Patrick Saint-Dizier
email: stdizier@irit.fr

Mining Incoherent Requirements in Technical Specifications

Keywords:

Requirements are designed to specify the features of systems. Even for a simple system, several thousands of requirements produced by different authors are often needed. It is then frequent to observe overlap and incoherence problems. In this paper, we propose a method to construct a corpus of various types of incoherences and a categorization that leads to the definition of patterns to mine incoherent requirements. We focus in this contribution on incoherences (1) which can be detected solely from linguistic factors and (2) which concern pairs of requirements. These represent about 60% of the different types of incoherences; the other types require extensive domain knowledge and reasoning.

Motivations and Objectives

Requirements (or business rules) form a specific class of documents with specific functions: they are designed to describe a task, a regulation or any kind of situation in a sound way. A requirement may be composed of a conclusion alone or be associated with one or more supports that justify it (these supports are usually called the rationale' of the requirement). In some specifications, warnings describe the consequences of not following requirements, while advice describe optional requirements that may improve e.g. the result of a task. Both warnings and advice are specific forms of arguments [START_REF] Fontan | Analyzing the explanation structure of procedural texts: dealing with advices and warnings[END_REF]. Requirements state in a declarative way e.g. the features a product should have, the way an organization should be managed via rules, etc. Even for a small product, several thousands of requirements are often necessary.

In spite of numerous authoring guidelines, unclear, ambiguous, incomplete or poorly written requirements are relatively frequent. This results in a significant waste of time to understand a specification, in difficulties to update, trace and re-use these specifications, and, more importantly, in risks of misconceptions by users or manufacturers leading to incorrect realizations or exposure to health or ecological risks. Controlling how requirements are written, independently of whether guidelines are followed or not, is a high and fast return-on-investment activity. For example, in the maintenance sector, poorly written requirements can entail extra costs up to 80% of the initial product development costs. DOI: 10.1007/978-3-319-59569- 6 8 The different protagonists involved in requirement production is a source of mismatches, inconsistencies and redundancies: stakeholders, technical writers, validators, users and manufacturers may all play different roles and have different views on the requirements. This is developed in e.g. [START_REF] Hull | Requirements Engineering[END_REF][START_REF] Schriver | Evaluating text quality: the continuum from text-focused to readerfocused methods[END_REF][START_REF] Unwalla | AECMA simplified English[END_REF][START_REF] Weiss | How to Write Usable User Documentation[END_REF][START_REF] Wyner | Properties and prospects[END_REF]. Most industrial sectors have now defined authoring recommendations, methods and tools (e.g. Doors), to elaborate, structure and write requirements of various kinds, e.g. for easier traceability, control and update. However, our experience shows that those recommendations, in spite of the existing tools, cannot strictly be observed in particular for very large sets of requirements. Authoring tools have emerged two decades ago, e.g. one of the first investigated at Boeing [START_REF] Wojcik | Controlled languages in Industry, in survey of the state of the art in human language technology[END_REF], then at IBM with Acrolink and Doors, and, more recently, Attempto [START_REF] Fuchs | First-order reasoning for attempto controlled English[END_REF] that has some reasoning capabilities. [3] among many others developed a number of useful methodological elements for authoring technical documents. A synthesis of controlled language principles and tools is presented in [START_REF] Kuhn | A principled approach to grammars for controlled natural languages and predictive editors[END_REF][START_REF] Kuhn | A survey and classification of controlled natural languages[END_REF]. Most of these principles and tools have been customized to requirement authoring, based on general purpose or domaindependent norms (e.g. INCOSE, IREB or ISO26262).

Several road-maps on requirement elicitation and writing (e.g. [START_REF] Wyner | Properties and prospects[END_REF]) show the importance of having consistent and complete sets of requirements, and rank it as a priority. However, no concrete solution so far, to the best of our knowledge, has been developed to characterize the coherence problem. Projects such as [START_REF] Kloetzer | Two-stage method for large-scale acquisition of contradiction pattern pairs using entailment[END_REF] aim at finding contradictions between lexico-syntactic patterns in Japanese, including spatial and causal relations. This is however quite different from the problem that is addressed here, since inconsistencies may have very diverse forms (Sect. 4). One of our main aim is indeed to characterize these forms w.r.t. requirement authoring principles. In [START_REF] De Marneffe | Manning CD finding contradictions in text[END_REF], the limits of textual entailment as a method to detect inconsistencies is shown, corpora is developed from which a typology of contradictions is constructed. The need of a very fine granularity in the data is advocated to avoid errors.

Finding out incoherences in specifications is recognized by requirement authors to be a crucial and a very hard problem. Of interest is the site: www. semanticsimilarity.org that offers several tools for measuring similarities in texts. The use of SAT solvers (satisfiability solvers, running efficiently and in a sound way on very large sets of propositions) can be foreseen but this means translating requirements into propositional logic or into Horn clauses. Both of these translations fail to capture several facets of requirements, in particular complex VPs, modals and the discourse structure. Our goal is rather to develop specific linguistic patterns that can identify incoherences between requirements. These patterns could be viewed, possibly, as instantiated SAT templates.

In this article, we first show how a corpus of incoherent requirements can be constructed and annotated. This corpus leads us to develop a categorization of inconsistencies based on linguistic considerations. This contribution opens new perspectives (1) on new forms of incoherence mining in texts and (2) on mining incoherent requirements and arguments more generally. The construction of this corpus is extremely challenging: large volumes of requirements are necessary, that experts are not ready to share. Furthermore, incoherent requirements are in general not adjacent in a text and involve various textual and discourse structures.

Construction of a Corpus of Incoherent Requirements

Constructing a corpus of incoherent requirements is very challenging, it is difficult (1) to get real and substantial specifications from companies and (2) to extract pairs of incoherent requirements which are relatively sparse and may appear in remote sections or chapters of a specification.

Incoherence between two requirements may be partial: they may include divergences without being completely logically opposed. Next, incoherence may be visible linguistically, or may require domain knowledge and inferences to be detected and characterized. We focus in this research on incoherences which can be detected from a linguistic and general semantic analysis: these incoherences are domain and style independent and therefore mining them is simpler and probably re-usable over domains. Finally, we focus on incoherences between pairs of arguments, leaving aside incoherences which may arise from larger sets of requirements. It would obviously be more interesting and useful to consider more complex configurations: requirement compared to a sequence of requirements, or chains of requirements, but this involves more language complexity, e.g. the taking into account of discourse, co-text, titles, etc. A gradual approach to such complex forms, based on use-cases will probably be developed in the future.

Corpus Compilation Method

Our analysis of requirement incoherences is based on a corpus of requirements coming from 5 companies in three different critical industrial sectors (energy, transportation and telecommunications). Companies have requested to be anonymous; named entities (e.g. equipment and process names) in these texts have been replaced by meaningless constants. Specification documents need to be relatively long (above 100 pages) to allow the detection of various forms of incoherences; short documents are easier to manually control and have much less incoherence problems. Most specification documents, even for a simple equipment can be very long, which motivates our project.

Our documents are in French or in English. Our corpus contains about 1200 pages extracted from 7 documents, where only the requirement parts of these documents have been kept (leaving aside e.g. introductions, summaries, contexts and definitions, but also diagrams and formula). The requirement part of each document is between 150 and 200 pages long, i.e. between 3000 and 4000 requirements in each document. This is not very large, but seems to be sufficient to carry out this first analysis. A total of about 26000 requirements are considered. Most documents do not follow very accurately the norms for writing requirements, nor do they follow the guidelines imposed by their company. Reasons are not investigated here.

