# How does low-input crop management perform? Economic, environmental and social assessment data on different arable crop management strategies in France

Simon Buresi, Chantal Loyce, Rémy Ballot

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Simon Buresi, Chantal Loyce, Rémy Ballot. How does low-input crop management perform? Economic, environmental and social assessment data on different arable crop management strategies in France. Data in Brief, 2026, 64, pp.112316. 10.1016/j.dib.2025.112316. hal-05504216

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Data Article

# How does low-input crop management perform? Economic, environmental and social assessment data on different arable crop management strategies in France

<figure><img src="image_2.png" /></figure>

Simon Buresi, Chantal Loyce, Rémy Ballot*

Université Paris-Saclay, AgroParisTech, INRAE, UMR Agronomie, F-91120 Palaiseau

ARTICLE INFO

Article history:

Received 17 December 2024

Revised 3 June 2025

Accepted 18 November 2025

Available online 22 November 2025

Dataset link: Référentiel de performances économiques, environnementales et sociales de pratiques culturales économies en intrants (Original data)

Keywords:

Pesticides

Fertilisers

Crop management route

Crop sequence

Expert knowledge elicitation

ABSTRACT

We analysed public raw data from field surveys and elicited knowledge from agricultural advisors to update data about low-input arable crop management performances in France. This dataset includes information about 26 arable crops in 10 zones covering the entire territory of metropolitan France. We differentiated five crop management strategies, ranging from conventional, which is fully input-reliant farming, to organic farming, through integrated pest management and agroecology. Typical crop management routes are given for each situation and assessed through a set of 30 economic, environmental and social performance indicators. This data could be useful to support public decision making regarding low-input agriculture.

© 2025 The Author(s). Published by Elsevier Inc.

This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/)

ARTICLE INFO

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: remy.ballot@inrae.fr (R. Ballot).</footnote>
Specifications Table

<table><tr><td>Subject</td><td>Agronomy and Crop Science</td></tr><tr><td>Specific subject area</td><td>Multicriteria assessment of arable crop low-input management</td></tr><tr><td>Type of data</td><td>Table</td></tr><tr><td>Data collection</td><td>Public data from field surveys were analysed and completed using expert knowledge elicited from agricultural advisors</td></tr><tr><td>Data source location</td><td>Metropolitan France</td></tr><tr><td>Data accessibility</td><td>Repository name: Référentiel de performances économiques, environnementales et sociales de pratiques culturales économies en intrants<br>Data identification number: https://doi.org/10.57745/LCA116<br>Direct URL to data: https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/LCA116</td></tr><tr><td>Related research article</td><td>None</td></tr></table>

1. Value of the Data

* In the European Union and especially in France, public policies target cuts in the use of agricultural inputs (e.g. pesticides and fertilisers) and thus require generic reference points regarding low-input crop management performances.

* A set of such reference points was collected for France around 2010. It needs to be updated, to account for progress in knowledge about low-input crop management.

* Combining 1) an analysis of public data from field surveys and 2) knowledge elicited from experts, namely agricultural advisors involved in on-farm experiments with low-input crop management systems, we compiled data covering the whole of metropolitan France.

* This data considers five crop management strategies, ranging from conventional agriculture, which is fully reliant on inputs, to organic farming, which prohibits the use of synthetic pesticides and fertilisers.

* We assessed the economic, environmental and social performances of most of arable crops for these five crop management strategies, according to 30 indicators.

* This data could be used by other researchers or public policy makers to support forecast studies about scaling-up low-input agriculture.

2. Background

In 2010, Butault et al. [1] published reference points about performances of low-input crop management. This data aimed at supporting the French Ecophyto plan, which targeted to halve the use of pesticides in agriculture. This target remains relevant, but reference points are no longer up to date. Indeed, knowledge have progressed in >10 years, as low-input crop management has been experienced, within the Ecophyto plan framework. Data presented here is an update of the reference points previously released by Butault et al.

3. Data Description

The data is composed of five CSV files.

3.1. 1_crop_managementRoutes.csv

The file "1_crop_managementRoutes.csv" contains quantitative descriptions of typical crop management routes [2] associated to each cropping situation considered (i.e. a given crop, cul-
tivated in a given zone, within a given crop sequence type and according to a given crop management strategy). Table 1 presents its content.

### 3.2. 2sol parameters.csv

The file "2sol_parameters.csv" contains information that is specific to each crop considered and needs to be combined with quantitative typical crop management route information to compute indicators values. Table 2 presents its content.

### 3.3. 3Input_parameters.csv

The file "3input_parameters.csv" contains information about inputs that is not crop-specific and was involved in computing the values of indicators. Table 3 presents its content.

### 3.4. 4Operation_parameters.csv

The file "4operation_parameters.csv" contains information related to the technical operations involved in computing indicator values. Table 4 presents its content.

### 3.5. 5Indicators_values.csv

The file "5indicators_values.csv" contains the values of indicators describing the economic, environmental and social performances of the cropping situations considered. These values were either pulled from the 1sol_management routes.csv file or computed from the four preceding files. Table 5 presents the content of this final file.

