# I-DRo: A new indicator to assess spatiotemporal diversity and ecosystem services of crop rotations

Olivier Keichinger, Loïc Viguier, Guénaëlle Corre-Hellou, Antoine Messéan,

Frédérique Angevin, Christian Bockstaller

► To cite this version:

Olivier Keichinger, Loïc Viguier, Guénaëlle Corre-Hellou, Antoine Messéan, Frédérique Angevin, et al. I-DRo: A new indicator to assess spatiotemporal diversity and ecosystem services of crop rotations. European Journal of Agronomy, 2025, 164, pp.127531. 10.1016/j.eja.2025.127531. hal-04945929

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# I-DRo: A new indicator to assess spatiotemporal diversity and ecosystem services of crop rotations

Olivier Keichinger<sup>a</sup>, Loïc Viguier<sup>b</sup>, Guénaëlle Corre-Hellou<sup>c</sup>, Antoine Messéan<sup>d</sup>, Frédérique Angevin<sup>e</sup>, Christian Bockstaller<sup>f,*</sup>

## ARTICLE INFO

### Keywords:

- Crop diversification

- Crop sequence

- Strip cropping

- Functional diversity

- Taxonomic diversity

- Scoring method

- CONTRA method

## ABSTRACT

Agroecological farming systems depend on ecosystem services (ES) to replace external anthropogenic inputs. Crop diversification is increasingly being put forward as a way to support ES service provision. Actors involved in promoting, designing and implementing diversified cropping systems presumably need to assess the degree of diversity and performance of diversified cropping systems at one stage or another. This article focuses on crop rotation diversity since it is a core component of annual cropping systems. In this study, we designed a new global indicator (I-DRo) based on a set of other indicators used to assess the temporal diversity of crop rotation (which includes functional diversity through ES provision,) as well as taxonomic diversity and spatial diversity. I-DRo covers various crop diversification strategies in the rotation: introduction of new crops or cover crops, multiple cropping, intercropping, relay cropping and strip cropping. The proposed indicators may be used separately or aggregated in a hierarchical way according to a fuzzy decision tree. Using I-DRo requires only data on field width and the main crop and cover crop species. Initial tests showed the indicator could potentially be used to support various actors in their decision-making, although results on the predictive quality were mixed given the degree of simplification. Contextualizing the calculation method for ES assessment would be one avenue of investigation. Lastly, use at EU level could support the implementation of new cross-compliance measures on crop diversification, but would require efforts to harmonize data on main crops and cover crops.

## 1. Introduction

Farming systems have become increasingly intensified and specialized since the Second World War following efforts to modernize agriculture, first in Western countries and later in other areas of the world. Mechanization, genetic progress and the systematic use of external anthropogenic inputs such as fertilizers and pesticides have been instrumental in this process. This agricultural intensification has lifted constraints on crop choices and rotations, giving farmers the ability to grow the most profitable crops more often (Hufnagel et al., 2020; Meynard et al., 2013; Stein and Steinmann, 2018). For example, cereal-based rotations with a large proportion of winter wheat can be found across a large swath of northern Europe (Sieling et al., 2005). An

extreme case of this intensification is the broad extent of maize monoculture in some regions like the USA (Plourde et al., 2013). While these modernization efforts have significantly increased agricultural yields, they have come at a cost, including undesirable environmental impacts. These impacts have been well documented since the 1990s. Indeed, modern agriculture is proving to be a major source of pressure on planetary boundaries. Agriculture plays a major role in biodiversity erosion, water use, and nitrogen and phosphorus biogeochemical flows (Campbell et al., 2017), and is a substantial contributor to climate change (IPCC., 2023).

Studies on agricultural sustainability have proliferated with a view to identifying alternative agricultural models to address society's growing concern about the downsides of modern agriculture, especially with

<footnote><sup>a</sup> Independent researcher, France</footnote>

<footnote><sup>b</sup> ARVALIS, Baziège 31450, France</footnote>

<footnote><sup>c</sup> École Supérieure d'Agriculture (ESA), INRAE, LEVA, Angers 49007, France</footnote>

<footnote><sup>d</sup> Université Paris-Saclay, AgroParisTech, INRAE, Agronomie, Palaiseau 91123, France</footnote>

<footnote><sup>e</sup> INRAE, Sols, Orléans 45075, France</footnote>

<footnote><sup>f</sup> Université de Lorraine, INRAE, LAE, Colmar 68000, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: christian.bockstaller@inrae.fr (C. Bockstaller).</footnote>
regards to soil quality degradation, water and air contamination from pesticides, biodiversity loss and landscape simplification. Historically, this quest for more sustainable farming systems dates back to the 1920s with pioneering work on organic farming and its expansion in recent decades (Francis and Wart, 2015), as well as the development of integrated pest management since the 1960s (Ferron and Deguine, 2005). More recently, agroecology has become a new paradigm (Bommarco et al., 2013; Duru et al., 2015; Therond et al., 2017; Wezel et al., 2009). Agroecological farming systems revolve around replacing external anthropogenic inputs with ecosystem services (ES), which are ecological processes or elements of an ecosystem structure that provide benefits to humans (Dardonville et al., 2022). Pollination, pest regulation and nutrient supply are all key regulating ES that support agriculture production and could potentially play a major role in the replacement of external inputs.

Researchers are increasingly advocating for the use of crop diversification to support the provision of regulating ES (Beillouin et al., 2021a, 2019; Degani et al., 2019; Tamburini et al., 2020; Wezel et al., 2014; Zhang et al., 2020). Crop diversification fosters a substantial reduction of external anthropogenic inputs, and in doing so, lowers nutrient losses and pollution of environmental compartments (Alletto et al., 2022b). Crop diversification also supports soil cover to mitigate nitrate leaching (Beillouin et al., 2021b) and soil erosion. It is also an important driver of biodiversity in agricultural landscapes (Sirami et al., 2019). In terms of economic benefits, most diversification strategies increase productivity (Beillouin et al., 2019). However, the available literature offers contradictory examples of the effect of diversification on stabilizing economic performance and risk mitigation (Hufnagel et al., 2020; Nilsson et al., 2022; Rosa-Schleich et al., 2019). The impact of crop diversification depends on whether the system is in transition or stabilized. Economic performance also depends on multiple and diverse factors, such as available knowledge on new crops, the local biophysical context (soil and climate factors, pest pressure, etc.) marketing conditions, and more (Messéan et al., 2021). The process of diversifying cropping systems includes both temporal and spatial aspects (Beillouin et al., 2021a; Viguier et al., 2021). Temporal diversification involves extending crop rotations by introducing new crops such as legumes into the rotation or growing several cash crops every year, either by growing one crop after another (multiple cropping) or sowing one crop into the previous one (relay cropping). Increasing the number of different crop species in a crop rotation sustains temporal and spatial diversification through the arrangement of rotation blocks within the fields, or at farm or landscape level. Within fields, spatial diversification involves simultaneously cropping several crops via intercropping or strip cropping (Juventia et al., 2022).

Despite the economic and environmental benefits of crop diversification, different barriers and lock-in effects hamper its implementation (Magrini et al., 2016; Meynard et al., 2018). A discussion of solutions to unlock such situations falls outside the scope of this article, and has been covered by other authors (e.g. Morel et al., 2020). However, we assume that stakeholders, including policymakers, involved in the design, promotion and implementation of diversified cropping systems will at one stage or another need to assess the degree of diversity and the performances of these systems. Both of these topics are important: the first makes it possible to address the degree of change, while the second sheds light on ways to cope with the impact of this change. Here, we have chosen to focus on crop rotation diversity since it is a core component of annual cropping systems (Stein and Steinmann, 2018).

In the past, single indicators, such as the number of crops (Uthes et al., 2020), a diversity index like the popular Shannon index (Fahrig et al., 2015; Nilsson et al., 2022; Uthes et al., 2020) or the inverted Simpson indicator (locola et al., 2020) were proposed. Used alone, these indicators provide limited information on quantitative diversity and no qualitative insights. To overcome this limitation, Viguier et al. (2021) used a set of 10 simple descriptors to assess crop rotation diversity, including rotation duration, number of botanical families and

percentage of cash crops intercropped. However, these indicators are still “causal” indicators according to the typology developed by Bockstaller et al. (2015). They provide descriptive information on the degree of diversification (i.e. at the level of a driver), but not the impact of diversification (i.e. the effect). A scoring method assessing the effect of crops on different environmental components (soil cover, soil structure, nitrogen supply to subsequent crop) was proposed by Vereijken (1997) and applied by Dogliotti et al. (2003) and Juventia et al. (2022). An average score at rotation level is calculated for each environmental component, but those scores were not aggregated in a single “rotation quality” score. Furthermore, this scoring method does not address pest regulation potential due to rotation or the effect of cover crops. The crop sequence indicator developed by Bockstaller and Girardin (1996); (2000) and implemented by Leteinturier et al. (2006) and Lechenet et al. (2014) encompassed these aspects of pest regulation as well as nitrogen supply and soil structure for the main and cover crops. Nevertheless, it was based on scores for each crop effect and did not explicitly address ES. Essentially, only the temporal dimension was covered.

