# Modeling power and workforce demand in French field agriculture highlights variability, fleet redundancy, and magnitude comparable to renewable electricity generation

Cyprien Drommi, Petros Chatzimpiros

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Cyprien Drommi, Petros Chatzimpiros. Modeling power and workforce demand in French field agriculture highlights variability, fleet redundancy, and magnitude comparable to renewable electricity generation. Energy Nexus, 2026, 21, pp.100654. 10.1016/j.nexus.2026.100654. hal-05513494

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Full Length Article

# Modeling power and workforce demand in French field agriculture highlights variability, fleet redundancy, and magnitude comparable to renewable electricity generation

Cyprien Drommi* ID, Petros Chatzimpiros*

Laboratoire Interdisciplinaire des Energies de Demain (LIED), UMR 8236, Université Paris Cité, 75013 Paris, France

## ARTICLE INFO

**Keywords:**

Agriculture

Field cropping

Energy transition

Power demand

Mechanization

Electrification

Agroecology

## ABSTRACT

Mechanized agriculture relies heavily on fossil fuels for field operations, while facing decarbonization and environmental sustainability challenges. Phasing out fossil fuels requires alternative energy supply that can provide sufficient power to perform field tasks. However, the variability of agricultural power demand remains unexplored, yet it is crucial for anticipating energy transition to renewable flows. Here, we fill this gap by modeling on-field power demand distribution over a full cropping season based on production practices from public surveys and using national-scale French agriculture as a case study. Results show high variability in power demand, ranging from below 5 to above 12 GW during autumn tillage. Compared to total installed engine power, the load factor of tractors and combine-harvesters peaks at about 18 % and 23 % respectively. This indicates substantial fleet redundancy, which is in part necessary to accommodate unpredictability in field working conditions. By assessing scenarios of agroecological practices, we show that no-till practices help reduce peak power demand, whereas organic practices result in a marginal power demand increase. In parallel, we assess workforce requirement for field driving of machinery. This requirement peaks simultaneously with power demand at one-third of total workers and accounts for 12 % of annual working time in agriculture. Our findings highlight trade-offs between farming system organization, practices and on-field power demand, revealing that the latter is currently comparable in scale to renewable (wind and solar) power generation in France. The results underscore the need to integrate power demand analysis of fossil-fueled activities into energy transition policies and planning.

## 1. Introduction

Human energy consumption, primarily from fossil fuels, has grown steadily for nearly two centuries, alongside the spread of machinery across all activities [1,2]. In the agricultural sector, the motorization of farms began in most countries in the mid-20th century and has led to the progressive displacement of farmers and draft animals by tractors and combine-harvesters. This shift also involved an energy transition from traditional biomass self-fueling to extensive use of fossil fuels [3,4]. In the context of climate change and fossil fuel supply risks, the dependence of current systems on fossil fuels is a key concern.

The issue of agricultural production's dependence on fossil fuels was first raised conceptually by Odum [5], who pointed out that agriculture is both an energy production and consumption system. Later, a seminal quantitative analysis for specific cropping systems in the USA revealed

negative energy balances of farms, i.e., energy inputs from fossil fuels exceeding biomass energy output [6]. Over the last years, the analysis of agricultural systems from an energy perspective has received increased attention and has allowed describing transition processes and the impact of production practices on energy balances and greenhouse gas emissions [3,7–9].

The reliance of current farming systems on fossil fuels is a major sustainability issue, and the sector's transition requires shifting machinery from fossil to renewable and low-carbon sources, particularly electricity [10]. Electrification, however, is challenged by the predominance of mobile, high-power machinery, which explains the current limited uptake of electric tractors [11,12]. More fundamentally, the temporal distribution of energy demand – i.e., power in Watts – becomes critical when replacing dispatchable liquid fuels with renewable power sources. Whereas fossil fuels can satisfy highly variable power

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: cyprien.drommi@etu.u-paris.fr (C. Drommi), petros.chatzimpiros@u-paris.fr (P. Chatzimpiros).</footnote>
requirements, solar and wind exhibit strong fluctuations making the alignment of supply and demand critical for reliable operations [13]. The challenge becomes increasingly acute for high-power and mobile machinery particularly during peak agricultural seasons. By aggregating power use and masking its variability over time, conventional energy balances obscure this issue that becomes central in the context of the energy transition.

For agricultural systems, the timing of power demand, in particular that of mobile machinery, remains unexplored. The only exception is intra-day consumption profiles in relation to specific field manoeuvres performed by a tractor [14,15]. The only article mentioning, without quantifying, the seasonal variation in agricultural energy demand dates back to the early 1990s [16]. This highlights that although concerns of the timeliness of agricultural tasks are not brand new, they have received very little scrutiny in the scientific literature so far. We argue that knowledge on the distribution of power demand is essential for supporting system design compatible with the transition to renewable power sources.