## 4. Experimental Design, Materials and Methods

### 4.1. Cropping situations described

In our dataset, the performances of low-input crop management are provided at the elementary scale of a cropping situation. By cropping situation, we mean a given crop, cultivated in a given zone, within a given crop sequence type and according to a given crop management strategy. 496 cropping situations are described.

#### 4.1.1. Crops

We considered 26 arable crops. These include major arable crops, which are considered by the "Pratiques culturales grandes cultures et prairies" field survey [3] (i.e. soft wheat, durum wheat, barley, triticale, grain corn, silage corn, oilseed rape, sunflower, oilseed flax, fibre flax, protein pea, faba bean, soybean, sugar beet, potatoes and temporary grasslands). We differentiated winter and spring types for barley, protein pea, faba bean and oilseed flax. We also considered additional minor crops, as they could be used as diversification crop or are frequently cultivated in organic farming (i.e. alfalfa, hemp, chickpea, lentil, buckwheat, sorghum and cereal-pulse intercrop).
<table><caption>**Table 1**

Description of the contents of the file "1_crop_managementRoutes.csv".</caption><thead><tr><td>Variable</td><td>Name</td><td>Description</td></tr></thead><tbody><tr><td>1</td><td>Crop</td><td>Crop considered (as defined in Section 3.1.1.)</td></tr><tr><td>2</td><td>Zone</td><td>Zone considered (as defined in Section 3.1.2)</td></tr><tr><td>3</td><td>crop_sequence_type</td><td>Crop sequence type considered (as defined in Section 3.1.3)</td></tr><tr><td>4</td><td>crop_management_strategy</td><td>Crop management strategy considered (as defined in Section 3.1.4)</td></tr><tr><td>5</td><td>duration</td><td>Number of years of cultivation of the crop considered (1 year for most of crops, except temporary grasslands or forage legumes)</td></tr><tr><td>6</td><td>composition</td><td>Number of species composing the crop considered (1 specie for most of crops, except temporary grasslands or intercrops)</td></tr><tr><td>7</td><td>plowing</td><td>Plowing frequency (0 if never plowed or 1 if systematically plowed)</td></tr><tr><td>8</td><td>secondary_tillage</td><td>Average number of secondary tillage operations</td></tr><tr><td>9</td><td>other_mechanical_operations</td><td>Average number of other mechanical operations</td></tr><tr><td>10</td><td>double_crop</td><td>Frequency of double crop sowing before the crop considered</td></tr><tr><td>11</td><td>double_crop_type</td><td>Type of double crop sown before the crop considered</td></tr><tr><td>12</td><td>covercrop</td><td>Frequency of cover crop sowing before the crop considered</td></tr><tr><td>13</td><td>covercrop_type</td><td>Type of cover crop sown before the crop considered</td></tr><tr><td>14</td><td>sowing</td><td>Sowing frequency of the crop considered (1 time a year for most of crops, except temporary grasslands or forage legumes)</td></tr><tr><td>15</td><td>sowing_rate</td><td>Average sowing rate (kg.ha<sup>-1</sup> or seed.ha<sup>-1</sup> depending on the crop)</td></tr><tr><td>16</td><td>harrow_weeding</td><td>Average number of harrow weeding operations</td></tr><tr><td>17</td><td>hoe_weeding</td><td>Average number of hoe weeding operations</td></tr><tr><td>18</td><td>hand_weeding</td><td>Average number of hand weeding operations</td></tr><tr><td>19</td><td>pesticide_spraying</td><td>Average number of pesticides spraying operations</td></tr><tr><td>20</td><td>localised_spraying</td><td>Average number of localised weeding operations</td></tr><tr><td>21</td><td>fertilisers_spreading</td><td>Average number of fertilisers spreading operations</td></tr><tr><td>22</td><td>dble_crop_fertilisers_spreading</td><td>Average number of fertilisers spreading operations on double crop</td></tr><tr><td>23</td><td>manure_spreading</td><td>Average number of manure spreading operations</td></tr><tr><td>24</td><td>manure_type</td><td>Type of manure spread</td></tr><tr><td>25</td><td>dble_crop_harvest</td><td>Average number of double crop harvest operations</td></tr><tr><td>26</td><td>crop_harvest</td><td>Average number of crop harvest operations (1 operation for most of crops, except temporary grasslands or forage legumes)</td></tr><tr><td>27</td><td>hay</td><td>Frequency of hay harvest (forage crops only)</td></tr><tr><td>28</td><td>silage</td><td>Frequency of silage harvest (forage crops only)</td></tr><tr><td>29</td><td>wrapping</td><td>Frequency of wrapping harvest (forage crops only)</td></tr><tr><td>30</td><td>seed_coating</td><td>Frequency of use of coated seed</td></tr><tr><td>31</td><td>herbicides_TFI</td><td>Average herbicides Treatment Frequency Index</td></tr><tr><td>32</td><td>fungicides_TFI</td><td>Average fungicides Treatment Frequency Index</td></tr><tr><td>33</td><td>insecticides_TFI</td><td>Average insecticides Treatment Frequency Index</td></tr><tr><td>34</td><td>in_furrow_ins_TFI</td><td>Average