Covid 19 Crisis Short part 3 – Short of Milk, Short of Cash

PhD Candidate: Théo Lenormand (Countryside and Community Research Institute)

This analysis is based on my master’s degree thesis from 2019 which was funded by the Carasso Foundation as part of a project aimed at understanding the impact of the Common Agricultural Policy (CAP) on farms in Europe. It was supervised by Sophie Devienne from AgroParisTech (Fr) and Prof. Janet Dwyer from the CCRI (University of Gloucestershire).

The impact of Covid19 on the consumption of dairy in the UK

In March 2020, no one would have predicted that our relatively secure milk production (the UK is 75% self sufficient in dairy products) supply chain would be reeling. As the food service industry closed, a sharp reduction in demand meant that nearly 8 million litres of milk [1] is not consumed every week (average weekly UK milk split between retail and foodservice). It was difficult to redistribute this excess milk to retailers or local dairies, despite the fact that they were experiencing a surge in demand from members of the public. This was due to the structure of the milk processing sector and the supply chain (most notably, but not only, hyper-specialization of milk processing sector and retailing channels).

What types of individual dairy farms were impacted by the Covid19 dairy crisis?

To answer this question, I built on findings from a French method of Agrarian Diagnosis.  This is an holistic all-compassing concept, capable of making sense of farming evolution and structure at a regional scale. This approach enables a farm-focused approach which works at the scale at which the farm operates – the landscape – but still takes into account the other impactful factors. (Lenormand, T. (2019), Cochet H. (2011), Cochet H., Devienne S., (2006))

In 2019, I completed a study in South Pembrokeshire, which is a lowland dairy powerhouse at the south-western tip of Wales. The farming industry here is diversified with a range of different methods of agricultural production. This research involved modelling 25 farms, based on the results of  work with 90 farmers on their farm’s history and farming systems.  This included 14 dairy farms which were grouped into 4 categories, and other farms including sheep, beef, and potato farming. This study was based on a qualitative analysis and a qualitative construction of modelled farms. This approach produces a detailed picture of how farms work and operate, a bit like wearing farmer’s goggles. Therefore, this is a great way to understand which farm types are adversely impacted by Covid19, in the extremely specialized dairy farming sector.

To carry out this research, I selected 5 archetypical dairy farms which ranged in size, calving patterns, and so forth as described in table 1. However, as each farm sold their milk through different contracts within? the milk processing industry, the price at which the milk was sold varied. Each farm has a different milk price because of a variety of factors, including the quality of the milk (bacterial quality, protein and butterfat contents, dependant on contract terms), the milk production pattern (a peak in
production in spring/autumn or a levelled production all year round) and the amount of milk produced.

Table 1: Table showing dairy farms selected for comparison: extracted from Lenormond (2019)

Investigating the impact on farms: different scenarios for different farms and outlets

To analyse the effect of Covid19 market upheaval on farms, a crisis scenario was constructed to look at possible price evolution and the trends of milk delivery according to the latest information available.  

Figure 1: UK milk deliveries as of the 22/05/2020 from the AHDB website, the 2020 milk production is down by approximately 5%. [2]

Dairy processors incentivized farmers to cut milk production at least by 5% (or surplus  milk was not collected). Milk production was also down by around 3-6% compared to last year [2]

Does this indicate a short-term reduction in milk production?

Unlike dairy markets, farming systems operate over long periods of time. For example, crops to feed cows are planned months in advance and money to plough them in is spent way before the harvest.  Similarly a cow can’t turn its milk production on or off to meet demand. The  inertia in the cow’s physiological milk production cycle means lactation continues whether or not the milk can be sold.

From the data and articles tracking Covid19 we listed impacts on dairy farming in 2020:

  • Farmer are to witness drops from 1 to 2 pence per litre (NFU/AHDB) from April onwards. This would cause a 3 to 6 % reduction in milk prices [3].
  • Delayed payments of up to 1 and a half months.
  • Reduction in the prices of key inputs to the farming system, most notably those matched to oil prices (whose price collapsed as a result of the Covid19 impact).

To investigate future scenarios, this study applied a 5% reduction in milk prices, a 5% reduction in volume, and a 5% reduction in prices on inputs such as petroleum-based feed, fertilizer, and fuel.

Impact on farms: which farm systems take the brunt of the Covid19 crisis

Note: Common Agricultural Policy payments were until this year based in euros (before conversion), thus the choice to keep €2018 as the currency. Thus, the economic model was constructed in € when the original study was realized.

