# Soil phosphorus budgets in organic farming differ according to plot managements and farm types

Pietro Barbieri<fnref n="1" /> Ⓒ · Josephine Demay<fnref n="1" /> · Morgan Maignan<fnref n="2" /> · Thomas Nesme<fnref n="1" /> · Gregory Vericel<fnref n="2" />

Accepted: 4 August 2025

© The Author(s) 2025

## Abstract

Soil fertility in organic farming systems relies on the use of organic fertilizers and on the introduction of N-fixing crops in rotations. In such systems, the management of phosphorus fertilization can be challenging. Field phosphorus nutrient budgets and soil phosphorus status are useful indicators for assessing the sustainability of fertilization practices. However, the factors driving soil phosphorus budgets of organic fields for different management practices and farm types are still not fully understood. Here we propose to fill this knowledge gap by investigating, through semi-directive interviews, the variability of 5-year soil phosphorus budgets in 177 French organic farms to identify relationships between the P budgets and farm characteristics, covering farm territorial specialization, and soil fertilization practices. Changes in Olsen-P measurements were also computed to test their coherence with the phosphorus budgets. Based on our unprecedented large and diverse dataset, we found contrasting soil P budgets ranging from -32.3 to 50.3 kg·P·ha⁻<fnref n="1" /> y⁻<fnref n="1" />. Our work also revealed that P management was not at the core of farmers’ preoccupations, as 72% of them did not pay any specific attention to P management practices. The two most important factors driving soil phosphorus budgets were the cumulated phosphorus inputs and the frequency of N-fixing crops. We also identified 5 plot types — through a cluster analysis — with contrasting phosphorus management practices and soil phosphorus budgets. Our results highlight the need for a strategic management of phosphorus resources to prevent the occurrence of soil phosphorus imbalances that may be challenging to rectify in the medium to long term. This is particularly important in the case of organic farms that rely on the use of N-fixing crops for their nitrogen fertilization strategy, leading to low or negative soil phosphorus budgets.

**Keywords** Organic farming · Phosphorus · Soil budgets · Olsen-P

## 1 Introduction

Current agricultural systems are pushing the world beyond planetary boundaries (Richardson et al. 2023). In order to reverse this trend and to sustain our livelihood on Earth, there is an urgent need to transform current, i.e., “conventional,” agricultural systems (Clark et al. 2020). One possible alternative is organic farming, which emits fewer greenhouse gases (GHGs) per hectare of cultivated crops (Hashemi et al. 2024) and bans the use of synthetic fertilizers and pesticides, thus reducing both our dependence

on fossil energy and the exposure of living beings to toxic substances. Yet, organic farming crop yields are on average 25% lower than conventional farming (Seufert et al. 2012) and this yield gap could widen significantly as organic farming expands due to nutrient limitations. Managing the soil nutrient fertility is thus a key challenge for organic farming.

Soil fertility management in organic farming (OF) systems differs significantly from conventional farming systems as it ideally relies on closing nutrient cycles rather than on the use of external mineral fertilizer inputs. Consequently, organic farms are expected to rely on techniques such as biological nitrogen fixation (BNF), on-farm nutrient cycling, and nutrient recycling from urban waste streams (Watson et al. 2010; Willer and Lernoud 2019). While nitrogen (N) fertility is relatively straightforward to manage through maximizing BNF, phosphorus (P) management is more challenging, as P fertilizers may be either banned (e.g., synthetic fertilizers, sewage sludge), prohibitively expensive (e.g., dehydrated

<footnote>Pietro Barbieri
pietro.barbieri@agro-bordeaux.fr</footnote>

<footnote n="1">¹ ISPA, Bordeaux Sciences Agro, INRAE,
F-33140 Villenave d'Ornon, France</footnote>

<footnote n="2">² Arvalis Institut du Végétal, 31048 Baziège, France</footnote>
poultry manure, processed animal proteins — PAT), diffi-
cult to transport (e.g., livestock manure), or relatively inef-
fective (e.g., ground rock phosphate (Möller et al. 2018). It
is therefore challenging to replace exported P with the har-
vested biomass, making organic systems vulnerable to soil P
depletion. In addition, nitrogen fertility management through
BNF could further exacerbate the tendency of organic farm-
ers to reduce fertilizer inputs, thus also reducing the inputs
of other nutrients, including P and potassium (Reimer et al.
2020a). Contrary to N availability that requires short-term
management, soil P availability is strongly dependent on
soil phosphorus reserves, that evolve over decades (Ringeval
et al. 2014; Demay et al. 2023). Soil P reserves may often be
sufficient to replenish the plant-available soil P pool in the
case of no annual fertilization. Therefore, lack of regular P
fertilization usually has little effect in the short term, but the
long-term effects could be critical. As P availability is one
of the main drivers of BNF efficiency (Mitran et al. 2018), it
can have a significant impact on N supply and, consequently,
on the overall productivity of cropping systems. All of these
challenges are very different from those of conventional
farming, where the main challenge lies in the sustainability
of P overloading. Therefore, the investigation of farm and
plot P nutrient flows through the calculation of P budgets
(Watson et al. 2002) is key to assess the sustainability of
organic fertilization practices.