in-furrow insecticides Treatment Frequency Index</td></tr><tr><td>35</td><td>other_TFI</td><td>Average other pesticides Treatment Frequency Index</td></tr><tr><td>36</td><td>molluscicide_TFI</td><td>Average molluscicides Treatment Frequency Index</td></tr><tr><td>37</td><td>growth_regulator_TFI</td><td>Average growth regulator Treatment Frequency Index</td></tr><tr><td>38</td><td>TFI</td><td>Average total Treatment Frequency Index</td></tr><tr><td>39</td><td>iron_phosphate_bio_TFI</td><td>Average iron phosphate biocontrol Treatment Frequency Index</td></tr><tr><td>40</td><td>tricho_bio_TFI</td><td>Average trichogramma biocontrol Treatment Frequency Index</td></tr><tr><td>41</td><td>copper_sulfate_bio_TFI</td><td>Average copper sulfate biocontrol Treatment Frequency Index</td></tr><tr><td>42</td><td>N_rate</td><td>Average nitrogen rate (kgN.ha<sup>-1</sup>)</td></tr><tr><td>43</td><td>dble_crop_N_rate</td><td>Average nitrogen rate on double crop (kgN.ha<sup>-1</sup>)</td></tr><tr><td>44</td><td>manure_rate</td><td>Average manure rate (t.ha<sup>-1</sup> or m<sup>3</sup>.ha<sup>-1</sup>)</td></tr><tr><td>45</td><td>irrigation</td><td>Frequency of irrigation</td></tr><tr><td>46</td><td>irrigation_operations</td><td>Average number of irrigation operation</td></tr><tr><td>47</td><td>irrigation_rate</td><td>Average rate of irrigation (mm)</td></tr><tr><td>48</td><td>dble_crop yield</td><td>Average double crop yield (t.ha<sup>-1</sup>)</td></tr><tr><td>49</td><td>crop yield</td><td>Average crop yield (t.ha<sup>-1</sup>)</td></tr></tbody></table>
<table><caption>**Table 2**
Description of the contents of the file "2sirop parameters.csv".</caption><thead><tr><th>Variable</th><th>Name</th><th>Description</th></tr></thead><tbody><tr><td>1</td><td>crop</td><td>Crop considered</td></tr><tr><td>2</td><td>destination</td><td>Destination of the crop</td></tr><tr><td>3</td><td>composition</td><td>Number of species composing the crop considered (1 specie for most of crops, except temporary grasslands or intercrops)</td></tr><tr><td>4</td><td>priceoj conv</td><td>Selling price of the crop in conventional farming – lowest price recorded over 2011–2021 (€.t<sup>-1</sup>)</td></tr><tr><td>5</td><td>priceoj conv</td><td>Selling price of the crop in conventional farming – highest price recorded over 2011–2021 (€.t<sup>-1</sup>)</td></tr><tr><td>6</td><td>priceoj conv</td><td>Selling price of the crop in conventional farming – average price recorded over 2011–2021 (€.t<sup>-1</sup>)</td></tr><tr><td>7</td><td>priceoj org</td><td>Selling price of the crop in organic farming – average price recorded over 2011–2021 (€.t<sup>-1</sup>)</td></tr><tr><td>8</td><td>expN straw</td><td>Nitrogen exportation when straw is exported (kgN.t<sup>-1</sup>)</td></tr><tr><td>9</td><td>expN straw</td><td>Nitrogen exportation when straw is returned (kgN.t<sup>-1</sup>)</td></tr><tr><td>10</td><td>lower heating value</td><td>Amount of heat released by combating a specified quantity (GJ.t<sup>-1</sup>)</td></tr><tr><td>11</td><td>seed cost cert</td><td>Cost of certified seeds in conventional farming (€.unit<sup>-1</sup>)</td></tr><tr><td>12</td><td>seed cost cert</td><td>Cost of certified seeds in organic farming (€.unit<sup>-1</sup>)</td></tr><tr><td>13</td><td>seed cost farm</td><td>Cost of farm-saved seeds in conventional farming (€.unit<sup>-1</sup>)</td></tr><tr><td>14</td><td>seed cost farm</td><td>Cost of farm-saved seeds in organic farming (€.unit<sup>-1</sup>)</td></tr><tr><td>15</td><td>seed cost</td><td>Unit employed for seeds cost</td></tr><tr><td>16</td><td>seed cert share</td><td>Share of certified seeds considered</td></tr><tr><td>17</td><td>seed farm share</td><td>Share of farm-saved seeds considered</td></tr><tr><td>18</td><td>seeder type</td><td>Type of seeder considered</td></tr><tr><td>19</td><td>cost herbicides</td><td>Cost of one unit of herbicides Treatment Frequency Index (€)</td></tr><tr><td>20</td><td>cost fungicides</td><td>Cost of one unit of fungicides Treatment Frequency Index (€)</td></tr><tr><td>21</td><td>cost insecticides</td><td>Cost of one unit of insecticides Treatment Frequency Index (€)</td></tr><tr><td>22</td><td>cost in furrow</td><td>Cost of one unit of in-furrow insecticides Treatment Frequency Index (€)</td></tr><tr><td>23</td><td>cost growth regulator</td><td>Cost of one unit of growth regulator Treatment Frequency Index (€)</td></tr><tr><td>24</td><td>cost molluscicide</td><td>Cost of one unit of molluscicides Treatment Frequency Index (€)</td></tr><tr><td>25</td><td>cost iron phosphate bio</td><td>Cost of one unit of iron phosphate biocontrol Treatment Frequency Index (€)</td></tr><tr><td>26</td><td>cost tricho bio</td><td>Cost of one unit of trichogramma biocontrol Treatment Frequency Index (€)</td></tr><tr><td>27</td><td>cost copper sulfate bio</td><td>Cost of one unit of copper sulfate biocontrol Treatment Frequency Index (€)</td></tr></tbody></table>

