The photograph is of Wrest Park, Silsoe, Bedfordshire. Home of the Silsoe Research Institute (SRI), my first Operational Research employer. In 2005-2006 the team “Maths and Decisions systems’ transferred to Silsoe College (Cranfield University) and then to the Cranfield Campus. My degrees are BSC Hons Agriculture, MSc Applied Environmental Science, and MSc Operational Research. My career sits in the middle of these. I transitioned from practical agriculture to practical OR by using the dissertation of my Masters to make contact with the environmental Life Cycle Assessment and Bioenergy communities in the UK and within weeks meet all the people who have employed and or managed me since. My thesis was a LCA of oilseed rape biodiesel production in the UK. As an agriculturalist I already knew my oilseed rape agronomy and farming systems so it was easy and I had time over to help a nursery deliver hanging baskets to garden Centres in the South East. It is amazing how therapeutic driving and traffic jams can be whilst wrestling with the most recent intellectual impasse in the dissertation. Win:Win I earned several £thousand that summer, got a distinction for the thesis, and made strategic contacts to facilitated a 20 year career.
How much of the countryside is undisturbed by man just as nature intended? Less than 1% of the UK is in an undisturbed climax vegetation, which for Bedfordshire is broadleaved woodland. Everything you see about you is the result of people going about their lives making a living, taking decisions, and doing something. 70% of the land scape is managed by farmers and there are over 300,000 farms in the UK
What does the landscape do for us? Provide food! Yes, that is what farmers get paid for. Is that all? No the combined value to the UK of all the ecosystems services exceeds that of food alone. Simply put it is an integral part of our life support system and well being. The natural landscape is managed and how it is managed matters to farmers and to society.
It would be reasonable to do OR to help farmers make optimal or at least informed choices about what best to do under the current business conditions. This forms a prescriptive recommendation to farmers. However, government/society are more interesting in knowing how the industry (300,000, farms) will react to change due to new policies, technologies, taxes, prices, crops, climates. We can use optimisation models such a linear programming to systematically quantify how the industry should respond and identify any unwanted side-effects
Policy interventions are often piecemeal targeting single issues using blunt instruments to persuade, coerce, or tax. Farming and the environment respond systemically. This gives rise to the law of unintended consequences. The main application of OR is to help foresee side effects as well as benefits. Taming the nitrogen cycle with its many loss pathways has been likened to chasing bubbles in carpets
The impact of crop X or machine, or policy, or prices or climates can be answered by linear programming. Introduce the change. If it is adopted then what else has changed and how profitable is it. If it is not adopted what price or performance is required for adoption. From this information we can construct a price performance surface showing scientists and engineers where they need to invest most effort to improve the crop/ technology. In this particular project, my first major piece of real OR I had to see if farmers would take up these crops at the prices and yields though possible.. The 12 crops are non-narcotic hemp, milkweed (cotton like down), nettles (see nettle lingerie), Miscanthus (elephant grass), reed canary grass, Linseed, Flax, Wheat straw, Oilseed rape straw, Willow coppice, Poplar coppice, Hollyhock, and Mallow. The short fibre sources can be used for paper and board making and the long fibre crops for yarns and textiles. It was great fun talking to engineers, agronomists and fibre processors and contextualising the significance of the data and work. Many of these crops were grown in preindustrial times, but agronomists couldn’t get many of them to reliably establish. Nettle grow when you don’t want them to but can you get them to grow when and where you do? The work showed that there was some chance of profitability in niches, but not as a widescale commodity because in all of the commodity markets there were already established and proven natural fibre sources. At this point the class chose to hear about the Environmental LCA so the SfarmMod section was skipped and placed last in these slides.
My second major method. Life Cycle Assessment. In order to systematically account for the environment of any production system or process we adopt a systems theory perspective and aim to have balanced mass and energy flows. The production system is encapsulated from craddle to grave. Resources traced right back to oil fields or quarries. These rules ensure that environmental pollution is not over looked, exported or swapped for something else.
The production system is encapsulated from cradle to grave. Resources traced right back to oil fields or quarries. These rules ensure that environmental pollution is not over looked, exported or swapped from one form to another.
Agriculture’s shop floor is also the natural environment so there are many area where interactions occur and burdens may arise. This shows some common ones.
The handling of livestock manures is an area of much concern to ensure that as greater proportion of the nutrients are recycled rather than lost to cause burdens. Which systems are likely to have the highest risk of odour nuisance as well as ammonia volatilisation? However the injectors cost money and need more power.
Many livestock farms have grown up in and around villages and their streams. Many are outgrowing this situations and have very out dated facilities. More modern facilities are covered tanks and lagoons
System A is a covered store and System B is a slurry injector. The covered store restricts gaseous losses to an extent and rainwater additions. The slurry injector requires more power and reduces ammonia losses. Both systems retain the ammoniacal N in the slurry, which is subsequently lost as nitrates due to poor crop-soil utilisation.
One Green technology concept is the use of centralised anaerobic digestion. It does mean additional vehicle movements on country lanes, but it enables farm and food wastes to be processed for bioenergy with the digestate being recycled to land. The centralised plant can more readily find outlets for heat and electricity 24/7 and avoid methane flaring or fugitive loses
Overall the system was a clear winner except for the management of the N in the digestate. The more it is constrained by low emission applicators the more care is needed to capture it in the crop-soil system by applying within the growing season and storing when crops not growing
Endoslug aimed to reduce and improve the processing of sewage sludges back to farm land by creating a high values fertiliser product
The overall impact of the end-o-sludg technologies is to reduce burdens. However, there is one exception. On the largest plats the sludge digestate is dewatered and pelleted to produce a fertilise product, but most of the available fertiliser nitrogen is lost by dewatering and drying so it is replaced by urea. The drying and the Urea create unwanted additional ammonia burdens.