In this study, we designed a new global indicator based on a set of new indicators to assess the temporal diversity of crop rotation and the spatial arrangement of crops. Temporal diversity covers the functional diversity of crop rotation through ES provision as well as taxonomic crop diversity. These indicators aim to assess the most common crop diversification practices: introduction of a new crop or cover crop, multiple cropping, intercropping, relay cropping (for main and cover crops) and strip cropping (for the spatial aspect). The different indicators may be used separately or aggregated according to a hierarchy based on a fuzzy decision tree designed using the CONTRA method (Bockstaller et al., 2017). We present the calculation method for these indicators, provide examples of implementation and discuss the initial results from the comparison of indicator output with field measurements. Potential implementation at EU level is also discussed.

## 2. Methods

### 2.1. Indicator presentation

#### 2.1.1. General structure

The global diversity indicator of crop rotation (I-DRo) is structured as a hierarchical tree that includes functional and taxonomic diversity at the temporal and spatial scales (Fig. 1). I-DRo results from the aggregation of an indicator for the temporal diversity of crop rotation (I-TDR) and another indicator for spatial diversity (I-SDR). Temporal diversity is linked to the functional and taxonomic diversity of the rotation, which are assessed using the ecosystem service indicator (I-ESR) for the former and the crop diversity indicator (I-CDR) for the latter. Functional diversity is assessed through the ES provided by functional crop traits and their interactions in the crop sequence (Gaba et al., 2014; Therond and Duru, 2019). Taxonomic diversity provides information that complements functional diversity, and is correlated with associated biodiversity (i.e. biodiversity that is not managed by farmers) for different taxonomic groups of soil microorganisms, invertebrates and vertebrates (Christel et al., 2021; Sirami et al., 2019). There is substantial evidence showing the role of spatial crop diversity as a driver of associated biodiversity in cropped landscapes (Fahrig et al., 2011; Martin et al., 2020; Sirami et al., 2019; Therond and Duru, 2019; Uthes et al., 2020). Diversifying the crop rotation by increasing the number of families and species can increase crop diversity at the farm or landscape level. We also introduced a specific subindicator covering the spatial diversity linked to rotation block arrangement. This subindicator is assessed by a proxy, so it was not separated into two subindicators like the temporal indicator was.

#### 2.1.2. Ecosystemic service indicator (I-ESR)

A total of 12 ES were identified as being influenced by crop rotation and diversification (Table 1), (Keichinger, 2001; Palomo-Campesino et al., 2018; Tibi and Therond., 2017). Among them, six ES concern
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** General structure of the global diversity indicator of the crop rotation (I-DRo). Spatial diversity is linked to rotation block arrangement within and between fields. It is assessed by a proxy, so it was not separated into two subindicators like the temporal indicator was.</figcaption></figure>

<caption>**Table 1**</caption>

Ecosystem services (ES) considered in the I-DRo ecosystem indicator (I-ESR). The ES described in the article are shown in bold type.

<table><thead><tr><th>ES for farmers</th><th>ES for farmers and society</th><th>ES for society</th></tr></thead><tbody><tr><td>Weed regulation</td><td>Carbon sequestration</td><td>Landscape</td></tr><tr><td><strong>Disease regulation</strong></td><td>Soil protection</td><td>Wildlife habitat</td></tr><tr><td>Pest regulation</td><td><strong>Nitrate leaching reduction</strong></td><td></td></tr><tr><td>N supply</td><td>Water provision to crops</td><td></td></tr><tr><td>Soil structure</td><td><strong>Pollination</strong></td><td></td></tr></tbody></table>

farmers only, four concern both farmers and society, and two concern only society. For simplicity's sake and to make implementation easier, the ES indicators deal with the effect of crop rotation only, without considering the effects of crop management or local conditions. Each indicator assessing the effect of rotation on the ES is expressed on a scale between 0 (high risk or negative effect) and 1 (no risk or positive effect). Data from the literature and expertise were used to design these indicators. Depending on the ES, scores may be attributed or calculated for each crop (e.g. pollination or nitrogen supply) or to each previous crop/current crop pair (e.g. for disease regulation, a matrix cross-referencing the crops sown in year *n* and the crops in year *n-1*). The scores, as well as the equation parameters, are drawn either from expertise (e.g. disease regulation), from the literature (e.g. C-factor for soil erosion) or from literature data (e.g. nitrogen absorption and supply). Depending on the variables evaluated, a result is obtained directly from the rotation (return time for diseases) or requires an additional step with an annual result before being aggregated at the rotation scale.

Only three ES will be detailed here to highlight the diversity of ES covered and calculation methods; they are linked to agronomic and environmental, biotic and abiotic issues. The nine other ES are described in the Supplementary Materials (S1 and S2).

### 2.1.2.1. Disease regulation: I-ESR diseases

#### 2.1.2.1.1. Background knowledge.
A diversified crop rotation mitigates the effect of diseases transmitted by soil-borne pathogens by interrupting their development cycles and reducing inoculum (Charles et al., 2011; Krupinsky et al., 2002; Ratnadass et al., 2012). This occurs when non-host crops (previous effect) are introduced into the rotation (Krupinsky et al., 2002). These effects of rotation diversification on

inoculum (succession and return time) combine with other effects such as the diversity of microflora and microfauna that is supported by the diversity of crop residues and roots that help regulate pathogens (Bailey and Lazarovits, 2003; Ghorbani et al., 2008; Ratnadass et al., 2012). Cover crops play a positive role in controlling diseases, through the diversity of microflora generated by the breakdown of fresh organic matter (Larkin et al., 2012). However, rotation diversification will not have an influence on diseases that have significant opportunities to spread across large areas or that are long-lived in some forms (Ratnadass et al., 2012).

#### 2.1.2.1.2. Input variables.
Based on the background knowledge summarized above, we selected two variables:

1. Previous effect (PE): We created a scoring matrix that cross-references all possibilities between the 43 crops that were identified as potential crops in the pedoclimatic context of Western Europe. These crops include major arable and forage crops, cereals, oilseeds, pulses, forage grasses and legumes (Table S2). A score assessing PE was assigned to every previous/current crop pair, mobilizing expert judgment (APCA., 2017; Jeangros and Courvoisier, 2019). PE varied from 0 for a previous/current crop pair with a high risk of disease transmission to 1 for a pair with no risk of disease transmission (Table S2). For example, a score of 0.75 was assigned to the wheat/wheat crop pair and 0.25 to the maize/maize crop pair. The former presents a high risk of disease transmission (such as take-all or eyespot), while for the latter, the risk is much lower because of hybrid cultivar resistance.

2. Return time: This refers to the amount of time required between growing the same crop to prevent biotic pressure: a reference return time was established based on expert judgment (APCA., 2017; Jeangros and Courvoisier, 2019) and ranged from as much as 6 years to just 1 year for a crop that can be grown every year (Table S2). This reference return time was set for each crop or group of crops affected by the same disease. An effective return time was calculated for each crop (or group of crops) in the rotation as the number of years where the crop (or group of crops) is grown. Here, we did not quantify the yield reduction based on the crop frequency in the rotation like Dogliotti et al. (2004), but we assumed that if the effective return time equals the reference time, the regulation ES is maximized.

#### 2.1.2.1.3. Calculation.
For the crop rotation, an index is calculated
according to Eq. 1.

$$I-ESR\_diseases = (\sum SED_{ij})/N \times \prod (ERT_{j}/RRT_{j}) \quad (1)$$

where SEDij = the effect of previous crop pair i on the risk of disease in the subsequent crop j (Table S2);

ERTj = the effective return time for crop (or group of crops) j, in number of years where the crop (or group of crops) is grown;

RRTj = the reference return time for crop (or group of crops) j (Table S2).

If ERTj/RRTj > 1 then ERTj/RRTj = 1

### Examples

#### Example 1

Maize/maize: I-ESR_diseases = 0.75.(1/2) = 0.38

where

SED maize/maize = 0.75, ERTmaize = 1 and RRTmaize = 2

#### Example 2

Maize/winter wheat: I-ESR_diseases = [(0.25+0.5)/2] × [(2/2) × (2/2)] = 0.38

where

SEDmaize/wheat = 0.25, SEDwheat/maize = 0.5;

ERTmaize = 2, ERTwheat = 2;

RRTmaize = 2 and RRTwheat = 2

#### Example 3

Alfalfa/alfalfa/winter wheat: I-ESR_diseases = [(1+1+1)/3] × [(1/1) × 1 × 1] = 1

where

SED alfalfa/alfalfa= 1, SED alfalfa/wheat= 1, SED wheat/alfalfa= 1;

ERTalfalfa = 1, ERTalfalfa = 2 ERTwheat = 3;

RRTalfalfa = 1, RRT wheat = 2;

RRTalfalfa/ERTalfalfa = 2/1 ⇒ = 1;

RRTwheat/ERTwheat = 3/2 ⇒ = 1.

For additional data, see the Supplementary Materials S2 and the Excel file in Table S2.

