Second Agri-Food Study Group with Industry

The Second Agri-Food Study Group with Industry will be held at ICMS in Edinburgh on the 21st – 23rd February 2018.
If you wish to take part, please register here.
Clean Growth, and the AI & the Data Economy are two of the four Industry Strategy Grand Challenges underpinning much of HMGs investment into R&D over the next few years. Additionally, we can expect  funding in Transforming Food Production to be made available as an Industrial Strategy Challenge Fund, this Study Group is a key way of establishing relationships between communities, refining industry challenges and preparing the mathematical science community for this upcoming opportunity.
 
The three problems being presented are:
 
Promar International – Identifying Drivers for Profitability in Cattle
 
We have a huge amount of data throughout the dairy farm supply chain. We have used this data predominantly for benchmarking, including the impact of different farming systems and geographical areas on profitability. We have done some analysis to identify drivers for profitability using physical and financial parameters but more recently management practices and attitudinal aspects of the farmers. Based on the datasets above, we would like to explore drivers and KPI’s to predict profitability (performance is often masked by the management ability of the farmer and other factors). Another potential area for exploration is in linking genetic and financial data on an individual cow basis. 
 
Phytoponics – Aeration Optimisation
 
Phytoponics Hydrosac is a hydroponic growing system module that holds a body of water to grow plants in. At the base of the module is an integrated aerator, which consists of a perforated strip of material that receives external air input from an air compressor, and emits bubbles to the body of water such that oxygenation of the water occurs. The scope of this challenge is to develop a mathematical model of the aeration system of the Hydrosac, including volumetric flow rate, input pressures, aerator strip material design parameters and costs therein, such that Phytoponics can use this model to improve the aeration of the Hydrosac design and select supporting ancillary air supply services or system parameters.
Syngenta – Scheduling Seed Production
Syngenta are one of the largest suppliers of agricultural seed globally. A key requirement of the business is the adequate supply of seeds to meet varied customer demands throughout the world. Scheduling seed production is complex and unpredictable. Crops must be planted one year in advance of when the resultant crop of seeds will be sold. A recurring problem is that of spatiotemporal variation of yield and the management of the associated risk of over / under production of seeds, which is extremely costly, and can severely damage the business. Syngenta have developed an interface for internal planning of production, which is purely based on historical yield. Syngenta would like to rationalise planting strategies which are informed by a judicious choice of objective function, which best optimises the business performance (which could include growth, profitability) and is robust against potential risks (natural, market risks etc). Can a more sophisticated approach “beat” the experts and / or strategies based on historical data simulations?

More information on these problems can be found on the Study Group website. 

The KTN staff and members of their Industrial Mathematics group worked with the University of Bath’s Institute for Mathematical Innovation (IMI) to organise a study group. The Agri-Food Study Group brought together over 40 mathematicians, engineers and computer scientists to work on challenges presented by representatives from the Agri-Food sector over the course of three days. The Study Group which ran from 16-18 January 2017, and was hosted by the IMI and sponsored by Innovate UK.

Three agri-food challenges were presented at the event, namely helping farmers to optimise the value of the pigs they sell (Innovent Technology) improving cocoa yields for the chocolate industry (Mondelez International), and refining the design of a hydroponics system for crop production (Phytoponics Ltd). Further details about these challenges are detailed here.

These challenges required varied expertise from across the mathematical sciences, and it was fascinating to see the three agri-food company representatives working closely with the maths experts over three full days to try and solve the problems presented.

On behalf of Dr. Matt Butchers, Knowledge Transfer Manager, Industrial Mathematics 
Knowledge Transfer Network