grazed and cut grassland systems in NI and assess the potential for improving production through precision management techniques. In this review, substantial within field variability in grass yield was found to exist in silage fields in NI, of as much as 4t/ha across a single field (Higgins 2015; McCormick, 2005; Bailey, 2005). Nitrogen (N) and potassium (K) deficiencies in part or all of the fields were found to be contributing to the lower yielding areas, whilst excess nutrients exist in other areas (Higgins et al. 2011). These inefficiencies in nutrient use could be improved using precision technologies, thus providing benefits to farm economy and reducing detrimental environmental effects (McCormick, 2005; Higgins et al., 2011; Bailey et al. 2011; Bailey & Higgins, 2011). Laborious manual methods of mapping herbage nutrients are expensive and impractical on a farm scale. Precision technologies can offer a rapid, cost-effective and non-invasive alternative (Bailey et al. 2000).  

Human capital has also been identified as the single most important factor in transfer and uptake of technology (Rogers, 1995). However, the ability of farmers to adapt to, and fully utilise new technologies is a much under-researched area. Hence this project will also contribute to our understanding of the support mechanisms required to assist technology uptake on commercial farms.

 

OUTCOMES

  1. Expansion of a weekly grass growth and quality monitoring network and grass growth forecasting service. Development of regional grass growth forecasts across Northern Ireland.
  2. New information on the ability of precision technologies to measure grass growth and quality in Northern Ireland for both ensiled and grazed pastures
  3. Adaptation of technologies suited to measuring grass growth and quality in Northern Ireland  to ensure user support.
  4. Development of a decision support tool to support grassland management decisions on commercial farms.

 

BENEFITS

Novel ideas / product creation

All of the objectives described for the project are innovative and will generate new data and knowledge. Generation of grass growth and quality information, coupled with grass growth forecasts will provide up to date information for livestock farmers on grass management.  This is a service still in development in other dairying regions e.g. Republic of Ireland, giving N.I. farmers a competitive advantage.

The project will provide information on novel technologies, detailing both their ability to assess grass dry matter yield and grass quality. These technologies are currently not in use on commercial farms in Northern Ireland and hence provide an innovative approach to grass measurement and management.

Currently grass biomass on grazing paddocks is measured via either a ‘cut and weigh’ method or using a rising platemeter. Both of these techniques require a farmer to visit each individual paddock to conduct herbage yield assessments and often, manual calculation and recording of data. Use of new technologies, such as drone reflectance imaging offer the potential to significantly reduce the amount of time required to conduct herbage yield assessments and to automate the recording of data.

 

Technological and scientific benefits / advances

There are a number of potential technological and scientific advancements from this proposed study:

  • Improved understanding of both between and within field variability in grass growth and quality of fields managed under intensive grazing and silage management
  • New information on the variability of grass growth potential across N.I. livestock farms
  • New evidence on the feasibility of novel precision technologies to measure herbage mass and herbage quality, specific to Northern Ireland climatic and geographical conditions.
  • Assessment of the usability of a range of precision techniques by commercial farmers and identification of any skills gaps

 

Benefits to the primary producer

There are a number of primary production sector benefits to this project including: 

  • Increasing grass growth and utilisation. Improving both grass utilisation by 1 t DM/ha and grass quality on an average N.I. dairy farm has been estimated to be worth an additional profit of £441/ha/year (Mayne & Bailey, 2016). This project, through providing up to date grass quality and quantity information will assist farmers in making grassland management decisions and help contribute towards an increase in grassland utilisation on farm. This is of considerable financial benefit to the industry. Assuming a 10% farmer adoption rate, a conservative improvement in grass utilisation of 1.0t DM/ha is worth £4.835 million to the N.I. dairy industry each year. Over a five year period, this represents at 56:1 return on investment from this project.
  • Increasing efficiency of agricultural inputs. The proposed grass measurement technologies which will be tested as part of this project will identify variability in grass yield . Identification of enables farmers to identify poorly performing areas/fields which then can be earmarked for improvement e.g. reseeding.
  • Reducing labour requirements. The proposed grass measurement technologies which will be tested as part of this project will reduce the time required to measure grass yield across fields on livestock farms. Across a typical 40ha grazing platform, employment of these new technologies is estimated to quarter the time requirements for grass growth monitoring.
  • Growing the agricultural skills base. This project, through the use of novel technologies on commercial farms will encourage farmers to embrace new technologies whilst developing the skills to interact with similar technologies and interrogate data from these devices. Given the rapid development of a range of agricultural technologies and the emergence of ‘digital agriculture’ it is essential that this skill set is developed.   

 

Sustainable development and environmental benefits

The data collected and published through this project will enable farmers to undertake grass measurement in a timely manner and support farmers in making grassland management decisions. This will assist in improving both the quality and utilisation of grass on farm, reducing the need for purchased feedstuffs to meet animal nutritional requirements. This reduction in purchased feedstuffs will both lower greenhouse grass emissions associated with milk production, and will contribute to lower phosphorus balances on dairy farms, reducing the risk of phosphorus loss to the environment in surface runoff. Increasing grass utilisation on dairy farms by 1 t DM/ha is estimated to reduce the phosphate balance from 11.3 kg to 3.0 kg P/ha/year (Mayne and Bailey, 2016).

In addition, better information on future grass growth rates will assist farmers in balancing grass growth against demand, and should reduce the waste of forage within the dairy system.