# Crop Establishment SIMulator: a qualitative aggregative model to predict the role of phytobiomes on field crop establishment

Jay-Ram Lamichhane, Ming Pei You, Martin John Barbetti, Jean-Noel J.-N. Aubertot

► To cite this version:

Jay-Ram Lamichhane, Ming Pei You, Martin John Barbetti, Jean-Noel J.-N. Aubertot. Crop Establishment SIMulator: a qualitative aggregative model to predict the role of phytobiomes on field crop establishment. Phytobiomes Journal, 2020, 4 (4), pp.327-339. 10.1094/PBIOMES-05-20-0036-R. hal-02912499

HAL Id: hal-02912499

https://hal.inrae.fr/hal-02912499

Submitted on 6 Aug 2020

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1

2

3

4

5

6

7

8

9

10

11

# Crop Establishment SIMulator: a qualitative aggregative model to predict the role of phytobiomes on field crop establishment

Jay Ram Lamichhane¹*, Ming Pei You², Martin J. Barbetti², Jean-Noël Aubertot¹

¹INRAE, Université Fédérale de Toulouse, UMR AGIR, F-31326 Castanet-Tolosan Cedex, France;

²UWA School of Agriculture and Environment and The UWA Institute of Agriculture, The University of Western Australia, Crawley, WA 6009, Australia

*Corresponding author: jay-ram.lamichhane@inrae.fr

Tel: +33 (0)5 61 28 52 50; Fax: +33 (0)5 61 28 55 37

## Abstract

The definition of phytobiomes can be transposed to any agroecosystem and applies to any phase of crop cycles. Here, we study the crop establishment phase using a generic modeling framework to assess the potential role of phytobiomes on field crop establishment. We first developed a generic model called Crop Establishment SIMulator (CESIM) that takes into account cropping practices, seed and seedling characteristics, seedbed components (physical chemical and biological), and weather, as well as their interactions. All these variables were integrated in a qualitative aggregative hierarchical network to predict the quality of field crop establishment. CESIM has 38 basic (input variables) and 20 aggregated attributes (19 state variables and 1 output variable) for a total of 58 attributes. The prediction quality of the model was evaluated for a dataset of 231 field observations across four states of Australia, and experimental results obtained
23 in the last 40 years. Accuracy of predictions of the final attribute (i.e. crop
24 establishment) was 91% and explained 29% of variability of the dataset, as described by
25 the quadratic weighted Cohen's κ. CESIM represents a unique and original generic model
26 capable of taking into account a large number of variables and their interactions to
27 predict the quality of field crop establishment. This model is flexible, transparent, user-
28 friendly, and therefore is suitable both for academic and non-academic users. CESIM can
29 be used across a wide range of situations not only to perform the *ex-ante* assessment of
30 potential establishment quality of a given crop but also *ex-post* assessment.

31 **Keywords:** damping-off, pre-emergence losses, post-emergence damage, seedling
32 blight, seedbed components, seed germination, seedling emergence, soil-borne
33 pathogens

34 **Introduction**

35 Crop establishment is the very beginning phase of a crop cycle that consists of three sub-
36 phases: sowing-seed germination, seed germination-seedling emergence, and seedling
37 emergence-early seedling growth (i.e. initial competition among plants; Aubertot et al.
38 2020). Crop establishment is the most important phase of any crop cycle as the quality
39 of crop establishment can directly affect crop productivity, both in terms of quantity and
40 quality (Arvidsson et al. 2014; Känkänen et al. 2011; Villalobos et al. 2016). For example,
41 a poor quality of crop establishment leads to several direct and indirect negative
42 consequences for farmers including the need for re-sowing (additional costs), yield
43 losses, and higher density of weeds with further problems owing to increased weed seed
44 bank in the soil (Lamichhane et al. 2018).

45 Crop establishment is affected by four major groups of drivers and their interactions
46 namely seed and seedling characteristics, seedbed components (physical, chemical and
biological) weather, and cropping systems (Lamichhane et al. 2018). Several studies have been carried out to understand the role of different factors on the quality of field crop establishment. However, these studies focused either on abiotic (Constantin et al. 2015; Dürr and Aubertot 2000; Souty and Rode 1993), or biotic factors (Baker 1971; Burdon and Chilvers 1975; Grogan et al. 1980), and cropping practices (Farooq et al. 2006; Leoni et al. 2013), without taking into account their overall impact and/or interactions. Therefore, there is a need to adopt "a systems-level approach" for a better understanding of these factors, their interactions and its overall impact on the quality of field crop establishment (Lamichhane and Aubertot 2018). This approach is consistent with the definition of phytobiomes, which consist of plants, their environment, associated micro- and macroorganisms, and their interactions (Leach et al. 2017), as reported in the Phytobiomes Roadmap (Anonymous 2016). The phytobiome concept in turn is fully consistent with the definition of Agronomy, as provided by Sebillotte (Sebillotte 1974), who viewed Agronomy as "a systemic approach to agriculture" that led to the development of a more holistic direction considering the entire agroecosystem, with a particular attention to cropping systems. Therefore, from here on we use the term phytobiomes to indicate all these key determinants affecting crop establishment.

Experimental approaches and regional agronomic diagnoses in commercial fields can be used to understand the effect of phytobiomes on field crop establishment. Nevertheless, both of these processes are time consuming and resource intensive. In addition, the limit of these approaches is that only a few variables can be studied at a time. In contrast, modeling allows taking into account all key factors affecting crop establishment, by integrating them into a same system over time. In particular, mechanistic models that simulate seed germination, seedling emergence and early seedling growth, as functions
of measured or estimated environmental variables seem to be the most promising approach to understand the quality of field crop establishment. However, mechanistic models are difficult to develop as they require a detailed knowledge of the system, by integrating all factors and their interactions (Forcella et al. 2000). This is especially true to study the quality of field crop establishment, which is affected by a large number of factors and their interactions (Lamichhane et al. 2018). Several models have been developed and used to achieve this objective. However, all of them take into account only a few factors affecting field crop establishment (Finch-Savage et al. 2005; Jame and Cutforth 2004; Wang et al. 2009). The SIMPLE crop emergence model (Dürr et al. 2001) is the most robust mechanistic model developed so far to study the quality of crop emergence. However, this model does not take into account biotic factors affecting crop establishment (Lamichhane et al. 2017) and our knowledge is not still advanced sufficiently to integrate this part into a mechanistic model. In addition, the SIMPLE model can not benefit from expert knowledge. Development and use of simpler models that benefit from experimental and observational data, in addition to published scientific and technical literature, existing simulation models, and expert knowledge, may be more useful to predict the quality of field crop establishment.

In order to do so, we propose a generic modeling framework called Crop Establishment SIMulator (CESIM). This framework is very simple in the way the modeled system is described, despite its complexity and the number of factors and processes involved, which would be very difficult to address using a mechanistic approach. Such a framework has been successfully developed and tested to predict risks of pest development on crops as a function of cropping practices, and abiotic and biotic environment (Aubertot and Robin 2013; Robin et al. 2013). CESIM is based on a simple qualitative hierarchical aggregative approach to represent the effects of various factors
affecting the quality of field crop establishment. The objectives of this study are three-
fold: i) present the basic principles of CESIM, ii) apply it to a concrete case as a proof of
concept: simulation of establishment quality of subterranean clover (Trifolium
subterraneum L.) in various Australian conditions, and iii) present a sensitivity analysis
of the model (i.e. measure how the model output reacts to changes in input or
aggregated attributes; Carpani et al. 2012) and assess its prediction quality.

103 **Materials and methods**

104 **Basic principales of CESIM**

Three sub-phases characterize the establishment phase of any crop, namely seed
germination, seedling emergence, and early seedling growth (Aubertot et al. 2020). A
conceptual scheme of CESIM, where crop establishment is the output variable is
presented in **Figure 1**. This scheme is an adaptation of a previously published scheme
(Lamichhane et al. 2018), where four different components: cropping systems, seed and
seedling characteristics, and seedbed components (physical, chemical and biological)
were the input variables while seed germination and seedling emergence were the
output variable. However, because CESIM goes beyond the seedling emergence phase
and considers the quality of crop establishment as its output variable, we integrated into
this scheme a fourth component represented by animal pests, especially the vertebrate
ones. This is because vertebrate pests are often responsible for post-emergence damage
of young seedlings (Dimitri et al. 2012; Firake et al. 2016; Nasu and Matsuda 1976). The
term "cropping system" indicates "a set of management practices applied to a given,
uniformly treated area, which may be a field, part of a field or a group of fields"
(Sebillotte 1990). This includes many technical operations such as the choice of the crop
sequence, cover cropping, cultivar, tillage or seedbed preparation practices, date, depth
and density of sowing etc. The term "system" is used here because these technical
choices are interdependent (Meynard et al. 2003). Crop establishment can be seen as the
result of hierarchical interactions among these components and the environment.

124 Implementation of CESIM

125 The development of CESIM was implemented using the DEXi software
126 (https://kt.ijs.si/MarkoBohanec/dexi.html). The underlying approach (called DEX
127 method) initially aimed at qualitative hierarchical multi-attribute decision modeling and
128 support, where a complex decision problem was broken down into smaller and less
129 complex sub-problems (Bohanec 2003). In the field of agronomy, this tool was applied
130 to the assessment of sustainability of agroecosystems (Craheix et al. 2015; Pelzer et al.
131 2012; Sadok et al. 2009). In addition, the DEXi software has also been used to implement
132 qualitative simulation models focusing on pest management (Aubertot and Robin 2013;
133 Robin et al. 2013; Robin et al. 2018). The sub-problems are hierarchically structured
134 into a tree of attributes that represents the "skeleton" of the model. Terminal nodes of
135 the tree, i.e. leaves or basic attributes, represent input variables of the model, which
136 must be specified by the user. The root node represents the main output, in our case it is
137 the quality of crop establishment. The root node and internal nodes of the model are
138 aggregated attributes. All the attributes in the model are qualitative (ordinal or nominal)
139 and not quantitative variables. They take only discrete symbolic values usually
140 represented by words. In the DEX method, the aggregation of values of the tree is
141 defined by "utility functions" based on a set of "if-then" aggregation rules. Here, we
142 renamed these functions "aggregating tables" as they are not related to the concept of
143 "utility" in decision theory (Aubertot and Robin 2013).

144 CESIM structure
As for building any DEXi model (Bohanec 2003), CESIM was designed in three steps: i) identification and organization of the attributes, ii) definition of attribute scales, and iii) definition of aggregating tables.

## Identification and organization of the attributes

CESIM aims at predicting the quality of any crop establishment in a given field based on a range of input variables. The spatial scale considered is the field while the temporal scale considered is a single growing season. Nevertheless, some input variables include the entire crop sequence (up to the preceding crops) as it affects the seedbed physical, chemical and biological characteristics. The hierarchical structure of the model is presented in **Figure 2**, which represents the breakdown of factors affecting the quality of crop establishment into specific explanatory variables, represented by lower-level attributes. CESIM has 58 attributes in total, including 38 basic and 20 aggregated attributes. The 38 basic attributes represent input variables of the model and are presented as the terminal leaves of the tree. The levels of the basic attributes are aggregated into higher levels according to aggregating tables. The aggregated attributes are internal nodes, which represent state variables or the output variable of CESIM, and they are determined by lower-level basic attributes (Bohanec et al. 2007). The importance of some basic attributes may differ not only from one crop species to another, but also within a given crop, depending on the growth phase considered. This means that some of basic attributes will have a higher impact than others on the final output (i.e. the quality of crop establishment). For example, seedbed chemical components such as organic matter, or inorganic nutrients, do not have any impact on seed germination which relies on seed reserves (autotrophic phase). In contrast, they markedly affect seedling growth, especially after emergence. Likewise, emergence losses
169 due to soil-borne animal pests can be more important for maize, oilseed rape or sugar
170 beet but less important for soybean or sunflower (BSV 2016; Furlan et al. 2020;
171 Lamichhane et al. 2020). Likewise, post-emergence seedling damage due to vertebrate
172 pests maybe very high for sunflower (Sausse et al. 2016) or soybean (Firake et al. 2016)
173 but less relevant for wheat. Similarly, crop compensation capacity is high for crops such
174 as soybean and wheat through ramification and tillering capacity. In contrast, crops such
175 as maize or sunflower do not have this capacity and thus exposed to higher pre-or post
176 emergence failure. At the same time, the importance of the same basic attribute on the
177 same crop may differ depending on production situations (Aubertot and Robin 2013).