### 2.1.2.2. Nitrate leaching reduction: I-ESR_NO3_leaching

#### 2.1.2.2.1. Background knowledge.
With nitrogen leaching, two conditions must be met: the soil must have a significant amount of nitrate in it, and water drainage must occur. While the amount of nitrogen can be high (from inputs) during the growing period, drainage is generally low because the water is consumed by the crops, except when intensive rainfall occurs. Similarly, just after harvest, if fertilization has been well managed, there is less nitrogen in the soil, except in crops with low nitrogen recovery efficiency (ten Berge, 2002). But after harvest, during the winter drainage period, nitrate amounts may be high and leaching will take place (Beaudoin et al., 2005; Padilla et al., 2018). We consider that the amount of mineral nitrogen in the soil at the beginning of the drainage period will therefore depend on the mineralization of the previous crop residues and the time between harvest and winter entry for soil organic matter mineralization. Conversely, this mineralized nitrogen can be taken up by the following crop (autumn sowing) or by cover crops to reduce the amount in the soil. Thus, the maximum leaching risk occurs when soil is left bare during winter under Western temperate conditions. We implemented a very simplified version from the model developed by Bockstaller et al. (2022) to assess the potential reduction of nitrate leaching by crops of the rotation taking into account those three variables, mineralization of previous crop residues and soil organic nitrogen during the fallow period after harvest, and nitrogen uptake by the next crop.

#### 2.1.2.2.2. Input variables.
Three variables expressed between 0 (low amount of nitrogen) and 1 (high amount of nitrogen) were selected:

1. Crop residue mineralization index (CRM; score assigned for each crop): from 1 (high mineralization) for pluriannual leguminous crops with a low C/N ratio the year when ploughed in, to 0 (low mineralization) for late harvested crops with a high C/N ratio;

2. Soil mineralization index (SM), which depends on the harvesting date: from 0 for green crops harvested late to 1 for early harvested crops;

3. Mineral nitrogen uptake by next crop index (cash or cover crops) before winter (CU): from 0 for bare soil and 0.1 for some winter cereals to 0.6 for early sown cover crops.

#### 2.1.2.2.3. Calculation.
For the rotation, an index is calculated according to Eq. 2.

$$I-ESR\_NO_3\_leaching = (\sum (1 - ((CRM_i + a \times SM_i)/(1+a)) - CU_j))/N \quad (2)$$

where

CRMi = Crop residue mineralization index of harvested crop i;

SMi = Soil mineralization index of the soil organic matter between harvesting of the current crop i and winter;

CUj = Mineral nitrogen uptake by next crop index by the following cash crop j (autumn sowing) or by cover crops j;

a = ratio between NO3 from mineralization of crop residues and soil organic matter;

Based on the typical values of these variables taken from the model developed by Bockstaller et al. (2022), we established: a = 2 for annual crops and a = 1 for pluriannual crops. For pluriannual crops, crop residue mineralization can be very high when the crop is ploughed in;

N = duration of the rotation (year).

### Examples:

#### Example 1

Maize / maize I-ESR_NO3_leaching = 0.5

#### Example 2

Maize / winter wheat: I-ESR_NO3_leaching = 0.48

#### Example 3

Alfalfa / alfalfa/ winter wheat: I-ESR_NO3_leaching = 0.64

where CRM, SM and CA were as follows: 0.1, 0.7, 0 for maize; 0.1, 0.9, 0.1 for winter wheat; and 1, 0.2, 0.3 for alfalfa for the year of destruction. These values are based on the model parameter according to Bockstaller et al. (2022) for Western temperate conditions (Supplementary Materials S2 and Excel file for Table S2).

### 2.1.2.3. Pollination: I-ESR_pollination

#### 2.1.2.3.1. Background knowledge.
In agricultural landscapes pollinators can feed on crops and semi-natural habitats (Holzschuh et al., 2016). Overall, pollination is affected not by crop diversity but floral traits (e.g. flower shape, colour) (Goulnik et al., 2020; Hass et al., 2018) and the quality of the nectar and pollen (Davaine, 2012). Thus, diversified landscapes do not necessarily have a positive effect on pollinator populations (diversity and abundance). This may be due either to cultural practices (frequent mowing for grasslands, intensively managed crops) (Andersson et al., 2014; Hass et al., 2018), or to a relatively short flowering period (Goulnik et al., 2020; Hass et al., 2018), which will have a diluting rather than a stimulating effect (Holzschuh et al., 2016). As such, it is also important to have crops in the rotation with staggered flowering periods to increase supply during the growing season. The crop sequence thus influences the crop rotation and so will have this effect every year on the spread of flowering date in the local area.

#### 2.1.2.3.2. Input variables.
Based on the background knowledge summarized in the previous paragraph, two variables were selected:
1. Capacity of the crop to provide quality nectar and pollen and be accessible to pollinators (QF): from 0 (no pollen or nectar) to 1 for crops with accessible, quality nectar and/or pollen.

2. Diversity of flowering period by crops in the rotation (PF): the active life of pollinators is divided in three periods and each crop is assigned to a period; the diversity depends on the number of periods with flowering.

**2.1.2.3.3. Calculation.** For the rotation, an index is calculated according to Eq. 3.

$$I-ESR\_pollination = f((\sum (QFi))/N); PF) \quad (3)$$

where

QFi = quality of flowering (pollen and nectar) of crop *i* (see Table S2);

PF = index indicating the number of seasons with a flowering crop during the rotation (spring, summer and autumn). PF equals 0, 0.25, 0.8 and 1 for respectivel zero, one, two or tree seasons with a flowering crop. We considered a) that one season with a flowering crop during the rotation was less than proportionally favourable to pollinators (0.25 instead 0.33) because of a lack of trophic provision continuity, and b) that two seasons with a flowering crop was more than proportionally favourable to pollinators (0.8 instead 0.67);

N = duration of the rotation (year);

f() = aggregation with use of a fuzzy decision tree with fuzzy sets (favourable and unfavourable) (see Section 2.1.5 for more details). The following rules were assigned: i) if QF and PF are favourable, then the final value is 1; ii) if QF is favourable and PF is unfavourable, then the final value is 0.4; iii) if QF is unfavourable (e.g. no nectar or pollen available), then the final value is 0.

**Examples:**

Example 1

Maize/winter wheat: I-ESR_pollination = 0.04 where

QFmaize = 0.1, QFwheat = 0 and PFmaize = 0, PFwheat = 0 (no flowering period for the crop)

Example 2

Rapeseed/winter wheat/winter barley: I-ESR_pollination = 0.34

where

QFrapeseed = 1, QFwheat and QFbarley = 0 and PF = 0.25 (one season with a flowering crop, i.e. spring for rapeseed)

Example 3

Rapeseed/winter wheat/sunflower/winter wheat: I-ESR_pollination = 0.73

where

QFrapeseed and QFsunflower = 1, QFwheat = 0 and PF = 0.8 (two seasons with a flowering crop, i.e. spring for rapeseed and summer for sunflower)

For additional data, see the Supplementary Materials S2 and the Excel file in Table S2

**2.1.2.4. Aggregation of the ES indicators: I-ESR.** After an individual assessment, the 12 ES indicators obtained are aggregated to determine the overall impact of the rotation on ecosystem services by calculating an average without any weighting. However, it will be possible to introduce weighting according to the objectives and any local constraints. To limit compensation between ES indicators (e.g. when a heterogeneous distribution of six extreme 0 and 1 values produces the same results as a homogeneous distribution of 12 median 0.5 values), a second term is added to Eq. 4 that considers the minimum and maximum value of the 12 ES as well as the standard deviation to penalize rotations providing very disparate services. This term is corrected by a factor determined empirically from real cases showing an acceptable level of compensation. With this mean correction, the equation provides a value of 0.5 when all the ES subindicators are equal to 0.5 and a value of 0.25 when six of the ES subindicators equal 1 and six equal 0.

$$I-ESR = Mean - [(Max - Min) \times SD \times 0.5] \quad (4)$$

where

Mean, Max, Min, SD = the mean, maximum, minimum and standard deviation of the 12 ES subindicators.

**2.1.3. Taxonomic diversity indicator: I-CDR**

The number of different crops (or crop families) or a diversity index such as the Shannon index (Sirami et al., 2019; Uthes et al., 2020) or Gini-Simpson index are used generally to assess the level of rotation diversification (Thuiller et al., 2014). However, the Shannon index is sensitive to low species numbers and the Gini-Simpson index to high species numbers, and the results of these indices are not always easily understandable. The inverted Simpson index (D) was chosen for I-DRo (Eq. 5) because it better accounts for taxonomic diversity (Chao et al., 2014; Hill, 1973; Jost, 2006a,2006b). It gives the effective number of species, also called true diversity, and the results are easier to understand: for a three-year rotation with three different crops, D = 3.

$$D = 1 / \sum p_i^2 \quad (5)$$

where

$p_i$: the proportion of crop *i* in the rotation.

The value of D can vary considerably depending on the rotation length or presence of cover crops. To aggregate it with the other I-DRo subindicators, the result of D was normalized and converted into an indicator (also varying from 0 to 1) using Eqs. 6 and 7.

$$I-CDR = 0.4328 \ln(D) \text{ for } D < 10 \quad (6)$$

$$I-CDR = 1 \text{ for } D \ge 10 \quad (7)$$

Eq. 6 was calibrated with four cases corresponding to 1, 2, 4 and 10 different crops (D value), to which the following values were assigned: 0, 0.3, 0.6 and 1, respectively. The aim of these four values is to give less weight to an additional crop as the total number of different crops increases (beyond D = 4) and to integrate the reference of the integrated production specifications: four different crops (D = 4) provides a relatively good level of diversification (Boller et al., 1997; Viaux, 1999).