Within a generation we have seen IT and quantitative approaches appear in most professions. OR focusses on decisions rather than the mechanistic replication of phenomena.
As agricultural Operational Researchers we typically reside within public sector research establishments and universities. Few farmers directly employ OR staff! However, there are related opportunities in the larger consultancies, government, NGOs, and supply chain companies. Not only does the OR community provide insights and evidence into decision it also provides trained people with some experience of our methods. Commonly, because of the higher costs of academia, we tend to get a lot of the jobs that don’t have obvious textbook solutions. Entry can be graduate, postgraduate, doctorate with an interdisciplinary team formed of mathematical, agricultural, and environmental science influences. This inter-set is for many people a stepping stone, but for a few it is a career. The number of teams is geographically sparse resulting in opportunities to travel or requirements to move. The next best job could well be in Europe. Within academia tenure is hard to obtain and the money just OK or just not (£20-50,000). The academic pressure to produce papers can conflict with the needs of the practitioner serving clients and securing repeat business. In extremis client funding can go into producing the academics next paper rather than solving the decision maker’s problem. However, academia is a fantastic setting for Continued Professional Developed with ready access to training courses, libraries, conferences, and public lectures on the latest ideas and results. Good personality traits are rigour, interdisciplinarity, creativity, autonomy, and good communications skills. An academic is trying to become a world leader in his field. It can get lonely at the top! The downside is long periods of self working with sparse contact with colleagues in a largely dispassionate setting –that can feel lonely and dry at times. Note the value of the dissertations as work experience (see first slide)!
I mentioned these to one or two more interested students and staff.
There was not enough time to do the LP examples…here for reference onlyThis is SfarmMod Linear programme. It has evolved of 40 years starting from crops and cultivation to including livestock and now environmental interactions and burdens. The basic concept is to maximize the profit of Crop X, Activity I, Machine J, in time period L for all X,I,J,K subject to constraints and penalties
Farms are very site specific. This shows the impact of soil type and weather on the ability to work the soil and schedule activities which in turn impacts the costs of work and the crops that are profitable. Light dry sandy soils offer the most flexibility, but are drought prone in summer and yield less. Conversely heavy wet soils are least flexible but can yield more. There is a trade off between increasing the size of the machinery fleet and accepting penalties for late planting and harvesting. The optimum is a compromise.
The case of crop X –what does it take to get it grown and what else changes due to operational conflicts such as harvest and planting
The law of hidden consequences revealed. If policy makers target nitrate leaching (blue baby syndrome) via nitrate fertilizer, which is something they can measure and regulate they don’t have much impact and do cost farmers profit. If farmers optimize for both high profit and reduced nitrate leaching they adopt a very different solution and achieve better results for themselves and the environment.
SfarmMod is now embedded within a suit of models allowing us to calculate future landuse under climate change across EU27. To ensure fast runtime we have meta-modelled by linear regression the Solution space of SfarmMod as a function of input variable
This project is delivered by a web interface that anyone can use for free. A vast array of inputs can be changed to enable the user to explore what if?
Oops where have all the reindeer gone? We are still OK for xmas Trees. The key vulnerable system is upland grazing where the soils are thin and drought prone and cannot be ploughed and reseeded with new varieties. By their nature these systems always have been opportunistic semi hunter gather ones
An agriculturalist's operational research career perspective
An agriculturalist’s Operational
Daniel Sandars; BSc(Hons.), 2xMSc, AFORS, MIAgrM:CEnv
Linear Programme SFarmMod
The case of 12 novel fibre crops
The LCA Concept
e.g. 1 kg
Pig carcass or
1 kg bread
e.g. ammonia, carbon
e.g. minerals, fossil energy, land
Cradle to grave is fullest
application of LCA
Consumption / use
Each step has is own set
of inputs and outputs,
like the production stage
Best environmental options?
Controlling the N
Fig. 6.6 LCA predictions of the overall effects abating ammonia losses from land spreading of
digestate on Global Warming, Acidification and Eutrophication, each normalised with respect to
current Western European environmental emissions inventory (Anon, 2005)
Where does Agric. OR fit in?
EA 300502/ ./17
Complex system and
decision system models
• Plà, L.M., Sandars, D.L., Higgins, A.J. A perspective on operational
research prospects for agriculture (2014) Journal of the Operational
Research Society, 65 (7), pp. 1078-1089. http://www.palgrave-
• Audsley, E., Sandars, D.L. A review of the practice and achievements
from 50 years of applying OR to agricultural systems in Britain OR
Insight (2009) 22, 2–18. doi:10.1057/ori.2008.1 http://www.palgrave-
• Linear programming
– Dantzig, George B. (1990). "The Diet Problem". Interfaces 20 (4):
43–7. doi:10.1287/inte.20.4.43 http://resources.mpi-
Soils and Weather
Crop and livestock
Silsoe Whole Farm
Linear programme, important
features timeliness penalties,
workability per task,
Heavy clay, 800 mm annual rainfall
Sandy loam, 500 mm annual rainfall
Workable hours v.
Period, fortnights Period, fortnights
Low gross margin crop
(Sown spring, harvested
£370/ha versus £600-750/ha
Nitrate leaching scenarios on an arable sandy loam farm: crop
areas; profit; N leaching and N use
Profit = £456/ha
N leach = 56.4 kg/ha
N use = 123.7 kg/ha
• N restricting policy increases Nitrate leaching - more spring crops increasing over-
• To decrease N leaching, grow crops which use the N applied efficiently
Base N < 100kg/ha Opt Profit + N leach
No Oilseed rape
No legumes. No
Climate & socio-economic scenarios