A number of studies have already investigated the rela-
tionship between farm-gate P budgets and farm type or
fertilization management practices in organic farming,
with contrasting results (Watson et al. 2002; Oelofse et al.
2010; Nesme et al. 2012; Reimer et al. 2020a). A recent
meta-analysis showed that P budgets can be highly variable
between organic farms, with values ranging from –12.1 to
+48 kg P ha⁻¹ y⁻¹, and between budgeting methods (with
farm-gate budgets showing higher values than field budg-
ets) (Reimer et al. 2020b)(Reimer et al. 2020b). No clear
relationships were found between the presence of livestock
on the farm, the stocking rate, and P budgets (Nesme et al.
2012), although some studies reported lower P deficits on
dairy farms than on stockless farms (Reimer et al. 2020a). In
addition, a high reliance on BNF for N inputs was negatively
correlated with positive farm-gate P budgets (Reimer et al.
2020a, 2024). However, most of the previous studies had
limitations because their farm gate or field P budgets were
calculated (i) for only 1 to 3 years, (ii) for specific geographi-
cal regions, (iii) focusing on either dairy or beef production
(rather than arable farms), and (iv) using a small sample
size (Watson et al. 2002; Kirchmann et al. 2008; Nesme
et al. 2012; Reimer et al. 2020b). These limitations make it
challenging to understand how management practices, terri-
torial specialization, and soil P budgets are correlated. Here,
we propose to fill this knowledge gap by computing field P
budgets over a 5-year time frame and for a wide diversity of

plot types across France. Our objective was to understand
the factors influencing the observed heterogeneity in soil
P budgets under organic farming. Therefore, we examined
the relationships between soil P budgets and farm types,
defined as a set of P management practices within a defined
regional agricultural specialization. Based on the high num-
ber of farm types and management practices in our dataset,
we tested the following hypotheses: (i) farm or field char-
acteristics and fertilization practices, taken separately, are
not effective predictors of soil P budgets; rather, it is the
combinations of these characteristics that are significant;
(ii) a high frequency of legumes in rotation is associated
with negative soil P budgets; (iii) positive soil P budgets are
positively correlated with increases in soil available Olsen-P
over time. We also examined the degree of attention paid by
farmers to the strategic management of P fertility.

## 2 Materials and methods

2.1 Study area plots selection

Between November 2021 and February 2023, 140 certified organic farms (EU certification standards) were surveyed (Figure 1). For each of them, we collected data on one or more of their plots to compute annual soil P budgets for a period of 5 years prior to the interview. In total, 177 plots were included in our database. The farms were selected within four contrasted arable farming French territories, which differ in terms of their regional main agricultural sector and pedoclimatic characteristics (Figure S1). In order to identify possible relationships between the farm and territorial characteristics, soil fertilization practices and soil P budgets, crop and mixed farms were also selected to cover a wide range of livestock densities, conversion dates, as well as cropping practices. For each farm, one or more plots were selected for both the

<figure><img src="image_1.png" /><figcaption>Fig. 1 One of the selected arable farming plots in the Gers region (South West) in November 2022. Photocredit: J. Demay</figcaption></figure>

<figure><img src="image_2.png" /></figure>
calculation of an annual field P budget over a period of 5 years (Watson et al. 2002) and the measurement of soil available Olsen-P. To ensure that the conversion period to organic farming has been completed, only plots that had converted to organic farming before 2016 were selected.

## 2.2 Data collection

Through semi-directive interviews, farmers were asked to describe their farming systems and to provide quantitative information on soil P inputs and outputs over the last five years for the plots studied. Based on the main crop grown each year for the 5 years studied, we were able to calculate the frequency of N-fixing crops over this period. This indicator varied from 0 to 5 and was used to assess the correlation between the intensity of leguminous crops in the rotations and the soil P budget. The information was collected either from written documentation, if available, or based on the farmers' memory. Most of the interviews were conducted in person. When this was not possible, farmers were interviewed via telephone. Open-ended questions were used to qualify the farmers' ability in sourcing organic P fertilizers or to question their attention paid to the P fertilization of their plots. This is because the characteristics of the region may influence the availability of organic fertilizers. Livestock and mixed-farming regions have been observed to have a higher supply than stockless regions (Nowak et al. 2015), which in turn may affect the P management practices and soil budgets of organic farms. Some quantitative variables with a high degree of dispersion describing the farm characteristics and the fertilization management practices were transformed into qualitative variables by assigning each value to a class to reduce the overall dataset variability (Table 1). It should be noted that when data were collected based on farmers' recall, there may have been some bias due to the quality of information provided by each farmer. Similarly, there may have been some uncertainty in obtaining P concentrations for either minor crops or unusual P fertilizers.