The main features considered to validate our corpus are the following, where diversity of form and contents captures the main linguistic features of requirements:

-requirements correspond to various professional activities, and have been written by different types of authors, over a relatively long time span (about a year), -requirements correspond to different conceptual levels, from abstract considerations to technical specifications, -requirements have been validated and are judged to be in a relatively 'final' state, -requirements follow various kinds of authoring norms imposed by companies, including predefined patterns (boilerplates).

Extraction of Incoherent Requirements

It is almost impossible to manually extract incoherent arguments over large texts since this means memorizing with great precision several thousands of requirements. Specification authors can intuitively, via their knowledge of the domain, identify a very limited number of incoherences when proofreading texts, but they know that there are more incoherences, and that these may have important consequences.

The hypothesis we consider to mine incoherent requirements is that they deal with the same precise topic, but they differ on some significant element(s) which leads to the incoherence. Since requirements should a priori follow strict authoring guidelines (no synonyms, limited syntactic forms), dealing with the same precise topic means that two requirements which are potentially incoherent have a relatively similar syntactic structure and a large number of similar words, but differ on a few words. This can be illustrated by the two following requirements found in two different chapters of a specification, concerning the same software: REQ12-7 A minimum of 10 simultaneous requests must be allowed. REQ34-5 At least 20 simultaneous requests must be allowed.

Our strategy to mine potentially incoherent arguments is the following:

(1) In a specification, identify requirements among other types of statements such as definitions. Requirements are often labeled. If this is not the case, a grammar is used that identifies requirements based on a few specific clues such as the use of modals [START_REF] Kang | Discourse structure analysis for requirement mining[END_REF].

(2) Mine pairs of requirements which are potentially incoherent based on the two metrics developed below. (3) Process the discourse structure of mined requirements. This is realized via the TextCoop discourse processing platform [START_REF] Saint-Dizier | Processing natural language arguments with the <TextCoop> platform[END_REF]. Of much interest are conditionals, circumstances, goals, purposes, illustrations and restatements. These structures are tagged in each requirement. For example: <circumstance> when the temperature is below 2 • C, </circumstance> the engines must not be switched off <purpose> to avoid any icing </purpose >. (4) Finally, manually inspect the results to identify, for each requirement pair that has been mined, if they are incoherent or not.

Mining pairs of potentially incoherent requirements is based on two metrics:

(1) a similarity metrics, since incoherent requirements deal with the same precise topic and therefore have very close linguistic contents, and (2) a dissimilarity metrics to characterize differences. Since, at this stage, the form of incoherent requirement pairs is unknown, we developed two metrics with general purpose constraints in order to mine a large diversity of situations, the noise being discarded manually in a later stage (step 4 above).

(a) The Similarity Metrics. Requirements follow style guidelines: requirements are short (in principle they must be smaller than 20 words, but in practice 30 words are frequently encountered), synonym terms are not allowed, the syntactic structure is very regular (passives are not allowed), negation must be avoided, adjectives and adverbs are limited to a few, fuzzy terms are not allowed, etc. Therefore, a similar content can be characterized, in a first experiment, by a large set of similar words shared by two requirements. Only the words that have a topical content are included in the metrics, to form the content requirement signature S. They are: nouns, proper nouns, numbers, verbs, symbol names, and boolean or scalar adjectives. Their identification is based on WordNet resources. The other terms, which mostly play a grammatical role, are not used in the metrics, they include: prepositions, adverbs, determiners, modals, auxiliaries if they are not the main verb, negation, connectors, coordination and pronouns. They may play a role in the incoherence analysis, but do not convey the topic of the requirement.

For example, in the maximum length of an imaging segment must be 300 km, the words that are considered in the metrics are: maximum, length, imaging, segment, be, 300 km. These form the content requirement signature of that requirement, composed of 7 terms. The similarity metrics considers the content requirement signatures S 1 and S 2 of two requirements R 1 and R 2 and computes their intersection I and union U , considering the noninflected forms of words. The similarity rate is:

I/U .
Requirements have in general between a total of 6 and 40 words, with an average size of 22 words. In this initial experiment, after some tuning from a small sample of requirements, it turns out that two requirements are in a similarity relation, if:

-between a total of 6 and 12 words long, the similarity rate is greater or equal to 80%, -between a total of 13 and 22 words long, the similarity rate is greater or equal to 70%, -above a total of 22 words long, similarity rate is greater or equal to 65%. Indeed longer requirements often include peripheral information which means that the threshold for the similarity weighted rate should be lower. This 3 level tuning can still be slightly improved, depending on the authoring style of the requirements, the domain via its terminology and the conceptual complexity.

(b) The Dissimilarity Metrics. The dissimilarity metrics considers those terms which are different independently of the discourse structure. It parallels the similarity metrics, searching for terms which introduce a clear difference. Intuitively, differences may be characterized by the main following situations:

-use of different values for the same statement, with a difference of at least 10% to be significant, -use of different arithmetical operators and constraints, -use of different named entities, -use of antonym prepositions or connectors, e.g.:before/after, neutral/after, etc. -use of contrasted modals: e.g.: must/recommended, -presence of typical structures such as manner or temporal adverbs in a requirement and not in the other, -presence of two main clauses (two events) in one requirement and a single main clause in the other (single event), -presence of a conditional or a circumstance structure in one the of the requirements and not in the other one.

Provided that two requirements have been ranked as similar by the similarity metrics, the presence of at least one of these criteria in a pair of requirements entails that they may be incoherent. More than one criteria reinforces the risk of incoherence, however, there is no dissimilarity rate at this level. These two metrics used jointly have been tested on our 7 texts separately since they deal with different topics. A total of 204 pairs of requirements have been mined as potentially incoherent. Then, from this corpus, via a manual analysis, 83 requirement pairs (on average 41%) that have been mined indeed show some form of incoherence. This is not a very high number of incoherent arguments: this confirms expert intuitions that incoherences are relatively sparse, but there are probably more incoherences. 18 pairs have been found in the same text section (i.e. among between about 80 to 150 requirements), while 65 pairs are more distant in the text and couldn't have probably been identified without a tool.

Even if it is small, this sample of 83 requirement pairs allows us to develop an analysis of incoherence among requirements, and then to develop relatively generic templates that go beyond these observations that should be able to mine a higher number of incoherent requirement pairs. Finally, a side effect of this analysis is to also mine coherent duplicates or very closely related requirements which could be eliminated. Via the similarity metrics used alone, with a threshold of 90%, our corpus contains at least 368 requirements which are very closely related and could be analyzed as duplicates.

Annotating Incoherence in Requirements

The next stage of our analysis is to identify more precisely the various forms incoherence takes in requirements. For that purpose, we defined a set of annotations. The discourse processing used to mine requirement pairs provides a first level of annotations as illustrated in the previous section. Annotations are made manually by the author, some confirmation were given by requirement authors, but their availability is often very limited.