<table><caption>**Table 3**
Description of the contents of the file "3input parameters.csv".</caption><thead><tr><th>Variable</th><th>Name</th><th>Description</th></tr></thead><tbody><tr><td>1</td><td>category</td><td>Category of the input considered (fertilisation, fuel or irrigation)</td></tr><tr><td>2</td><td>input</td><td>Input considered</td></tr><tr><td>3</td><td>cost_low</td><td>Purchase cost of the input considered – lowest price recorded over 2011–2021 (€/unit)</td></tr><tr><td>4</td><td>cost_average</td><td>Purchase cost of the input considered – average price recorded over 2011–2021 (€/unit)</td></tr><tr><td>5</td><td>cost_high</td><td>Purchase cost of the input considered – highest price recorded over 2011–2021 (€/unit)</td></tr><tr><td>6</td><td>unit</td><td>Unit of cost considered</td></tr><tr><td>7</td><td>N_content</td><td>Nitrogen content of the input (kgN.t<sup>-1</sup>)</td></tr><tr><td>8</td><td>share_use</td><td>Share of fertilisers use</td></tr><tr><td>9</td><td>indirect energy cost</td><td>Indirect energy cost of fertiliser production (GJ.kgN<sup>-1</sup>)</td></tr><tr><td>10</td><td>direct energy cost</td><td>Direct energy cost of irrigation (GJ.mm<sup>-1</sup>)</td></tr><tr><td>11</td><td>CO2 factor emission</td><td>CO2 emission factors used to calculate GHG (teq CO2) emissions from fertilisation, fuel, irrigation and livestock unit</td></tr><tr><td>12</td><td>N2O factor emission</td><td>N2O emission factor used to calculate GHG (teq CO2) emissions from nitrogen fertiliser use</td></tr></tbody></table>
<table><caption>**Table 4**

Description of the contents of the file "4 operation_parameters.csv".</caption><thead><tr><td>Variable</td><td>Name</td><td>Description</td></tr></thead><tbody><tr><td>1</td><td>operation</td><td>Operation considered</td></tr><tr><td>2</td><td>description</td><td>Details of the operation</td></tr><tr><td>3</td><td>fixed_cost</td><td>Cost of the operation, excluding fuel and labour cost (€.ha<sup>-1</sup>)</td></tr><tr><td>4</td><td>cost_fuel_incl</td><td>Cost of the operation including fuel cost (€.ha<sup>-1</sup>)</td></tr><tr><td>5</td><td>cost_labor_incl</td><td>Cost of the operation including labour cost (€.ha<sup>-1</sup>)</td></tr><tr><td>6</td><td>cost_fuel_and_labor_incl</td><td>Cost of the operation including fuel and labour cost (€.ha<sup>-1</sup>)</td></tr><tr><td>7</td><td>ha_h</td><td>Acreage covered per hour (ha.h<sup>-1</sup>)</td></tr><tr><td>8</td><td>h_ha</td><td>Time required to cover 1 hectare (h.ha<sup>-1</sup>)</td></tr><tr><td>9</td><td>fuel_cons</td><td>Fuel consumption (l.ha<sup>-1</sup>)</td></tr><tr><td>10</td><td>energy_cost</td><td>Energy cost (GJ.ha<sup>-1</sup>)</td></tr></tbody></table>

### 4.1.2. Zone

We subdivided the territory of metropolitan France into 11 zones, which we considered as homogeneous from an agricultural point of view.

We obtained these 11 zones by classifying metropolitan France's 96 départements (administrative counties) according to 19 variables representing the agricultural characteristics of each one (Table 6).

We fed these variables into a Principle Component Analysis (PCA) using the R package FactoMineR 2.1's PCA() function [7]. The first seven components in this PCA, which account collectively for 88 % of the variance, were fed into the same R package's HCPC() function (hierarchical clustering on principle components), which yielded our 11 groups.

We set aside the first group, which only included the island of Corsica, and ceased to take it into account as no further data were available.

### 4.1.3. Crop sequence type

Each of the 11 zones we had outlined could encompass a diverse array of production situations [8]. We assumed that the diversity of production situations in a given zone should be reflected by the diversity of crop sequences observed. For example, in a given zone, crop sequences that include temporary grassland should be used more frequently at medium altitudes or in sub-zones presenting limited potentialities, which specialize in livestock farming, whereas crop sequences that include root crops should be confined to sub-zones which present high potentialities and are specialised in arable crop farming. This is why the dataset differentiated values according to crop sequence typology. This typology is based on a classification of crop sequences drawn from the French LPIS [5], according to the method proposed by Ballot et al. [9]. First, for each crop sequence, we calculated temporal frequencies over the period 2015–2019 of 14 (groups of) crops considered as typical of production situations: soft wheat, durum wheat, barley, triticale, corn, oilseed rape, sunflower, soybean, pea, sugar beet, potatoes, fibre flax, temporary grasslands, forage legumes.