Half of the plots analyzed were located in stockless regions (56% - 99/177 plots), while the rest were divided between livestock (25% - 44/177 plots) and mixed (19% - 34/117 plots) regions. With regard to the farm types, half of the surveyed farms did not have any livestock (48% - 85/177 plots), while the remaining were characterized by either “small” (less than 0.5 Livestock Units (LU) ha⁻¹ - 14%, 25/177 plots), “medium” (between 0.5 and 1 LU ha⁻¹ - 25%, 44/177 plots), or “large” stocking rates (more than 1 LU ha⁻¹ - 13%, 23/177 plots). In line with these characteristics, 46% (82/177 plots) of the surveyed plots were cultivated

with arable crops, while the remaining 54% (95/177 plots) were characterized by the presence of temporary or permanent pastures, which were associated with the presence of livestock. Most of the plots (55% - 97/177 plots) were converted to organic management after 2006, while plots that had been organic for more than 30 years represented only 9% (16/177 plots) of the sample.

Soil sampling and analysis was carried out on all plots during the winter 2021 and 2022. In each plot, 15 soil samples were collected within a radius of 10 m at a depth of 20 cm, and then pooled together. The soil sampling was performed by an external service (Aurea AgroSciences). Soil P availability was assessed using the Olsen method (Olsen et al. 1954). In addition, farmers were requested to provide, if available, previous soil analyses reporting Olsen-P concentrations, irrespective of the year of the analysis. Note that Olsen-P was excluded from the variables used to characterize soil P budgets since a small minority of the farmers know and apply a P fertilization recommendation method (Table S1), thus making the variable of little importance in the farmers' P management strategic decisions.

## 2.3 P calculations

For each surveyed plot, annual soil P budgets (Watson et al. 2002) were calculated according to Eq. 1 for the 5 years prior to the interview. Soil inputs and outputs were converted into P flows by multiplying each flow by its respective P concentration. P concentrations were either obtained from the literature (Levasseur P. et al. 2019) or provided directly by farmers (for a limited range of organic fertilizer products, where a chemical analysis or the composition was provided). Specific P concentrations for organically managed crops were used to calculate the P flows exported in plant biomass. These specific coefficients were assessed and obtained from the project itself (Supplementary Dataset). Due to the lack of appropriate data, we had to formulate hypotheses for two specific situations. First, when livestock grazed on permanent pasture, we assumed that P inputs as manure were equal to P in the grazed biomass, resulting in a zero P budget. Secondly, for all associated crops for which individual crop yields were not available, we assumed that all crop species contributed equally to the total plot yield. For example, if farmers reported a single yield for a mixture of wheat and broad beans, we assumed that this yield represented 0.5 ha of wheat and the same for broad beans. In addition, atmospheric P deposition and erosion losses were not taken into account, as the uncertainty of their estimation is very high (Welikhe et al. 2023).

$$ \text{SoilBudget}_i = \sum \text{Pinput}_k \times [P]_k - (\text{Poutput}_{\text{grain}_s} \times [P_{\text{grain}}]_s + \text{Poutput}_{\text{cropresidues}_s} \times [P_{\text{cropresidues}}]_s) \quad (1) $$