178 For example, a diversified cropping system is less favorable for soil-borne pathogens to
179 cause pre- or post-emergence damping-off disease than monocropping or less-
180 diversified cropping system (Abdel-Monaim and Abo-Elyousr 2012; Hwang et al. 2008;
181 Lamichhane et al. 2017). Likewise, the risk of post emergence damage due to vertebrate
182 pests depends on a number of factors including the field, landscape and regional
183 characteristics (Bayani et al. 2016; Sausse and Lévy 2020). Therefore, none of the basic
184 attributes can be omitted from the model structure due to the generic nature of CESIM.
185 However, we excluded weeds as their impact on crop growth, development and yield
186 become important only after the crop establishment phase (provided that a crop is sown
187 in weed-free conditions) when they are sufficiently developed to compete with crops for
188 light and nutrients (Chauhan and Johnson 2011; Chauhan and Opeña 2013).

189 *Definition of the Attribute Scales*

190 The choice of ordinal or nominal scales for basic and aggregated attributes represents
191 the second step of a DEXi model design. To this objective, sets of discrete values were
192 defined for the attributes of the model and described by symbolic value scales
expounded by words. These values were explicated based on the literature knowledge as well as expertise when deemed necessary. CESIM uses no more than a three-grade scale value (i.e. "Unfavourable", "Moderately favourable", "Favourable") for the aggregated and basic attributes, which alludes to crop establishment. The value "Favourable" means that the attribute is favourable to seed germination, seedling emergence, stand uniformity (i.e. the uniformity of emerged seedlings in terms of growth stage including height, biomass, stem diameter etc.), and finally to crop establishment.

Different values for basic attributes can be specified using quantitative data, which are subsequently converted into qualitative values. For example, the seed mass, seedbed temperature or moisture can be converted into qualitative values using literature references or expertise. This conversion takes into account not only the regional context but also the crop in question. For instance, a relatively low seedbed moisture could be classified as "Moderately favorable" for oilseed rape or wheat (quite tolerant to water stress in the seedbed) but "Unfavorable" for soybean (very sensitive to water stress in the seedbed) establishment (Dürr et al. 2015; Lamichhane et al. 2020). In contrast, other attributes, such as "crop rotation" or "crop residue management", can be directly described qualitatively.

For seed and seedling attributes, all characteristics provided by seed suppliers, and when available, those obtained by experimental results and from expertise can be used. Likewise, for the seedbed physical, chemical and biological characteristics, the information can be retrieved from field experiments. Information related to weather attributes, such as rainfall and evapotranspiration can be either measured using sensors or recovered from nearby meteorological stations. **Figure 3** reports the scales which are
ordered from unpromising values to the propitious ones for crop establishment. All these characteristics are clearly noticeable in the DEXi software as detrimental, neutral and favorable values to the end user are, by convention, coloured in red, black and green, respectively.

Any initial quantitative or qualitative input attribute values can be converted into qualitative appreciation, based on two to three scales. These scales are defined relying on available information in the literature, models or expertise. For the same attribute, a two-value scale ("Unfavourable", and "Favourable") was used for the seed germination and seedling emergence phase (e.g. Temperature, Moisture, etc.) while a three-value scale ("Unfavourable", "Moderately favourable", and "Favourable") was used after seedling emergence (Fig. 3). This is because seed germination is indeed strongly affected by a relatively high or low level of seedbed temperature or moisture that have an important impact on seedling growth and the stand uniformity that together determine the final crop establishment (Constantin et al. 2015).

## Definition of Aggregating Tables

The choice of aggregating tables that determine the aggregation of attributes in the tree and their interactions is the third step in the design of a DEXi model. A set of "if-then" rules determine the value of the considered attribute, for each aggregated attribute in the model, as a function of the values of its immediate descendants in the model. The rules that correspond to a single aggregated attribute are assembled together and easily represented in tabular form. Each table defines a mapping of all value combinations of lower-level attributes into the values of the aggregate attribute. **Figure 4** presents decision rules that correspond to the "seed germination" aggregated attribute and define the value of this attribute for the 12 possible combinations of the three seed
characteristics, the 2 levels of seedbed characteristics, and the 2 levels of seed predation.

For example, if seed and seedbed characteristics are favorable and the seed predation
rate is low, then the “seed germination” will be high and thus favorable to crop
establishment (Fig. 5).

All CESIM aggregating tables have been generated using literature, experimental data,
and expert knowledge when deemed necessary. Other aggregating tables of the model
are reported in **Supplementary Figures S1-S4**.

## Evaluation of the prediction quality of CESIM: the establishment of subterranean clover establishment in Australia as a case study

### *Description of the Dataset*

A detailed description of the dataset used to test the prediction quality of the model is
presented in **Supplementary Table S1**. The quality of subterranean clover
establishment over the last four decades has been severely affected across Western
Australia (Burnett et al. 1994; Foster et al. 2017; O'Rourke et al. 2009), which has
encouraged research to investigate the key underlying causes. Soil-borne pathogens
have been reported to cause severe economic losses thereby threatening the viability of
this forage crop (Barbetti et al. 2007; Barbetti et al. 1986; Wong et al. 1985a) and four
major soil-borne pathogens: namely *Phytopthora clandestina* (Simpson et al. 2011; You
et al. 2005; You and Barbetti 2017), *Pythium irregulare* (Wong et al. 1984; Wong et al.
1985b; You et al. 2017), *Rhizoctonia solani* (Maughan and Barbetti 1983; Wong et al.
1985a; You et al. 2008; You and Barbetti 2017) and *Aphanomyces trifolii* (Ma et al. 2008;
You et al. 2016; 2018) have been reported to cause root rot of subterranean clover. The
severity of the disease caused by these soil-borne pathogens mainly depends on
cropping system (e.g. cultivar choice) or seedbed soil and weather characteristics (e.g.
soil texture, structure, moisture, temperature and rainfall; (Barbetti and MacNish 1984; Hochman et al. 1990; Wong et al. 1985a, 1985b; You et al. 2017; You and Barbetti 2017).
In addition, there are also other components affecting the quality of subterranean clover establishment, such as the impact of animal pests like nematodes (Barbetti et al. 2007; Pung et al. 1988).

Research conducted over the last four decades in Australia focused on a number of issues spanning from ecology and epidemiology of damping-off disease caused by soil-borne pathogens (Barbetti et al. 1986; Sivasithamparam, 1993; Wong et al. 1984, 1985b, 1985a; You et al. 2008) to identification and deployment of available host resistance (Nichols et al. 2014, 2013; You et al. 2005; You et al. 2016), use of mineral nutrients (O'Rourke et al. 2012), soil and plant management (Smiley et al. 1986). More recent research has been aimed at better understanding of soil and weather effects and their interactions with cropping practices and the overall impact on disease development and levers towards better disease management (You and Barbetti 2017; You et al. 2017; You et al. 2018). All this research conducted across Australia in the last four decades allowed us to generate an important amount of primary knowledge on how different factors and their interaction may affect the quality of subterranean clover establishment. This dataset thus represents an unprecedented opportunity for modeling frameworks.

The required datasets had to provide information for input attributes of CESIM (description of seed, seedling and seedbed characteristics, cropping practices, climate) and its output (crop establishment) which was challenging. Therefore, the predictive quality of CESIM was tested using two kinds of dataset: i) that originating from an Australia-wide survey that was carried out in 2014 to identify the quality of subterranean clover establishment across four states (SA, NSW, VIC & WA) in Australia,
289 and ii) that generated from different greenhouse and field trials conducted in Australia
290 over the last four decades.

291 Although the model structure considers different levels of crop rotation or cropping
292 practices, out dataset included only a low level of variability for some of the input
293 variables. For example, subterranean clover is an annual pasture crop that naturally
294 reseeds each growing season and therefore no crop rotation data were included in our
295 dataset. Likewise, although most field crops are subjected to seed treatment
296 (Lamichhane et al. 2020b) no seed treatment was performed for subterranean clover for
297 two reasons. First, this crop is considered a low-value crop and second, given that
298 damping-off disease limiting the establishment and productivity of this crop is most
299 often caused not only by one soil-borne pathogen but by a pathogen complex. In such a
300 case, fungicide seed tratment is not effective in managing the disease (You et al. 2020).
301 Nevertheless, all data used to assess the prediction quality of the model included
302 information corresponding to the real field situations. In total, we used data collected
303 across 231 production situations. All these data were transformed into qualitative
304 values and used as input basic attributes to feed CESIM-Subterranean clover.

305 *Converter*

306 As for any other DEXi-based models (Robin et al. 2018), CESIM-subterranean clover is
307 based on qualitative attributes and aggregative tables with nominal or quantitative
308 variables. The latter are generally available for users dealing with their specific
309 situations. A converter was designed to transform these variables into ordinal ones
310 using specific regional references adapted to the local pedo-climatic situations and
311 cropping practices. A detailed description of the converter used to this aim is presented
312 in **Supplementary Table S2**.
313 *Simulations with DEXi*

314 The qualitative final attribute value (final rate of crop establishment) is calculated by
315 DEXi. The estimation consists of calculating all aggregated attribute values according to:
316 (i) the structure of the tree; (ii) the considered simulation unit, defined as a set of input
317 variables (basic attribute values); and (iii) the aggregating tables for the aggregation of
318 attributes.

319 *Evaluation of the Predictive Quality of CESIM-Subterranean clover*

320 CESIM was evaluated for its ability to predict crop establishment classes. To this aim,
321 quantitative observed values of crop establishment were transformed into ordinal
322 values, using the same discrete categories as the model (i.e., 0 to 40, 40 to 60, 60 to 80,
323 and 80 to 100%). Each of the observed crop establishment percentages was related to a
324 value simulated by the model using the corresponding observed input attributes. To
325 assess the predictive quality of the model, a confusion matrix was computed as a table
326 layout, where each column represents the instances in a predicted class, while each row
327 represents the instances in an observed class. Accuracy (proportion of correctly
328 predicted situations) is a widely used performance metric. However, it cannot be the
329 only statistical criterion to consider since our dataset was unbalanced. Both Matthews
330 Correlation Coefficient (MCC) (Matthews 1975) and Cohen's quadratic weighted κ
331 (Cohen 1960; Fleiss and Cohen, 1973) correct this bias, but the former is prefered for
332 unbalanced cases (Delgado and Tibau 2019). Matthews Correlation Coefficient (MCC) is
333 a special case of Pearson Correlation Coefficient and leads to similar interpretations
334 (Matthews, 1975). It takes into account true and false positives and negatives and is
335 generally regarded as a balanced measure, which can be used even if the classes are of
336 very different sizes. We also used the Cohen's quadratic weighted κ because it can be
337 interpreted as the proportion of variability explained by the model (Fleiss and Cohen, 1973). The dataset used only had two classes of crop establishment quality: Low (<=40%), Moderately low (41-60%). Two additional statistics for binary classifiers were therefore also considered (Agresti 2002): the sensitivity (measurement of the proportion of situations with actual low crop establishment correctly predicted), and the specificity (measurement of the proportion of situations with moderately low crop establishment quality correctly predicted). These computations were carried out using Mathematica 10.1.0.0 (Wolfram Research 2015).