We used these frequencies as input variable in a PCA [9]. The 10 first components, which account together for 85 % of the variance, were used as input of a hierarchical clustering differentiating 10 groups of crop sequence, as presented in Table 7.

### 4.1.4. Crop management strategy

We followed the typology proposed by Butault and al. [1] and Jacquet et al. [10] to identify five crop management strategies (Table 8). Based on the Efficiency-Substitution-Redesign framework [11], this typology remains accurate to differentiate crop management strategies according to their degree of reliance on pesticides or synthetic inputs in general.
<table><caption>**Table 5**
Description of the contents of the file "5_indicators_values.csv".</caption><thead><tr><th>Variable</th><th>Name</th><th>Description</th></tr></thead><tbody><tr><td>1</td><td>crop</td><td>Crop considered (as defined in Section 3.1.1.)</td></tr><tr><td>2</td><td>zone</td><td>Zone considered (as defined in Section 3.1.2)</td></tr><tr><td>3</td><td>crop_sequence_type</td><td>Crop sequence type considered (as defined in Section 3.1.3)</td></tr><tr><td>4</td><td>crop_management_strategy</td><td>Crop management strategy considered (as defined in Section 3.1.4)</td></tr><tr><td>5</td><td>crop yield</td><td>Crop yield (t.ha<sup>-1</sup>), as defined in 1_crop_managementRoutes.csv</td></tr><tr><td>6</td><td>herbicides_TFI</td><td>Herbicides Treatment Frequency Index, as defined in 1_crop_managementRoutes.csv</td></tr><tr><td>7</td><td>fungicides_TFI</td><td>Fungicides Treatment Frequency Index, as defined in 1_crop_managementRoutes.csv</td></tr><tr><td>9</td><td>insecticides_TFI</td><td>Insecticides Treatment Frequency Index, as defined in 1_crop_managementRoutes.csv</td></tr><tr><td>10</td><td>other_TFI</td><td>Other Treatment Frequency Index, as defined in 1_crop_managementRoutes.csv</td></tr><tr><td>11</td><td>TFI</td><td>Total Treatment Frequency Index, as defined in 1_crop_managementRoutes.csv</td></tr><tr><td>12</td><td>bio_TFI</td><td>Biocontrol Treatment Frequency Index, as defined in 1_crop_managementRoutes.csv</td></tr><tr><td>13</td><td>total_N_rate</td><td>Total nitrogen rate (kg<sub>N</sub>.ha<sup>-1</sup>) as defined in 1_crop_managementRoutes.csv for crop and double crop: total_N_rate = N_rate + dble_crop_N_rate</td></tr><tr><td>14</td><td>N_balance</td><td>Nitrogen balance (kg<sub>N</sub>.ha<sup>-1</sup>), difference between nitrogen inputs, given in 1_crop_managementRoutes.csv and outputs, considering nitrogen exportation by yield unit, given in 2_crop_parameters.csv: N_balance = N_rate + manure_rate.N_content - crop yield.exp_N (straw exp or rest)</td></tr><tr><td>15</td><td>irrigation_rate</td><td>Irrigation rate (mm), as defined in 1_crop_managementRoutes.csv</td></tr><tr><td>16</td><td>livestock_density</td><td>Livestock density (livestock unit.ha<sup>-1</sup>), equal to 0 for non-forage crops</td></tr><tr><td>17</td><td>milk_production</td><td>Milk production (l.ha<sup>-1</sup>), equal to 0 for non-forage crops</td></tr><tr><td>18</td><td>meat_production</td><td>Meat production (kg.ha<sup>-1</sup>), equal to 0 for non-forage crops</td></tr><tr><td>19</td><td>gross_product</td><td>Gross product (€.ha<sup>-1</sup>), production, given in 1_crop_managementRoutes.csv multiplied by its price, given in 2_crop_parameters.csv: gross_product = crop yield.priceoj average (or price_org) Forage crops gross product corresponds to the sale of hay or silage.</td></tr><tr><td>20</td><td>gross_product_livestock_incl</td><td>Gross product (€.ha<sup>-1</sup>), same as gross_product, but for forage crops, gross product correspond to the sale of milk or meat.