**2.1.4. Spatial diversity indicator: I-SDR**

Biodiversity depends on both compositional heterogeneity (i.e. the diversity of crops in the crop rotation) and spatial heterogeneity (i.e. the crop distribution and configuration in a given landscape) (Fahrig et al., 2011; Hass et al., 2018). The effect of spatial heterogeneity is partly due to the existing field margins between crops, and field size and shape plays an essential role (Uthes et al., 2020). Although crop rotation is linked to crop distribution in space, it is difficult to develop an indicator to assess spatial heterogeneity when no spatialized data is available. However, at field level, the narrower a plot or strip, the greater the field margin effect, which can support biotic interactions such as natural pest regulation, or the barrier effect, which slows the spread of pests and diseases. These benefits underpin the practice of strip cropping (Ditzler et al., 2021). Accordingly, we suggest assessing spatial diversity in a simplified way through the plot width. The I-SDR indicator was based on a function calibrated with three data points: width = 3 m and I-SDR = 1; width = 30 m and I-SDR = 0.5; width = 250 m and I-SDR = 0. The first data point corresponds to the minimal width that can be sown with a standard seeder (Ditzler et al., 2021), while the last data point corresponds to a square field of about 6 ha. This value is suggested by Sirami et al. (2019) as a threshold below which field size has a clear effect on biodiversity. Here, this threshold is meant to be valid for ES. Sirami et al. (2019) also showed that the relationship is not linear and may have a logarithmic shape. The intermediate data point depends on this result
and on the assumption that tenfold increase of the width decreases the effect on the ES by half. The following equation was obtained by regression with those three data points:

$$I-SDR = -0.228 \ln(l) + 1.25 \quad (8)$$

where

l = the field width (in metres).

### 2.1.5. Indicator of global diversity: I-DRo

As Fig. 1 shows, the global diversity indicator (I-DRo) is derived from the successive aggregations of the three indicators I-ESR, I-CDR and I-SDR (presented in detail in Sections 2.1.2, 2.1.3 and 2.1.4, respectively). The aggregation method used involved decision trees linked by if/then rules, as such rules are easily understood, with the determination of two fuzzy classes (favourable – F and unfavourable – U) for each variable (Bockstaller et al., 2017). Simple linear membership functions were implemented to calculate membership values for the fuzzy classes F and U when values were between extremes, i.e. 0 and 1 for I-ESR, I-CDR, I-SDR, or 0 and 10 for I-TDR (see Fig. 1). Assigning a score to each rule of the decision tree based on expertise is challenging and is often affected by subjectivity. For greater transparency, the CONTRA method was used and the results of the two successive aggregations, the weights and the corrections are given in Table 2 (Bockstaller et al., 2017). For I-TDR (Table 2a), we assigned a lower weight to the diversity index than for the ES (67 % for I-ESR and 33 % for I-CDR): we decided that favouring all 12 ES was more important than the taxonomic diversity of crops, because ES may support the use of fewer inputs and reduce some environmental impacts, which is more favourable to biodiversity than taxonomic crop diversity alone. In one case (Table 2a, row 3), a negative correction is attributed to the output value of I-TDR because it is considered that a good taxonomic diversity index cannot compensate for the lack of services rendered (−0.08). For I-DRo (Table 2b), we weighted temporal diversity I-TDR much more heavily (80 %) than spatial diversity I-SDR (20 %). I-SDR is calculated using a very simplified method compared with I-TDR. In one case (Table 2b, row 3), we consider that spatial diversity can partially compensate for a deficit in temporal diversity by the field margin effect that it produces (hence a positive correction of 2). For each variable, we define the values of the limits of the fuzzy classes F and U. These values correspond to the extreme values that these indicators can take: 0 and 1 for I-CDR and I-SDR and 0 and 10 for I-TDR. However, for I-ESR, we found the maximum values to be around 0.6 (see the Results section), so we lowered the threshold from 1 to 0.7.

## 2.2. Data acquisition

### 2.2.1. Calculation of I-DRo

The calculation is done automatically using a computerized tool in Excel. This tool can calculate a crop sequence of 10 years with the possibility of harvesting three cash crops per year (in anticipation of adaptation to market gardening); two crops can be combined each time. Between two cash crops, cover crops can be planted in combination with up to six different crops. The tool was parameterized for 46 cash crops (or potential crop pairs) as well as 46 cover crops (see Supplementary Materials S2). The only input data needed by the user is i) the crop rotation or sequence (i.e. a succession of crops that is not repeated) to calculate I-ESR and I-CDR, and ii) the field width to calculate I-SDR, so the global indicator I-DRo can be calculated. We assume that the user will apply the tool under the same agroecological and management conditions for which the tool was parameterized.

I-DRo and the associated indicators were calculated for 19 rotations (see Table 4): most of the rotations were chosen based on the expertise of the authors and are commonly found in France (rotations #1 to #14), while rotations #15 to #19 were taken from the European Union-funded DiverIMPACTS project (DiverIMPACTS, 2023). The rotation length increases from #1 to #19. Four field widths were tested: 3, 24, 100 and 250 m to account for the effect of strip cropping. While the widths of 3 m and 250 m correspond to favourable and unfavourable cases (see Eq. 8), 24 m is a multiple of the width of the most commonly used agricultural machinery (3, 4, 6, 8, 12 or 24 m), while 100 m corresponds to a minimum area of 1 ha. The corresponding I-SDR value for these widths were 1, 0.53, 0.20 and 0.

### 2.2.2. Sensitivity analysis

Following the recommendation of Bockstaller et al. (2008), a sensitivity test was carried out to identify weights of variables in the aggregated indicators. The first analysis tested the effect of the variation of a single ES value when the 11 others are set at given value. The same is done with the other two aggregations that produce the indicators I-TDR and I-DRo (see Fig. 1). Each single tested variable had different value intervals (0 to 1), while the values of the other variables were set at the low (unfavourable), medium or high (favourable) value (respectively 0, 0.5 or 1) corresponding to the median value and extreme value of the fuzzy sets.

### 2.2.3. Validation of output regarding ecosystem services

The next step in the methodology of Bockstaller et al. (2008) is the validation of the indicator, and more specifically its predictive quality, by comparing indicator output with field observations or measurements.

<caption>**Table 2**</caption>

Indicator decision tree: a) I-TDR temporal diversity indicator, with a value between 0 (unfavourable) and 1 (favourable), which results from the aggregation of the ecosystem services indicator (I-ESR) and the taxonomic crop diversity indicator (I-CDR) with a weight of 67 % for I-ESR and 33 % for I-CDR; b) I-DRo global diversity indicator, with a value between 0 (unfavourable) and 10 (favourable), which results from the aggregation of I-TDR and the spatial diversity indicator (I-SDR) with a weight of 80 % for I-TDR and 20 % for I-SDR. The CONTRA aggregation method (Bockstaller et al. 2017) was used. This method allows modifications (column “absolute adjustment”) of the automatically generated results (“calibrated” result column), thus yielding a “corrected result” to offset compensation between variables. Input variables belong either to the favourable set (F) or unfavourable set (U). The truth value of each rule is calculated with a minimum operator (Bockstaller et al., 2017).

<table><thead><tr><th colspan="8">a)</th></tr><tr><th>Fuzzy classe for I-ESR</th><th>Weight 1</th><th>Fuzzy classe for I-CDR</th><th>Weight 2</th><th>Calibrated I-TDR</th><th>Absolute adjustment</th><th>Corrected I-TDR</th></tr></thead><tbody><tr><td>F</td><td>67</td><td>F</td><td>33</td><td>1</td><td></td><td>1</td></tr><tr><td>F</td><td>67</td><td>U</td><td>0</td><td>0.67</td><td></td><td>0.67</td></tr><tr><td>U</td><td>0</td><td>F</td><td>33</td><td>0.33</td><td>-0.08</td><td>0.25</td></tr><tr><td>U</td><td>0</td><td>U</td><td>0</td><td>0</td><td></td><td>0</td></tr><tr><th colspan="8">b)</th></tr><tr><th>Fuzzy classe for I-TDR</th><th>Weight 1</th><th>Fuzzy classe for I-SDR</th><th>Weight 2</th><th>Calibrated I-DRo</th><th>Absolute adjustment</th><th>Corrected I-DRo</th></tr><tr><td>F</td><td>80</td><td>F</td><td>20</td><td>10</td><td></td><td>10</td></tr><tr><td>F</td><td>80</td><td>U</td><td>0</td><td>8</td><td></td><td>8</td></tr><tr><td>U</td><td>0</td><td>F</td><td>20</td><td>2</td><td>2</td><td>4</td></tr><tr><td>U</td><td>0</td><td>U</td><td>0</td><td>0</td><td></td><td>0</td></tr></tbody></table>
For aggregated indicators like I-DRo, it is helpful to focus on the basic non-aggregated indicators (in this case, the ES indicators). The challenge was to find data sets with crop rotation as the only variable compared in experiments. When rotation diversification is included in experiments, it is often only one of the agronomic levers implemented. For two ES, we found experimental data sets. For “weed regulation” (I-ES, weeds), data for the 2002–2010 period from the environmental weed monitoring network set up by the competent national authorities in France (“Bio-vigilance Flore”), were used (Fried et al., 2012; 2008). Fields were monitored over a period of 7 to 10 years in several regions of France. Most rotations on those fields were poorly diversified. The data were obtained from weed surveys (on abundance and diversity) conducted in autumn and spring each year on non-sprayed controls to account for seasonal variations in weed populations. In this case, the I-ES, weeds indicator output were compared with the abundance (plants/m²) or the diversity (number of species/m²). For the I-ES, NO₃ reduction indicator, the data used were obtained from a French study on NO₃ losses in conventional and organic farming measured using porous cup samples (Benoit et al., 2016). Only data on organic fields without fertilization were retained to test the effect of crop sequence on nitrate leaching. For other ES, we collected data from publications focusing on the effect of crop rotation on one ES. We then validated five ES: weed regulation, disease regulation, NO₃ leaching reduction, carbon sequestration and soil structure (see Table 3 and Supplementary Materials S3). For each validation test, a correlation coefficient *r* rather than *r*² was calculated, because it indicates the degree of relationship that needs to be assessed (Pierlot et al., 2017a, 2017b). A chi-squared test was carried out on the distribution of the “indicator/measurements” pairs according to how the results changed (same or opposite direction).