345 *Sensitivity analysis*

346 Sensitivity analysis was conducted using the automatic procedure integrated into the
347 DEXi software (Bohanec 2009), which computes the standardized local and global
348 weights of each attribute as a function of the aggregative tables using a linear regression
349 method (Bohanec 2009). Previous studies that used DEXi-based models performed
350 sensitivity analysis to measure the behaviour of the model output to changes in
351 parameters or other input values (Carpani et al. 2012; Robin et al. 2018). These weights
352 are important as they are comparable to a sensitivity analysis for quantitative models
353 (Robin et al. 2018). The higher the weight, the more influential the attribute. The “local”
354 and “global” weights are calculated in two different ways. “Local” weights are assigned
355 to each aggregated attribute individually so that the sum of weights of its immediate
356 descendants in the hierarchy equals 100%. The “global” weights are determined at a
357 given level of aggregation and signify the importance of each attribute on the value of
358 the output attribute. The “global” weights are computed by multiplying the local weight
359 of a given attribute at a given level of aggregation, by local weighting of its ascendants.
360 Only standardized values are presented since non standardized weights calculated by
DEXI do not take into account the number of classes in the scales used and prevent the structural bias they induce (Bohanec, 2009).

## 363 Results

### 364 Sensitivity analysis

Table 1 reports the weights of each of the 58 attributes of the model, providing an overview of the model's structure. CESIM has 5 levels of aggregation (Fig. 3), the fifth one being the leaves (i.e. the model input basic attributes). For example, the output attribute "Final percentage of establishment" is defined at a level of 40% by the attribute "Seedling emergence"; 20% by the attribute "Stand uniformity", and 40% by the attribute "Crop compensation capacity". As a proof of the complexity of the underlying processes at stake, the most influential input attribute (leaves), after the attribute "Crop compensation capacity" (40%), is only 4% (attribute "Seed predation").

### 373 Evaluation of the Predictive Quality of CESIM-Subterranean clover

The relatively high number of observed situations in the dataset (231) allowed an acceptable evaluation of the CESIM-Subterranean clover predictive quality. The accuracy of the confusion matrix (Fig. 5) revealed that the model correctly predicted 91.3% of the observations (sum of the italic numbers reported in the diagonal of the matrix in green). However, the MCC (0.342) revealed a fair agreement as confirmed by the Cohen's κ criterion (0.297) (Landis and Koch, 1977). The model has a good sensitivity (0.62), and an excellent specificity (0.92). As expected, the predictive quality of the model was excellent for the lowest crop establishment class (i.e. the most frequently observed class in the dataset): 92% of the observed values between 0 and 40% were correctly
383 simulated (as described by the specificity). Consequently, the overall predictive quality
384 of CESIM-Subterranean clover considered as satisfactory.

385 **Discussion**

386 *Complexity of the crop establishment phase and the need for multidisciplinary research*

387 The quality of crop establishment under field conditions is affected by several factors
388 and their interactions, depending on cropping practices and production situations
389 (Lamichhane et al. 2018). For example, drought represents the most important limiting
390 factor to cover crop establishment in Southern France (Constantin et al. 2015), while
391 soil-borne pathogens are the major limiting factor for the establishment of forage crops
392 across Southern Australia (Barbetti et al. 2007; Foster et al. 2017; You and Barbetti,
393 2017; You et al. 2018). However, in both cases, these abiotic and biotic factors interact
394 not only among them but are also under the influence of cropping practices that finally
395 determine the quality of crop establishment. Focusing only on some of the factors
396 determining the quality of crop establishment may not allow development of sustainable
397 solutions. This is especially true taking into account the complexity behind the crop
398 establishment phase requiring knowledge and expertise from agronomy,
399 phytopathology, entomology, weed science, soil science and soil microbial ecology. The
400 modeling framework proposed herein is a telling example that highlights how a complex
401 issue can be disentagled into more simple problems and how all this can be addressed
402 using a broader approach integrating literature knowledge and experise (notably from
403 scientific specialists, but also from farmers and agricultural advisers).

404 *Potentials of CESIM generic modeling framework*
Despite the availability of a rich dataset on factors affecting the establishment quality of subterranean clover, no modeling framework has been developed to date undertaking a system approach. A small part of this dataset has been recently used to develop generalized linear models and boosted regression trees (You et al. 2017, 2018), but these models did not take into account all key factors and their interactions. In addition, although these generalized linear models may have parameters with biological significance at discretion of the « experts » tuning, or creating a model, this was not taken into account in these recent works. On the other hand, a quantitative modeling approach, such as the use of mechanistic models, is not realistic yet to study this complex system. This is because they need precise quantitative data on each variable of the model and their interactions although they can still provide a good range of uncertainty around an estimation even without being fully calibrated (e.g. good sensitivity and uncertainty analysis procedures help targeting the right parameters to accomplish it). Overall, a quantitative approach may lead to models difficult to use due to the challenge to provide input variables. Also, propagation errors is a common pitfall of complex mechanistic models. However, these issues can be overcome by using a qualitative generic modeling framework. The dataset and knowledge available on the agroecosystems with subterranean clover allowed us to test the prediction quality of a complex generic model such as CESIM, which is not yet possible for other crops due to incomplete data availability.

CESIM represents the first model that takes into account all key abiotic and biotic factors, as well as cropping practices affecting the establishment quality of a given crop. The example of modeling framework reported herein is innovative because it allows aggregation of key information from different sources (i.e., technical and scientific literature, expert knowledge, experimental data or data from field diagnoses). In this
way, a high level of complexity can be addressed given that it deals with qualitative variables (sometimes derived from quantitative variables). Such a qualitative framework is very appropriate, while modeling complex systems for which a high precision level is not necessarily a pre-requisite. The DEXi software thus offers a relevant environment for organizing the available knowledge and developing models. In addition, DEXi-based models allow a high level of generality (Aubertot and Robin 2013; Robin et al. 2018). A major innovation of CESIM is its flexibility and adaptability as the model promptly integrates any variables at any time with the possibility of adjusting certain scales or aggregative rules. In addition, the simple and user-friendly DEXi interface is another key advantage of this approach that represents a powerful communication and educational tool. Finally, as for any DEXi-based model, CESIM can be used across a wide range of situations not only to perform the *ex-ante* assessment of potential establishment quality of a given crop but also *ex-post* assessment, taking any possible changes in cropping practices, seed and seedling as well as seedbed characteristics thereby facilitating decision making process.

## *Limits of CESIM generic modeling framework*

One of the limits of the CESIM approach is a high number of attributes and scales used in the model. This is due, in part, to the fact that the crop establishment phase is composed of three sub-phases (seed germination, seedling emergence and crop uniformity). This means that the same attributes and their related variables repeat several times within these sub-phases. For instance, seedbed characteristics such as moisture and temperature appear throughout all three sub-phases. Likewise, seedbed physical, chemical and biological characteristics appear along two phases (seedling emergence and seedling uniformity). All this makes the model structure quite complicated. In
addition, the scales used for each of the attributes that repeat throughout different sub-phases are not necessarily the same. This would further increase the complexity of aggregative tables, which would be more difficult to complete. This could be due to many factors including: i) lack of knowledge in the literature, ii) lack of consistency in experimental and observational results, or iii) lack of consensus among experts on the potential effects of different factors, given that attributes and scales were selected from literature, experimental data, and expert knowledge. As a consequence, the model may not be able to integrate these factors. An example, is the effect of seedbed temperature and moisture on disease severity index. Because different soil-borne pathogens, including oomycetes (Simpson et al. 2011; You and Barbetti, 2017; You et al., 2017), and true fungi (You and Barbetti 2017; You et al. 2016), affect the establishment quality of subterranean clover, the ranges of soil moisture and temperature triggering their epidemics could be different. In addition, most often these pathogens are subjected to synergistic interactions that lead to disease complexes. However, while this latter information is not precisely integrated into the model due to poor knowledge and expertise available to date, model inputs representing the outcomes of such synergistic interactions were integrated into this CESIM approach and well-represented by variables such as "seed germination ability" and "crop compensation ability".

Certain values of the basic indicators are difficult to estimate objectively. For instance, the role of beneficial organisms in the soil, in particular that of antagonist microorganisms, in reducing negative impact of soil-borne pathogens is very difficult for users to measure. Consequently, we did not include this variable into the model. Nevertheless, this variable is somehow well-represented in the model by the variable "organic matter", as soil organic matter has been reported to be directly correlated to disease suppressiveness due to the induction of physicochemical and biological changes
in soils (Campos et al. 2016; Vida et al. 2020). This means that the higher the soil organic matter in the soil, the lower the risk of disease development due to soil-borne pathogens. Likewise, the variable "crop rotation", which has been reported to enhance disease suppressive potential in soils when diversified (Peralta et al. 2018) is also integrated into the model, which to some extent represent the role of beneficial microorganisms. Another variable difficult to measure for users is "seed latently infected by seed-borne pathogens" which depends on the seed quality and thus belongs to seed characteristics. Although we did not include this variable into the model, this characteristic is already well-represented by the variable "seed germination ability" and "seed treatment". Indeed, the higher the percentage of latent seed infection in a given seed lot, the lower the germination ability, if the seeds are not treated. Finally, we did not include host resistance or tolerance into the model as a variable, neither for pre-emergence nor for post emergence. This is because the variables "seedling characteristics" and "crop compensation ability" represent host resistance/tolerance pre-and post-emergence, respectively. This is more realistic than including specific host resistance as a variable for two reasons: first, no crop cultivar is fully resistant to all soil-borne pathogens causing damping-off (Lamichhane et al. 2017) and a cultivar resistant to one soil-borne pathogen can be susceptible to another soil-borne pathogen or to a specific race of a given pathogen (Nichols et al. 2014; You et al. 2005). Second, a resistant cultivar can rapidly become susceptible due to multiple resistance-breaking as for example was the case for new races of *P. clandestina* (You et al. 2005). In general, the variable "crop compensation ability" also includes any potential pre- or post-emergence losses due to abiotic or biotic stresses via ramification, tillering, indeterminate or semi-determinate growth to ensure a good coverage of the seedbed.
In a qualitative model such as CESIM, the basic attribute values need to be defined qualitatively, and some of them have to be originated from quantitative values with simple transformation. This operation can be time consuming, in some cases, especially when the number of attributes is high. In addition, such a procedure raises questions about the precision of the model although the precision is not the main objective of such qualitative models (Aubertot and Robin 2013; Robin et al. 2018).