Here, we address this research gap by embracing a power-based approach rather than the usual energy balance approach. We focus on on-farm machinery, which typically represents about half of total energy consumption of agricultural systems. The other half is indirect (or embodied) energy use [4,17], in particular in the form of fertilizers and pesticides inputs for which power demand occurs in industry. We use France as a case study. Field work in French agriculture is practically fully mechanized on the basis of combustion engines, and the associated fuel consumption amounts to about 120 PJ yr⁻¹ [18].

The article pursues as follows. First, we model the power demand profile of field operations in current French agriculture for a full cropping season, based on public agricultural surveys. Second, we define the load factor of agricultural machinery by putting the calculated power profile in perspective with the installed engine power. Third, we explore the impact of changing practices on power demand, in particular organic and reduced tillage as recommended in agroecological scenarios [19]. Such practices are reported to reduce energy consumption and associated GHG emissions [20,21,16]. Finally, we quantify the corresponding workforce demand for machinery driving and discuss our findings

within the broader context of agricultural system constraints and the energy transition.

## 2. Methods

### 2.1. Power demand calculation

We examine the power (Watt) demand of mechanized agriculture in continental France, focusing on field operations of tractors and combine-harvesters which underlie direct energy consumption of agriculture. The analysis covers 20 major field crops (11,6 million hectares (Mha)) and permanent and temporary grasslands (12 Mha), together representing 89 % of the national agricultural area. The studied crops include wheat, maize, other cereals, oilcrops, pulses, potatoes and sugar beets. From the most recent surveys of the French Ministry of Agriculture, we extract detailed data on field tasks for these crops [22] and grasslands [23] and estimate the total motor power required over an entire cropping season, from sowing to harvest. Field tasks include tillage, sowing, fertilizers and pesticides application, and crop harvesting. For these tasks, the data reports the implemented practices and associated calendar. Vineyards, fruits and vegetables, which cover 2.7 Mha (10 % of French agricultural land), are excluded from the analysis due to unavailable data on field tasks. Out-of-field operations such as transport, hauling and on-farm handling are also excluded for the same reason. The studied crops and associated operations are detailed in Supplementary Tables S1 and S3, and the scope of the study is summarized in Fig. 1.

Power (Watt) is the rate of energy conversion and can be calculated at different stages. *Fuel power* is the combustion rate of fuel (usually diesel) over a given time. *Motor power* is the mechanical power output of the machinery's engine. The efficiency between fuel and motor power for agricultural machinery is typically 30 to 40 % [24]. Motor power is the metric used to express the power of machinery (i.e., a tractor of 200 kW refers to motor power). In this paper, we calculate the power demand of agriculture in terms of motor power to allow for straightforward comparisons with the installed engine power of machinery.

For each task (noted i) and crop (noted j) the corresponding motor power requirement ($P_{i,j}$) is estimated by combining data on the above-

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Summary of energy use in French agriculture and scope of the study. The category “Others” includes irrigation, other farm buildings, crop drying, cold chambers, on farm transformation. Orders of magnitude for comparative size of energy use categories are from Harchaoui and Chatzimpiros [4] and INSEE [18].</figcaption></figure>
mentioned field tasks with data on unitary energy consumption for different crops from the literature using Eq. (1). $P_{ij}$ (W) represents the instant consumption of fuel by the machinery fleet, considering the intra-day variability of the task performed during the workday. $P_{ij}(t)$ distributions obtained for each crop and task sum up to total power distribution of field tasks over the year ($P_{tot}(t)$) following Eq. (2). For better readability, $P_{tot}(t)$ is the envelope of power demand excluding intra-day variations.

$$P_{ij}(t) = \frac{S_j * N_{ij}}{T_{ij} * t_i} * E_i * \eta_{motor} * \alpha_p * f_{ij}(t) \quad (1)$$

$$P_{tot}(t) = \sum_{\substack{i \text{ in tasks} \\ j \text{ in crops}}} P_{ij}(t) \quad (2)$$

Where $S_j$ (ha) is the cultivated area of each crop, $N_{ij}$ the number of passes per task and crop, $T_{ij}$ (days) the time window per task (i) and crop (j), $t_i$ (hours) the effective working hours per task per day, $E_i$ (J ha⁻¹) the unitary energy consumption per hectare and pass, $\eta_{motor}$ the motor efficiency, $\alpha_p$ a corrective term of power variability within a given field task, and $f_{ij}(t)$ a time-dependent distribution function.

$S_j$ and $N_{ij}$ are reported from agricultural statistics [23,22]. For a crop j, $N_{ij}$ represents the number of passes for task i during the allocated time $T_{ij}$ (e.g. how many tillage passes are made, how many fertilizer or pesticide applications are made).