### 2.2.4. Validation of output regarding input reduction

We tested the relation between nitrogen fertilizer use and I-ESR, Nsupply in comparison with other simple descriptors that are easy to obtain, such as the percentage of legumes in the rotation, the number of crops or the taxonomic crop diversity indicators. The data set used came from the field trials and long-term experiments in the Diver-IMPACTS project (DiverIMPACTS, 2023).

## 3. Results

### 3.1. Calculation of the ES indicators for a wide range of crop rotations

Table 4 shows that as crop rotations get longer, the values of the ES indicators appear to increase (with the exception of water provision to crops), although drivers aside from ES were identified. Sowing a cover crop plays a significant role in increasing several ES (soil structure and protection, N supply, NO₃ leaching and carbon sequestration) but also contributes to a decrease in other ES (disease regulation and water provision to crops). For the disease regulation ES, longer rotations have a positive effect because the return time increases and the previous crop is more often favourable. However, there are some exceptions, especially for rotations with high rate of legumes in cash crops and legumes and grasses as cover crops (rotations #17 and #19, see Table 4). Nevertheless, introducing legumes and cover crops promotes the N supply ES. The soil structure ES has more to do with the presence of a structuring root system (alfalfa) and cover crops than with longer rotations. Pluriannual crops and cover crops also promote some ES by how

long they are in the field (soil protection and wildlife habitat) and the capacity to take up nitrogen before winter (NO₃ leaching). Conversely, this same duration in the field is unfavourable to the water provision to crops ES because crop evapotranspiration is increased.

### 3.2. Calculation of temporal diversity and I-DRo on a wide range of crop rotations

Regarding temporal diversity (I-ESR, I-CDR and I-TDR), the indicator values tend to increase as the rotations get longer (Table 5). Longer rotations, and especially crop diversification (pulses, pluriannual and cover crops) as seen in the previous section, have a positive effect on ES provision (Table 4), hence an increased I-ESR indicator. For the same reasons, the diversity indicator (I-CDR) will increase as well as I-TDR, which is a result of the aggregation of the first two indicators. We note the effect of field width on the final value of I-DRo: a field width from 3 m to 250 m results in a loss of between 2.3 and 3.6 indicator points. With a 3 m width, for relatively short and low diversified rotations (#6 to #12), I-DRo values are around 7. But for a large field, the rotation must be very diversified to obtain a value close to 7 (rotation #17). Whatever the field width, I-DRo clearly distinguishes between different rotations, from monoculture to highly diversified systems (up to five indicator points).

### 3.3. Comparison of the ES indicator with basic descriptors of crop rotations

To assess the degree of diversification of crop rotation, the most used descriptors are the rotation length, the number of crops (or crop families) or diversity indices. To know if the calculation of the I-ESR indicator was more informative, it was compared with these descriptors (Fig. 2). The four curves have the same aspect: an increase until 60 % of variation of the descriptor and then a stagnation of the I-ESR results (Fig. 2), because the rotation length, number of crops and crop families, and I-CDR are related. In any case, for a low level of diversity, I-ESR variation is important. Thus, for short rotations or those with little diversity, the choice of crops in the rotation will strongly influence the result. In this case, we note the importance of the crop sequence and the previous crop effect. For the second part of the curve, the I-ESR indicator caps at around 0.6, which means that a longer rotation or crop diversification no longer allows for an overall increase in ES provision. This cap may be explained by compensation between the ES because antagonisms exist between services (nitrogen supply versus disease regulation when legumes are introduced, or soil protection and NO₃ leaching versus water provision to crops when intercropping is introduced). Even if I-ESR does not increase anymore, diversification will influence I-CDR through the number of different crops present in the rotation and thus the indicators from the two successive aggregations (I-TDR and I-DRo). These results show the utility of the new I-ESR subindicators in comparison with simple descriptors like the rotation length, even if the correlation coefficients are quite high.

### 3.4. Indicator sensitivity

Fig. 3 illustrates the weight of the variables in the aggregate indicators. If the 11 ES are unfavourable, the variation of I-ESR was almost null, while a substantial variation of I-ESR was observed (about 0.2

<table><caption>**Table 3**
Published data used to validate five ecosystem service (ES) indicators (for details on the indicator calculation, see section 2.1.2 and Supplementary Materials S2).</caption><thead><tr><th rowspan="2"></th><th colspan="5">Ecosystem services</th></tr><tr><th>Weed regulation</th><th>Disease regulation</th><th>Nitrate leaching reduction</th><th>Carbon sequestration</th><th>Soil structure</th></tr></thead><tbody><tr><th>Number of publications</th><td>9</td><td>9</td><td>8</td><td>5</td><td>10</td></tr><tr><th>Number of comparisons</th><td>30</td><td>32</td><td>40</td><td>30</td><td>19</td></tr></tbody></table>
<caption>**Table 4**</caption>

Examples of crop rotations (common rotations in France and those tested in the DiverIMPACTS project) and their values obtained for the 12 ES. For crops in rotation, two successive growing seasons are separated by a slash (//), cash crops and cover crops planted during the same growing season are separated by an underscore (\_), two cash crops planted in the same growing season are separated by a hypen (-) and crops sown in mixture are indicated with a plus sign (+). For cash crops: M = maize, WW = winter wheat, DW = durum wheat, SUN = sunflower, R = rapeseed, WB = winter barley, SB = sugar beet, Pot = potato, A = alfalfa, SP = spring pea, BW = buckwheat, Soy = soybean, Sor = sorghum, Len = lentil, WP = winter pea, CP = chickpea and B = spring barley. For cover crops: Vet = vetch, Mus = mustard.

