Intra-Farm Variation


Farms across the country experience many variables that set them apart and effect their performance. The most discussed variable (particularly this harvest) is of course the weather, which dictates an agricultural operation. Others include soil quality & type and topography.  

However, even within a single farm operation, field level variables are present which are reflected in the productivity of each individual field. 

With the emergence of many SFI schemes and the continuing declination of BPS, it is increasingly important to understand your farm performance in as much granular detail as possible – including the exact value and productivity of each of your fields. This empowers your decision-making when it comes to efficient allocation of your land. 

This month, I have been exploring ‘intra-farm variation’, being the difference in field performances across a farm, analysing yield spread and Cost of Production difference at a field level. This analysis is specifically focusing on Winter Wheat, the most popular combinable crop in the UK.  

Average yield spread across a single farm site:     

This first graph represents the average differences in yield performance across single farm sites. I split the results into three sections for visibility: top and bottom 25% of fields, with the remaining 50% in the middle.  

This shows the significant average spread in yields taking place. On average – top fields are producing 3.04t higher yields than the least productive. There is an enormous 10 tonne gap between the very poorest and very highest results. The lower ends of best performing fields are yielding twice as much grain as that of the poorest.  

I’m sure you can picture a field in your operation that you believe performs better or worse than the others? But having the verified data proves or disproves that assumption and therefore helps to justify a decision on how best to take it forwards. 

Differences in Cost of Production per Hectare (COP £/ha) across fields:   

Representing Cost of Production per Hectare (COP £/ha), we see here costs are relatively flat regarding inputs. There is no major discerning factor between the inputs & costs being assigned on a per field basis.

Differences in Cost of Production per Tonne (COP £/t) split across fields: 

Regarding Cost of Production, this graph is where the story comes to life. When analysing COP £/t it is evident that producing crop in the bottom 25% of fields, based on performance, is on average costing £25.20/t more than the top performing fields. With the potential for even greater variation.  

This means if two 20ha fields are averaging 9t yields (= 180t per field) then the poorer performing field has cost an additional £4’536 to take to harvest. One of the reasons for this increase in cost is visualised in the following graph.  

If you could easily and accurately visualise your average Cost of Production at this field level, and the differences between your fields, would it affect your decision-making process? 

Applied Nitrogen split across fields by performance:   

This final graph visually displays the differences in applied Nitrogen per tonne of wheat harvested across different performing fields. The picture painted is clear – poorer performing fields are receiving on average 9.65kg more Nitrogen per tonne of Wheat harvested.  

Using the previous example of a 20ha field, this is an extra 1’737kg of Nitrogen being applied per tonne of wheat harvested to a lower performing field. With the current market prices of fertiliser, it is easy to recognise this a costly exercise.  

We believe these insights lend themselves to the need for an optimal rate chart for Nitrogen, and Nitrogen use efficiency is something we’ll be analysing in upcoming articles... If you'd like know more about optimising your field performances, Cost of Production & Gross Margin, click here.

In conclusion, the observation of significant yield variation within a single farm underscores the potential for enhancing productivity and efficiencies. Whilst the cost of production per hectare remains relatively steady, discrepancies and inefficiencies are evident in fields with lower performance when analysing costs per tonne harvested.  

This inefficiency is exemplified by the application of higher Nitrogen levels per yield tonnage, which not only increases costs but also fails to correspond to increased yield.  

Integrating YAGRO’s analytics tool with a farming operation creates an opportunity for farmers. Dissecting and visualising the field level performance of a farm provides a crucial initial step towards targeted improvements.  

This data-driven approach holds the potential for elevating subpar fields to higher standards whilst informing decisions around inputs on better performing land. In addition, regarding the many SFI options farms have today, field level data is a true indicator of land value and can be used when considering any future allocations your land.  

I believe that strategic insights such as these make YAGRO an essential tool for navigating the complexities of yield variability and operational costs, leading the way to more efficient and sustainable farming practices.  

Note: We have used normalised data to conduct this study, which is the process of creating performance indicators by analysing field level data and conducting cross comparisons with farm averages to reveal accurate insights on performance. 

“If you understand your farm from the bottom up, you can solve the problems from the top down.”  

ABOUT THE AUTHOR: Thomas Gate is an Analyst in the Data Team. With a passion for data and agriculture, Thomas grew up around farming and agronomy. With a day-to-day role of cleaning, processing and analysing complex data sets for bespoke farm projects, Thomas and the data team are exploring the endless possibilities of how data can be best used to aid and inform farmers.   Outside of work...Thomas enjoys getting outdoors through playing football, running or a bit of gardening. He also likes to expand his programming skills with a variety of small projects.