where i indicates the plot number, k the input type, and s the crop species
<table><caption>**Table 1** Summary of the main characteristics of the selected plots and their belonging farms. The regional agricultural specialization levels are based on: https://cdonline.articque.com/share/display/regions-agricoles</caption><thead><tr><th>Variable type</th><th>Variable</th><th>Levels</th><th>Levels definition</th><th>Frequency [%]</th></tr></thead><tbody><tr><td rowspan="3">Regional characteristics</td><td rowspan="3">Regional agriculture specialization</td><td>Stockless</td><td>Region mainly characterized by arable crop production</td><td>56 (99/177)</td></tr><tr><td>Mixed</td><td>Region mainly characterized by both arable crop and livestock production</td><td>19 (34/177)</td></tr><tr><td>Stocked</td><td>Region mainly characterized by livestock production</td><td>25 (44/177)</td></tr><tr><td rowspan="3">Farm characteristics</td><td rowspan="3">Time since conversion to OF</td><td>Very old</td><td>Conversion to OF before 1990</td><td>9 (16/177)</td></tr><tr><td>Old</td><td>Conversion to OF between 1990 and 2006</td><td>36 (64/177)</td></tr><tr><td>New</td><td>Conversion to OF between 2006 and 2016</td><td>55 (97/177)</td></tr><tr><td rowspan="4">Stocking rates</td><td rowspan="4"></td><td>No livestock</td><td>0 LU ha<sup>-1</sup></td><td>48 (85/177)</td></tr><tr><td>Small</td><td>&lt;0.5 LU ha<sup>-1</sup></td><td>14 (25/177)</td></tr><tr><td>Medium</td><td>[0.5 – 1] LU ha<sup>-1</sup></td><td>25 (44/177)</td></tr><tr><td>Large</td><td>&gt;1 LU ha<sup>-1</sup></td><td>13 (23/177)</td></tr><tr><td rowspan="3">Farm land use</td><td rowspan="3"></td><td>Cropland</td><td></td><td>46 (82/177)</td></tr><tr><td>Cropland with pastures</td><td></td><td>37 (65/177)</td></tr><tr><td>Permanent pasture</td><td></td><td>17 (30/177)</td></tr><tr><td rowspan="3">Fertility management</td><td rowspan="3">Phosphorus management</td><td>Off-farm produced fertilizers</td><td></td><td>59 (104/177)</td></tr><tr><td>No P use</td><td></td><td>21 (37/177)</td></tr><tr><td>On-farm produced fertilizers</td><td></td><td>20 (36/17)</td></tr><tr><td rowspan="3">Fertilization frequency</td><td rowspan="3"></td><td>Low</td><td>The plot is fertilized 0 or 1 year out of 5</td><td>36 (64/177)</td></tr><tr><td>Medium</td><td>The plot is fertilized 2 or 3 years out of 5</td><td>35 (62/177)</td></tr><tr><td>High</td><td>The plot is fertilized 4 or 5 years out of 5</td><td>29 (51/177)</td></tr><tr><td rowspan="6">Fertilizer type</td><td rowspan="6"></td><td>Poultry manure</td><td></td><td>14 (24/177)</td></tr><tr><td>Multiple inputs</td><td></td><td>30 (53/177)</td></tr><tr><td>None</td><td></td><td>21 (37/177)</td></tr><tr><td>Manure (other than poultry)</td><td></td><td>23 (41/177)</td></tr><tr><td>Processed Animal Proteins</td><td></td><td>6 (11/177)</td></tr><tr><td>Other</td><td></td><td>6 (11/177)</td></tr><tr><td rowspan="3">N-fixing crops frequency</td><td rowspan="3"></td><td>Low</td><td>Less than 1.5 years</td><td>22 (39/177)</td></tr><tr><td>Medium</td><td>Between 1.5 and 2.5 years</td><td>65 (115/177)</td></tr><tr><td>High</td><td>Higher than 2.5 years</td><td>13 (23/177)</td></tr></tbody></table>

We defined and calculated P use efficiency (PUE) as the ratio of P inputs to P outputs, following (Reimer et al. 2020a). For the 67 farms that had provided a historical soil P analysis, we calculated the annual change in Olsen P by taking into account the number of years between the provided value and the one carried out in this study. It should be noted that 76% (52/67) of the provided historical P analysis came from the South-West region, while only 16% (9/67), 9% (5/67) and 1% (1/67) came from the Paris Basin, West, and Rhone Alps regions, respectively (Figure S1).

## 2.4 Statistical analysis

A factor analysis of mixed data (FAMD, using the R package FactoMineR (Lê et al. 2008) was used to reduce and select the key variables driving soil P budgets. The FAMD

was run as many times as the number of variables retained, dropping one variable each time in order to calculate their mean contribution. For each loop, we retained all the axes with an eigenvalue greater than 1. Variables with a mean contribution of less than 5% were then excluded from the analysis (namely: time since conversion to OF, soil texture and region of plot location). Confidence ellipses were used to visually confirm the absence of plot discrimination driven by the three variables mentioned above. The variable “total P exports” was also excluded as it was both highly correlated with the variable “total P imports” and had a lower contribution score. The quantitative variable “soil P budget” was included as a supplementary, i.e., non-active variable. Similarly, both the soil texture and the region of plot location were included as supplementary variables (Figure S2).

<figure><img src="image_3.png" /></figure>
The data were then analyzed using descriptive statistical tools. The correlations between the soil budgets and the different quantitative variables were calculated using Pearson's coefficient (cor.test R function). To determine the factors influencing the soil budgets (effect size — η²), a linear model (Eq. 2) was developed using the lme R package

(Bates et al. 2015). The data were subjected to a visual assessment of the residual vs. fitted plot and normal Q-Q plot to ascertain their homogeneity of variance and normal distribution. When necessary, the original data were transformed (square root) to ensure conformity to a normal distribution.

$$Mean soil budget \sim Phosphorus management + Regional agricultural specialization + Stocking rates + Farm land use + N fixing crops frequency + fertilization frequency + fertilizer type + Total P imports \quad (2)$$

Hierarchical Clustering on Principal Components (HCPC) was then performed to identify clusters of similar plots and associations between management, farm, and regional variables (Table 1) and analyzing the similarity between the surveyed plots. The HCPC was performed using the first five axes of the FAMD analysis (accounting for 56% of the total variance). The differences in soil P budget between the different clusters were then assessed using a linear model and a post-hoc Tukey test (R package agricolae, Felipe de Mendiburu 2023). Similarly, the differences in the annual variation of Olsen-P availability were tested using a linear model as in Eq. 2 and substituting the variable “Mean soil budget” with “Olsen-P annual variation” Statistical analysis was conducted using the R statistical computing environment (R 4.3.1, R Core Team 2022).