<table><thead><tr><th>Rotation number</th><th>Crops in rotation</th><th>Rotation length</th><th>Number of crops</th><th>Weeds</th><th>Diseases</th><th>Pests</th><th>Pollination</th><th>Soil structure</th><th>Soil protection</th><th>Water provision</th><th>N supply</th><th>NO3 leaching</th><th>Carbon storage</th><th>Habitat</th><th>Landscape value</th></tr></thead><tbody><tr><td>1</td><td>M</td><td>1</td><td>1</td><td>0.13</td><td>0.38</td><td>0.50</td><td>0.08</td><td>0.35</td><td>0.24</td><td>0.25</td><td>0.00</td><td>0.50</td><td>0.90</td><td>0.28</td><td>0.11</td></tr><tr><td>2</td><td>WW</td><td>1</td><td>1</td><td>0.22</td><td>0.13</td><td>0.80</td><td>0.00</td><td>0.58</td><td>0.60</td><td>0.51</td><td>0.00</td><td>0.47</td><td>0.80</td><td>0.70</td><td>0.53</td></tr><tr><td>3</td><td>M/WW</td><td>2</td><td>2</td><td>0.44</td><td>0.38</td><td>1.00</td><td>0.04</td><td>0.47</td><td>0.42</td><td>0.38</td><td>0.00</td><td>0.48</td><td>0.85</td><td>0.49</td><td>0.32</td></tr><tr><td>4</td><td>DW/ SUN</td><td>2</td><td>2</td><td>0.44</td><td>0.50</td><td>1.00</td><td>0.39</td><td>0.77</td><td>0.48</td><td>0.52</td><td>0.00</td><td>0.57</td><td>0.65</td><td>0.49</td><td>0.43</td></tr><tr><td>5</td><td>M/M/WW</td><td>3</td><td>2</td><td>0.33</td><td>0.38</td><td>0.83</td><td>0.05</td><td>0.43</td><td>0.36</td><td>0.34</td><td>0.00</td><td>0.49</td><td>0.87</td><td>0.42</td><td>0.25</td></tr><tr><td>6</td><td>R/WW/WB</td><td>3</td><td>3</td><td>0.48</td><td>0.47</td><td>0.97</td><td>0.34</td><td>0.69</td><td>0.62</td><td>0.62</td><td>0.05</td><td>0.54</td><td>0.83</td><td>0.61</td><td>0.59</td></tr><tr><td>7</td><td>R/WW/WW</td><td>3</td><td>2</td><td>0.45</td><td>0.42</td><td>0.93</td><td>0.34</td><td>0.69</td><td>0.62</td><td>0.57</td><td>0.05</td><td>0.56</td><td>0.87</td><td>0.61</td><td>0.58</td></tr><tr><td>8</td><td>R/WW/WB/WW</td><td>4</td><td>3</td><td>0.44</td><td>0.46</td><td>0.93</td><td>0.30</td><td>0.67</td><td>0.61</td><td>0.59</td><td>0.04</td><td>0.52</td><td>0.83</td><td>0.64</td><td>0.57</td></tr><tr><td>9</td><td>WW/SB/WW/M</td><td>4</td><td>3</td><td>0.44</td><td>0.69</td><td>1.00</td><td>0.02</td><td>0.50</td><td>0.44</td><td>0.36</td><td>0.04</td><td>0.57</td><td>0.78</td><td>0.47</td><td>0.38</td></tr><tr><td>10</td><td>Pot/WW/SB/WW</td><td>4</td><td>4</td><td>0.43</td><td>1.00</td><td>1.00</td><td>0.00</td><td>0.50</td><td>0.46</td><td>0.41</td><td>0.08</td><td>0.58</td><td>0.61</td><td>0.43</td><td>0.41</td></tr><tr><td>11</td><td>Pot/WW_Vet/SB/WW_Mus</td><td>4</td><td>6</td><td>0.43</td><td>0.90</td><td>0.90</td><td>0.27</td><td>0.66</td><td>0.56</td><td>0.30</td><td>0.15</td><td>0.83</td><td>0.71</td><td>0.51</td><td>0.52</td></tr><tr><td>12</td><td>R/WW/WB/SB/WW</td><td>5</td><td>4</td><td>0.48</td><td>0.75</td><td>0.98</td><td>0.27</td><td>0.62</td><td>0.55</td><td>0.51</td><td>0.06</td><td>0.58</td><td>0.78</td><td>0.55</td><td>0.53</td></tr><tr><td>13</td><td>A/A/A/WW/M</td><td>5</td><td>3</td><td>0.89</td><td>0.90</td><td>0.96</td><td>0.30</td><td>0.84</td><td>0.77</td><td>0.14</td><td>0.74</td><td>0.73</td><td>0.98</td><td>0.72</td><td>0.59</td></tr><tr><td>14</td><td>SP/WW/SB/WW/Pot/WW</td><td>6</td><td>6</td><td>0.41</td><td>1.00</td><td>1.00</td><td>0.20</td><td>0.56</td><td>0.47</td><td>0.47</td><td>0.27</td><td>0.52</td><td>0.61</td><td>0.47</td><td>0.43</td></tr><tr><td>15</td><td>SP_Mus/BW_Vet/SB/WW/R_Mus/Soy_Mus/Hemp/WW_Mus</td><td>8</td><td>9</td><td>0.64</td><td>0.86</td><td>0.88</td><td>0.95</td><td>0.79</td><td>0.57</td><td>0.33</td><td>0.36</td><td>0.84</td><td>0.79</td><td>0.49</td><td>0.55</td></tr><tr><td>16</td><td>R/M/Sun/WW_Vet/SP/WW_vet/WB/Sor/Len_Vet/DW</td><td>9</td><td>9</td><td>0.46</td><td>0.80</td><td>0.96</td><td>0.71</td><td>0.80</td><td>0.56</td><td>0.42</td><td>0.32</td><td>0.69</td><td>0.80</td><td>0.61</td><td>0.51</td></tr><tr><td>17</td><td>WP-BW/DW_Vet/Sun/WW_Oat/CP/R/DW/WB-Oat/Sor</td><td>9</td><td>10</td><td>0.51</td><td>0.26</td><td>0.93</td><td>0.98</td><td>0.89</td><td>0.67</td><td>0.41</td><td>0.31</td><td>0.70</td><td>0.86</td><td>0.70</td><td>0.62</td></tr><tr><td>18</td><td>SB/WW_Oat/WP/WW_Oat+Vet/M/WP/R_Vet/Pot/WW_Oat+Vet</td><td>9</td><td>8</td><td>0.45</td><td>0.73</td><td>0.92</td><td>0.45</td><td>0.69</td><td>0.58</td><td>0.44</td><td>0.40</td><td>0.72</td><td>0.75</td><td>0.58</td><td>0.53</td></tr><tr><td>19</td><td>SB/WW_vet/WP-Oat+Vet/Hemp/WW_Vet/SB/SP/R/WW-Oat+Vet/B_Oat</td><td>10</td><td>8</td><td>0.53</td><td>0.30</td><td>0.90</td><td>0.66</td><td>0.75</td><td>0.61</td><td>0.41</td><td>0.33</td><td>0.69</td><td>0.80</td><td>0.61</td><td>0.58</td></tr></tbody></table>
<caption>**Table 5**</caption>

Examples of crop rotations (common rotations in France and those tested in the DiverIMPACTS project) and their values obtained for temporal and spatial indicators and for I-DRo (see Table 4 for more details on the crops in rotation).

<table><thead><tr><th>Rotation number</th><th>Crops in rotation</th><th>I-ESR</th><th>I-CDR</th><th>I-TDR</th><th>I-DRo (3 m)</th><th>I-DRo (24 m)</th><th>I-DRo (100 m)</th><th>I-DRo (250 m)</th></tr></thead><tbody><tr><td>1</td><td>M</td><td>0.23</td><td>0.00</td><td>2.17</td><td>5.30</td><td>4.20</td><td>3.25</td><td>1.74</td></tr><tr><td>2</td><td>WW</td><td>0.35</td><td>0.00</td><td>3.34</td><td>6.00</td><td>4.87</td><td>3.92</td><td>2.67</td></tr><tr><td>3</td><td>M/WW</td><td>0.35</td><td>0.30</td><td>4.42</td><td>6.65</td><td>5.34</td><td>4.53</td><td>3.53</td></tr><tr><td>4</td><td>DW/ SUN</td><td>0.44</td><td>0.30</td><td>5.00</td><td>7.00</td><td>5.54</td><td>4.86</td><td>4.00</td></tr><tr><td>5</td><td>M/M/WW</td><td>0.32</td><td>0.25</td><td>4.13</td><td>6.48</td><td>5.22</td><td>4.37</td><td>3.30</td></tr><tr><td>6</td><td>R/WW/ WB</td><td>0.50</td><td>0.48</td><td>5.68</td><td>7.41</td><td>5.78</td><td>5.25</td><td>4.54</td></tr><tr><td>7</td><td>R/WW/WW</td><td>0.49</td><td>0.25</td><td>5.20</td><td>7.12</td><td>5.60</td><td>4.98</td><td>4.16</td></tr><tr><td>8</td><td>R/WW/WB/WW</td><td>0.48</td><td>0.42</td><td>5.43</td><td>7.26</td><td>5.68</td><td>5.11</td><td>4.34</td></tr><tr><td>9</td><td>WW/SB/WW/M</td><td>0.38</td><td>0.42</td><td>4.83</td><td>6.90</td><td>5.49</td><td>4.76</td><td>3.86</td></tr><tr><td>10</td><td>Pot/WW/SB/WW</td><td>0.39</td><td>0.42</td><td>4.89</td><td>6.93</td><td>5.51</td><td>4.80</td><td>3.91</td></tr><tr><td>11</td><td>Pot/WW_Vet/SB/WW_Mus</td><td>0.49</td><td>0.55</td><td>5.77</td><td>7.46</td><td>5.82</td><td>5.30</td><td>4.61</td></tr><tr><td>12</td><td>R/WW/WB/SB/WW</td><td>0.48</td><td>0.55</td><td>5.74</td><td>7.45</td><td>5.81</td><td>5.29</td><td>4.59</td></tr><tr><td>13</td><td>A/A/A/WW/M</td><td>0.63</td><td>0.47</td><td>7.15</td><td>8.29</td><td>6.49</td><td>6.09</td><td>5.72</td></tr><tr><td>14</td><td>SP/WW/SB/WW/Pot/WW</td><td>0.46</td><td>0.48</td><td>5.44</td><td>7.26</td><td>5.69</td><td>5.11</td><td>4.35</td></tr><tr><td>15</td><td>SP_Mus/BW_Vet/SB/WW/R_Mus/Soy_Mus/Hemp/WW_Mus</td><td>0.61</td><td>0.85</td><td>7.86</td><td>8.72</td><td>6.94</td><td>6.49</td><td>6.29</td></tr><tr><td>16</td><td>R/M/Sun/WW_Vet/SP/WW_vet/WB/Sor/Len_Vet/DW</td><td>0.59</td><td>0.90</td><td>7.90</td><td>8.74</td><td>6.97</td><td>6.51</td><td>6.32</td></tr><tr><td>17</td><td>WP-BW/DW_Vet/Sun/WW_Oat/CP/R/DW/WB-Oat/Sor</td><td>0.58</td><td>0.99</td><td>8.71</td><td>9.23</td><td>7.61</td><td>7.09</td><td>6.97</td></tr><tr><td>18</td><td>SB/WW_Oat/WP/WW_Oat+Vet/M/WP/R_Vet/Pot/WW_Oat+Vet</td><td>0.57</td><td>0.72</td><td>6.93</td><td>8.16</td><td>6.37</td><td>5.96</td><td>5.55</td></tr><tr><td>19</td><td>SB/WW_vet/WP-Oat+Vet/Hemp/WW_Vet/SB/SP/R/WW-Oat+Vet/B_Oat</td><td>0.56</td><td>0.91</td><td>7.67</td><td>8.60</td><td>6.81</td><td>6.38</td><td>6.14</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Comparison between I-ESR (aggregation of the 12 ecosystem services (ES), see Section 2.1.2.4.) and different descriptors of rotation diversification: a) rotation length (year), b) I-CDR (see Section 2.1.3.), c) number of crop and d) number of crop families.</figcaption></figure>

points) when the 11 ES are favourable (Fig. 3a). This is consistent with the goal of aggregation; one ES with a favourable value cannot compensate for the 11 others when they are unfavourable (U curve) and a single unrealized ES will lower the I-ESR score (F curve). For the test on I-DRo (Figs. 3b, 3c and 3d), the weights of the three variables (respectively I-ESR, I-CDR and I-SDR) were consistent with the conclusions of the decision rules. We note that the effect of I-ESR and I-SDR was not the same when the other variables are favourable (variation between 4.5 and 2 points, respectively) or unfavourable (variation between 5.5 and 4 points, respectively). This difference highlights the decision rule to give more weight to a single variable when the others have low values and

less weight when the others are high.