The energy intensity $E_i$ (fuel use in J ha⁻¹) is determined by averaging values from the available literature cited in Supplementary Table S1. We calculate uncertainty based on standard deviation of collected values. Fuel consumption values are converted into motor power output by considering $\eta_{motor}$ equal to 35 % [24].

$T_{ij}$ is given by the latest crop calendar publicly available from the Ministry of Agriculture [25] and complementary sources. Detailed bibliographic references for each crop are given in Supplementary Table S3. The effective working hours per day $t_i$ is assumed to vary between 6 and 12 depending on the task type. The value of $t_i$ for each task is detailed in Supplementary Table S1.

The corrective term $\alpha_p$ is added to account for power discrepancies during a field task. Indeed, mission profile studies show that during a field task, power varies between high strokes and baseline activity during U-turns and maneuvers. The disparity between the hourly averaged and the peak power strokes is about +17 % [14,15]. The term $\alpha_p$ is thus taken equal to 1.17, so that $P_{ij}(t)$ captures the peak power during a task. Note that $\alpha_p$ does not affect total energy consumption, which is an input variable to the calculation, but only affects its temporal distribution.

The expression is smoothed over the calendar time slot and within a workday following a normal distribution, given by the time-dependent distribution function $f_{ij}(t)$. In the calculation, the granularity of the distribution is a 1-hour step. To illustrate this calculation, an example is shown in supplementary material (section C), along with the exact expression of $f_{ij}(t)$. Parameters used in the study are summarized in Supplementary Table S2.

## 2.2. Installed power of machines and load factor calculation

We define the load factor for agricultural machinery ($L_{machine}$) that we calculate separately for tractors ($L_{tractor}$) and combine-harvesters ($L_{combine}$) over time. The load factor is the ratio between $P_{tot}$ (the motor power demand calculated in the previous section), and total installed engine power for a given fleet of machinery ($P_{installed}$), following Eq. (3):

$$L_{machine}(t) = \frac{P_{tot}(t)}{P_{installed}} \quad (3)$$

$P_{installed}$ for tractors is calculated from published values of the French tractor fleet [26]. These data report the number of tractors by category

of rated power and farm specialization. Three categories are reported: tractors below 80 hp, above 170 hp and in-between. We consider the following mean power values for the three categories: 60, 120 and 180 hp. $P_{installed}$ for tractors accounted for in this study sums to 64 GW, which is very consistent with a previous accounting [4].

For combine-harvesters, $P_{installed}$ is derived from Harchaoui and Chatzimpiros [4] and equaled 20 GW in 2016. We kept this same value for this study.

## 2.3. Alternative scenarios

We construct alternative scenarios to assess the effect of changing production practices, in particular agroecological practices, on power demand. We construct two scenarios: one considering organic agriculture (Scenario A) and one considering conservation practices, in particular no till (Scenario B). Note that no-till systems do not exclude the use of synthetic agrochemicals such as industrial fertilizers and pesticides, whereas in organic systems such inputs are banned and weed control relies on additional mechanical tillage [27,28].

Organic and conservation practices are gaining momentum. For instance, in 2023, 10.4 % of agricultural land in France was labelled organic (1.6 Mha for grassland and 0.74 Mha for cropland according to Agence Bio [29]). The European Commission has set the goal of reaching 25 % of organic land by 2030 [30]. Accordingly, in scenario A, we consider the expansion of current organic practices for fertilization (quantities, number of passes and burying method) as well as the replacement of herbicides spreading by mechanized weed control as described in Agreste [22] on 25 % of agricultural land (the remaining 75 % correspond to current practices). Concerning conservation agriculture, 25 % of cropland received reduced tillage in France in 2020, out of which 3 % received zero tillage [31]. Although there is no official target for further expansion of conservation practices, such practices are frequently advocated for in the literature. In scenario B, we assume a 25 % penetration of no-till and direct seeding practices cropland (the remaining 75 % correspond to current practices) matching the same percentage used in scenario A for a consistent comparison.