The results obtained when assessing the predictive quality of CESIM rely not only on the model itself, but also on the diversity of the dataset. The more representative the data are of a range of soil, weather, cropping systems and crop establishment, the more robust the evaluation. Although a relatively large dataset (i.e. 231 situations) was used to represent all these variabilities, the dataset did not cover a wide range of situations. For instance, there was no variability in seed treatment (all non-treated seeds), germination ability (all medium), seed predation (all low), evapotranspiration (all favorable), seedbed structure (all favorable), sowing density (all favorable), crop rotation (all unfavorable), sowing depth (all average), and vertebrate pests (all favorable). In addition, although four classes of the crop establishment quality was defined in the model, the dataset contained only low (<40%) and moderately low (41-60%) classes while the other two classes (moderately high; 61-80% and high >80%) were not represented in the dataset. Soil-borne pathogens, in interaction with pedo-climatic factors, cropping practices and production situations, cause devastating losses of subterranean clover across Australia (Barbetti et al. 1986, 2007). Disease management is extremely challenging for different reasons. First, host resistance is available to specific soil-borne pathogens but not to all soil-borne pathogen complexes (You et al. 2005). Likewise, while chemical seed treatment is generally effective against individual pathogens it is ineffective to pathogen complexes (You et al. 2020). Due to
permanent nature of annually self-regenerating subterranean clover forages, there is no
crop rotation and, most often, farmers systematically tend to renovate semi-permanent
forages by making new sowings into pre-existing clover fields that have severely
declined. This makes the clover establishment extremely challenging, as newly planted
cultivars often fail to successfully establish within existing forage systems due to
competition from surviving seedbank. In addition, resowing new cultivars into a field
already infected by soil-borne pathogens can fail due to pre- and post-emergence
damping-off from soil-borne pathogen complexes. All this explains the frequently low to
moderately low crop establishment rates in the dataset. This did not allow us to test the
predictive quality of the model for relatively high or high rates of crop establishment.
539 *Perspectives*

540 Here, for the first time, we developed a qualitative modeling framework based on a
541 systems approach, that assesses the role of phytobiomes on field crop establishment.

542 Development of such a model was a challenging task that has been achieved. The next
543 step should focus on its improvement, in particular, to increase its predictive quality. To
544 this objective, two approaches can be used : i) optimization of the model by modifying
545 the aggregative tables (equivalent to parameter optimization for quantitative models;

546 Aubertot and Robin 2013), and ii) improvement of the dataset used herein to test the
547 prediction quality of the model. The latter can be done including those situations where
548 the establishment quality of subterranean clover is high to very high in three different

549 ways as suggested previously (Robin et al. 2018): i) setting up specific experiments, (ii)
550 performing agronomic diagnoses in commercial fields, and (iii) integrating data from
551 other countries. Also, the use of other simulation models could be considered in order to
552 generate data, provided that their quality of prediction is sufficient.

553
555 Acknowledgements

556 We thank Gauthier Quesnel for his kind support and interaction on the model evaluation
557 part, as well as Marie-Hélène Robin and David Camilo Corrales for their suggestions on
558 the DEXi-based modeling approach. We also thank Kunmei Guo, Dr He Bo, John Quealy,
559 Kevin Foster, Daniel Kidd, Peter Skinner, Paul Wilson, Robert Creasy, and Bill Piasini
560 from the University of Western Australia for their technical support with collection of
561 some data used in this modelling study. This work was partly supported by the FAST
562 project (Faisabilité et Evaluation de Systèmes de Cultures Economies en pesticides en
563 l'Absence répétée de Semences Traitées) funded by the French Agency for Biodiversity,
564 by credits from the royalty for diffuse pollution, attributed to the funding of the
565 Ecophyto plan. A thank also goes to Institut National Polytechnique of Toulouse (INPT)
566 for providing a short-term visiting Professor grant that allowed M. J. Barbetti to come in
567 France and work with the first and last author.
569

570 **Literature cited**

571

572 Abdel-Monaim, M.F., and Abo-Elyousr, K.A.M. 2012. Effect of preceding and intercropping crops on suppression of lentil damping-off and root rot disease in New Valley - Egypt. Crop Prot. 32: 41-46.

575 Agresti, A. 2002. Categorical Data Analysis. 2nd ed. Wiley Series in Probability and Mathematical Statistics. Wiley, Hoboken, NJ.
577 https://doi.org/10.1002/9780470594001.

578 Anonymous 2016. Phytobiomes: A Roadmap for Research and Translation. American Phytopathological Society, St. Paul, MN. www.phytobiomes.org/roadmap.

580 Arvidsson, J., Etana, A., and Rydberg, T. 2014. Crop yield in Swedish experiments with shallow tillage and no-tillage 1983-2012. Eur. J. Agron. 52: 307-315.

582 Aubertot, J.-N., Deguine, J.-P., Lamichhane, J.R., Robin, M.-H., Sarthou, J.-P., and Steinberg, C. 2020. Vers une protection agroécologique des cultures en phase d'implantation, in: Réussir l'implantation Des Cultures. pp. 107-134 (in French).

585 Aubertot, J.-N., and Robin, M.-H. 2013. Injury Profile SIMulator, a Qualitative Aggregative Modelling Framework to Predict Crop Injury Profile as a Function of Cropping Practices, and the Abiotic and Biotic Environment. I. Conceptual Bases. PLoS One 8(9): e73202.

589 Baker, R. 1971. Analyses involving inoculum density of soil-borne plant pathogens in epidemiology. Phytopathology 61: 1280-1292.

591 Barbetti, M., You, M.P., Li, H., Ma, X., and Sivasithamparam, K. 2007. Management of root diseases of annual pasture legumes in Mediterranean ecosystems - a case study of subterranean clover root diseases in the South-Wesf of Western Australia. Phytopathol. Mediterr. 46: 239-258

595 Barbetti, M.J., and MacNish, G.C. 1984. Effects of cultivation and cultural practices on subterranean clover root rot. Aust. J. Exp. Agric. Anim. Husb. 24: 550-554.

597 Barbetti M.J., Sivasithamparam K., and Wong, D. 1986. Root rot of subterranean clover. Rev Plant Pathol 65: 287-295.

599 Bayani, A., Tiwade, D., Dongre, A., Dongre, A.P., Phatak, R., and Watve, M. 2016. Assessment of Crop Damage by Protected Wild Mammalian Herbivores on the Western Boundary of Tadoba-Andhari Tiger Reserve (TATR), Central India. PLoS One 11: e0153854.

603 Bohanec, M. 2015. DEXi: Program for Multi-Attribute Decision Making, User's Manual, Version 5.00. IJS Report DP-11897, Jožef Stefan Institute, Ljubljana, Slovenia. http://kt.ijs.si/MarkoBohanec/pub/DEXiManual500.pdf.

606 Bohanec, M. 2009. DEXi: Program for Multi-Attribute Decision Making, Version 3.02. Online publication. Jozef Stefan Institute, Ljubljana, Slovenia.

608 Bohanec, M. 2003. Decision support., in: Mladeniæ D, Lavrae` N, Bohanec M, M.S. (Ed.), Data Mining and Decision Support: Integration and Collaboration. Kluwer Academic Publishers, pp. 23-35.

611 Bohanec, M., Cortet, J., Griffiths, B., Žnidaršič, M., Debeljak, M., Caul, S., Thompson, J., and Krogh, P.H. 2007. A qualitative multi-attribute model for assessing the impact of cropping systems on soil quality. Pedobiologia (Jena). 51: 239-250.

614 BSV 2016. Résultats de l'enquête dégâts de mouche (géomyze) sur maïs en Bretagne. Bulletin de Santé vegetal (in French).

616 Burdon, J.J., and Chilvers, G.A. 1975. Epidemiology of damping-off disease (Pythium irregulare) in relation to density of Lepidium sativum seedlings. Ann. Appl. Biol. 81:
618 135-143.

619 Burnett, V.F., Coventry, D.R., Hirth, J.R., and Greenhalgh, F.C. 1994. Subterranean clover decline in permanent pastures in north-eastern Victoria. Plant Soil 164: 231-241.

620 Campos, S.B., Lisboa, B.B., Camargo, F.A.O., Bayer, C., Sczyrba, A., Dirksen, P., Albersmeier, A., Kalinowski, J., Beneduzi, A., Costa, P.B., Passaglia, L.M.P., Vargas, L.K., and Wendisch, V.F. 2016. Soil suppressiveness and its relations with the microbial community in a Brazilian subtropical agroecosystem under different management systems. Soil Biol. Biochem. 96: 191-197.

621 Carpani, M., Bergez, J.E., and Monod, H. 2012. Sensitivity analysis of a hierarchical qualitative model for sustainability assessment of cropping systems. Environ. Modell. Softw. 27-28: 15-22.

622 Chauhan, B.S., and Johnson, D.E. 2011. Row spacing and weed control timing affect yield of aerobic rice. F. Crop. Res. 121: 226-231.

623 Chauhan, B.S., and Opeña, J. 2013. Implications of plant geometry and weed control options in designing a low-seeding seed-drill for dry-seeded rice systems. F. Crop. Res. 144: 225-231. Cohen, J. 1960. A Coefficient of Agreement for Nominal Scales. Educ. Psychol. Meas. 20: 37-46.

624 Constantin, J., Dürr, C., Tribouillois, H., and Justes, E. 2015. Catch crop emergence success depends on weather and soil seedbed conditions in interaction with sowing date: A simulation study using the SIMPLE emergence model. F. Crop. Res. 176: 22-33.

625 Craheix, D., Bergez, J.-E., Angevin, F., Bockstaller, C., Bohanec, M., Colomb, B., Doré, T., Fortino, G., Guichard, L., Pelzer, E., Méssean, A., Reau, R., and Sadok, W. 2015. Guidelines to design models assessing agricultural sustainability, based upon feedbacks from the DEXi decision support system. Agron. Sustain. Dev. 35: 1431-1447.

626 Delgado, R., and Tibau, X.-A. 2019. Why Cohen's Kappa should be avoided as performance measure in classification. PLoS One 14, e0222916.

627 Dimitri, G., Yuri, V., Albores-Barajas, N., Emilio, B., Lorenzo, V., and Cecilia, S. 2012. Feral Pigeons: Problems, Dynamics and Control Methods, Integrated PestManagement and Pest Control Current and Future Tactics Dr. Sonia Soloneski(Ed.), ISBN: 978-953-51-0050-8, InTech.

628 Dürr, C., and Aubertot, J.-N. 2000. Emergence of seedlings of sugar beet (Beta vulgaris L.) as affected by the size, roughness and position of aggregates in the seedbed. Plant Soil 219: 211-220.

629 Dürr, C., Aubertot, J.N., Richard, G., Dubrulle, P., Duval, Y., and Boiffin, J. 2001. SIMPLE: a model for SIMulation of PLant Emergence predicting the effects of soil tillage and sowing operations. Soil Sci. Soc. Am. J. 65: 414-442.

630 Dürr, C., Dickie, J.B., Yang, X.-Y., and Pritchard, H.W. 2015. Ranges of critical temperature and water potential values for the germination of species worldwide: Contribution to a seed trait database. Agric. For. Meteorol. 200: 222-232.

631 Farooq, M., Barsa, S.M.A., and Wahid, A. 2006. Priming of field-sown rice seed enhances germination, seedling establishment, allometry and yield. Plant Growth Regul. 49: 285-294.

632 Finch-Savage, W.E., Rowse, H.R., Dent, and K.C. 2005. Development of combined imbibition and hydrothermal threshold models to simulate maize (Zea mays) and chickpea (Cicer arietinum) seed germination in variable environments. New Phytol. 165: 825-838

633 Firake, D.M., Behere, G.T., and Chandra, S. 2016. An environmentally benign and cost-effective technique for reducing bird damage to sprouting soybean seeds. F. Crop.
667 Res. 188: 74-81.

668 Fleiss, J.L., and Cohen, J. 1973. The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educ. Physchol. Meas. 33: 613-619.

670 Forcella, F., Arnold, R.L.B., Sanchez, R., and Ghersa, C.M. 2000. Modeling seedling emergence. F. Crop. Res. 67: 123-139. Foster, K., You, M.P., Nietschke, B., Edwards, N., and Barbetti, M.J. 2017. Widespread decline of subterranean clover pastures across diverse climatic zones is driven by soilborne root disease pathogen complexes. Crop Pasture Sci. 68: 33-44.