The annotations we add are those that characterize the string of words which differ between the elements of a pair and the nature of the difference(s). This task allows to categorize errors (Sect. 4) and to define templates to mine incoherent arguments in specifications. Incoherences are specified informally as an attribute of the main tag <incoherence>. Here are a few examples that illustrate the method and the difficulties. Examples 1 and 2 are relatively straightforward whereas Examples 3 and 4 are much more complex to annotate and to characterize. Differences are between <diff> tags, <req> identifies a requirement, and <event> tags the events in requirements that contain two or more explicit events.

Example 1. <incoherence type = "numerical variation" incoherence = "300 = 100"> <req> the maximum length of an imaging segment is <diff> 300 </diff> km. </req> <req> the maximum length of an imaging segment is <diff> 100 </diff> km. </req> </incoherence> Example 2. <incoherence type = "arithmetical expression divergence" incoherence = "(<30) = 50"> <req> In S, the probe X30 shall be preheated <diff> up to 30 </diff> degrees <circumstance> <diff> before doing</diff> E. </circumstance> </req> <req> the probe X30 in S shall be preheated <diff> to 50 </diff> degrees <purpose> <diff> to realize </diff> E. </purpose></req> </incoherence>

In this example, while the arithmetical expression introduces the incoherence, the distinction between circumstance and purpose must be made flexible so that the synonymy between 'doing' and 'to realize' can be identified.

Example 3. English gloss of French example: at flight level 30 in normal flight conditions, the maximal 20 • flap extension speed is 185 kts versus at flight level 30 in normal flight conditions, extend flaps to 20 • and then reduce speed below 185 kts: <incoherence type = "event synchronization with mismatches" incoherence = "( < kts) = unknown"> <req> <circumstance> A FL 30 ASL et dans les conditions normales </circumstance>, la vitesse <diff> maximale de </diff> sortie des volets à 20 • <diff> est </diff> 185 kts. </req> <req> <circumstance> A FL 30 ASL et dans les conditions normales </circumstance>, <event> sortir les volets à 20 • </event> puis <event> <diff> réduire la vitesse en dessous de </diff> 185 kts. </event> </req> </incoherence>

In this example, a general rule (first requirement) is contrasted with a requirement that describes a procedure composed of two events. In the second requirement, the temporal connector 'puis' (then) suggests that the constraint stated in the first requirement is split into two consecutive parts, with the risk that the constraint is not met as it should, e.g. flaps are extended at an unknown speed, which is then reduced to 185 kts. Example 4. <incoherence type = "incompatibility between events" incoherence = "(stop every 24 h) = (no stop during the entire update)"> <req> the system S administrator must make sure <diff> to stop the system S every 24 h <purpose> for maintenance. </purpose> </diff> </req> <req> the system S administrator must make sure <diff> that the update of system S database is not interrupted. </diff></req> </incoherence>.

In this example, a potential incoherence may arise if the system is stopped for maintenance while its associated database is being updated. The terms 'stop' and 'not interrupted' are opposed and may create the incoherence.

These annotations remain informal, in particular for the 'type' attribute. Incoherences are very diverse and do not lend themselves easily to a simple characterization. The goal is to have a first level of analysis, before implementing templates that would mine incoherent pairs of requirements. The examples given above are composed of independent statements. Connecting these statements via e.g. conditional statements could improve their global coherence.

A Preliminary Categorization of Incoherence in Requirements

From our sample of 83 requirement pairs which are incoherent, a preliminary categorization of incoherence types can be elaborated on a linguistic and conceptual basis. This categorization shows that incoherence has multiple facets, and that some pairs that have been mined may be a symptom of a form of incoherence. This categorization leads to the definition of templates and lexical resources to mine pairs of incoherent requirements. The examples below are direct corpus samples (translated into English for French examples). They are numbered Ri a/b where i numbers examples and a and b are the 2 elements of an incoherent pair.

Partial or Total Incompatibilities Between Expressions

The most direct and simple type of incoherence is composed of 'local' divergences:

-incompatible numerical values or arithmetical expressions, with more than 10% difference: R1a-Make sure to preheat the probe X30 to a maximum of 30 • . R1b-The probe X30 must be preheated at at least 50 • . R'1a-The maximum length of an imaging segment is 300 km. R'1b-The minimum length of an imaging segment is 30 km.

-incompatibility between various types of temporal, spatial or instrumental restrictions expressed as verb complements: R2a-Service D must be available only from the screen.

R2b-Service D must be available in any configuration.

A domain ontology is necessary to detect most of these incompatibilities. However, the fact that a different term is used can be identified as a potential incompatibility source, since in technical writing synonyms are not allowed. -incompatible temporal structures or temporal organization between events:

R3a-Update data marking when transferring data. R3b-Update data marking before transferring data. -modal variation, involving e.g. a different critical level:

R4b-It is recommended to stop the engine before E. R4b-The engine shall be stopped before E. -adverbial incompatibilities or discrepancies in particular in manners: carefully/quickly, etc. -some specific quantification aspects such as the difference between every/each, most/all.

Among these various forms of incoherences, the three first cases are the most frequent. Adverbial uses and quantification are less frequent: they are strongly controlled in requirement authoring because they often introduce underspecified or fuzzy expressions. Our corpus includes 33 cases that fall in this category, among which 24 belong to the three first cases.

Incompatible Events

In this category fall pairs of requirements that describe events which show some form of incoherence. Such incoherences often require some domain dependent knowledge, but a number of them remain at a 'surface' level and can be detected solely on a linguistic basis. Examples 3 and 4 in Sect. 3 are typical incoherent statements: R5a-The system S must be stopped every 24 h for maintenance. versus R5b-The update of the database via the system S must not be interrupted. is a type of situation that is difficult to predict besides the antonym stop/not interrupted. R6a-the maximum 20 • flap extension speed is 185 kts. versus R6b-extend flaps to 20 • and then slow down to 185 kts. is a typical business error that any domain expert should be able to avoid. R6a is a general rule which should be preferred to R6b which is a procedural statement.

Contextual Incompatibilities

This category includes various types of discourse structures (e.g. condition, circumstance, goal, illustration, etc.) associated with the main body of two similar requirements which could induce forms of incompatibility:

R7a-during the project specification phase, both on-line and off-line access must be available. R7b-during the project implementation, both on-line and off-line access must be available. R8a-a rule includes facts and its critical. R8b-a rule includes facts and a description.

R7 illustrates a case where the context (circumstance) is different but expects the same behavior. One may expect a negation in one of the two main propositions 'must not be available' to motivate these two requirements or may want to merge the two circumstances to avoid any doubt. shows two enumerations which are different and should probably be adjusted. R8 is a frequent case where enumerations are either incomplete (not recommended in requirement guidelines), or manipulate objects at different levels of abstraction.

Terminological Variations and Other Discrepancies

In this category, requirements which largely overlap are considered. Their slight differences may however be symptomatic of an partial inconsistency. These cases are relatively frequent and typical of documents produced either by several authors or over a long time span (where e.g. equipment names may have changed). Typical examples are:

R8a-the up-link frequency, from earth to space, must be in the s band. R8b-the down-link frequency, from space to earth, must be in the s band. R9a-those tests aim at checking the rf compatibility of the ground stations used during the mission and the tm/tc equipment on board. R9b-those tests aim at checking the rf compatibility of the ground stations used during the mission and the on board equipment. R10 is somewhat ambiguous and contains implicit knowledge which makes the identification of the incoherence slightly more challenging: R10a-the upper attachment limit must not exceed 25 GB. R10b-It must be possible to specify a maximum limit for the storage capacity of an attachment.