## 2.4. Calculation of workforce requirement for machinery driving

Many agricultural machines (tractors or combine-harvesters) are operated simultaneously to carry out field tasks within a specified time window. Because machinery is operated by humans in its entirety (no driverless machinery), the number of machines operating in parallel equals the workforce mobilized simultaneously for driving. For this calculation, we introduce $Q_i$ as the field capacity of a field task i (in ha h⁻¹), and $\Pi_{ij}$ as the simultaneous workforce requirement for machinery driving for a given task and crop (in persons). Eq. (4) gives the relation between $Q_i$ and $\Pi_{ij}$, where the other variables of Eq. (4) are the same as in Section 2.1

$$\Pi_{ij}(t) * Q_i = \frac{S_j * N_{ij}}{T_{ij} * t_i} * f_{ij}(t) \quad (4)$$

Values and literature references of the field capacity $Q_i$ per field task are given in table S1. Following the same normal distributions $f_{ij}(t)$ as described in Section 2.1, the value of $\Pi_{ij}(t)$ is distributed and summed up for all crops and operations to give the distribution of the total simultaneous workforce requirement for machinery driving ($\Pi_{tot}(t)$), as per Eq. (5).

$$\Pi_{tot}(t) = \sum_{\substack{i \text{ in tasks} \\ j \text{ in crops}}} \Pi_{ij}(t) \quad (5)$$

We introduce $W_{tot}$ (in h yr⁻¹) as the integral of $\Pi_{tot}(t)$ over a year representing the cumulative annual working time for field driving. $\Pi_{tot}(t)$ and $W_{tot}$ are compared respectively with the 533.000 workers and 457.000 Annual Working Units reported by Eurostat [32] and
corresponding to the study's agricultural sector.

# 3. Results

## 3.1. Power demand assessment

Fig. 2 shows machinery power distribution for a full cropping season (diurnal variation is not shown for readability reasons) per type of production (Fig. 2a) and field operation (Fig. 2b). Because cropping seasons last more than 12 months, operations between successive seasons overlap. In particular, harvest in year *n* overlaps with soil preparation in year *n+1*. Accordingly, in Fig. 2 total power demand corresponds to the stack graph where the power demand of previous and following seasons are shadowed in grey.

The results show significant seasonal power demand variability, mainly driven by tillage and harvesting operations, particularly for winter wheat. Power demand peaks at around 12 GW during late summer and autumn operations, contrasting with winter periods which are characterized by a much thinner and more distributed demand. The successive peaks for grasslands (May-July and September-October) correspond to their double harvest. In contrast, the harvest period is unique for major crops that are dominant in French agriculture, like winter cereals. The sowing operation between March and May concerns maize and other spring cereals. Uncertainties over the estimation of power distribution are also calculated and available in Supplementary Figure S3.

Fig. 3 shows the evolution of $L_{tractor}$ and $L_{combine}$ throughout the

cropping season. $L_{tractor}$ peaks at 18% during the tillage period in the fall and otherwise is below 7%. $L_{combine}$ shows two distinct peaks, reaching 23% during the harvest of winter crops in July and 9% during the harvest of spring crops in October. As expected, harvesting machines are completely out of use for the rest of the period.

Fuel energy consumption is the integral of power distribution divided by $\eta_{motor}$. Total fuel consumption is estimated at 57 PJ, with average energy intensity of 3.8 GJ ha⁻¹ in cropland and 1.1 GJ ha⁻¹ in grassland. Table 1 summarizes energy consumption per type of production and field operation as well as total area and associated energy intensity. Energy consumption detailed per crop is given in Supplementary Table S3.

## 3.2. Effect of agroecological practices on power demand

Figs. 4a and 4b compare power distribution respectively in scenario A (organic) and Scenario B (conservation) with the previously estimated reference case (dashed line). The bar chart (Fig. 4c) compares the energy use per field operation between Scenarios A, B and the reference case. The peak power demand in scenario A is about 1 GW above the reference case, which lies within the uncertainty margin (see supplementary Figure S3). On the one hand, organic systems require increased power for mechanical weed control and organic fertilizers spreading which are more voluminous than synthetic fertilizers. On the other hand, reduced pesticides spreading results in lesser passes, thus tends to reduce power demand. In scenario B, peak power is about 2 GW below the reference case as a result of reduced tillage and direct sowing.

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Motor power distribution (in GW) for field operations in French agriculture over a cropping season (year *n*) per type of production (a) and per task category (b). The grey areas represent the previous (*n*-1) and following (*n*+1) cropping seasons that overlap with the considered season.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Load factor (%) for tractors and combine-harvesters in France over a cropping season (year n). This represents the ratio between motor power demand and total installed power for each category.</figcaption></figure>