# 3 Results and discussion

## 3.1 Plots display contrasting soil P budgets

Average annual soil P budgets ranged from -32.3 to 50.3 kg P ha⁻¹ y⁻¹, with a mean value of 1.7 kg P ha⁻¹ y⁻¹. Our results highlight a strong variability between plots (Figure 2c), with 55% of the plots showing a negative soil P budget. In addition to this strong variability, our results also

highlight a strong variability between years for a given field. Over the 5-year period studied, only 7% of the plots exhibited a consistently positive soil P budget, while 30% of the plots exhibited recurrent negative soil P budgets. (Reimer et al. 2020b) Average P inputs were also highly variable, ranging from 0 to 70 kg P ha⁻¹ y⁻¹ (mean 12.7 kg P ha⁻¹ y⁻¹). Similarly, P exports ranged from 0 (assumed for pastures that are only grazed, see Methods) to 37 kg P ha⁻¹ y⁻¹ (mean 11 kg P ha⁻¹ y⁻¹, Figure 2a and b).

A variety of inputs were used on the farms studied (Table 1). Our results revealed that 23% of the fields were fertilized with manure, composted or not, while 20% were fertilized with organic P-rich inputs (i.e., poultry manure and processed animal proteins). The use of plant-based recycled fertilizers such as compost, digestate, or other organic products was limited (6%). There were no mineral sources of P, as none of the farms used rock phosphate as an input, most probably due to its low solubility (Casanova et al., 2002). Note that, apart from rock phosphate, there is no organic fertilizer containing only P, making it difficult to manage P fertilization independently from other nutrients. Concerning exogenous P sources, approximately one-third of the farmers imported fertilizing material from conventional (32%) or organic (26%) farms, while the remaining third could not specify the conventional vs. organic origin of their fertilizers (Table S1). Fertility transfers from conventional to organic

<figure><img src="image_4.png" /><figcaption>**Fig. 2** Distribution of the 5-year average P inputs [kg P ha⁻¹ y⁻¹] (a), average P exports [kg P ha⁻¹ y⁻¹] (b), and average soil P budget [kg P ha⁻¹ y⁻¹] (c) across the 177 plots studied. The blue dotted lines represent the mean values. Note the differences in both X and Y axes across the panels.</figcaption></figure>
farms are a common practice and have been documented in previous studies (Nowak et al. 2013; Maltais-Landry et al. 2019). A recent report by the French Ministry of Agriculture and Food Sovereignty has estimated that only 14% of the P demand of organically managed crops could be met nationally by livestock manure from certified organic farms, highlighting the dependence of current organic systems on conventional livestock (AND International 2022). This dependence could become an issue both if conventional farms increase their reliance on organic fertilizers to the determination of inorganic fertilizers and if organic farming were to expand in the future (Muller et al. 2017; Barbieri et al. 2021). The availability of P fertilizer would become even scarcer, with possible negative effects on the performance of organic systems.

<table><caption>**Table 2** Influencing factors on the 5-year soil P budgets based on the linear model presented in the Methods section. Degrees of freedom (Df), *p*-value, and effect size (η²) are shown.</caption><thead><tr><th></th><th>Df</th><th>p-value</th><th>η<sup>2</sup></th></tr></thead><tbody><tr><td>Cumulated P Inputs</td><td>1</td><td>&lt;0.01***</td><td>0.39</td></tr><tr><td>Phosphorus management</td><td>2</td><td>&lt;0.01***</td><td>0.16</td></tr><tr><td>N-fixing crops frequency</td><td>2</td><td>&lt;0.01***</td><td>0.06</td></tr><tr><td>Fertilization frequency</td><td>2</td><td>&lt;0.01***</td><td>0.06</td></tr><tr><td>Regional agriculture specialization</td><td>2</td><td>&lt;0.01***</td><td>0.04</td></tr><tr><td>Farm Land Use</td><td>2</td><td>0.003**</td><td>0.02</td></tr><tr><td>Fertilizer type</td><td>5</td><td>0.04*</td><td>0.02</td></tr><tr><td>Stocking rates</td><td>3</td><td>0.41</td><td>&lt;0.01</td></tr><tr><td>Adjusted R<sup>2</sup></td><td></td><td>0.72</td><td></td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 3** a Correlation between the 5-year average P inputs [kg P ha⁻¹ y⁻¹] and the 5-year average soil P budget [kg P ha⁻¹ y⁻¹]. The red line shows the linear regression between the two variables. The lighter orange area indicates the standard error. b Relationship between the 5-year average P inputs and outputs [kg P ha⁻¹ y⁻¹]. The light and</figcaption></figure>