</td></tr><tr><td>21</td><td>variable_cost</td><td>Variable cost (€.ha<sup>-1</sup>) corresponding to seeds, fertilisers, manure, pesticides and water. Sum of all input rates, given in 1_crop_managementRoutes.csv multiplied by their unit costs, given in 3_input_parameters.csv: variable_cost = &Sigma;<sub>input</sub> rate.unit_cost</td></tr><tr><td>22</td><td>mechanisation_cost</td><td>Mechanisation cost (€.ha<sup>-1</sup>), sum of all operations frequencies, described in 1_crop_managementRoutes.csv, multiplied by their cost, given in 4_operations_1_crop_managementRoutes.csv (fuel and labour were not considered): mechanisation_cost = &Sigma;<sub>operation</sub> frequency fixed_cost</td></tr><tr><td>23</td><td>livestock_cost</td><td>Feed, veterinary and breeding costs (€.ha<sup>-1</sup>).</td></tr><tr><td>24</td><td>grossMargin</td><td>Gross margin (€.ha<sup>-1</sup>), difference between gross product and variable cost: grossMargin = gross_product - variable_cost</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 5 (continued)**</caption><thead><tr><th>Variable</th><th>Name</th><th>Description</th></tr></thead><tbody><tr><td>25</td><td>grossMargin_livestock_incl</td><td>Gross margin (€.ha<sup>-1</sup>), same as grossMargin but for forage crops, gross product including milk or meat sale and livestock cost were considered:<br>grossMargin_livestock_incl =<br>gross_product_livestock_incl - variable_cost - livestock_cost</td></tr><tr><td>26</td><td>netMargin</td><td>Net margin (€.ha<sup>-1</sup>), gross margin minus mechanisation cost:<br>netMargin = grossMargin - mechanization_cost</td></tr><tr><td>27</td><td>netMargin_livestock_incl</td><td>Net margin (€.ha<sup>-1</sup>), same as netMargin but for forage crops, gross margin including milk or meat sale and livestock cost was considered:<br>netMargin_livestock_incl =<br>grossMargin_livestock_incl - mechanization_cost</td></tr><tr><td>28</td><td>total_fuel_cons</td><td>Total fuel consumption (l.ha<sup>-1</sup>), sum of all operations frequencies, described in 1_crop_managementRoutes.csv, multiplied by their consumption, given in 4_operations_1_crop_managementRoutes.csv:<br>total_fuel_cons = &Sigma;<sub>operation</sub> frequency.fuel_cons</td></tr><tr><td>29</td><td>total_energy_cost</td><td>Total energy cost (GJ.ha<sup>-1</sup>), sum of all operations frequencies, described in 1_crop_managementRoutes.csv, multiplied by their energy cost, given in 4_operations_1_crop_managementRoutes.csv, direct energy cost related to irrigation and indirect energy cost related to nitrogen fertilisers production, given in 3_input_parameters.csv:<br>total_energy_cost = &Sigma;<sub>operation</sub> frequency.energy_cost +<br>irrigation_rate.direct_energy_cost + N_rate.indirect_energy_cost</td></tr><tr><td>30</td><td>total_energy_product</td><td>Total energy product (GJ.ha<sup>-1</sup>): production, given in 1_crop_managementRoutes.csv, multiplied by lower heating value, given in 2_crop_parameters.csv for crop and double crop:<br>total_energy_product = crop_yield.lower_heating_value +<br>dble_crop_yield.lower_heating_value</td></tr><tr><td>31</td><td>energy_efficency</td><td>Ratio of total energy product to total energy cost:<br>energy_efficency = total_energy_product / total_energy_cost</td></tr><tr><td>32</td><td>number_operations</td><td>Number of operations, as described in 1_crop_managementRoutes.csv</td></tr><tr><td>33</td><td>working_time</td><td>Working time (h.ha<sup>-1</sup>), sum of all operations frequencies, described in 1_crop_managementRoutes.csv, multiplied by the time required to cover 1 hectare, given in 4_operations_1_crop_managementRoutes.csv:<br>working_time = &Sigma;<sub>operation</sub> frequency.h_ha</td></tr><tr><td>34</td><td>GHG_emissions</td><td>Greenhouse gases emissions (t<sub>CO2 equivalent</sub>/ha) due to fuel consumption, energy use for irrigation and fertilisers production, taking into account emission coefficients given in 3_input_parameters.csv:<br>GHG_emissions = total_fuel_cons.emission_coef +<br>irrigation_rate.emission_coef + total_N_rate.emission_coef</td></tr><tr><td>35</td><td>GHG_emissions_livestock_incl</td><td>Greenhouse gases emissions (t<sub>CO2 equivalent</sub>/ha), same as GHG-emissions, but including livestock GHG emissions for forage crops:<br>GHG_emissions_livestock_incl =<br>GHG_emissions + livestock_density.emission_coef</td></tr></tbody></table>