<table><caption>**Table 1**
Summary of the energy consumption in PJ for each production and task, surface cultivated and energy per ha.</caption><thead><tr><th>Unit: PJ</th><th>Wheat</th><th>Maize</th><th>Other Cereals</th><th>Oilcrops</th><th>Pulses</th><th>Potatoes and Beets</th><th>Grasslands</th><th>Total</th></tr></thead><tbody><tr><td>Tillage</td><td>6,91</td><td>5,17</td><td>3,29</td><td>2,79</td><td>0,68</td><td>1,41</td><td>5,15</td><td>25,40</td></tr><tr><td>Sowing</td><td>1,70</td><td>0,90</td><td>0,73</td><td>0,58</td><td>0,13</td><td>0,21</td><td>0,77</td><td>5,01</td></tr><tr><td>Fertilizer</td><td>1,94</td><td>1,58</td><td>0,75</td><td>0,64</td><td>0,03</td><td>0,30</td><td>2,49</td><td>7,74</td></tr><tr><td>Pesticide</td><td>1,94</td><td>0,41</td><td>0,62</td><td>0,68</td><td>0,10</td><td>0,47</td><td>0,00</td><td>4,23</td></tr><tr><td>Harvesting</td><td>3,74</td><td>1,92</td><td>1,59</td><td>1,23</td><td>0,27</td><td>1,30</td><td>4,87</td><td>14,91</td></tr><tr><td>Total (PJ)</td><td>16,24</td><td>9,98</td><td>6,97</td><td>5,92</td><td>1,21</td><td>3,68</td><td>13,29</td><td>57,29</td></tr><tr><td>Area (Mha)</td><td>4,73</td><td>2,42</td><td>2,00</td><td>1,55</td><td>0,35</td><td>0,54</td><td>12,18</td><td>23,77</td></tr><tr><td>Total (GJ ha<sup>-1</sup>)</td><td>3,43</td><td>4,12</td><td>3,48</td><td>3,81</td><td>3,50</td><td>6,83</td><td>1,09</td><td>2,41</td></tr></tbody></table>

### 3.3. Workforce requirement for machinery driving

Fig. 5 shows $\Pi_{tot}(t)$, the workforce requirement for machinery driving over time for the reference case. $\Pi_{tot}(t)$ has a distribution similar to power demand ($P_{tot}(t)$, shown by the black dotted line) and peaks at 160.000 parallel drivers in fall. This suggests that, at that moment, 30 % of total workers are simultaneously and full-time employed in driving machinery in the field. The relative contribution of tasks in $\Pi_{tot}(t)$ and $P_{tot}(t)$ is different. Workforce requirement is inversely proportional to the field capacity $Q_i$, meaning that for a given task the use of bigger equipment reduces workforce requirement but increases power demand. Harvest operations illustrate well this trade-off. In cropland, harvest relies on a relatively little number of high-power machines (i.e. combine-harvesters involving few operators) whereas grassland harvesting requires twice as many operators per hectare but less power. The cumulated annual working time in field driving $W_{tot}$ amounts to approximately $10^8$ h yr$^{-1}$. The cumulated working time for field driving is not reported in official statistics which only report total working time in agriculture. Our estimated $W_{tot}$ suggests that annual field driving represents about 12 % of total agricultural working time (the calculation is detailed in Supplementary section G). The relative contribution of crop types and tasks in $W_{tot}$ and in energy consumption (the integral of $P_{tot}$) are shown in Supplementary Figure S6.

## 4. Discussion

### 4.1. Field work constraints and machinery fleet redundancy

The quantification of seasonal variability in power demand and workforce requirement for field driving is a novel approach in the scientific literature. It allows discussing redundancy in mechanical equipment of farms and brings up new perspectives on the energy transition of farming systems. In the current paradigm of fossil-fueled systems, the variability of power demand is uncritical because fossil fuels are dispatchable and energy-dense sources, but adequate power supply becomes a key concern in the forthcoming transition to renewable and non-dispatchable flows (i.e., wind and solar). This concern has been

very briefly mentioned in the scientific literature [16] and the power demand of on-field agricultural machinery has remained elusive so far.

A direct comparison and validation of our results with the literature is impossible due to the absence of studies on the power demand of mobile machinery. An indirect validation can be done by considering energy consumption which is the integral of the power distribution curve. For the crops and field operations considered, we obtain total fuel consumption of 57 PJ, which aligns closely with published figures for direct fuel consumption in French agricultural statistics [18]. This latter reports total fuel consumption at 120 PJ, of which approximately half falls within the scope of our study (see Supplementary Information, section F). Our results also align with average diesel fuel inputs reported per hectare for various crops in European Union (EU) countries [17]. Notably, their results align closely with our findings for potatoes, beets, and straw cereals, where the difference is minimal (0–20 %). The discrepancy is higher for maize and rapeseed (50 %), and for sunflowers and soybeans (100 %). Given the substantial variability in diesel use across EU countries reported in their study, such differences are expected, and the consistent orders of magnitude support our estimates.

The highly variable load factors of tractors and combine harvesters – peaking at just 18 % and 23 % of installed engine power respectively – highlight significant fleet redundancy. Previous research has shown that field machines typically operate at less than 70 % of their engine power [33], suggesting that a load factor of 70 % can be regarded as a practical upper limit. The gap between this practical limit and our findings underscores the influence of additional factors driving fleet redundancy. Given that agricultural machinery represents costly capital investments, such redundancy has a substantial economic impact on farms operations – an aspect that warrants further attention.