## 3.2 Budgets explained by a few variables

The variables we identified as critical for discriminating soil P budgets, listed in Table 2, explained 72% of the observed variance. However, the qualitative variables alone explained only 30% of the total variation. The most influential factor was the cumulated P inputs, which accounted for 39% of the total variability (Table 2). Thus, we found that P inputs were significantly correlated with P budgets both in the correlation analysis (R² = 0.88, Figure 3a) and in the FAMD (Figure S2). These results confirm the relationship found in previous studies, but in different geographical and socio-economic contexts (Reimer et al. 2020a). Cumulative P inputs were followed in order of importance by P management, the frequency of N-fixing crops, and the fertilization frequency (Table 2). In contrast, soil P budgets were not correlated with P outputs (R² = 0.14, *p*-value > 0.05). The average P use efficiency (PUE, calculated as export/ inputs) was relatively high and above 1 (1.13), but highly variable between plots (Figure 3b). In addition, about one third of the plots had a PUE > 125%, and were therefore in a situation of soil P depletion. This may increase the risk of negative effects on crop performance, especially when combined with low plant-available soil P status.

An interesting result was that a high frequency of N-fixing crops was associated with low P inputs, low fertilization frequency, and negative P budgets (Figure 4). This inverse relationship between the frequency of N-fixing crops in rotations and the P budgets is significant (Table 2) and raises concerns about the long-term P fertility of organic farms that strongly rely on BNF for N fertility

<figure><img src="image_6.png" /></figure>

dark shaded areas represent situations with high (PUE > 125%, light grey) and low (PUE < 75%) Phosphorus Use Efficiency, respectively, indicating plots at risk of either soil P mining or soil P accumulation. P use efficiency (PUE) is calculated as the ratio of P inputs to P outputs.

<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 4** Distribution of the 5-year mean soil P budget [kg P ha⁻¹ y⁻¹] as a function of the frequency of P fertilization for varying frequency of N-fixing crops in the rotation (a number of years lower than 1.5, b number of years between 1.5 and 2.5, and c number of years larger than 2.5). Purple, blue, and green bars show the proportion of plots with low, medium, and high fertilization frequency, respectively,</figcaption></figure>

within each corresponding N-fixing crop frequency group. Mean soil P budget [kg P ha⁻¹ y⁻¹] as a function of the 5-year mean soil P inputs [kg P ha⁻¹ y⁻¹] for varying frequency of N-fixing crops in the rotation (d number of years lower than 1.5, e number of years between 1.5 and 2.5, and f number of years larger than 2.5).

management, confirming our second hypothesis. This is particularly important since a large body of the literature has shown that BNF efficiency is strongly impacted by plant-available P resources (Bello Suleiman Kehindeand Muraina 2023; Míguez-Montero et al. 2020; Zhong et al. 2023). This result corroborates the findings of previous publications analyzing farm-gate P budgets (Nowak et al. 2013; Reimer et al. 2020a). At a larger scale, a potential increase of leguminous species in European crop rotations was shown to reduce soil available P by -0.22 mg P kg⁻¹ of soil (Muntwyler et al. 2024). As fodders have been shown to fix more N than pulse crop species (Peoples et al. 2021; Barbieri et al. 2023), we wondered whether more fodders than pulses in crop rotations would be associated with lower soil P budgets. From our dataset, we found similar P budgets regardless of the type of legume crops grown (pulse vs fodder), invalidating this hypothesis (Figure S3).

## 3.3 Sustainability management of soil P fertility is not a priority for farmers

With regards to the availability of organic fertilizers, we found that 45% of farmers reported no particular difficulties in retrieving organic fertilizers, while 38% reported difficulties (Table S1). Interestingly, this distribution was similar between farms in stockless regions (42% vs. 37% respectively) and farms in mixed or livestock regions (48% vs. 39%, respectively), suggesting that the farmers' ability to retrieve organic fertilizers was not correlated with their farming environment. This is due to the fact that farms in stockless regions mainly used processed animal proteins or poultry manure, which are easily transportable fertilizers and can therefore be sourced far from the local environment of the farm. This finding is in contradiction with the common idea that farms in stockless regions may have difficulties in
finding organic fertilizers (Nowak et al. 2015). Nevertheless, farms in stockless regions that source their fertilizers outside their close environment are likely to be more vulnerable to fertilizers shortage or price increases. This phenomenon has already been observed following the increase in synthetic fertilizer prices after the Ukrainian crisis (Ihle et al. 2022). Concerning P management, 72% of the farmers reported not to pay any specific attention to P fertilization management (Table S1), which could be a problem in organic farming, especially in cropping systems rich in leguminous crop species, due to the higher risk of having negative P budgets (21% of the plots were not fertilized at all during the 5 years studied). In addition, 23% of farmers reported to be unsatisfied with the P fertility status of their plots.