## 4.2. Data collection

### 4.2.1. Analysis of public data from field surveys

We analysed raw data from the "Pratiques culturales grandes cultures et prairies" field survey to complete the "Average conventional" and "N3" levels. This survey is carried out every five years by the French Ministry of Agriculture to monitor agricultural public policies. The latest campaign
<caption>**Table 6**</caption>

Variables used to classify the 96 French metropolitan départements into 11 homogeneous agricultural zones.

<table><thead><tr><th>Agricultural characteristic</th><th>Variable</th></tr></thead><tbody><tr><td>Production systems</td><td>Share of the département's UAA used for arable crops [4]<br>Share of the département's UAA used for dairy cattle [4]<br>Share of the département's UAA used for beef cattle [4]<br>Share of the département's UAA used for other livestock farming [4]<br>Share of the département's UAA used for monogastric livestock [4]<br>Share of the département's UAA used for mixed crop-livestock farming [4]</td></tr><tr><td>Weather conditions</td><td>Latitude of the département's centroid<br>Longitude of the département's centroid</td></tr><tr><td>Crop successions</td><td>Share of crop sequences including oilseed rape in the département's arable land area [5]<br>Share of crop sequences including corn in the département's arable land area [5]<br>Share of crop sequences including temporary grassland in the département's arable land area [5]<br>Share of crop sequences including cereals and temporary grassland in the département's arable land area [5]<br>Share of crop sequences including root crops in the département's arable land area [5]<br>Share of crop sequences including sunflower in the département's arable land area [5]<br>Share of crop sequences including protein pea in the département's arable land area [5]<br>Share of crop sequences including soybean in the département's arable land area [5]<br>Share of crop sequences including forage legumes in the département's arable land area [5]<br>Share of crop sequences including fiber flax in the département's arable land area [5]</td></tr><tr><td>Pedoclimatic conditions</td><td>Best average soft wheat yield [6]</td></tr></tbody></table>

available when we produced this dataset concerned the cropping year 2017. Information from around 25,000 fields were collected during in-person interviews with farmers, according to a sampling plan designed to ensure representativeness at the national scale and at the scale of administrative regions. The information collected includes all technical operations carried out on surveyed fields, from the harvest of the preceding crop until the crop's harvest. This includes tillage, sowing, fertilisation, crop protection, with details about the date of the operation and the nature and amounts of the inputs it involved. Basic information about the five preceding years, including the crop sequence, was also collected.

We assigned each field surveyed to a zone, as defined in Section 3.1.2, according to its location. We also assigned a crop sequence type, as defined in Section 3.1.3, according to the observed crop sequence. We tried to assign a crop management strategy, as defined in Section 3.1.4, to each field. It was easy to differentiate between fields under conventional or organic farming. However, within the sub-sample of fields under conventional farming, crop management strategies "N2a" or "N2c" were poorly represented, and "N0" and "N1" were difficult to differentiate. This is why we used raw data from this survey only to fill in the "Average conventional" and "N3" categories of crop management strategies.

We concluded by aggregating the raw data by crop, zone, crop sequence type and crop management strategy (i.e. "Average conventional" if under conventional farming or "N3" if under organic farming). For each cropping situation represented in the aggregated data, we derived a typical crop management route, such as the one illustrated by Fig. 1.

### 4.2.2. Expert elicitation with agricultural advisors

To complete the "N0", "N1", "N2a" and "N2c" and consolidate the "N3" crop management strategies, we individually interviewed 51 experts. These experts were identified by their involvement in the DEPHY FERME network, which has been active for over a decade and now brings together around 2000 farmers. They were mostly agricultural advisors working at local chambers of agriculture or in other agricultural services. We selected them for their day-to-day work with innovative farmers and the expertise they had thus developed regarding what low input crop management strategies look like and how they perform. Interviews were mostly carried
<caption>**Table 7**</caption>

Crop sequence types identified, spatial frequency at the scale of France and average temporal frequency of dominant (groups of) crops within the crop sequence.

<table><caption>**Table 8**</caption><thead><tr><th>Crop sequence group</th><th>Spatial frequency at the scale of France over the period 2015–2019</th><th>Average temporal frequencies of dominant (groups) of crops within the crop sequence</th></tr></thead><tbody><tr><td>Crop sequence with oilseed rape</td><td>25 %</td><td>Soft wheat: 0,4<br>Rapeseed: 0,2<br>Barley: 0,2</td></tr><tr><td>Crop sequence with corn</td><td>14 %</td><td>Corn: 0,61<br>Soft wheat: 0,26</td></tr><tr><td>Crop sequence with temporary grassland</td><td>12 %</td><td>Temporary grassland: 0,7</td></tr><tr><td>Crop sequence with cereals and temporary grassland</td><td>12 %</td><td>Triticale: 0,3<br>Corn: 0,2<br>Soft wheat: 0,18<br>Temporary grassland: 0,16</td></tr><tr><td>Crop sequence with root crops</td><td>10 %</td><td>Soft wheat: 0,46<br>Sugar beet: 0,21<br>Potatoes: 0,09</td></tr><tr><td>Crop sequence with sunflower</td><td>7 %</td><td>Durum wheat: 0,37<br>Sunflower: 0,26<br>Soft wheat: 0,12</td></tr><tr><td>Crop sequence with protein pea</td><td>6 %</td><td>Soft wheat: 0,41<br>Protein pea: 0,21<br>Barley: 0,15<br>Oilseed rape: 0,11</td></tr><tr><td>Crop sequence with soybean</td><td>5 %</td><td>Soybean: 0,28<br>Soft wheat: 0,28<br>Corn: 0,27</td></tr><tr><td>Crop sequence with forage legumes</td><td>4 %</td><td>Forage legumes: 0,63<br>Soft wheat: 0,13</td></tr><tr><td>Crop sequence with fiber flax</td><td>3 %</td><td>Soft wheat: 0,47<br>Fibre flax: 0,21<br>Sugar beet: 0,08<br>Potatoes: 0,05</td></tr></tbody></table>

Crop management strategies identified.