The observed fleet redundancy possibly relates to the operational timeliness constraint of field tasks. Indeed, for each operation, the time windows of favorable working conditions can vary significantly from year to year due to weather variability. These windows are typically shorter than the agronomic windows, which are primarily determined by long-term climate patterns [34]. Consequently, the agronomic windows define the maximum timeframe, which is further constrained by short-term weather conditions. The data used in our calculations reflect
<figure><img src="image_5.png" /><figcaption>Fig. 4. Motor power distribution (in GW) for field operations in French agriculture over a cropping season (year n) per task category for scenario A (25 % organic area) (a) and scenario B (25 % no-till area) (b). The black dotted line shows the envelope of power demand of the reference case for comparison. (c) Associated fuel consumption (PJ) per task category for the reference case and scenarios A and B.</figcaption></figure>

to the average agronomic windows across France. At the national scale, these windows aggregate conditions across a large number of farms, resulting in a smooth normal distribution of power demand per task. However, at the farm scale, weather conditions directly influence field trafficability and inevitably narrow the optimal working slots. Consequently, given the high unpredictability of weather conditions and the

strategic importance of agricultural production, it is logical for farms to invest in oversized equipment as a precautionary measure. This ensures that tasks can be completed even in years with severely limited optimal working periods. In general, the greater the reduction in optimal periods, the faster the work must be performed and, thus, the higher the power requirement.
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Cumulated workforce requirement for machinery driving (x10³ drivers) per type of production (a) and task category (b) in French agriculture for the reference case. The black dotted line shows the envelope of power demand (GW) for comparison. The grey areas indicate workforce requirement overlapping with previous (n-1) and following (n+1) cropping seasons.</figcaption></figure>

Furthermore, both weather patterns and agronomic windows operate at a regional scale, covering areas of several hundred square kilometres. Farm machinery fleets are frequently deployed simultaneously across these large areas, which limits opportunities for mutualizing equipment and, thereby, improving load factors. At even larger scales, the low mobility of heavy machinery makes interregional sharing practically impossible. However, it is worth noting that the heaviest agricultural equipment – particularly combine-harvesters – is already widely shared among farms, as demonstrated by the success of cooperative machinery-sharing initiatives like the CUMA movement that regrouped 225.000 farmers in 2015 within 11.500 local cooperatives [35]. This mutualization is made possible by the remarkably high field capacity of these machines, which – combined with their cost – makes sharing economically advantageous. In contrast, tractors are rarely shared among farms, as most farms own at least one tractor.

Fleet oversizing in particular for tractors can also relate to social factors. When purchasing a new tractor, farmers may be influenced by peer pressure from neighboring farms, incentives from local machinery dealers, and personal pride in owning a machine that plays a central role in the farming system. These factors can drive the decision to invest in more powerful and expensive models. However, this social dimension remains largely unexplored in the social sciences and would benefit from rigorous field analysis. Further investigation into these factors could

help explain the warnings and recommendations issued by local authorities, which encourage farmers to acquire appropriately sized equipment to avoid oversizing and reduce fuel consumption (see [36]).

The load factor of tractors can also be put in perspective with that of other machines. For instance, private vehicles are reported to be unused over 95 % of their lifetime [37]. Given that the power used during operation is typically only a fraction of the engine power, the average utilization rate of private vehicles is necessarily below 5 %. By comparison, the load factor of non-mobile and common-interest machines is higher, ranging from 60–90 % for industrial assets and dispatchable electricity plants, to 10–30 % for intermittent (wind and solar) power generation systems [38]. We find that field machines occupy an intermediate position, with load factor falling between those of private vehicles and industrial assets.

## 4.2. Challenges in the context of the energy transition

Dispatchable energy sources can supply power at practically unconstrained rates, making the timing of energy demand less of a concern. Even in electricity networks, where consumption corresponds to instantaneous production, dispatchable sources like fossil-fueled or nuclear power plants ensure a stable balance between consumption and production. In contrast, solar energy harvesting through photovoltaics
(PV) and wind turbines faces intrinsic variabilities (seasonal, diurnal, weather-related), making the timing of total energy demand crucial for supply alignment. This interplay affects storage requirements and the sizing of power harvest areas [13]. Thus, the stock-to-flow transition challenges the stability of energy supply and the structure of demand. By highlighting the profile of power demand of agriculture, the present study shows how the current system is anchored in a stock-based operating mode. The high peaks of power demand can be instantly met by unconstrained supply from fossil fuels, and may become a constraint in the transition to non-dispatchable flows.