## 3.4 Soil P budgets differ between plot types

The Hierarchical Cluster Analysis highlighted five separate clusters, i.e., “types” of different plots (Table S2), providing strong evidence in support of hypothesis 1, that combining farm or field characteristics and fertilization practices discriminates soil P budgets.

The plots belonging to Cluster 1 are located in stockless regions and are strongly characterized by the absence of P inputs. Consequently, the fertilization frequency is low, and the frequency of N-fixing crops is medium. Such plots are often found on farms without livestock. Due to the absence of P inputs, their average P budget is negative (−7.4 kg P ha⁻¹ y⁻¹, Figure 5), suggesting that they are at risk of long-term soil P depletion. As most of these plots are located in a stockless environment or have no livestock on the farm, access to fertilizers to compensate for their soil P exports may be challenging. One potential solution is to purchase processed organic fertilizers, but the price and origin of the products could be prohibitive for many farmers.

Conversely, all the other clusters are composed of P-fertilized plots, although the origin of the organic fertilizers used is highly heterogeneous. The plots belonging to Cluster 5 are distinguished by the use of fertilizers produced on the farm (P endogenous) due to the presence of animals on the farm. These plots belong to mixed farming systems with both cropland and pastures and are located in livestock regions. The frequency of P fertilization is high or medium, and the fertilizer is mainly livestock manure, with an average P input of less than 17 kg P ha⁻¹ y⁻¹. Finally, the frequency of N-fixing crops is medium. These plots are slightly P deficient, with an average soil P budget of −0.55 kg P ha⁻¹ y⁻¹. In the long term, their soil P fertility is not particularly endangered, as P circulates within the farm from feed to manure to feed again.

The remaining three clusters are distinguished by the use of exogenous P inputs. Cluster 2 and 4 are mainly composed of arable plots located in stockless regions.

The remaining three clusters were in the type of fertilizer used, the frequency of fertilization, and the presence of livestock animals on the farm. On the one hand, the plots of Cluster 2 belong to stockless farms. They are fertilized on a medium-to-low frequency basis using various external P sources. On the other hand, plots of Cluster 4 belong either to stockless farms or to farms with high stocking density. These plots are often fertilized with exogenous poultry manure, which is a P-rich organic substrate. Thus, if plots of Cluster 2 show a relatively balanced P budget (0.7 kg P ha⁻¹ y⁻¹), plots of Cluster 4 are characterized by a highly positive P budget (+10.9 kg P ha⁻¹ y⁻¹, Figure 6). Plots with highly positive soil P budgets may have the opportunity to increase the frequency of N-fixing crops in their rotations to supply N without adding excess P to their soils. Such P budget values are quite high compared to a recent study that reported a range of −2.7 to +4.3 kg P ha⁻¹ y⁻¹ within the EU countries under non-organic conditions (Panagos et al. 2022). It should be noted that (Panagos et al. 2022) included P atmospheric deposition, P chemical weathering, and P erosion in their calculation. However, these variables only accounted for 3.5%, 0.5%, and 2.5% of total P inputs, respectively, confirming the small contribution of these factors to soil P budgets.

Finally, Cluster 3 is characterized by plots belonging to farms with both permanent pasture and cropland with temporary pasture, as well as a medium number of livestock animals on the farm. The plots are fertilized with various external inputs with a medium frequency. The frequency of N-fixing crops is also medium. The average P budgets of these plots are close to zero (0.06 kg P ha⁻¹ y⁻¹). Note that we found an uneven distribution and a range of P budgets of the plots between clusters. In particular, Cluster 4 and 5 are characterized by a wide range of P budgets (Figure 6).

The results of our cluster analysis revealed contrasting situations with respect to P management practices and thus soil P budgets. Interestingly, the absence of livestock on the farms led to divergent situations with, on the one hand, cropland plots that were not fertilized at all (Cluster 1) and, on the other hand, plots that received significant amounts of exogenous P inputs (Cluster 4). The latter plots may be indicative of organic farms with management practices that are similar to those of conventional farms, with a high reliance on imported P fertilizers driven by economic considerations (Darnhofer et al. 2010). Conversely, plots on livestock farms exhibited more balanced soil P budgets.

## 3.5 Cluster types are not correlated with changes in plant available soil Olsen-P

Our objective was to understand the factors influencing the observed heterogeneity in soil P budgets under organic farming. We found no relationship between the rate of change of

<figure><img src="image_9.png" /></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 5** Cluster characteristics resulting from the hierarchical cluster analysis on 5 principal components (see methods). Base images assembled from “Freepik”.</figcaption></figure>