675 Furlan, L., Benvegnù, I., Chiarini, F., Loddo, D., and Morari, F. 2020. Meadow-ploughing timing as an integrated pest management tactic to prevent soil-pest damage to maize. Eur. J. Agron. 112: 125950.

678 Grogan, R.G., Sall, M.A., and Punja, Z.K. 1980. Concepts for modelling root infection by soilborne fungi. Phytopathology 70: 361-363.

680 Hochman, Z., Osborne, G.J., Taylor, P.A., and Cullis, B. 1990. Factors contributing to reduced productivity of subterranean clover (*Trifolium subterraneum*) L.) pastures on acidic soils. Aust. J. Agric. Res. 41: 669-682.

683 Hwang, S.F., Ahmed, H., and Turnbull, G.D. 2008. Effect of crop rotation on canola seedling blight and soil pathogen population dynamics. Can. J. Plant Pathol. 30: 369.

685 Jame, Y.W., Cutforth, and H.W. 2004. Simulating the effects of temperature and seeding depth on germination and emergence of spring wheat. Agric. For. Meteorol. 124: 207-218.

688 Känkänen, H., Alakukku, L., Salo, Y., and Pitkänen, T. 2011. Growth and yield of spring cereals during transition to zero tillage on clay soils. Eur. J. Agron. 34: 35-45.

690 Lamichhane, J.R., and Aubertot, J.-N. 2018. A conceptual framework to better understand interactions between seedbed abiotic and biotic factors under the influence of cropping systems and their overall impact on field crop establishment, in: International Phytobiomes Conference. 4th-6th December. Montpellier, France.

694 Lamichhane, J.R., Constantin, J., Schoving, C., Maury, P., Debaeke, P., Aubertot, J.-N., and Dürr, C. 2020a. Analysis of soybean germination, emergence, and prediction of a possible northward establishment of the crop under climate change. Eur. J. Agron. 113: 125972.

698 Lamichhane, J.R., Debaeke, P., Steinberg, C., You, M.P., Barbetti, M.J., and Aubertot, J.-N. 2018. Abiotic and biotic factors affecting crop seed germination and seedling emergence: a conceptual framework. Plant Soil 432: 1-28.

701 Lamichhane, J.R., Dürr, C., Schwanck, A.A., Robin, M.-H., Sarthou, J.-P., Cellier, V., Messéan, A., and Aubertot, J.-N. 2017. Integrated management of damping-off diseases. A review. Agron. Sustain. Dev. 37: 10. https://doi.org/10.1007/s13593-017-0417-y

704 Lamichhane, J.R., You, M.P., Laudinot, V., Barbetti, M.J., and Aubertot, J.N. 2020b. Revisiting sustainability of fungicide seed treatments for field crops. Plant Dis. 104: 610-623.

707 Landis, J.R., and Koch, G.G. 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics 33: 159-174.

709 Leach, J.E., Triplett, L.R., Argueso, C.T., and Trivedi, P. 2017. Communication in the Phytobiome. Cell 169: 587-596.

711 Leoni, C., de Vries, M., ter Braak, C.J.F., van Bruggen, A.H.C., and Rossing, W.A.H. 2013. *Fusarium oxysporum* f.sp. *cepae* dynamics: in-plant multiplication and crop sequence simulations. Eur. J. Plant Pathol. 137: 545-561.

714 Ma, X., Li, H., O'Rourke, T., Sivasithamparam, K., and Barbetti, M.J. 2008. Co-occurrence of an *Aphanomyces* sp. and *Phytophthora clandestina* in subterranean clover pastures
716 in the high rainfall areas of the lower south-west of Western Australia. Australas.

717 Plant Pathol. 37: 74-78.

718 Matthews, B.W. 1975. Comparison of the predicted and observed secondary structure of
719 T4 phage lysozyme. Biochim. Biophys. Acta - Protein Struct. 405: 442-451.

720 Maughan, R.D., Barbetti, M.J. 1983. Rhizoctonia root rot of white clover. Australas. Plant
721 Pathol. 12: 13-14.

722 Meynard, J.M., Doré, T., and Lucas, P. 2003. Agronomic approach: Cropping systems and
723 plant diseases. Comptes Rendus - Biol. 326: 37-46.

724 Nasu, H., and Matsuda, L. 1976. The damage to soybean by pigeons and doves and
725 its control methods. Agr. Hort. 51: 563-566.

726 Nichols, P.G.H., Foster, K.J., Piano, E., Pecetti, L., Kaur, P., Ghamkhar, K., and Collins, W.J.,
727 2013. Genetic improvement of subterranean clover (*Trifolium subterraneum* L.). 1.
728 Germplasm, traits and future prospects. Crop Pasture Sci. 64: 312-346.

729 Nichols, P.G.H., Jones, R.A.C., Ridsdill-Smith, T.J., and Barbetti, M.J. 2014. Genetic
730 improvement of subterranean clover (*Trifolium subterraneum* L.). 2. Breeding for
disease and pest resistance. Crop Pasture Sci. 65: 1207-1229.

731 O'Rourke, T.A., Ryan, M.H., Scanlon, T.T., Sivasithamparam, K., and Barbetti, M.J. 2012.
732 Amelioration of root disease of subterranean clover (*Trifolium subterraneum*) by
733 mineral nutrients. Crop Pasture Sci. 63: 672-682.

735 O'Rourke, T.A., Scanlon, T.T., Ryan, M.H., Wade, L.J., McKay, A.C., Riley, I.T., Li, H.,
736 Sivasithamparam, K., and Barbetti, M.J. 2009. Severity of root rot in mature
737 subterranean clover and associated fungal pathogens in the wheatbelt of Western
738 Australia. Crop Pasture Sci. 60: 43-50.

739 Pelzer, E., Fortino, G., Bockstaller, C., Angevin, F., Lamine, C., Moonen, C., Vasileiadis, V.,
740 Guérin, D., Guichard, L., Reau, R., and Messéan, A. 2012. Assessing innovative
cropping systems with DEXiPM, a qualitative multi-criteria assessment tool derived
741 from DEXi. Ecol. Indic. 18: 171-182.

743 Peralta, A.L., Sun, Y., McDaniel, M.D., and Lennon, J.T. 2018. Crop rotational diversity
744 increases disease suppressive capacity of soil microbiomes. Ecosphere 9: e02235.
745 https://doi.org/10.1002/ecs2.2235

746 Pung, S.H., Barbetti, M.J., and Sivasithamparam, K. 1988. Association of *Meloidogyne arenaria* with root rot of subterranean clover in Western Australia. New Zeal. J. Exp. Agric. 16: 91-96.

749 Robin, M.-H., Bancal, M.-O., Cellier, V., Délos, M., Felix, I., Launay, M., Magnard, A., Olivier,
750 A., Robert, C., Rolland, B., Sache, I., and Aubertot, J.-N. 2018. IPSIM-Web, An Online
751 Resource for Promoting Qualitative Aggregative Hierarchical Network Models to
752 Predict Plant Disease Risk: Application to Brown Rust on Wheat. Plant Dis. 102:
753 488-499.

754 Robin, M.-H., Colbach, N., Lucas, P., Montfort, F., Cholez, C., Debaeke, P., and Aubertot, J.-
755 N. 2013. Injury Profile SIMulator, a Qualitative Aggregative Modelling Framework
756 to Predict Injury Profile as a Function of Cropping Practices, and Abiotic and Biotic
757 Environment. II. Proof of Concept: Design of IPSIM-Wheat-Eyespot. PLoS One 8: 1-
758 13. https://doi.org/10.1371/journal.pone.0075829

759 Sadok, W., Angevin, F., Bergez, J.-E., Bockstaller, C., Colomb, B., Guichard, L., Reau, R.,
760 Messéan, A., and Doré, T. 2009. MASC, a qualitative multi-attribute decision model
761 for ex ante assessment of the sustainability of cropping systems. Agron. Sustain.
762 Dev. 29: 447-461.

763 Sausse, C., Lecomte, V., Martin-Monjaret, C., Raimbault, J., and Vogrincic, C. 2016. Dégâts
d'oiseaux et petits gibiers – Synthèse de l'enquête Terres Inovia (in French).
765 Sausse, C., and Lévy, M. 2020. Dégâts d'oiseaux au tournesol: situation internationale et perspectives. OCL, In press (in French).

767 Sebillotte, M. 1990. Système de culture, un concept opératoire pour les agronomes, in: 768 Combe, L., Picard, D. (Eds.), Les Systèmes de Culture. INRA, Versailles, pp. 165-196 769 (in French).

770 Sebillotte, M. 1974. Agronomie et agriculture, analyse des tâches de l'agronome. Cah. 771 ORSTOM, série Biol. 24, 3-25 (in French).

772 Simpson, R.J., Richardson, A.E., Riley, I.T., McKay, A.C., McKay, S.F., Ballard, R.A., Ophel-773 Keller, K., Hartley, D., O'Rourke, T.A., Li, H., Sivasithamparam, K., Ryan, M.H., and 774 Barbetti, M.J. 2011. Damage to roots of *Trifolium subterraneum* L. (subterranean 775 clover), failure of seedlings to establish and the presence of root pathogens during 776 autumn-winter. Grass Forage Sci. 66: 585-605

777 Sivasithamparam, K. 1993. Ecology of root-infecting pathogenic fungi in Mediterranean 778 environments. Adv. Plant Pathol. 10: 245-279.

779 Smiley, R.W., Taylor, P.A., Clarke, R.G., Greenhalgh, F.C., and Trutmann, P. 1986. 780 Simulated soil and plant management effects on root rots of subterranean clover. 781 Aust. J. Agric. Res. 37: 633-645.

782 Souty, N., and Rode, C. 1993. Emergence of sugar beet seedlings from under different 783 obstacles. Eur. J. Agron. 2: 213-221.

784 Vida, C., de Vicente, A., and Cazorla, F.M. 2020. The role of organic amendments to soil 785 for crop protection: Induction of suppression of soilborne pathogens. Ann. Appl. 786 Biol. 176: 1-15.

787 Villalobos, F.J., Orgaz, F., and Fereres, E. 2016. Sowing and Planting, in: Villalobos, F.J., 788 Fereres, E. (Eds.), Principles of Agronomy for Sustainable Agriculture. Springer 789 International Publishing, Cham, pp. 217-227.

790 Wang, H., Cutforth, H., McCaig, T., McLeod, G., Brandt, K., Lemke, R., Goddard, T., and 791 Sprout, C. 2009. Predicting the time to 50% seedling emergence in wheat using a 792 Beta model. NJAS - Wageningen J. Life Sci. 57: 65-71

793 Wolfram Research, I. 2, 2015. Mathematica, version 10.1.0.0 (Mac OS X x86 platform). 794 Wolfram Research, Inc., Champaign, IL.

795 Wong, D.H., Barbetti, M.J., and Sivasithamparam, K. 1985a. Pathogenicity of *Rhizoctonia* 796 spp. associated with root rots of subterranean clover. Trans. Br. Mycol. Soc. 85: 797 156-158.

798 Wong, D.H., Barbetti, M.J., and Sivasithamparam, K. 1985b. Fungi associated with root rot 799 of subterranean clover in Western Australia. Aust. J. Exp. Agric. 25: 574-579.

800 Wong, D. H., Barbetti, M.J., and Sivasithamparam, K. 1984. Effects of soil temperature and 801 moisture on the pathogenicity of fungi associated with root rot of subterranean 802 clover. Aust. J. Agric. Res. 35: 675-684.

803 You, M. P., Barbett, M. J., and Nichols, P.G.H. 2005. New sources of resistance identified in 804 *Trifolium subterraneum* breeding lines and cultivars to root rot caused by *Fusarium* 805 *avenaceum* and *Pythium irregulare* and their relationship to seedling survival. 806 Australas. Plant Pathol. 34: 237-244.