Potential alternatives to fossil fuels include biofuels or electrification through batteries or hydrogen. One significant advantage of future electrification is the high motor efficiency of electrical engines, which is nearly 100 %. In contrast, thermic motors require nearly three times more fuel input to achieve the same output. Accordingly, renewable electrification may be promising given the progress of battery-based vehicles and on-farm PV panels [39–41].

The present study allows gaining insights on the variability of power demand which is critical for electrification planning. Fig. 6 puts in perspective the modeled power profile with total wind and solar power generation in France in 2022 (latest available data). Field works not accounted for in the study (fruits and vegetables, hauling, etc.), could further increase on-field peak power demand, as they occur simultaneously with the activities analyzed (see discussion in Supplementary Information, section F). As field work is currently not at all electrified, there is no overlap between the modeled power demand and the renewable production curves. However, the curves are of the same order of magnitude and largely out of phase. In addition to daily-scale variability, solar and wind power generation peaks during summer and winter when power demand of the agricultural sector is relatively low and vice-versa. Assuming there is enough electricity and that machinery fleet electrification is possible, inter-seasonal storage may be necessary to balance supply and demand. However, large-scale electrification still faces numerous challenges such as mature technology availability, rural electric infrastructure limitations, vehicle lifetime, charging delays, increased capital costs, low usage rates and high-power of machinery [11,12,10]. Along with these challenges, our findings call for integrating the agricultural peak power demand into the policies of rural energy networks development.

To comply with operational constraints, electrification of machines may require adjustments to the nominal power and autonomy of vehicles. A key challenge lies in matching the autonomy and field capacity of

current fossil-fueled machines through electrified alternatives. However, since batteries have far lower energy density than fossil fuels, achieving comparable performance could necessitate a substantial increase in weight. This, in turn, risks exacerbating soil compaction, which harms soil health and ultimately reduces crop yields [42]. Recent research indicates that electrified technologies – such as on-field battery exchange systems – could achieve operability comparable to diesel-powered equipment. However, this performance depends on three key conditions: relatively high battery capacity and energy density, a robust grid electricity supply, and secondary applications for batteries – such as grid services – to maximize their utilization and ensure economic viability [43,41]. From a practical standpoint, deploying battery-powered tractors would necessitate revising work organization to accommodate battery charging during peak work periods. Additionally, the national-scale approach of this study obscures farm-level dynamics, where power demand variability is likely even more pronounced. This underscores the critical need for tailored work organization and charging infrastructure to support effective electrification at the farm scale.

Contrary to electricity, bioenergy such as liquid biofuels and biogas are convenient to store and allow delivering high power on demand [45]. Consequently, they can allow absorbing virtually any variation in power demand, and could be a solution for heavy machinery, such as combine harvesters but also large tractors, which are hard to electrify [10]. However, in order to not compete with food production, bioenergy must come from agricultural residues rather than from primary crops. In 2023, the production of biogas and liquid biofuels in France equaled 75 PJ and 147 PJ respectively [46]. Biogas, which according to the public directive must result for at least 85 % from biomass residues [47], was mainly directed towards cogeneration and injection in the gas network. Biofuels, which are almost exclusively produced from primary crops thus competing with food production, were also directed towards other sectors, in particular the transportation sector [46]. These figures suggest that the agricultural sector analyzed in this paper could theoretically achieve energy self-sufficiency, as defined by the energy balance between input (here 57 PJ) and recovery (biogas and biofuels). However, a large share of current bioenergy production competes with food production and previous work has suggested that important structural changes are needed for the system to achieve energy self-sufficiency without food-energy competition [3].

<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Renewable power generation in GW compared to the envelope of power demand for field tasks ($P_{tot}$, black dotted line) over one year. Wind power curve shows the daily min-max range. Solar power curve shows the daily max value (the min value is zero and excluded for visibility). Data for renewable power generation is from RTE [44].</figcaption></figure>
### 4.3. Potential future trade-offs in farming system

Despite the stability in installed engine power – which has plateaued since the late 1980s at 70 GW for tractors and 20 GW for combine-harvesters (Supplementary Figure S4) – there has been a major shift towards fewer and larger farms, less workforce and less machines with an increasing nominal power and field capacity (Supplementary Figure S5). This shift highlights a fundamental trade-off between power demand (captured by $P_{tot}$) and workforce requirement (captured by $\Pi_{tot}$). While supporting economies of scale, fewer and larger farms also entrench the agricultural system in its current paradigm: shrinking farmer populations and increasingly powerful machinery depending on energy-dense fuels, which hinders the energy transition.