<table><thead><tr><th>Mean</th><th>Average conventional farming practices, according to public statistics</th></tr></thead><tbody><tr><td>N0</td><td>Crop management strategy based on a systematic use of synthetic pesticides and fertilisers</td></tr><tr><td>N1</td><td>Crop management strategy based on an efficient use of synthetic pesticides and fertilisers, thanks to scouting and decision support tools</td></tr><tr><td>N2a</td><td>Crop management strategy based on Integrated Pest Management (IPM) principles, which includes substitution by non-chemical methods (e.g. mechanical weeding) or redesigned crop management routes to prevent pest damages</td></tr><tr><td>N2c</td><td>Crop management strategy based on agroecology principles [12], which includes cropping systems diversification to promote natural means of pest regulation</td></tr><tr><td>N3</td><td>Crop management strategy based on organic farming specifications, which excludes the use of synthetic pesticides and fertilisers.</td></tr></tbody></table>

out remotely and lasted from one to three hours. They began by an introductory talk explaining the context and framework of our research. We then asked the expert to describe a typical crop management route for each cropping situation in which they are competent. Here, we used the typical crop management routes described in the "Pratiques culturales grandes cultures et prairies" field survey raw data as a support for discussion. We questioned the expert as follows: "According to public statistics, a typical crop management route for crop X, in zone Y and in crop sequence Z looks like that.", at which point we would show them the typical crop management route, "If the crop was managed according to crop management strategy N0, what would
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Representation of the typical crop management route for soft wheat, in the intermediate limestone plateau zone, cultivated in a crop sequence with oilseed rape and following an “average conventional” crop management strategy.</figcaption></figure>

have changed? And for N1? N2a?...”. On average, five experts were interviewed for each cropping situations, thus enabling us to take into account the subjectivity of expert knowledge. After the round of individual interviews, we sent the experts the typical crop management routes described for each cropping situations by e-mail for validation and corrected crop management route descriptions according to their reactions.

## 4.3. Indicator computation

### 4.3.1. Translating crop management routes into quantitative data

Typical crop management route descriptions, as the one illustrated by Fig. 1, were a good support to discuss low-input crop management practices with experts. However, they were inadequate to efficiently compute values for the 30 indicators and 496 cropping situations considered. Thus, we translated these typical crop management routes descriptions into quantitative data, which correspond to the file 1_crop_managementRoutes.csv, described in Section 2.1. Basically, this step associated each cropping situation with the number and nature of the technical operations it involved and the amount and nature of the input it required and of its yield.

### 4.3.2. Collecting additional parameters required to compute indicator values

Additional parameters were required to compute economic, environmental and social performances indicators from the quantitative data resulting from the previous step. For example, selling price and input costs were required to calculate profitability from yield and input quantity information. We compiled such parameters values from numerous technical publications from agricultural services, into files 2_crop_parameters.csv, 3_input_parameters.csv and 4 Operation_parameters.csv, described in Section 2.2, 2.3 and 2.4 respectively.

### 4.3.3. Computing indicator values from quantitative descriptions of crop management routes

We used quantitative data associated to each cropping situation and additional parameters to compute indicator values, following the equations presented in Section 2.5. In addition to the indicators relating to crop management, we also included 8 indicators relating to livestock farming. These are specific to forage crops (silage corn, temporary grassland and alfalfa) and have been developed using a different approach, due to the complementary nature of crop production and its use by livestock. For different production areas in France, case studies provide a detailed description of the zootechnical functioning of one or more livestock production systems and the associated technical and economic performance. The farming system is associated with a crop rotation that determines the forage calendar. The approach developed allows us to link one or more case-types to our different cropping situations for forage crops and get new indicators. This resulted in 5_indicators_values.csv.
## Limitations

Combining public data analysis and expert knowledge elicitation, we produced data about the economic, environmental and social performances of five crop management strategies showing contrasting degrees of reliance on inputs (i.e. synthetic pesticides and fertilisers).

While our aim was for the spatial validity domain of our dataset to cover the whole agricultural area of metropolitan France, it was in fact limited to arable crops and grasslands, which cover 88 % of that area. [6] Vineyards, orchards and vegetable production were not considered.

The temporal validity domain was limited by the current stage of knowledge regarding low-input crop management practices, as well as by the sociotechnical or biophysical context in which they perform. For example, the economic performances assessed can only represent the range of crops prices or input costs found in recent years, and not how those will evolve in future market conditions.

## Ethics Statement

The authors have read and follow the ethical requirements for publication in Data in Brief and confirm that the work presented here does not involve human subjects, animal experiments, or any data collected from social media platforms.

## Credit Author Statement

**Simon Buresi:** Software, Formal analysis, Investigation, Writing - Original Draft; **Chantal Loyce:** Methodology, Writing - Review & Editing. **Rémy Ballot:** Conceptualisation, Methodology, Writing - Review & Editing, Supervision, Funding acquisition.

## Data Availability

Référentiel de performances économiques, environnementales et sociales de pratiques cultureles économes en intrants (Original data) (Dataverse).

## Acknowledgements

This study is financially supported by the "Facilitate public Action to exit from peSTicides (FAST)" project as part of the French National Research Agency's (ANR) French Priority Research Programme "Growing and Protecting Crops Differently" and by the French National Research Agency (ANR) through the Investments for the Future (*Investissements d'Avenir*) programme, grant ANR-17-EURE-0010.

The authors thank all the experts who agreed to be interviewed to produce this dataset.

## 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.
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