807 You, M. P., and Barbetti, M.J. 2017. Severity of phytophthora root rot and pre-emergence 808 damping-off in subterranean clover influenced by moisture, temperature, nutrition, 809 soil type, cultivar and their interactions. Plant Pathol. 66: 1162-1181.

810 You, M. P., and Barbetti, M.J. 2017. Environmental factors determine severity of 811 Rhizoctonia damping-off and root rot in subterranean clover. Australas. Plant 812 Pathol. 46: 357-368.

813 You, M. P., and Barbetti, M.J., Sivasithamparam, K., 2005. Characterization of
814

*Phytophthora clandestina* races on *Trifolium subterraneum* in Western Australia.

815

Eur. J. Plant Pathol. 113: 267-274. You, M. P., Guo, K., Nicol, D., Kidd, D., Ryan, M.H.,

816

Foster, K., and Barbetti, M.J. 2017. Cultivation offers effective management of

817

subterranean clover damping-off and root disease. Grass Forage Sci. 72: 785-793

818

You, M.P., Lamichhane, J.R., Aubertot, J.-N., and Barbetti, M.J. 2020. Understanding why

819

Effective Fungicides against Individual Soilborne Pathogens are Ineffective with

820

Soilborne Pathogen Complexes. Plant Dis. 104: 904-920.

821

You, M.P., Lancaster, B., Sivasithamparam, K., and Barbetti, M.J. 2008. Cross-

822

pathogenicity of *Rhizoctonia solani* strains on pasture legumes in pasture-crop

823

rotations. Plant Soil 302: 203-211.

824

You, M.P., O'Rourke, T.A., Foster, K., Snowball, R., and Barbetti, M.J. 2016. Host

825

resistances to *Aphanomyces trifolii* root rot of subterranean clover: first opportunity

826

to successfully manage this severe pasture disease. Plant Pathol. 65: 901-913.

827

You, M.P., Rensing, K., Renton, M., and Barbetti, M.J. 2018. Critical factors driving

828

Aphanomyces damping-off and root disease in clover revealed and explained. Plant

829

Pathol. 67: 1374-1387.

830

You, Ming P, Rensing, K., Renton, M., and Barbetti, M.J. 2017. Modeling Effects of

831

Temperature, Soil, Moisture, Nutrition and Variety As Determinants of Severity of

832

Pythium Damping-Off and Root Disease in Subterranean Clover. Front. Microbiol. 8:

833

2223. https://doi.org/10.3389/fmicb.2017.02223.

834

835

836

837

838

839

840

841

842

843

844

845

846

847

848

849

850

851

852

853

854

855

856

857

858

859

860

861

862
863

864

865

866

Figure legends :

867

868

869

870

871

872

873

874

875

876

877

878

879

880

881

882

**Figure 1.** Conceptual framework that highlights how each component of phytobiomes and their interactions affect field crop establishment (adapted from Lamichhane et al. 2018). Four major components of phytobiomes namely cropping system, seed and seedling characteristics, seedbed components (physical chemical and biological), and weather as well as their interactions affect seed germination, seedling emergence and crop establishment. Post emergence seedling damage can be caused by soil-borne pests and pathogens (internal component of the seedbed) or by animal pests coming from outside (external component of the seedbed, such as birds, wild animals etc.). Air-borne pathogens are not included as their impact on crop growth and development is important only after the crop establishment phase and that all key pathogens affecting the crop establishment phase are soil-borne (Rojas et al. 2016; Lamichhane et al. 2017, 2020b; You et al. 2017a). Likewise, weeds are excluded as their impact on crop growth, development and yield is important only after the crop establishment phase when they are sufficiently developed to compete with crops for light and nutrients (Chauhan and Johnson 2011).

883

884

885

**Figure 2.** Hierarchical structure of CESIM (screenshot of the DEXi software). Bolded and non-bold terms represent aggregated and basic attributes, respectively.

886

887

888

889

890

891

**Figure 3.** Attribute scales of CESIM (screenshot of the DEXi software). The scales are ordered from unfavorable values for crop establishment (on the left-hand side) to favorable ones (on the right-hand side). This difference is clearly noticeable in the DEXi software, because, by convention, values favorable to the user are coloured in green, detrimental in red, and neutral in black.

892

893

894

895

**Figure 4.** Aggregating table for the “seed germination” aggregated attribute (screenshot of the DEXi software). Aggregation rules for the 12 possible combinations of the 3 seed characteristics, the 2 levels of seedbed characteristics and the 2 levels of seed predation.

896

897

898

899

900

901

902

903

904

905

906

**Figure 5.** Confusion matrix of the CESIM-Subterranean clover model and marginal distributions. Numbers in italic indicate overall percentages calculated as the ratio of the number of instances in a given situation, or marginal sums, to the total number of observations × 100 (n = 231). Green and red color codes indicate the minimum and maximum difference between observed and simulated values, respectively. Intermediate colors were arbitrarily defined by the ColorFunction option in Mathematica (Wolfram Research, Inc. 2015), according to the number of the classes considered (four in this case). ‘Mod. Low’ and ‘Mod. High’ mean moderately low and high classes, respectively.
<table><caption>**Table 1.** Normalized attributes weights of CESIM-subterranean clover establishment computed by DEXi (Bohanec, 2015). The impact of each attribute on the value of the immediate descendant attribute in the hierarchy is represented by local weights while the influence of each attribute on the value of the final attribute is defined by global weights. Local and global weights are distributed in five levels of aggregation.</caption><thead><tr><th>Attributes defining the final percentage of subterranean clover establishment</th><th>Local level 1</th><th>Local level 2</th><th>Local level 3</th><th>Local level 4</th><th>Local level 5</th><th>Global level 1</th><th>Global level 2</th><th>Global level 3</th><th>Global level 4</th><th>Global level 5</th></tr></thead><tbody><tr><td><strong>1. Seedling emergence</strong></td><td>40</td><td></td><td></td><td></td><td></td><td>40</td><td></td><td></td><td></td><td></td></tr><tr><td>1.1. Seed germination</td><td></td><td>27</td><td></td><td></td><td></td><td></td><td>27</td><td></td><td></td><td></td></tr><tr><td>1.1.1. Seed characteristics</td><td></td><td></td><td>27</td><td></td><td></td><td></td><td></td><td>3</td><td></td><td></td></tr><tr><td>1.1.1.1. Seed mass</td><td></td><td></td><td></td><td>26</td><td></td><td></td><td></td><td></td><td>1</td><td></td></tr><tr><td>1.1.1.2. seed treatment</td><td></td><td></td><td></td><td>23</td><td></td><td></td><td></td><td></td><td>1</td><td></td></tr><tr><td>1.1.1.3. Seed germination ability</td><td></td><td></td><td></td><td>51</td><td></td><td></td><td></td><td></td><td>2</td><td></td></tr><tr><td>1.1.2. Seedbed characteristics</td><td></td><td></td><td>36</td><td></td><td></td><td></td><td></td><td>4</td><td></td><td></td></tr><tr><td>1.1.2.1. Moisture</td><td></td><td></td><td></td><td>50</td><td></td><td></td><td></td><td></td><td>2</td><td></td></tr><tr><td>1.1.2.2. Temperature</td><td></td><td></td><td></td><td>50</td><td></td><td></td><td></td><td></td><td>2</td><td></td></tr><tr><td>1.1.3. Seed predation</td><td></td><td></td><td>36</td><td></td><td></td><td></td><td></td><td>4</td><td></td><td></td></tr><tr><td>1.2. Seedling characteristics</td><td></td><td>15</td><td></td><td></td><td></td><td></td><td>6</td><td></td><td></td><td></td></tr><tr><td>1.2.1. Seedling emergence force</td><td></td><td></td><td>50</td><td></td><td></td><td></td><td></td><td>3</td><td></td><td></td></tr><tr><td>1.2.2. Shoot/root elongation rate</td><td></td><td></td><td>50</td><td></td><td></td><td></td><td></td><td>3</td><td></td><td></td></tr><tr><td>1.3. Seedbed characteristics</td><td></td><td>34</td><td></td><td></td><td></td><td></td><td>14</td><td></td><td></td><td></td></tr><tr><td>1.3.1. Seedbed physical characteristics</td><td></td><td></td><td>33</td><td></td><td></td><td></td><td></td><td>5</td><td></td><td></td></tr><tr><td>1.3.1.1. Temperature</td><td></td><td></td><td></td><td>22</td><td></td><td></td><td></td><td></td><td>1</td><td></td></tr><tr><td>1.3.1.2. Moisture</td><td></td><td></td><td></td><td>22</td><td></td><td></td><td></td><td></td><td>1</td><td></td></tr><tr><td>1.3.1.2.1. Rainfall</td><td></td><td></td><td></td><td></td><td>39</td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>1.3.1.2.2. Texture</td><td></td><td></td><td></td><td></td><td>35</td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>1.3.1.2.3. Evaporation</td><td></td><td></td><td></td><td></td><td>26</td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td>1.3.1.3. Crusting</td><td></td><td></td><td></td><td>26</td><td></td><td></td><td></td><td></td><td>1</td><td></td></tr><tr><td>1.3.1.3.1. Rainfall</td><td></td><td></td><td></td><td></td><td>32</td><td></td><td></td><td></td><td></td><td>0</td></tr></tbody></table>
<table><tbody><tr><td>1.3.1.3.2. Texture</td><td></td><td>22</td><td></td><td>0</td></tr><tr><td>1.3.1.3.3. Evaporation</td><td></td><td>24</td><td></td><td>0</td></tr><tr><td>1.3.1.3.4. Structure</td><td></td><td>22</td><td></td><td>0</td></tr><tr><td>1.3.1.4. Crop residue management</td><td></td><td>31</td><td></td><td>1</td></tr><tr><td>1.3.2. Seedbed chemical characteristics</td><td>33</td><td></td><td>5</td><td></td></tr><tr><td>1.3.2.1. Organic matter</td><td></td><td>25</td><td></td><td>1</td></tr><tr><td>1.3.2.2. Inorganic nutrients</td><td></td><td>15</td><td></td><td>1</td></tr><tr><td>1.3.2.3. pH</td><td></td><td>60</td><td></td><td>3</td></tr><tr><td>1.3.3. Seedbed biological characteristics</td><td>33</td><td></td><td>5</td><td></td></tr><tr><td>1.3.3.1. Disease index</td><td></td><td>24</td><td></td><td>1</td></tr><tr><td>1.3.3.1.1. Disease caused by oomycetes</td><td></td><td>50</td><td></td><td>1</td></tr><tr><td>1.3.3.1.2. Disease caused by true fungi</td><td></td><td>50</td><td></td><td>1</td></tr><tr><td>1.3.3.2. Risks of animal pests</td><td></td><td>24</td><td></td><td>1</td></tr><tr><td>1.3.3.2.1. Invertebrate pests</td><td></td><td>50</td><td></td><td>1</td></tr><tr><td>1.3.3.2.2. Vertebrate pests</td><td></td><td>50</td><td></td><td>1</td></tr><tr><td>1.3.3.3. Sowing density</td><td></td><td>21</td><td></td><td>1</td></tr><tr><td>1.3.3.4. Crop rotation</td><td></td><td>15</td><td></td><td>1</td></tr><tr><td>1.3.3.5. Crop residue management</td><td></td><td>15</td><td></td><td>1</td></tr><tr><td>1.4. Sowing depth</td><td>24</td><td></td><td>10</td><td></td></tr><tr><td>2. Stand uniformity</td><td>20</td><td></td><td>20</td><td></td></tr><tr><td>2.1. Seedbed physical characteristics</td><td>33</td><td></td><td>7</td><td></td></tr><tr><td>2.1.1. Moisture</td><td></td><td>50</td><td></td><td>3</td></tr><tr><td>2.1.2. Temperature</td><td></td><td>50</td><td></td><td>3</td></tr><tr><td>2.2. Seedbed chemical characteristics</td><td>33</td><td></td><td>7</td><td></td></tr><tr><td>2.2.1. Organic matter</td><td></td><td>50</td><td></td><td>3</td></tr><tr><td>2.2.2. Inorganic nutrients</td><td></td><td>25</td><td></td><td>2</td></tr><tr><td>2.2.3. pH</td><td></td><td>25</td><td></td><td>2</td></tr><tr><td>2.3. Seedbed biological characteristics</td><td>33</td><td></td><td>7</td><td></td></tr><tr><td>2.3.1. Disease index</td><td></td><td>50</td><td></td><td>3</td></tr><tr><td>2.3.1.1. Disease caused by oomycetes</td><td></td><td>50</td><td></td><td>2</td></tr></tbody></table>
<table><tr><td>2.3.1.2. Disease caused by true fungi</td><td></td><td>50</td><td></td><td>2</td></tr><tr><td>2.3.2. Risks of animal pests</td><td></td><td>50</td><td></td><td>3</td></tr><tr><td>2.3.2.1. Invertebrate pests</td><td></td><td>50</td><td></td><td>2</td></tr><tr><td>2.3.2.2. Vertebrate pests</td><td></td><td>50</td><td></td><td>2</td></tr><tr><td>3. Crop compensation capacity</td><td>40</td><td></td><td>40</td><td></td></tr></table>
**Supplementary materials:**