Indeed, either the limit is set to 256 GB or it can be specified, but R10b may be understood as saying that it can be specified but always below 256 GB.

Category Synthesis

The distribution observed on our corpus is the following, it remains indicative because of the small size of our corpus:

The implementation of these categories could possibly produce slightly different results. Most of the incoherences in these categories can be detected on the basis of linguistic analysis and general purpose lexical semantics data, in particular antonyms e.g. for prepositions, temporal connectors, arithmetical operators.

Category nb of occurrences and rate

(1) incompatibilities between expressions 33, about 40%

(2) incompatible events 11, about 13%

(3) contextual incompatibilities 27, about 33% (4) variations between requirements 12, about 14%

Analysis of Errors in the Incoherence Analysis

The main challenge is now the definition of templates to mine incoherent pairs of requirements, with their associated linguistic resources. The 41% accuracy of the current mining is not sufficient: a rate of 75% is the minimum acceptable accuracy for requirement authors. This means refining both metrics and analyzing the reasons of the noise to reduce it. For that purpose, a first step is to analyze why some requirement pairs have been incorrectly recognized as incoherent. The main reasons are the following:

(1) Description of complex cases via several coherent requirements: requirements are often detailed descriptions of a precise case within a given context. For complex cases, a set of closely related requirements may be produced where a few terms, dealing with the different cases, may be different, yet requirements remain coherent. (2) Different forms for a similar content: two requirements may deal with the same point using slightly different expression means, e.g. for values. For example, values+units may be different, intervals or arithmetical constraints may be used instead of a single value, but these expressions remain globally equivalent. For example, these two requirements are equivalent according to the similarity metrics: R11a-the A320 neo optimal cruise altitude is FL380 in normal operating conditions versus R11b-the A320 neo optimal cruise altitude is between FL 360 and 380, depending on weather conditions. R11b is just more precise. (3) Use of more generic terms: it is also frequent that two requirements differ only in a general purpose term or a business term where one is more generic than the other, or with a language level that is different. Detecting this situation requires a domain ontology. (4) Two or more differences between two requirements: when two requirements have more than two groups of terms which are different, it turns out that in most cases they deal with different aspects of a given topic. The dissimilarity analysis should be revised so that the case of several observed differences is constrained, for example manner or temporal adverbs and different values or arithmetical expressions as advocated in 2.2(b) can co-exist, but not two groups of different values. (5) Presence of negative terms: the negation, although not recommended in technical writing, or negatively oriented terms (verbs, adjectives) may appear in one requirement and not in the other, but these requirements are in fact similar to a large extent. For example R12a-Acid A must not be thrown in any standard garbage versus R12b-Acid A must be thrown in a dedicated garbage. (6) Influence of the co-text: requirements dealing with a given activity are often grouped under a title, subtitle, an enumeration or in a chart. Two arguments that belong to two different groups may seem to be incoherent, but if the context, given by the title or by the enumeration introduction head is different, then these two requirements may deal with different cases as in (1) and may not be incoherent. Co-text aspects are crucial in incoherence, but quite difficult to take into account because the link between a co-text and the requirement needs to be analyzed at the discourse level.

In terms of silence, two requirements have not been detected as incoherent because of: (7) implicit elements: this is the case in the pair R10 (detected in a different way) where the implicit 'for the storage capacity' expression is unexpressed in pair a. The same situation is observed with pronouns, although their use should be very limited in technical authoring. Missing information prevents the similarity metrics from mining requirements dealing with the same precise topic. (8) an external context: Similarly to (6) above, but in the other direction, two requirements may be exactly identical but incoherent if the sections in which they appear have titles which are opposed in some way. The incoherence may then be 'external' to the requirements. However the case structure evoked in (1) may also be considered here.

More observations are probably necessary before a model and an implementation for the detection of incoherent requirement pairs can be carried out. However, a few simple remarks can be made at this stage. Category 1 in Sect. 4 should be relatively simple to implement, with a base of antonyms, various forms of divergence expression, and a limited syntactic analysis for verb complements. Some elements that fall in category 3 should also be relatively simple to implement, after a discourse analysis of the requirements, but this category may include more complex cases than those given as illustrations. Categories 2 and 4 are much more challenging and probably the most interesting ones from a scientific point of view.

The similarity metrics clearly needs to be refined: a simple word to word match and count is obviously not accurate enough, even if it allowed us to investigate the incoherence problem from scratch. Several problems such as pronominal references, the use of more generic terms, and implicit terms, and ellipsis must be taken into account in some way.

Conclusion and Perspectives

We have presented in this paper the construction of a corpus of incoherent requirements and a preliminary categorization. We have restricted ourselves to incoherences which can be detected on a linguistic basis. Mining incoherences is really challenging as shown by the analysis provided for each category. Modeling and implementation is ongoing, but requires more data. This contribution opens new perspectives (1) on new forms of incoherence in texts and (2) on mining incoherent requirements, and arguments more generally.

Our corpus examples show that incoherence may take a large diversity of forms.

A major difficulty is evaluation. Since it is not possible to have a test corpus where incoherent requirements have been identified manually, it is only possible to evaluate noise, but not silence. Both dimensions are important to evaluate the accuracy and also the linguistic adequacy and soundness of templates. In addition, requirement authors certainly do not want to be bothered by getting alerts about pairs of requirements which are not incoherent, they also wish to have an estimate of what the system found.


Christophe Fatras
email: christophe.fatras@onera.fr

Pierre Borderies

Nicolas Baghdadi
email: nicolas.baghdadi@teledetection.fr

Mehrez Zribi
email: mehrez.zribi@ird.fr

Mohammad El Hajj
email: mohammad.el-hajj@teledetection.fr

Frédéric Frappart
email: frederic.frappart@get.obs-mip.fr

Éric Mougin

Eric Mougin
email: eric.mougin@get.obs-mip.fr

Radar Backscattering Coefficient Over Bare Soils at Ka-Band Close to Nadir Angle

teaching and research institutions in France or abroad, or from public or private research centers.



Abstract-The behavior of the Ka-band backscattering coefficient at nadir and close-to-nadir angles for land applications is poorly documented. The measurements made during a ground-based campaign at Ka-band were performed at nadir and close-to-nadir angles over bare soils for different surface roughness and soil moisture conditions. The resulting backscattering levels exhibited a dynamic range of approximately 23 dB at nadir for soil moisture contents between 5 and 50 % m 3 /m 3 over both smooth and rough surfaces. These results were then compared to the Geometrical Optics (GO) and Millimeter MicroWave (MMW) models. Generally, GO finely fit the backscattering coefficients close to nadir, and MMW appeared to fit for larger incidence angles or rough surfaces. The results obtained in this study can address pre-launch science and engineering considerations for the interferometry-altimetry SWOT mission operating at Ka-band. Index Terms-Radar, Ka-band, Bare soils, Soil moisture, Surface roughness.