In the future, agricultural systems will need to navigate a dual challenge: the phasing-out of fossil fuels and the declining farming population, a trend observed across industrialized countries [48]. The reduction in fossil fuel availability could constrain $P_{tot}(t)$ whereas the declining farming population may constraint $\Pi_{tot}(t)$. Simultaneously, agriculture will face additional key challenges: adapting to climate change and implementing best management practices and agroecological practices to reduce environmental impacts. These challenges may further influence the demand for $P_{tot}$ and $\Pi_{tot}$, potentially inducing additional trade-offs between them.

Climate change may drive up power demand for field operations – particularly during peak periods. Scientific consensus suggests that worsening weather extremes are likely to further restrict the workable time windows for field operations [49]. As a result, this may accentuate peak power demand as illustrated in Supplementary Figure S3d. Such an increase in power demand is manageable by fossil-fueled machines within the current trend of economies of scale toward larger farms. However, because electrification may reduce the nominal power and autonomy of vehicles, an increase in $P_{tot}$ would require a corresponding rise in $\Pi_{tot}$. This requirement could be challenged by the declining agricultural population and highlights the vulnerability of agricultural production to the combined lack of workforce and climate-related constraints. Climate change may impact the crop mix as well: as an adaptation measure, crop cultivars and production practices are likely to evolve [50,51], thereby also impacting the demand for $P_{tot}$ and $\Pi_{tot}$.

Similarly, if the transition away from fossil fuels constrains power availability, tensions may also arise between best management practices and economies of scale. Best management practices often involve an increased number of passes on agricultural land. For instance, split fertilization – a measure for improving nutrients use efficiency of crops [52] – necessitates more frequent passes than usual fertilization [53]. Likewise, organic farming practices use mechanical weed control as a substitute for herbicides, requiring more passes [54]. Over the past decades, the increase in the nominal power of individual machines – alongside a reduction in the total number of machines (Supplementary Figure S4) – has allowed farms to accommodate more passes while simultaneously expanding in size. Conversely, constraints from electrification on the nominal power and autonomy of machines, could necessitate a greater number of machines, thereby leading to a corresponding increase in $\Pi_{tot}$. Under such power constraints – coupled with a potentially shrinking agricultural workforce – tensions may emerge between the ongoing push for economies of scale (favouring larger farms) and the adoption of best management practices, which could be compromised as a result. The interplay between power and workforce constraints may pose distinct challenges depending on the type of agricultural production. While not considered in this study, the adoption of driverless autonomous machinery – which are currently practically unavailable – could be accelerated by persisting workforce shortages [55], offering a potential long-term solution.

To mitigate the tensions between power constraints, workforce availability and the adoption of sustainable practices, we suggest that agroecological practices could help adapt agricultural systems to the energy transition. For instance, no-till practices reduce power demand

(Fig. 4b) without impairing yield stability [28,56], thereby easing the pressure on $P_{tot}$ while maintaining productivity. Further research could explore complementary measures within conservation agriculture, such as cover cropping, to assess their cumulative impact on the demand of workforce, power and overall energy balance.

Regarding organic systems (Fig. 4a), our results suggest a slight increase in peak power demand due to the use of mechanical weed control as a substitute to pesticides. This slight increase falls within uncertainty margins and is thus insignificant. However, indirect energy savings achieved by avoiding inputs – primarily the exclusion of industrial fertilizers – are substantial [19,57,58] and result in overall reduction in energy use, provided the energy cost for nutrient circularity through careful territorial planning is low [59]. Since organic and no-till practices are rarely combined in Europe, we analyzed separately. However, their potential synergistic benefits warrant further investigation [60].

## 5. Conclusion

We assess variability in power demand and workforce requirement in field agriculture, an aspect unaddressed in the current literature, which focuses on energy balance approaches. Our findings reveal significant seasonal variability, with peaks during critical periods like fall tillage. This variability poses challenges for transitioning from fossil fuels to non-dispatchable renewable flows. The study highlights low load factors for tractors and combine-harvesters, indicating current fleet redundancy. This redundancy is driven by various factors including on-field constraints, particularly by the need to complete tasks within narrow, fluctuating and largely unpredictable weather-driven time windows. The transition to renewable flows presents several challenges and contrasts with current trends toward fewer, heavier and more powerful machines. The analysis highlights critical trade-offs between the energy transition, sustainable agronomic practices and the ongoing trend of increasing farm sizes. These findings underscore the need for rural energy infrastructure policies that address peak power demand challenges, as well as farming system designs that adapt to the constraints of renewable fueling for machinery.

### CRediT authorship contribution statement

**Cyprien Drommi:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Data curation, Conceptualization. **Petros Chatzimpiros:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

This work is part of the project SLAM-B of the exploratory research program FairCarboN and received government funding managed by the Agence Nationale de la Recherche under the France 2030 program, reference ANR-22-PEXF-0003.

### Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.nexus.2026.100654.
# Data availability

Data will be made available on request.

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