By Raw Product we mean the value of the output of the farm. By Intermediate Consumption we mean the value of all intermediate inputs used to produce the output on a regular basis (i.e. bought-in feed or fertilizer, but not buildings). For more information on how  we determine  the different economic values you can go to the extended post [5].

By looking at the agricultural revenue (before tax), we can compare different farms regardless of their business structure, from the point of view of a family operated farm. The agricultural revenue is therefore examined at an individual level, attributed to each family farm worker.

Delayed payment vs monthly spending on intermediate consumption, a cashflow squeeze

Figure 2: Modelization of the impact of Covid19 on farm potential cashflow – from Lenormand, T. (2019)

For this study, we compared the monthly intermediate consumption cost (knowing that there are some months when  farmers are strained because there are a lot of bills to pay) to the raw product of the farm, before and during the crisis.

Intermediate consumption represents at least 2/3 of every farm’s monthly raw product. Figure 2 shows how difficult it can be if farmers cannot get access to an overdraft or government loans  for the dairy. Due to the crisis, farms can lose anywhere between 5 and 20 K€ every month in terms of raw product value.

Crisis impact on farmers’ revenue  – A dependence on subsidies and lending

Figure 3: Results of archetypes modelization – Lenormand, T. (2019)

As seen in Figure 3 above, the biggest archetype (3c) is now operating at a loss.

Those with the lowest milk prices, the spring calvers (1) and the small farm (4), are particularly affected because their differentiated price is already low and spring is when they are producing most of their milk. The spring calvers have a low input and low-cost capital structure, which gives it a little leeway, but proves the system to be still sound enough to survive (thanks to subsidies). The smallest archetype (4a) at 90 dairy cows is fully dependent on subsidies (93%) and is not remunerating its farmers.

Those who fare the best are those with the highest milk price in general, and an average size of around 200 DC (2 and 5), though they still depend more on subsidies than in normal times.

The greatest danger for farms is a cashflow squeeze, not being able to increase their overdraft. It is likely that there will be indebted farms which can’t redeem themselves with their  capital structure. To their benefit, the Governments of England and Wales, for example, have pledged an extra £10,000 dairy business support in direct grants to help.

Bigger farms like 1a or 3c will tend to have easier access to an overdraft. Banks have already lent them so much that they must support them. Family operated and owned farms that are average-to-small-sized (80-200 dairy cows) are the ones most at risk. Notably, this includes those who have just invested in their farms to increase production.

One flaw of the approach taken is that the farm adaptation to the market scenario was rather simplistic. It is probable that farmers would have gone further in reducing their costs to try to reduce their losses. For example, archetype 3c is relying heavily on bought-in feed.  Another problem of this simulation is that we have taken the assumption that all prices fell from 5%. Every dairy gives a different price because it has a different product mix.

In times of crisis, we see the true worth of Agricultural Subsidies (for these particular farms direct subsidies only, they are not part of any agri-environmental scheme – The First Pillar of the Common Agricultural Policy, acreage payment to farmer in return for farming the land and complying with some criterias) because every archetype is dependent on it for its revenue (at least 20% and up to 100%. For some, subsidies compensate losses before providing a revenue). They act as a safety net during these difficult times.

Is it really that bad?

A 5% reduction in price and in volume is relatively tame compared to what dairy farmers suffered in 2015/2016 pictured in figure 4 [4]. Prices were down more than 20% compared to the 5-year average, roughly amounting to a 15% drop over the year. But 5% is already enough to put strain on farmers and the very fragile supply chains.

Figure 4: EU Weighted milk price evolution from 2015 to 2019 in nominal currency [4]

To summarise: We have seen that getting paid a premium for the milk produced is a way to stay profitable in difficult market conditions. Alternative transformation channels, for example local milk delivery/transformation (Calon Wen, Rachel’s Dairy) or organic producers that are protected niche markets, are others available solutions for farms to hike up their milk price. Farms can also diversity their outputs to increase system resilience. Another possibility is too cut cost by being more autonomous. But what will be the implications of this for the amount of time spent farming? Furthermore, during a transition period farms would be in a very fragile situation.

Thanks to Caitlin Hafferty, PhD researcher in environmental planning at the CCRI, for helping me to proof read this article. @CaitlinHafferty on Twitter.

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