I. INTRODUCTION

adar altimetry measurements are commonly used to monitor water levels over land [START_REF] Frappart | Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin[END_REF] and land surface properties, including surface soil moisture (SSM) estimation, mostly at C and Ku bands (e.g., [START_REF] Fatras | Estimating surface soil moisture over Sahel using ENVISAT radar altimetry[END_REF], [START_REF] Fatras | Spaceborne altimetry and scatterometry backscattering signatures at C-and Ku-band over West Africa[END_REF]). Since the launch of SARAL (Satellite with Argos and AltiKa) in 2013, which carried AltiKa, the first altimeter to operate at Ka-band (26-40 GHz), this frequency is also used to observe the Earth at nadir and near-nadir incidence angles [START_REF] Vincent | AltiKa: a Ka-band altimetry payload and system for operational altimetry during the GMES period[END_REF]. It will be followed by the Ka-band Radar Interferometer (KaRIn) on board the NASA (U.S. National Aeronautics and Space Administration) and CNES (French space agency) Surface Water and Ocean Topography (SWOT) mission, to be launched in 2020 [START_REF] Durand | The Surface Water and Ocean Topography Mission: Observing terrestrial surface water and oceanic submesoscale eddies[END_REF]. This mission will provide a global survey of Earth's ocean and inland water elevation in a 120km-wide swath at an incidence ranging from 0.6° to 4.5°. One of the major goals of hydrology is to inventory all terrestrial surface water bodies whose surface areas exceed 250 m² (e.g., lakes, reservoirs, and wetlands) and rivers whose widths exceed 100 m. This will be based on the ability to discriminate between land and inland waters using the backscattering coefficient.

The Ka band does not need another frequency band to correct the ionosphere delay. Due to its short wavelength ( ~8 mm), its use for interferometric purposes implies a reasonable baseline length compared to lower frequencies. Its main detrimental effect is its important attenuation and phase disturbances in the presence of water vapor.

Although the far-from-nadir radar response at C-and Kubands over land is well documented, the near-nadir radar response of soils for the Ka band is poorly studied. This is primarily because the Ka band was not used for Earth observations until recently. In particular, the range of the radar response over agricultural areas with different SSM and roughness conditions is not well known for close-to-nadir incidence angles (SWOT pointing angles). Backscatter response measurements of bare soils were examined at 35 GHz (Ka-band) and 94 GHz (millimeter band) on three soil surfaces with both dry and moderately wet soil conditions [START_REF] Nashashibi | Measurement and modeling of the millimeter-wave backscatter response of soil surfaces[END_REF]. Unfortunately, the measurements were made at 20°, 45° and 70° incidence angles, which are far from the nadir. This paper, for altimetry purposes, focuses on the nadir and close-to-nadir angles, with measurements made at pointing angles of 0°, 7° and 25° for six different surface roughness and SSM conditions. The measurement angles cover the nadir and close-to-nadir angles used on the SARAL/AltiKa (0°) and SWOT (0.6° to 4.5°) missions. This campaign will lead to the first data set at Ka-band over bare soils for conditions similar to those of the SWOT acquisitions. Section II introduces the dataset and presents the radar measurements and the soil parameters (soil moisture and roughness measurement). In Section III, the results obtained are presented and analyzed.

A. Radar backscattering measurements 1) Radar measurements and experimental setup

The radar backscattering coefficients are calculated from near-field measurements made at Ka-band (34.5 GHz) at VV polarization. The experimental setup consists of an offset parabolic reflector with a 60-cm diameter that is fixed on a mast controlled by two step motors, which induce a rotation in both azimuth and incidence (Fig. 1). The near-field configuration is chosen to ensure a constant incidence angle on the ground illumination spot. The goal of this experiment is to estimate the backscattering coefficient 𝜎 0 of a given soil through N successive and independent measurements. For each radar incidence angle 𝜃 𝑖 , 91 measurements (N) were performed per angle for each plot by rotating the azimuthal motor with a step of 2° over the ground, covering a 180°-wide area.

𝜎 0 corresponds to the incoherent field 𝐸 𝑖𝑛𝑐 , which is extracted from the total field observed over N successive measurements 𝐸 𝑡𝑖 (i from 1 to N) and the coherent field 𝐸 𝑐 :

𝐸 𝑐 = ∑ 𝐸 𝑡𝑖 𝑁 (1)
𝜎 0 ∝ |𝐸 𝑖𝑛𝑐 | 2 = | √∑|(𝐸 𝑡𝑖 -𝐸 𝑐 ) 2 | 𝑁 | 2 (2)
The precision obtained for 𝜎 0 increases by 1 √𝑁 .

2) Data processing

From a standalone measurement, we can calculate the impulse response in distance. Then, it can be manually filtered to extract only the ground backscattered signal that corresponds to the peak of interest. From the 91 independent measurements corresponding to the 91 azimuthal angles over each plot, we consequently extract the incoherent field using ( 1) and (2). To retrieve the expected backscattering coefficient 𝜎 0 , we use the center frequency value (34.5 GHz). Calibration is performed using Eq. 3, which is valid if both the soil spot and the corner reflector are in the near field of the parabola and the latter is in the far field of the corner reflector:

𝜎 𝑐𝑎𝑙𝑖𝑏𝑟𝑎𝑡𝑒𝑑 0 = 𝑃 𝑟 𝑃 𝑡𝑟 ( 𝐷 𝑠𝑜𝑖𝑙 𝐷 𝑡𝑟 ) 2 𝜎 𝑡𝑟 𝑆 𝑎𝑛𝑡 (3)
with -𝑃 𝑟 as the measured incoherent field power -𝑃 𝑡𝑟 as the measured calibration trihedral corner reflector returned power -𝐷 𝑠𝑜𝑖𝑙 as the propagation distance (see Fig. 1), which is a function of the azimuthal motor height and incidence angle of the system -𝐷 𝑡𝑟 as the distance between the parabolic antenna and the corner reflector position during the measurement -𝜎 𝑡𝑟 as the trihedral corner reflector theoretical radar cross-section of the edge length 𝑎, expressed in [START_REF] Ruck | Radar Cross Section handbook[END_REF] as

𝜎 𝑡𝑟 = 4𝜋𝑎 4 3𝜆 2 (with 𝑎 = 7 𝑐𝑚) (4)
-𝑆 𝑎𝑛𝑡 as the antenna surface projection over the ground, changing with the incidence angle 𝜃 𝑖 and the antenna radius 𝑅 𝑎𝑛𝑡

B. Ground measurements

The radar measurements were taken over an agricultural area in the city of Montpellier, South of France. The soil is 35% clay and 15% sand. Three different SSM levels (dry, moderately wet and saturated) are considered, with both low and high roughness each time. The six different ground configurations, measured at 0°, 7° and 25° of incidence, allow us to cover a wide range of soil conditions. A seventh ground configuration has been added by splitting the soft and wet ground surfaces into two parts (N° 6 in Table 1), as one part was mud (N°7, very smooth and very wet) and the other part exhibited the expected soil condition.

The ground plots covered by the measurements have an approximate surface area of 3.4 m 2 , 4.3 m 2 and 7.4 m 2 for the 0°, 7° and 25° incidence angles, respectively.

The bare soil was dry when the field campaign started (SSM < 0.06 m 3 /m 3 ). The ground watering and mixing over a 10-cm layer led to homogeneous, moderately wet and watersaturated surfaces. The surface roughness was manually changed by raking the ground coarsely or thinly.

1) Soil roughness parameters

Measurements of soil roughness were carried out for each reference plot using a RIEGL VZ-400 terrestrial laser scanner mounted on a 2-m-high platform. The point density was approximately 1 pts/mm 2 . The laser provides scan data acquisition with 5-mm accuracy.