**Supplementary Table 1.** Description of the dataset used in this study to test the prediction quality of the model

**Supplementary Table S2:** Transformation of nominal and quantitative variables into ordinal variables using a converter

**Supplementary Figure S1.** Aggregating table for the “seedbed characteristics” aggregated attribute (screenshot of the DEXi software). Aggregation rules for the 27 possible combinations of physical, chemical and biological characteristics, each of 3 levels.

**Supplementary Figure S2.** Aggregating table for the “seedling characteristics” aggregated attribute (screenshot of the DEXi software). Aggregation rules for the 9 possible combinations of emergence force and shoot/root elongation rate, each of 3 levels.

**Supplementary Figure S3.** Aggregating table for the “seedling emergence” aggregated attribute (screenshot of the DEXi software). Aggregation rules for the 81 possible combinations of seed germination, seedling characteristics, seedbed characteristics and sowing depth, each of 3 levels.

**Supplementary Figure S4.** Aggregating table for the “stand uniformity” aggregated attribute (screenshot of the DEXi software). Aggregation rules for the 27 possible combinations of seedbed physical, chemical and biological characteristics, each of 3 levels.
<figure><img src="image_2.png" /></figure>

Post-
emergence
damage
Attribute

Crop establishment (%)

Seedling emergence (%)

Seed germination (%)

Seed characteristics

Seed mass

Treatment

Germination ability

Seedbed characteristics

Moisture

Temperature

Seed predation

Seedling characteristics

Emergence force

Shoot/root elongation rate

Seedbed characteristics

Physical characteristics

Temperature

Moisture

Rainfall

Texture

Evapotranspiration

Crusting

Rainfall

Texture

Evapotranspiration

Structure

Crop residue management

Chemical characteristics

Organic matter

Inorganic nutrients

pH

Biological characteristics

Disease index (%)

Oomycetes

True fungi

Risk of animal pests

Invertebrate pests

Vertebrate pests

Sowing density

Crop rotation

Crop residue management

Sowing depth

Stand uniformity (%)