### 3.5. Predictive quality of ecosystem services assessment

#### 3.5.1. With the existing database

3.5.1.1. For weed regulation. With the Biovigilance Flore data (Fig. 4), we note no relationship between the result of the I-ESR-weeds indicator and the field measurements (weed diversity and abundance). There is a high level of variability that could be expected, because weed
<figure><img src="image_5.png" /><figcaption>Fig. 3. Analysis of sensitivity of I-ESR (a) and I-DRo (b, c and d). The tested variable varied from 0 to 1 while other are fixed at unfavourable (U), median (M) or favourable (F) respectively at 0, 0.5 and 1.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Validation of the I-ESR-weeds indicator with data from the Biovigilance Flore network (Fried et al., 2012, 2008): abundance (left) and diversity (right).</figcaption></figure>

management requires many other agronomic levers in addition to rotation. However, there are very few points above 0.55 indicator points and the variability is substantially decreased (zone defined by the blue lines). This suggests that this indicator plays an important role only when the rotation provides a relatively high level of ES for weed regulation, for instance with the presence of pluriannual crops.

### 3.5.1.2. For NO₃ leaching.
The comparison between the concentration of NO₃ in porous cup samples (beyond the roots) and the results of I-ESR_NO₃ reduction shows a mixed correlation with regard to the level of simplification of the indicator (Pierlot et al., 2017a, 2017b) (Fig. 5). Nevertheless, for values of I-ESR_NO₃ reduction below 0.5, there is a high variability in NO₃ concentration, while above this value, concentrations were low. The indicator calculates a nitrogen balance before winter, so values below 0.5 mean there is a lot of nitrogen in the soil. However, the concentrations under the roots will also depend on drainage and therefore on the pedoclimatic conditions (soil type, depth, rainfall). In this case, it can be assumed that for large amounts of nitrogen, drainage will modulate the concentration under the roots and

<figure><img src="image_7.png" /><figcaption>Fig. 5. Comparison of the I-ES_NO₃ reduction indicator with the measurement of nitrate concentrations using porous cups in winter.</figcaption></figure>
generate the variability. Furthermore, above 0.6 indicator points, where the amount of nitrogen is quite low, there will be little difference in concentrations, even for variable drainage.

### 3.5.2. With published data

Predictive quality assessments of the five ES indicators with diverse published data (Table 3) yielded mixed results (r from 0.33 to 0.74 and p-value of the chi-squared test from 0.007 to 0.086; see Fig. 6). For I-ESR-weeds (Fig. 6a), although the medium r value shows a loose relationship, the chi-squared test is significant. As with the Biovigilance Flore data, and even if most of the other agronomic levers are not implemented in the selected publications, there are still parameters that are not controlled, such as the field history with the seed stock and the previous crop's weed control efficiency. For I-ESR_diseases (Fig. 6b), the r and the chi-squared test show a good relationship between indicator and measurement. However, it should be noted that the indicator has been slightly modified to consider only the disease studied in the publication, whereas the indicator normally includes all the cryptogamic diseases for each crop. We also observed a large variability of the effect (from no effect to a nearly total suppressive effect) when the indicator varied between 0.1 to 0.4 (between diversified rotation and the control). For I-ESR_NO₃ leaching (Fig. 6c), the r and the chi-squared test show a

good relationship. The variability of NO₃ losses is largely due to the amount of water drained (Padilla et al., 2018) but, in our case, the diversified rotation and the control are in the same pedoclimatic situation, so there is no difference. For I-ESR_carbon sequestration (Fig. 6d), if the chi-squared test is significant, r is low. This is mainly due to the response variability: for an indicator gain around 0.1 point, the organic matter rate can be increased from 2 % to 25 %. These results were as expected because the change in organic matter rate depends not only on organic inputs but also on many other factors that impact mineralization (history of inputs, initial rate, soil texture, irrigation or tillage; see Pellerin et al., 2019). For I-ESR_soil structure (Fig. 6e), r is average and chi-squared is not significant for an alpha risk = 0.05. But the studied rotations rarely exceed four years (only 10 % of cases), which is too short to have a significant effect on soil density. Other factors are also known to come into play, such as crop residue management, tillage and machinery compaction (Boizard et al., 2002).

### 3.5.3. Predictive quality of input-use assessment

Fig. 7 shows that the indicator I-ESR_Nsupply of I-DRo is found to be the best predictor and confirmed the need for an accurate assessment of this ES. However, there is substantial variability for a given value of I-ESR_Nsupply, up to 75 kg N/ha (Fig. 7a). This variability occurs

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Results of the validation test for five ES indicators using published data: a) for I-ESR-weeds, b) for I-ESR_diseases, c) for I-ESR_NO₃_leaching, d) for I-ESR-carbon sequestration and e) for I-ESR_soil structure. The x-axis shows a gain (positive value) or loss (negative value) of the ES indicator when comparing the diversified rotation and the control. For a), b) and c), the y-axis provides a ratio of the measured effect between the diversified rotation and the control (a value of 1 corresponds to equality and a value below 1 a suppressive effect). For d) and e), a difference of the measured effect between diversified rotation and the control is calculated and expressed in % of the control (organic matter rate and soil density, respectively); in this case, a value of 0 corresponds to equality. The number of points of each of the four zones defined by the dotted green line and the y-axis are used in the chi-squared test.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Comparison between nitrogen input for rotations in the DiverIMPACTS project and diversification descriptors: a) I-ESR_Nsupply, b) crop diversity (calculated with I-CDR), c) number of crops and d) number of pulses. In Fig. 7a, green dots represent rotations with legumes and non-fixing crops with low needs while red dots represent rotations with multiple cropping (two cash crops in the same year).</figcaption></figure>

because the indicator assesses the need for nitrogen input or the capacity to fix atmospheric nitrogen and not the crop's need for fertilizer. Green dots correspond to situations where in addition to pulses, non-fixing crops with low needs (e.g. sunflower) are in the rotation (Machet et al., 2017). The red dots (Fig. 7a) are particular rotations where two cash crops are harvested in the same year (e.g. winter barley then soybean), which may increase total needs.

# 4. Discussion

## 4.1. Originality of the indicator

The I-DRo indicator and its components were developed to support reflections on diversifying crop rotations to support the agroecological transition that emerged in the 2010s. Previous studies on rotation assessment (Bockstaller and Girardin, 2000; Vereijken, 1997) were carried out in the context of integrated agriculture or pest management in the late 1990s and early 2000s (Ferron and Deguine, 2005; Girardin and Spiertz, 1993). At the time, the main focus was already on reducing the use of external anthropogenic inputs. Various tables mainly assessing the effect of previous crops on pest, disease and weed regulation and providing return time of a given crop were published in the grey literature (APCA., 2017; Jeangros and Courvoisier, 2019; Maillard et al., 1995; Viaux, 1999). The methodology described here presents several enhancements in comparison with the previous indicators assessing the effect of crop rotation on physical and biological variables. First, I-DRo covers more numerous forms of diversification in the rotation than previous indicators: not only does it consider rotation length and crop sequence, but it also covers intercropping, multiple cropping and relay cropping for cash and cover crops. A second improvement is the explicit introduction of the concept of ES in I-DRo; previous indicators were developed just before or at the same time as seminal work on ES by

ecologists (Costanza et al., 1997; Daily, 1997), which means the concept of ES had not yet spread among agronomists. Furthermore, while the scoring proposed by Vereijken (1997) covers the effect on soil cover and structure and nitrogen supply, and the method of Bockstaller and Girardin (2000); (1996) covers pest regulation, nitrogen supply and soil structure, I-DRo covers many more ES in addition to those mentioned above. It also includes water provision to crops, nitrate leaching reduction, carbon sequestration, pollination, wildlife habitat quality to support biodiversity and aesthetic aspects related to landscape. This scope is broader than that presented in the literature review of Palomo-Campesino et al. (2018), which did not cover the nitrate leaching reduction ES or wildlife habitat quality. Lastly, I-DRo addresses several aspects of diversity (both functional and taxonomic), which is also covered by the crop sequence indicator of Bockstaller and Girardin (2000); (1996), but with the addition of temporal and spatial diversity.