<figure><img src="image_11.png" /></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 6** a Mean soil P budgets (and Standard Error) and b. distribution on the soil P budget (in kg P ha⁻¹ y⁻¹) for each of the five clusters. In panel b, the black vertical line shows the value 0, while the dotted blue line represents the mean cluster value.</figcaption></figure>

soil Olsen-P and the cluster classification (Table S2). Similarly, no direct relationship was found between the annual change in Olsen-P and average P budgets, thus not confirming our third hypothesis. For example, plots belonging to Cluster 1, which have the most negative soil P budgets, show increasing Olsen-P values in time (+2.2 mg P kg⁻¹ y⁻¹). Similarly, plots belonging to Cluster 4 showed slightly decreasing Olsen-P values (−0.1 mg P kg⁻¹ y⁻¹), despite strongly positive soil P budgets. Moreover, none of the variables included in this study could explain the observed variability in soil Olsen-P variation (Table S2). While a relationship between the temporal evolution of soil available P and cumulated soil P budgets might be expected, all studies investigating such a relationship have failed to show a direct relationship (Nesme et al. 2012; Le Noë et al. 2018) probably due to the fact that the annual P flows are small compared to the total soil P stocks. This would suggest that Olsen-P is more influenced by long-term practices, i.e., soil P legacies. Cumulative budgets over a longer time period (i.e., 30–50 years) would therefore be required to explain variations in soil available P over time (Zhang et al. 2020). However, the lack of relationship could also be due to the uncertainties associated with soil sampling, most probably performed by different practitioners, and analytical procedures between the recent and historical soil analyses used in this study. The relatively short interval between soil sampling in this study and older analyses (~6 years; range between 1 and 33 years, data not shown) may also explain the difficulties in detecting a relationship between soil P budgets and soil available P variations. These limitations may explain the differences with other studies that have found a direct relationship between the P budget and the rate of change of Olsen-P (Messiga et al. 2012; Shen et al. 2014; Hua et al. 2016).

## 4 Conclusions

Our comprehensive database allowed for the first time to identify contrasting plot types in terms of their soil P budget and risk of long-term soil P fertility depletion, showing, as expected, that soil P budgets are driven by a combination of farm or field characteristics and fertilization practices. On the contrary, we could not show any correlation between positive P budgets and an increase in soil available Olsen-P over time. As expected, plots with a high frequency of legume crops in rotation and no P inputs are at high risk in terms of their long-term soil P fertility. This calls for a systemic management of N and P fertilization. On the contrary, plots with the highest soil P budgets relied mainly on high imports of exogenous P, independently of the presence of livestock on the farm. Such “conventional-like” management, i.e., based on high use of imported P fertilizers, may be potentially useful to maintain and eventually increase the soil P stocks in the long term, but at the risk of higher P leakage to the environment. Our research also shows that the farmers’ degree of attention to P management was low. This calls for more work that evaluates farmers’ attitudes to soil fertility to prevent long-term soil P depletion and associated risks for productivity. A strategic management of P fertilization is necessary to avoid soil P imbalances that may be difficult to reverse in the medium to long term. To better understand the choices farmers make, further research could investigate the influence of social criteria (e.g., age, education, farm ownership, entrepreneurship) on farmers’ strategic choices in P management. Our analysis has shown that the presence of livestock on farms limits the soil P deficit but does not necessarily ensure a balanced P budget either. The reintegration of crops and livestock has

<figure><img src="image_13.png" /></figure>
many advantages. In particular, it allows the nitrogen fixed by forage crops to be transferred to cash crops, thereby reducing the import of external nitrogen inputs. However, in terms of P, it poses a risk of long-term soil P depletion. Finally, other strategies aiming at closing P cycles should be planned. Among such strategies, the recycling of P resources within the whole food system and value chain (e.g., from consumption and urban waste resources) is crucial to ensure the long-term P sustainability of organic farming systems.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s13593-025-01047-w.

**Acknowledgements** The authors would like to express their gratitude to all the students from Bordeaux Sciences Agro (Tanguy Thiery, Pascale Edelmann, Lucie Cerise, Lara Pace, Sylvain Mesplou, Amandine Dellac, Romane Tandonnet and Audric Argillier) and ISARA Lyon (Jean Paul Epiphane Dogue, Sebastien Galland, Anna Gautier, Pauline Lefranc), as well as to Florian Celette, who contributed to the collection of the data used in this study. The authors would also like to thank all the farmers for their availability, Jean-Pierre da Costa for the statistical support, and all the reviewers that greatly contributed to improving our manuscript.

**Authors' contributions** Conceptualization, all authors; methodology, P.B., J.D.; investigation, P.B., J.D., M.M., T.N., G.V.; writing — original draft, P.B and J.D; writing, review and editing, all authors; funding acquisition, T.N. and G.V.

**Funding** This study was financed by the CASDAR project 'Phospho-Bio' n° 20AIP1626879.

**Data availability** The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

## Declarations

**Ethics approval** Not applicable.

**Consent to participate** Not applicable.

**Consent for publication** Not applicable.

**Conflict of interest** The authors declare no competing interests.

**Declaration of generative AI in scientific writing** During the preparation of this work, the authors used DeepL Write to improve readability and the overall language quality. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

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