Different statistical approaches have been proposed for the description of roughness [START_REF] Zribi | Evaluation of a rough soil surface description with ASAR-ENVISAT Radar Data[END_REF]. In the case of radar applications, the surface height is generally considered to be stationary and ergodic. For different soil samples, roughness estimation is conducted over profiles extracted from 3D surfaces. The roughness description of each profile is based on the computation of its surface height autocorrelation function. Two roughness parameters are extracted to describe the surface geometry: the standard deviation of the surface height (root mean square surface height, (s)), defining the vertical scale of the roughness; and the surface correlation length (l), defined as the horizontal displacement for which the autocorrelation function of the profile decreases to 1/e. As shown in Table 1, computations are performed in two directions (along the X-and Y-axes) to consider the limited anisotropic effects due to tillage work. The results illustrated in Table 1 show large roughness dynamic range, with s values ranging from 0.29 to 2.07 cm and l ranging from 2.7 cm to 6.9 cm. The considered grounds all have exponential autocorrelation functions, except for ground N°2, which exhibits Gaussian behavior.

2) Surface soil moisture

Simultaneously with the radar measurements, ground measurements of soil moisture were obtained on six reference plots. Eight gravimetric soil moisture samples were collected for each reference plot over depths of 0-3 cm. The volumetric soil moisture (SSM) was then obtained by multiplying the gravimetric soil moisture with the dry soil bulk density (eight measurements per reference plot). The volumetric soil moisture was then calculated for each reference plot using the mean of all of the soil moisture measurements collected on the reference plot. The soil moistures ranged from 0.045 to 0.501 m 3 /m 3 (Table 1), with standard deviations between 0.023 and 0.074 m 3 /m 3 . This sampling depth matches the small penetration depths of microwave signals for small radar wavelengths (the first few centimeters, [START_REF] Zribi | Evaluation of a rough soil surface description with ASAR-ENVISAT Radar Data[END_REF]).

III. MEASUREMENT RESULTS

The 21 backscattering coefficient measurements corresponding to the seven soils and the 0°, 7° and 25° incidence angles are presented in Table 1. The absolute error is +/-1.4 dB, as detailed in [START_REF] Boisot | Ka-band backscattering from water surface at small incidence : a wind-wave tank study[END_REF], with a relative error less than 0.5 dB. The corresponding backscattering coefficients at the 0° incidence angle range from -1.8 dB (moderately wet and rough) to 21.4 dB (very smooth and very wet), thus presenting a maximal dynamic range of ~23 dB between plots 4 and 7. At 7°, we have a maximal dynamic range of ~14 dB, and at 25°, a maximal dynamic range of ~8 dB. At 0° of incidence over plot N°7, the backscattering coefficient of 21.4 dB is the result of water-saturated ground (~0.50 m 3 /m 3 ), which could correspond to riverbanks or bare soils after a strong rainfall. This level is consistent with the backscattering value observed at the same frequency over water for small roughness [START_REF] Fjørtoft | KaRIn on SWOT: Characteristics of Near-Nadir Ka-Band Interferometric SAR Imagery[END_REF]. For dry soils (N°1-2), 𝜎 0 decreases by approximately 10 dB when the roughness increases from 0.3 to approximately 1.2 cm at nadir. This decrease becomes smaller with the incidence angle (5.4 dB at 7° and 1.7 dB at 25°). For moderately wet soils (N°3-4), 𝜎 0 decreases by only 1 dB when the roughness increases from approximately 0.4 to 1.7 cm at nadir. This decrease becomes smaller for increased incidence angles (3.5 dB at 7° and 0.4 dB at 25°). For wet soils (N°5-6), 𝜎 0 decreases by 7.4 dB when the roughness increases from approximately 0.7 to 2 cm. This decrease is slightly less important with the incidence angle (4.6 dB at 7° and 4.3 dB at 25°). Between measurements N°1 and N°3, for similar roughness parameters (~0.4 cm), the increase in the soil moisture (from ~0.05 m 3 /m 3 to ~0.21 m 3 /m 3 ) induces a decrease in the 𝜎 0 of 10 dB (3.6 dB at 7°) and an increase of 1.5 dB at 25°. Between measurements N°4 and N°6, for similar roughness parameters (~1.8 cm), the increase in the soil moisture (from ~0.18 m 3 /m 3 to ~0.41 m 3 /m 3 ) induces an increase in the 𝜎 0 of 1.8 dB (4 dB at 7° and 2.2 dB at 25°).

Globally, we observe a decrease in or stable behavior of the backscattering coefficient with the incidence angle, which was expected. In particular, for large roughness, the backscattering coefficient is nearly constant over the observation range. The strong dependence of the backscattering coefficient with the incidence angle (N°1 and 7, notably) may be explained by low roughness parameters, implying strong specular effects.

The results show how the combined effects of the various parameters impact the backscattering. Nevertheless, for the 0° and 7° incidence angles, one can verify that the lower the roughness is, the higher the 𝜎 0 is for equivalent SSM (see N°1-2 and, alternately, N°5-6 in Table 1). 𝜎 0 thus seems to be more dependent on roughness parameters than on the soil moisture. Unfortunately, due to the important range of roughness parameters covered in this study, it was not possible to analyze the evolution of 𝜎 0 with the SSM for similar roughnesses, but some trends in the behavior of 𝜎 0 can be inferred.

IV. COMPARISON WITH SCATTERING MODELS

A. Scattering models

In this section, the simulations from ground scattering models currently used at lower frequencies or higher incidence angles are compared with the previous experimental ones, both at Ka-band and at low incidence. According to the roughness measurements in our reference plots, the Geometric Optics (GO) model is in its validity domain [START_REF] Ulaby | Microwave Remote Sensing[END_REF] for all of them. Thereby, it was taken into consideration. Alternately, to the best of our knowledge, the only work presenting comparisons of experimental and simulated data for rough surfaces at Ka-band is [START_REF] Nashashibi | Measurement and modeling of the millimeter-wave backscatter response of soil surfaces[END_REF]. It showed that the semi-empirical Millimeter MicroWave (MMW) model was relevant at high incidence angles (beyond 20°), so we found it interesting to investigate in our case.

1) The Geometric Optics (GO) model

The GO model, also called the Kirchhoff model for the stationary phase, is based on tangent plane approximation that considers the surface as a pattern of randomly oriented planes, each one locally tangent to the surface. We used the backscatter coefficient given by the stationary phase model.

2) The MMW model

The MMW model, for millimeter wavelengths, is defined in [START_REF] Nashashibi | Measurement and modeling of the millimeter-wave backscatter response of soil surfaces[END_REF] as an adaptation of the model defined by Oh in [START_REF] Oh | An empirical and an inversion technique for radar scattering from bare soil surfaces[END_REF]. The main advantage of this model is that it does not rest on the surface correlation length, the estimation of which is nontrivial [START_REF] Mattia | A comparison between soil roughness statistics used in surface scattering models derived from mechanical and laser profilers[END_REF]. It has not been developed for nadir-looking angles because it relies on the polarization ratio being beyond 10°. It gave satisfactory simulations compared to actual backscattering measurements, presented in [START_REF] Nashashibi | Measurement and modeling of the millimeter-wave backscatter response of soil surfaces[END_REF] for 20°, 45° and 70° incidence angles.