## 4.2. Design methodology

To design I-DRo and its subindicators, we tried to avoid the pitfall of summing scores or calculating a weighted mean, which are common aggregation methods (e.g. Dardonville et al., 2022). Such aggregation methods raise several methodological issues, such as comparing distinct elements, subjective weighting or compensation between variables (Bockstaller et al., 2017). For the aggregation of ES subindicators, we kept a simple mean calculation but introduced terms to avoid compensation of variables. For several ES indicators, an original equation was developed to address the main mechanisms and interactions between drivers. The ES subindicators rest of data from scientific and grey literature as well as expertise. In some rare cases, we took data directly from a published paper, such as that for soil erosion (Panagos et al., 2015), but had to extrapolate a coefficient for crops not covered by the article. This makes the assessment of the predictive quality essential. We
also did not integrate factors such as climate, soil, crop management or landscape context for simplicity's sake and feasibility. For instance, field history and initial conditions are major drivers of carbon sequestration (Pellerin et al., 2019), while soil and weather conditions are major drivers of both nitrate leaching and mitigation (Padilla et al., 2018). Thus, I-DRo in its current state must be considered a tier 1 approach, like calculating emissions using a simple emissions coefficient. I-DRo can be considered to assess a potential impact according to the definition given by Freyer et al. (2000).

For this first version of I-DRo, we focused the assessment on the temporal scale with two components addressing functional diversity (I-ESR) and taxonomic diversity (I-CDR) while spatial diversity (I-SDR) was only based on field width (or crop width, for strip cropping). Spatial diversification based on strip cropping remains a very innovative management option and knowledge is lacking on how the practice affects ES. It was introduced to make users aware of the possibility of implementing spatial diversification as an additional lever. Thus, the assessment of the spatial diversity in I-DRo should be considered as a first attempt that requires further improvement.

Regarding the temporal component of I-DRo, calculations (data not shown) with the tested rotations (see Tables 4 and 5) showed that the correlation between I-CDR and I-ESR is quite good ($r = 0.82$), just as between I-CDR and I-DRo ($r = 0.95$). This raises the question of redundancy. We chose to keep both indicators because they are complementary: I-CDR provides information on crop diversity potential at the crop rotation scale, which is an important driver of biodiversity (Sirami et al., 2019), and I-ESR assesses functional diversity with data on ES that can be useful to users such as farmers.

### 4.3. Predictive quality of the indicator

As the previous section showed, the design of the indicator can be partially justified by results drawn from the scientific literature, but expert choices and data from the grey literature were also used to design I-DRo. As such, the scientific soundness of the output and results from the indicator must be assessed (Bockstaller et al., 2008). Outcomes from the test of a broad range of rotations appear to be consistent with current knowledge (Tables 4 and 5). For instance, the introduction of legumes favours nitrogen supply and nitrogen input reduction at the expense of disease regulation (Moussart et al., 2019). Another trade-off is suggested by the lack of correlation between rotation diversity and the water provision to crops indicator. The introduction of many cover crops and multiple cropping increases the water consumption, which may cause early water stress in the next crop and an increased demand for irrigation (Alletto et al., 2022a). The introduction of a pluriannual crop (here, alfalfa for three years) increases the beneficial effect on most ES and thus on the I-ESR indicator, as some authors have suggested (Martin et al., 2020; Piutti et al., 2015).

Overall, five indicators ES were tested against field measurements. Nitrogen supply was tested against input-use data. Six other indicators remain to be tested. If soil erosion and water provision to crops are based on model parameters, additional efforts to find data for pest, pollination, landscape value and habitat quality should be made. The five ES valuations (Fig. 6) produced mixed results in most cases ($r > 0.5$) with regards to the degree of simplicity sought (Pierlot et al., 2017a,2017b), with the exception of one data set on weeds and another on carbon sequestration. For the weeds, the data set did not include results that isolated the effect of rotation, while in the data set on carbon sequestration, other factors such as initial conditions or soil properties played a greater role than rotation. The exception for weeds highlights the difficulty of finding usable data sets, hence the choice to build our own databases from studies where rotation was the only factor tested. Overall, correlations are loose if it were to be used for modelling work with between 25 % and 55 % of variance explained. One way of improving the predictive quality for each ES would be to integrate the context into the structure, but this would also increase the amount of

required data. For I-ESR, local conditions (e.g. climate and soil data, slope, resource availability, presence or absence of diseases, etc.) could be integrated by weighting the ES during aggregation. Nevertheless, despite the low predictive quality of its subindicators, I-DRo was not designed to predict C sequestration or N leaching but rather to quickly assess the weaknesses of an actual crop rotation to give users clues about how to improve it and help them compare it with feasible alternatives.

### 4.4. Implementation and use of the indicators

The current version of the Excel file used to calculate I-DRo (available from the authors upon request) is easy for researchers and agronomists to use to assess crop rotations in field experiments, in farm networks or in designing new diversified rotations. Such a tool may give agronomists or even farmers new insights into the potential effect of rotations on different ES and trade-offs between ES, which is important from a sustainability perspective. One use example can be found in Soulé et al. (2023), who calculated the ES subindicators for 4 to 8 fields in a network of 33 farms in the Champagne Crayeuse region in Eastern France. Data for 17 of the 18 crops grown on this group of farms were available in the I-DRo database. Those ES indicators were used along with environmental impact indicators to develop an analytical framework highlighting the complementary between ES and environmental impact indicators. The I-DRo ES indicators were easier to implement than those of Dardonville et al. (2022), which provide more comprehensive information on ES.

At regional, national or even EU level, data on crops in each field are available in the Integrated Administration and Control System – IACS (Uthes et al., 2020). Applying I-DRo indicators would be possible provided an ad hoc calculator is developed and data are processed to obtain a crop sequence for each field (Levavasseur et al., 2016). Another issue at these levels is the lack of data on cover crops. This remains a major gap because cover crops play a major role in some ES, including reducing nitrate leaching and erosion (Blanco-Canqui, 2018) or carbon sequestration (Pellerin et al., 2019). Conversely, cover crops may also negatively affect pest regulation or water provision to crops (Lamichhane and Alletto, 2022). Given available information on crops, I-DRo could therefore be implemented on a broad scale (national or EU-wide), but only partially for cash crop rotations excluding cover crops, to provide a “rough” assessment. Applying I-DRo in this way could support the implementation of new cross-compliance measures on crop diversification included in the last reform of the common agriculture policy (CAP). Calculating and mapping I-DRo and its components at EU level would make it possible to assess the impacts in terms of ES provision and identify “hotspots” and “coldspots” (i.e. regions where policies to support crop diversification do or do not meet stakeholders’ expectations regarding impacts). Furthermore, the aggregated indicators I-ESR and I-DRo would be useful for comparing typical rotations to define crop diversification incentives based on the level of ES calculated by I-DRo. This would probably require simplifying the tool, such as by working with crop family instead crop species. Some contextual information, like climate soil data or pest and disease pressure (see Section 4.3) could be integrated to refine the indicators’ results and prioritize the actions of decision makers in regions where the context (e.g. high disease pressure and pesticide use) most needs ES provision (e.g. disease regulation).

## 5. Conclusion

I-DRo is a new indicator developed to provide a global assessment of crop rotation diversification using a set of subindicators and an aggregated subindicator. It has several original features compared with other diversity indicators. I-DRo deals with both taxonomic and functional diversity by assessing ecosystem services provided by different aspects of crop rotation diversification, including the introduction of new crops, longer rotations, cover cropping, intercropping, multiple cropping and
strip cropping. The predictive quality is moderate but acceptable given the degree of simplification of the indicator. Contextualization of the calculation method for ES assessment would be an avenue for further investigation.

The indicator's broad scope gives it unique potential for use compared with other crop rotation indicators. Initial tests showed the potential use of the indicators in the context of sustainability assessment in combination with other indicators. To further explore all use possibilities, the indicator should be applied to other contexts, such as a design process for innovative agroecological cropping systems. Applying I-DRo at a large scale (e.g. at the EU level) could support the implementation of new cross-compliance measures on crop diversification included in the latest CAP reform. Doing so would require substantial work on data collection and processing at the EU level. Remote sensing could fill this data gap (e.g. for cover crops), but would require additional research. Additionally, the indicator parameters were set according to background knowledge that is valid for temperate Western conditions in Europe. For southern and very cold northern and eastern conditions, further work would need to be carried out.

## CRediT authorship contribution statement

**Keichinger Olivier:** Conceptualization, Methodology, Writing – original draft. **Viguier Loïc:** Writing – review & editing. **Corre-Hellou Guénaëlle:** Formal analysis, Writing – review & editing. **Messéan Antoine:** Conceptualization, Funding acquisition, Writing – review & editing. **Angevin Frédérique:** Conceptualization, Funding acquisition, Writing – review & editing. **Bockstaller Christian:** Formal analysis, Methodology, Writing – original draft, Writing – review & editing.

## 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

The authors would like to extend their thanks to the European project 'Diversification through Rotation, Intercropping, Multiple Cropping, Promoted with Actors and value-Chains towards Sustainability' (DiverIMPACTS), funded by the European Commission under Grant Agreement no. 727482, for the financial support of this study. The authors are also grateful for the scientific support provided through two workshops and the financial assistance of the GIS Grande Culture. Finally, we would also like to thank to the SYPPRE platform partners for providing data and their support in developing this paper: Clotilde Rouillon and Anne-Laure Toupet de Cordoue (ARVALIS), Stéphane Cadoux (Terres-Inovia) and Rémy Duval (ITB), as well as Marie Benoît (ISARA) and Guillaume Fried (ANSESS) for providing data for validation.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.eja.2025.127531.

## Data availability

Data will be made available on request.

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