Agri-Food Study Group with Industry
Jan 21, 2018 - Jan 23, 2018
The Agri-Food industry is making rapid advances to adopt cutting-edge technology, to help it address the challenges it faces, such as feeding a rising world population, reducing environmental impacts, and improving food quality and safety. For example, agriculture is already utilising cutting edge technologies such as satellite imaging to monitor crop growth, and robots to milk cows, and many other technologies are under development and likely to be introduced in the coming years.
As with other industries, all these technologies generate massive data sets, and have the potential to make food production systems more complex. Thus, there is an increasing need for agriculture to work with other sectors to turn ‘data into decisions’ that can help the farmer.
At the KTN Mathematics in Agriculture Workshop at Harper Adams in 2015, Professor Graeme Wake commented that “Modern-day applied mathematics can be used with high impact on farm systems and precision agriculture. It provides excellent decision-support tools and brings a degree of rigour to the industry, which has often been lacking in the past. The agricultural industry has been relatively late in choosing to bring mathematics to bear on the processes involved”.
The First Agri-Food Study Group with Industry was held in January 2017, and a write-up can be found here.
Format: Representatives from industry will present their problems on the first day. Researchers in mathematics, statistics, engineering, computer science and related areas will work together towards practical solutions, and first steps in approaching problems. Early stage career academics, Ph.D students, and postdocs are particularly welcome.
The Problems: 4 problems are proposed for the Study Group. Outline details for each problem are provided below:
1. Promar - 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.
2. Agricompas - Sugar Beet Growth Monitor and Production Optimiser
In the sugar beet industry, harvest date is negotiated between grower and factory prior to the growing season depending on a set of constraints. However actual crop growth potential per individual field is not taken into account. By knowing the actual yield per hectare and potential yield in the future we could optimise the production across multiple fields by bringing harvest dates forward or delaying them. With help of phenotyping data (e.g. crop emergence, canopy cover and biomass under and above ground) the actual crop performance can be compared with the modelled outcomes. This provides valuable knowledge on crop performance, why it deviates from the optimal, and what the projected outcomes could be with or without interventions.
Currently, crop performance models are quite simplistic (REF). Of interest is how to incorporate additional parameters (up to 30) which are known to affect yield into these crop performance models. Also of interest is how to optimise the production and harvesting schedule across multiple locations ultimately to provide real-time decision supporting the entire supply chain.
3. 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.
4. 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?
Why take part in the Study Group?
Get new problems
Expand research portfolio
Vital contact with industry
Meeting academics from different fields
Get new solutions
Access to highly qualified individuals
More information to follow
The majority of the Study Group is paid for by the sponsoring organization (Innovate UK), however to cover some costs, there will be a nominal registration fee of £45. This fee will cover accommodation and meals for the three days. We also ask that travel costs should be sought from attendees’ own institutions.