B. Soil permittivity calculation

The complex permittivity of soil is a key variable in the scattering models. Typical models (e.g., [START_REF] Dobson | Microwave Dielectric Behavior of Wet Soil -Part I: Empirical Models and Experimental Observations[END_REF], [START_REF] Dobson | Microwave Dielectric Behavior of Wet Soil -Part II: Dielectric mixing models[END_REF], [START_REF] Mironov | Generalized Refractive Mixing Dielectric Model for Moist Soils[END_REF]) are limited to frequencies lower than Ka-band. For example, the widely used Dobson model [START_REF] Dobson | Microwave Dielectric Behavior of Wet Soil -Part I: Empirical Models and Experimental Observations[END_REF] is valid up to 18 GHz. In [START_REF] Nashashibi | Measurement and modeling of the millimeter-wave backscatter response of soil surfaces[END_REF], the determination of the permittivity discussed in [START_REF] Dobson | Microwave Dielectric Behavior of Wet Soil -Part II: Dielectric mixing models[END_REF] has been extended to Ka-band. This model, which relies on the mixture of soil and water dielectric permittivities with respect to the air-voids volume fraction of the soil, has been extended to the Ka band. The dielectric constant of water has been determined using [START_REF] Strogyn | Equations for Calculating the Dielectric Constant of Saline Water[END_REF] as a function of temperature (20°C), salinity (here set to zero) and frequency. This model was then applied to Ka-band frequencies, and the calculated values were compared to [START_REF] Nashashibi | Measurement and modeling of the millimeter-wave backscatter response of soil surfaces[END_REF] with good agreement.

C. Comparison with measured data

The two models described in the previous section have been applied to the first six measurement configurations. Due to the range of the roughness parameters for each type of soil, the range of the corresponding backscattering coefficient is displayed for each measurement configuration for both GO and MMW, as seen in Fig. 2. The differences between the measured and the mean backscattering coefficient values estimated from both GO and MMW are shown in Table 2.

GO simulations provide good agreement with measurements at 0° and 7° in most cases (measurements N°1-2-5-6). For the case of N°4, the agreement is good at nadir but poorer at 7°. The case of N°3 is unique: the agreement is very poor for both angles. At a 25° incidence angle, the agreement is fair for the rough soils (cases N°2-4-6) but poor for the other ones. The MMW model is supposed to be valid beyond 20°. As a matter of fact, at 25°, in cases N°2-4-6, it presents fair agreement with the experimental results, and it Fig. 2. Backscattering coefficients from measurements (in black) compared with GO (in red) and MMW (in blue). For the six first measurement surfaces N°1-6, the surface soil moisture (SSM) and roughness state are indicated (for specifications, see Table 1). The total measurements are also displayed.

can be seen that this agreement is also good for the other incidence angles (0° and 7°), which was not expected. For the smooth soils, the agreement is poor (there is an underestimation) even at 25°. Measurement N°3 must be considered apart from the others: it shows no correlation with GO, regardless of the incidence angle, and presents a fair agreement with MMW. Actually, the corresponding soil presents surface periodic features (the period between lines is approximately 2.5 cm), which have been induced by the raking. These periodic effects are not taken into account by GO. The better behavior of MMW in this case might be explained by its lack of sensitivity to the horizontal variations, such as the correlation length used in the GO model. For such a surface, a specific model, such as the one in [START_REF] Mattia | Coherent and incoherent scattering from tilted soil surfaces[END_REF], should be used.

Globally, GO provides satisfactory simulated backscattering coefficient values for angles close to nadir, whereas MMW is correct for larger angles and for the rough soils.

V. CONCLUDING REMARKS

The global dynamic range of the backscattering coefficient at Ka-band reaches 23 dB at nadir, 14 dB at 7° and 8 dB at 25° for measurements that encompass large variations in roughness and moisture content. Some values measured over bare soils are similar to those measured over continental water surfaces, which is important in the context of the SWOT mission for water/soil discrimination.

The results show that the impact of roughness on the backscattering coefficient seems to prevail compared to the effect of soil moisture. A comparison with models has been carried out. First, a prior derivation of the complex permittivity at Ka-band proved to be satisfactory based on the global comparisons. For the Ka band and close-to-nadir angles, GO presents the best agreement for nearly all cases, whereas MMW is accurate for rough surfaces at all angles. Future prospects include the analysis of remote sensing data at Ka-band [START_REF] Frappart | Radar altimetry backscattering signatures at Ka, Ku, C, and S bands over West Africa[END_REF] using these results and models.

Fig. 1 .

 1 Fig. 1. Experimental setup. The azimuthal and incidence motors are indicated, along with the parabolic antenna, its counterweight, and the propagation distance of the electromagnetic field. The antenna is connected to a millimeter-band Vector Network Analyzer (VNA, Anritsu 37269D model) through a flexible Radio Frequency (RF) cable. Both the motion controllers (Newport XPS) and the VNA are monitored with a personal computer. The VNA emits and receives a frequency ramp of width f=1 GHz, with a center frequency of 34.5 GHz and an IF filter of 10 kHz. The Fourier transform of the acquired complex data gives the impulse response, where the range resolution is 𝑅 = c 2Δf = 0.15 m and the unambiguous range is d =

TABLE 1

 1

		Summary of ground-truth measurements and radar
			observations (VV-polarization)
		SSM	Roughness	Correlation		𝝈 𝟎
	N°	(m 3 /m 3 ) Mean	s (cm) X-Y-	length l (cm) X-Y-	Angle	(dB) Angle	Angle
		± std	axis	axis	axis	axis	0°	7°	25°
	1	0.045 ± 0.039	0.29	0.4	3.09	6.87	8.9	3.6	-1.4
	2	0.057 ± 0.074	1.13 1.36	4.74	6.23	-0.9	-1.8	0.3
	3	0.213 ± 0.035	0.44 0.54	2.67	4.78	-1	0	0.1
	4	0.181 ± 0.05	1.52 1.81	4.09	5.65	-1.8	-3.5	-0.3
	5	0.501 ± 0.023	0.69 0.81	3.10	4.39	7.4	5.1	6.2
	6	0.414 ± 0.024	1.84 2.07	3.53	5.40	0	0.5	1.9
	7	0.501 ± 0.023	N/A N/A	N/A	N/A	21.4	10.5	5.9

  The GO simulated values are in accordance with the MMW model for the N°2 and 4 measurements. At 25°, the GO values do not fit with the measurements.

				TABLE 2
	Absolutes differences (in dB) between the measured and mean
		modeled backscattering coefficient values
	Angle	0°		7°		25°
	Model	GO	MMW	GO	MMW	GO	MMW
	N°1	2.1	15.7	0.5	10.5	45.3	7.5
	N°2	1.1	2.3	0.4	1.5	5.4	4.3
	N°3	9.6	1.5	6.6	2.6	12.1	4.3
	N°4	1.6	1.3	3.2	3.0	2.4	1.8
	N°5	0.1	6.2	1.6	4.1	8.8	6.7
	N°6	0.3	2.2	0.4	1.5	2.9	1.1

ACKNOWLEDGEMENT

We wish to thank Laurent Albrech and Alain Debreuil for their help during the field measurements.

submitted your paper for review. It will also contain support information, including sponsor and financial support acknowledgment. Christophe Fatras is supported by a CNES/ONERA PhD grant.