Seedbed physical characteristics

Moisture

Temperature

Seedbed chemical characteristics

Organic matter

Inorganic nutrients

pH

Seedbed biological characteristics

Disease Index

Oomycetes

True fungi

Risk of animal pests

Invertebrate pests

Vertebrate pests

Crop compensation capacity
<figure><img src="image_3.png" /></figure>
<table><thead><tr><th></th><th>Seed characteristics</th><th>Seedbed characteristics</th><th>Seed predation</th><th>Seed germination (%)</th></tr></thead><tbody><tr><td>1</td><td>Unfavorable</td><td>Unfavorable</td><td>High</td><td>Low (&lt;70)</td></tr><tr><td>2</td><td>Unfavorable</td><td>Unfavorable</td><td>Low</td><td>Low (&lt;70)</td></tr><tr><td>3</td><td>Unfavorable</td><td>Favorable</td><td>High</td><td>Low (&lt;70)</td></tr><tr><td>4</td><td>Unfavorable</td><td>Favorable</td><td>Low</td><td>Low (&lt;70)</td></tr><tr><td>5</td><td>Moderately favorable</td><td>Unfavorable</td><td>High</td><td>Low (&lt;70)</td></tr><tr><td>6</td><td>Moderately favorable</td><td>Unfavorable</td><td>Low</td><td>Low (&lt;70)</td></tr><tr><td>7</td><td>Moderately favorable</td><td>Favorable</td><td>High</td><td>Low (&lt;70)</td></tr><tr><td>8</td><td>Moderately favorable</td><td>Favorable</td><td>Low</td><td>Medium (71-90)</td></tr><tr><td>9</td><td>Favorable</td><td>Unfavorable</td><td>High</td><td>Low (&lt;70)</td></tr><tr><td>10</td><td>Favorable</td><td>Unfavorable</td><td>Low</td><td>Low (&lt;70)</td></tr><tr><td>11</td><td>Favorable</td><td>Favorable</td><td>High</td><td>Low (&lt;70)</td></tr><tr><td>12</td><td>Favorable</td><td>Favorable</td><td>Low</td><td>High (&gt;90)</td></tr></tbody></table>
<figure><img src="image_4.png" /></figure>
<table><caption>**Supplementary Figure S1.** Aggregating table for the “seedbed characteristics” aggregated attribute (screenshot of the DEXi software). Aggregation rules for the 27 possible combinations of physical, chemical and biological characteristics, each of 3 levels.</caption><thead><tr><th></th><th>Physical characteristics</th><th>Chemical characteristics</th><th>Biological characteristics</th><th>Seedbed characteristics</th></tr></thead><tbody><tr><td>1</td><td>Unfavorable</td><td>Unfavorable</td><td>Unfavorable</td><td>Unfavorable</td></tr><tr><td>2</td><td>Unfavorable</td><td>Unfavorable</td><td>Moderately favorable</td><td>Unfavorable</td></tr><tr><td>3</td><td>Unfavorable</td><td>Unfavorable</td><td>Favorable</td><td>Unfavorable</td></tr><tr><td>4</td><td>Unfavorable</td><td>Moderately favorable</td><td>Unfavorable</td><td>Unfavorable</td></tr><tr><td>5</td><td>Unfavorable</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Unfavorable</td></tr><tr><td>6</td><td>Unfavorable</td><td>Moderately favorable</td><td>Favorable</td><td>Unfavorable</td></tr><tr><td>7</td><td>Unfavorable</td><td>Favorable</td><td>Unfavorable</td><td>Unfavorable</td></tr><tr><td>8</td><td>Unfavorable</td><td>Favorable</td><td>Moderately favorable</td><td>Unfavorable</td></tr><tr><td>9</td><td>Unfavorable</td><td>Favorable</td><td>Favorable</td><td>Unfavorable</td></tr><tr><td>10</td><td>Moderately favorable</td><td>Unfavorable</td><td>Unfavorable</td><td>Unfavorable</td></tr><tr><td>11</td><td>Moderately favorable</td><td>Unfavorable</td><td>Moderately favorable</td><td>Unfavorable</td></tr><tr><td>12</td><td>Moderately favorable</td><td>Unfavorable</td><td>Favorable</td><td>Unfavorable</td></tr><tr><td>13</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Unfavorable</td><td>Unfavorable</td></tr><tr><td>14</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Moderately favorable</td></tr><tr><td>15</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Favorable</td><td>Moderately favorable</td></tr><tr><td>16</td><td>Moderately favorable</td><td>Favorable</td><td>Unfavorable</td><td>Unfavorable</td></tr><tr><td>17</td><td>Moderately favorable</td><td>Favorable</td><td>Moderately favorable</td><td>Moderately favorable</td></tr><tr><td>18</td><td>Moderately favorable</td><td>Favorable</td><td>Favorable</td><td>Moderately favorable</td></tr><tr><td>19</td><td>Favorable</td><td>Unfavorable</td><td>Unfavorable</td><td>Unfavorable</td></tr><tr><td>20</td><td>Favorable</td><td>Unfavorable</td><td>Moderately favorable</td><td>Unfavorable</td></tr><tr><td>21</td><td>Favorable</td><td>Unfavorable</td><td>Favorable</td><td>Unfavorable</td></tr><tr><td>22</td><td>Favorable</td><td>Moderately favorable</td><td>Unfavorable</td><td>Unfavorable</td></tr><tr><td>23</td><td>Favorable</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Moderately favorable</td></tr><tr><td>24</td><td>Favorable</td><td>Moderately favorable</td><td>Favorable</td><td>Moderately favorable</td></tr><tr><td>25</td><td>Favorable</td><td>Favorable</td><td>Unfavorable</td><td>Unfavorable</td></tr><tr><td>26</td><td>Favorable</td><td>Favorable</td><td>Moderately favorable</td><td>Moderately favorable</td></tr><tr><td>27</td><td>Favorable</td><td>Favorable</td><td>Favorable</td><td>Favorable</td></tr></tbody></table>
<table><caption>**Supplementary Figure S2.** Aggregating table for the “seedling characteristics” aggregated attribute (screenshot of the DEXi software). Aggregation rules for the 9 possible combinations of emergence force and shoot/root elongation rate, each of 3 levels.</caption><thead><tr><th></th><th>Emergence force</th><th>Shoot/root elongation rate</th><th>Seedling characteristics</th></tr></thead><tbody><tr><td>1</td><td>Low</td><td>Low</td><td>Unfavorable</td></tr><tr><td>2</td><td>Low</td><td>Medium</td><td>Unfavorable</td></tr><tr><td>3</td><td>Low</td><td>High</td><td>Unfavorable</td></tr><tr><td>4</td><td>Medium</td><td>Low</td><td>Unfavorable</td></tr><tr><td>5</td><td>Medium</td><td>Medium</td><td>Moderately favorable</td></tr><tr><td>5</td><td>Medium</td><td>High</td><td>Moderately favorable</td></tr><tr><td>7</td><td>High</td><td>Low</td><td>Unfavorable</td></tr><tr><td>3</td><td>High</td><td>Medium</td><td>Moderately favorable</td></tr><tr><td>9</td><td>High</td><td>High</td><td>Favorable</td></tr></tbody></table>
<table><caption>**Supplementary Figure S3.** Aggregating table for the “seedling emergence” aggregated attribute (screenshot of the DEXi software). Aggregation rules for the 81 possible combinations of seed germination, seedling characteristics, seedbed characteristics and sowing depth, each of 3 levels.</caption><thead><tr><th></th><th>Seed germination (%)</th><th>Seedling characteristics</th><th>Seedbed characteristics</th><th>Sowing depth</th><th>Seedling emergence</th></tr></thead><tbody><tr><td>1</td><td>Low (&lt;70)</td><td>Unfavorable</td><td>Unfavorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>2</td><td>Low (&lt;70)</td><td>Unfavorable</td><td>Unfavorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>3</td><td>Low (&lt;70)</td><td>Unfavorable</td><td>Unfavorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>4</td><td>Low (&lt;70)</td><td>Unfavorable</td><td>Moderately favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>5</td><td>Low (&lt;70)</td><td>Unfavorable</td><td>Moderately favorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>6</td><td>Low (&lt;70)</td><td>Unfavorable</td><td>Moderately favorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>7</td><td>Low (&lt;70)</td><td>Unfavorable</td><td>Favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>8</td><td>Low (&lt;70)</td><td>Unfavorable</td><td>Favorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>9</td><td>Low (&lt;70)</td><td>Unfavorable</td><td>Favorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>10</td><td>Low (&lt;70)</td><td>Moderately favorable</td><td>Unfavorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>11</td><td>Low (&lt;70)</td><td>Moderately favorable</td><td>Unfavorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>12</td><td>Low (&lt;70)</td><td>Moderately favorable</td><td>Unfavorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>13</td><td>Low (&lt;70)</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>14</td><td>Low (&lt;70)</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>15</td><td>Low (&lt;70)</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>16</td><td>Low (&lt;70)</td><td>Moderately favorable</td><td>Favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>17</td><td>Low (&lt;70)</td><td>Moderately favorable</td><td>Favorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>18</td><td>Low (&lt;70)</td><td>Moderately favorable</td><td>Favorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>19</td><td>Low (&lt;70)</td><td>Favorable</td><td>Unfavorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>20</td><td>Low (&lt;70)</td><td>Favorable</td><td>Unfavorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>21</td><td>Low (&lt;70)</td><td>Favorable</td><td>Unfavorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>22</td><td>Low (&lt;70)</td><td>Favorable</td><td>Moderately favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>23</td><td>Low (&lt;70)</td><td>Favorable</td><td>Moderately favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>24</td><td>Low (&lt;70)</td><td>Favorable</td><td>Moderately favorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>25</td><td>Low (&lt;70)</td><td>Favorable</td><td>Favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>26</td><td>Low (&lt;70)</td><td>Favorable</td><td>Favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>27</td><td>Low (&lt;70)</td><td>Favorable</td><td>Favorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>28</td><td>Medium (71-90)</td><td>Unfavorable</td><td>Unfavorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>29</td><td>Medium (71-90)</td><td>Unfavorable</td><td>Unfavorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>30</td><td>Medium (71-90)</td><td>Unfavorable</td><td>Unfavorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>31</td><td>Medium (71-90)</td><td>Unfavorable</td><td>Moderately favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>32</td><td>Medium (71-90)</td><td>Unfavorable</td><td>Moderately favorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>33</td><td>Medium (71-90)</td><td>Unfavorable</td><td>Moderately favorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr></tbody></table>
<table><tr><td>34</td><td>Medium (71-90)</td><td>Unfavorable</td><td>Favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>35</td><td>Medium (71-90)</td><td>Unfavorable</td><td>Favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>36</td><td>Medium (71-90)</td><td>Unfavorable</td><td>Favorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>37</td><td>Medium (71-90)</td><td>Moderately favorable</td><td>Unfavorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>38</td><td>Medium (71-90)</td><td>Moderately favorable</td><td>Unfavorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>39</td><td>Medium (71-90)</td><td>Moderately favorable</td><td>Unfavorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>40</td><td>Medium (71-90)</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>41</td><td>Medium (71-90)</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>42</td><td>Medium (71-90)</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Optimal</td><td>Medium (51-70)</td></tr><tr><td>43</td><td>Medium (71-90)</td><td>Moderately favorable</td><td>Favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>44</td><td>Medium (71-90)</td><td>Moderately favorable</td><td>Favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>45</td><td>Medium (71-90)</td><td>Moderately favorable</td><td>Favorable</td><td>Optimal</td><td>Medium (51-70)</td></tr><tr><td>46</td><td>Medium (71-90)</td><td>Favorable</td><td>Unfavorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>47</td><td>Medium (71-90)</td><td>Favorable</td><td>Unfavorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>48</td><td>Medium (71-90)</td><td>Favorable</td><td>Unfavorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>49</td><td>Medium (71-90)</td><td>Favorable</td><td>Moderately favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>50</td><td>Medium (71-90)</td><td>Favorable</td><td>Moderately favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>51</td><td>Medium (71-90)</td><td>Favorable</td><td>Moderately favorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>52</td><td>Medium (71-90)</td><td>Favorable</td><td>Favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>53</td><td>Medium (71-90)</td><td>Favorable</td><td>Favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>54</td><td>Medium (71-90)</td><td>Favorable</td><td>Favorable</td><td>Optimal</td><td>Medium (51-70)</td></tr><tr><td>55</td><td>High (&gt;90)</td><td>Unfavorable</td><td>Unfavorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>56</td><td>High (&gt;90)</td><td>Unfavorable</td><td>Unfavorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>57</td><td>High (&gt;90)</td><td>Unfavorable</td><td>Unfavorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>58</td><td>High (&gt;90)</td><td>Unfavorable</td><td>Moderately favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>59</td><td>High (&gt;90)</td><td>Unfavorable</td><td>Moderately favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>60</td><td>High (&gt;90)</td><td>Unfavorable</td><td>Moderately favorable</td><td>Optimal</td><td>Medium (51-70)</td></tr><tr><td>61</td><td>High (&gt;90)</td><td>Unfavorable</td><td>Favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>62</td><td>High (&gt;90)</td><td>Unfavorable</td><td>Favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>63</td><td>High (&gt;90)</td><td>Unfavorable</td><td>Favorable</td><td>Optimal</td><td>Medium (51-70)</td></tr><tr><td>64</td><td>High (&gt;90)</td><td>Moderately favorable</td><td>Unfavorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>65</td><td>High (&gt;90)</td><td>Moderately favorable</td><td>Unfavorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>66</td><td>High (&gt;90)</td><td>Moderately favorable</td><td>Unfavorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>67</td><td>High (&gt;90)</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>68</td><td>High (&gt;90)</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>69</td><td>High (&gt;90)</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Optimal</td><td>Medium (51-70)</td></tr><tr><td>70</td><td>High (&gt;90)</td><td>Moderately favorable</td><td>Favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>71</td><td>High (&gt;90)</td><td>Moderately favorable</td><td>Favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>72</td><td>High (&gt;90)</td><td>Moderately favorable</td><td>Favorable</td><td>Optimal</td><td>Medium (51-70)</td></tr><tr><td>73</td><td>High (&gt;90)</td><td>Favorable</td><td>Unfavorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>74</td><td>High (&gt;90)</td><td>Favorable</td><td>Unfavorable</td><td>Average</td><td>Low (&lt;50)</td></tr><tr><td>75</td><td>High (&gt;90)</td><td>Favorable</td><td>Unfavorable</td><td>Optimal</td><td>Low (&lt;50)</td></tr><tr><td>76</td><td>High (&gt;90)</td><td>Favorable</td><td>Moderately favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>77</td><td>High (&gt;90)</td><td>Favorable</td><td>Moderately favorable</td><td>Average</td><td>Medium (51-70)</td></tr><tr><td>78</td><td>High (&gt;90)</td><td>Favorable</td><td>Moderately favorable</td><td>Optimal</td><td>Medium (51-70)</td></tr><tr><td>79</td><td>High (&gt;90)</td><td>Favorable</td><td>Favorable</td><td>Poor</td><td>Low (&lt;50)</td></tr><tr><td>80</td><td>High (&gt;90)</td><td>Favorable</td><td>Favorable</td><td>Average</td><td>High (&gt;70)</td></tr><tr><td>81</td><td>High (&gt;90)</td><td>Favorable</td><td>Favorable</td><td>Optimal</td><td>High (&gt;70)</td></tr></table>
<table><caption>**Supplementary Figure S4.** Aggregating table for the “stand uniformity” aggregated attribute (screenshot of the DEXi software). Aggregation rules for the 27 possible combinations of seedbed physical, chemical and biological characteristics, each of 3 levels.</caption><thead><tr><th></th><th>Seedbed physical characteristics</th><th>Seedbed chemical characteristics</th><th>Seedbed biological characteristics</th><th>Stand uniformity (%)</th></tr></thead><tbody><tr><td>1</td><td>Unfavorable</td><td>Unfavorable</td><td>Unfavorable</td><td>Low (&lt;50)</td></tr><tr><td>2</td><td>Unfavorable</td><td>Unfavorable</td><td>Moderately favorable</td><td>Low (&lt;50)</td></tr><tr><td>3</td><td>Unfavorable</td><td>Unfavorable</td><td>Favorable</td><td>Low (&lt;50)</td></tr><tr><td>4</td><td>Unfavorable</td><td>Moderately favorable</td><td>Unfavorable</td><td>Low (&lt;50)</td></tr><tr><td>5</td><td>Unfavorable</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Low (&lt;50)</td></tr><tr><td>6</td><td>Unfavorable</td><td>Moderately favorable</td><td>Favorable</td><td>Low (&lt;50)</td></tr><tr><td>7</td><td>Unfavorable</td><td>Favorable</td><td>Unfavorable</td><td>Low (&lt;50)</td></tr><tr><td>8</td><td>Unfavorable</td><td>Favorable</td><td>Moderately favorable</td><td>Low (&lt;50)</td></tr><tr><td>9</td><td>Unfavorable</td><td>Favorable</td><td>Favorable</td><td>Low (&lt;50)</td></tr><tr><td>10</td><td>Moderately favorable</td><td>Unfavorable</td><td>Unfavorable</td><td>Low (&lt;50)</td></tr><tr><td>11</td><td>Moderately favorable</td><td>Unfavorable</td><td>Moderately favorable</td><td>Low (&lt;50)</td></tr><tr><td>12</td><td>Moderately favorable</td><td>Unfavorable</td><td>Favorable</td><td>Low (&lt;50)</td></tr><tr><td>13</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Unfavorable</td><td>Low (&lt;50)</td></tr><tr><td>14</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Medium (51-80)</td></tr><tr><td>15</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Favorable</td><td>Medium (51-80)</td></tr><tr><td>16</td><td>Moderately favorable</td><td>Favorable</td><td>Unfavorable</td><td>Low (&lt;50)</td></tr><tr><td>17</td><td>Moderately favorable</td><td>Favorable</td><td>Moderately favorable</td><td>Medium (51-80)</td></tr><tr><td>18</td><td>Moderately favorable</td><td>Favorable</td><td>Favorable</td><td>Medium (51-80)</td></tr><tr><td>19</td><td>Favorable</td><td>Unfavorable</td><td>Unfavorable</td><td>Low (&lt;50)</td></tr><tr><td>20</td><td>Favorable</td><td>Unfavorable</td><td>Moderately favorable</td><td>Low (&lt;50)</td></tr><tr><td>21</td><td>Favorable</td><td>Unfavorable</td><td>Favorable</td><td>Low (&lt;50)</td></tr><tr><td>22</td><td>Favorable</td><td>Moderately favorable</td><td>Unfavorable</td><td>Low (&lt;50)</td></tr><tr><td>23</td><td>Favorable</td><td>Moderately favorable</td><td>Moderately favorable</td><td>Medium (51-80)</td></tr><tr><td>24</td><td>Favorable</td><td>Moderately favorable</td><td>Favorable</td><td>Medium (51-80)</td></tr><tr><td>25</td><td>Favorable</td><td>Favorable</td><td>Unfavorable</td><td>Low (&lt;50)</td></tr><tr><td>26</td><td>Favorable</td><td>Favorable</td><td>Moderately favorable</td><td>Medium (51-80)</td></tr><tr><td>27</td><td>Favorable</td><td>Favorable</td><td>Favorable</td><td>High (&gt;80)</td></tr></tbody></table>