Christophe Fatras is with the Office National


Vincent Soulignac

François Pinet

Myriam Vallas

Jean-Louis Ermine

Experiments on the use of knowledge management tools for agriculture

Keywords:

Agriculture must be both sustainable and economically viable. Sustainable agriculture requires new knowledge and expertise. However, knowledge management is not sufficient in current sustainable agriculture. To overcome this, we have made the assumption that the knowledge management practices used in industry can be transferred in agriculture. In this paper, we propose to apply to agriculture, the methodological tools developed by the French knowledge management group. These tools are based on theoretical approaches for the transformation of explicit and tacit knowledge within an organization. These tools are generic and suitable for any knowledge. We have tested the tool CFK for knowledge criticality in order to identify the knowledge to make explicit within a farm. This knowledge is critical because they are valuable, rare, complex and difficult to formalize. They must therefore be managed. We have determined the criticality of knowledge in a farm for organic agriculture field crops, in prairies and on various flora grain legumes. In the French research project TATABOX related to the agro-ecological transition study in a land between Tarn and Aveyron in France, we also have experimented another methodological tool (TRACO) for characterizing the most appropriate knowledge transfer tools to use between an agricultural cooperative and farmers. Among 16 knowledge transfer methods proposed, TRACO allow highlighting supervised self-education but also traditional teaching courses, communities of practices and workshops. Our conclusion is that the proposed knowledge management tools seem relevant to manage knowledge in agriculture, but they still require training and adaptation to agricultural fields.

Introduction

Agriculture must become more environmentally sustainable while being economically viable. Taking better account of ecosystems, this type of agriculture called agro-ecology is the inverse of an intensive agriculture. The production and the acquisition of knowledge by farmers are one of the strategic conditions to develop this sustainable (but productive) farming. Currently, knowledge for productivity improvement is available, but knowledge that meets requirements for environment, territory and economic viability must be developed. Therefore, agro-ecology requires new knowledge and expertise. The diversity of the stakeholders and the difficulty to perform experiments because of the long duration of the production cycles constitute obstacles to knowledge capitalization. Thus, knowledge management is not sufficient in agro-ecology [START_REF] Meynard | La reconception est en marche ! Conclusion au Colloque « Vers des systèmes de culture innovants et performants : De la théorie à la pratique pour concevoir, piloter, évaluer, conseiller et former[END_REF][START_REF] Guichard | AgroPEPS, un outil web collaboratif de gestion des connaissances pour Produire, Echanger, Pratiquer, S'informer sur les systèmes de culture durables[END_REF].

To remedy this problem, we make the assumption that knowledge management methods applied in the industrial world can be transposed in agriculture [START_REF] Soulignac | A knowledge management system for exchanging and creating knowledge in organic farming[END_REF]. This paper focuses on the methodological tools developed by the French knowledge management group ("Le club de gestion des connaissances" -http://www.clubgc.asso.fr/accueil_gc). These tools are based on the transformation of explicit and tacit knowledge within organizations [START_REF] Nonaka | The knowledge-creating theory revisited : Knowledge creation as a synthesizing process[END_REF][START_REF] Ermine | Management et ingénierie des connaissances : Modèles et méthodes[END_REF]. They are generic and usually suitable for any type of organizations. In this paper, we present the use of two of these tools in real cases in agriculture. The first tool is used to identify the "critical" knowledge on which organizations should focus. The second tool allows the organizations to better define the knowledge transfer to implement.

Knowledge management

The creation of explicit knowledge in an organization is a complex task. The knowledge management process involves a first step for the identification of the most important knowledge (called critical knowledge). The second step is the definition of an action plan to reduce risks related to a poor control of knowledge. There are two main action types. The first type of action plan is a skill management. Knowledge is transferred by people, with learning processes or through recruitment. The second type of action is indirect. It requires a phase in which explicit knowledge is written, before a step of knowledge appropriation. Since the early 2000s, the French knowledge management group has developed a methodology for knowledge management. At each step of this methodology, the group proposes the use of a software tool, as indicated in Figure 1. Each of these tools uses the results of surveys conducted among stakeholders in the studied areas. It is not always necessary to use all the tools. For example, an organization can choose to successively use the following tools:

-The tool called CKF for evaluating the criticality of knowledge, -The decision support system TRACO to identify the most relevant knowledge transfer methods, -The RPC guide for writing knowledge, if the indirect knowledge transfer is an appropriate solution.

All these tools have been extensively used in industry in France, by private companies or public institutions. Their interest was proven in industrial and service activities. These tools are generic, as they are suitable for any of these areas, but a question arises about the usability of these tools for knowledge management in agro-ecology; this latter area having certain particular characteristics. For example, agro-ecology implies an anticipation strategy to manage pests. In this context, a crop rotation over a long period, more than five years, can be considered and implemented in order to limit the occurrence of plant diseases or weeds.

Local characteristics are very important, in particular the soil and climatic conditions make it complex the production of knowledge to design a rotation taking into account different issues (pests, production, etc.). Efficient solutions to be implemented locally are often the result of a combination of both empirical and scientific knowledge. The challenge is to determine if these tools also provide a solution for complex cases in which different types of knowledge must be combined, according to their origins (e.g., operational and scientific knowledge) .

We experimented the use of the methodology described in Figure 1 in several projects in organic farming or in agro-ecology. In this paper, we report the experience feedback on two knowledge management tools described above i.e. the tool for critical knowledge CKF and decision support tool TRACO for knowledge transfer. Factors having impacts on this criticality are the knowledge usefulness, its rarity, its complexity and its difficulty to be implemented. We tested CKF in multiple environments including the Melibio project led by the Organic Agriculture Centre for Massif Central in France. This research and development project aims at the enhancement of the diversity of species, forage varieties and farming practices in organic farming in order to secure food systems of ruminants in Massif Central in France. This project involves researchers, agricultural advisors, experts and teachers. In this paper, we illustrate the use of CKF on an example of research work related to the varied plant prairie management, conducted in the Melibio project. The CKF method includes several steps:

-Agricultural advisors and researchers having knowledge in varied plant prairies in organic farming were chosen. We also could involve farmers. -The knowledge areas are defined collectively. They are disjoint as possible in order to facilitate their analysis. Thus, 23 knowledge areas on varied plant prairie management have been identified related to pest management or nutritional needs of animals, etc. The complete list is in table 1. -Each of these areas is rated by an expert according to the four criteria (usefulness, rarity, complexity, implementation difficulty). A high score means that the knowledge area is highly critical.

The knowledge areas are ranked from #1 (the most important) to #23 (the less important).

The results are presented in The project Melibio committed since 2011 and will finish in 2018. The methodological tool CKF was applied to the varied plant prairie management from the beginning of the project. The result analysis highlights the priority areas, but also those in which cognitive investment is not needed. Thus, subjects such as fertilization or disease treatment will not be studied in Melibio because these technical processes are well controlled. Subjects related to plant dynamics such as varied plant prairie diversity and sustainability are preferred. Detailed analysis of the results facilitates the creation of an action plan. The rating of the four factors (usefulness, rarity, complexity, implementation difficulty) is analysed, but also the differences between the opinions of researchers and agricultural advisors:

-Each CKF rating of factors refer to an action plan: o When the usefulness factor rating is low, even if the overall score is high, the question about the relevance of the field study is raised. Consequently, the pest control is not taken into account. o When a domain is rare, i.e. only little knowledge is associated with it, then a scientific research work or an empirical knowledge collection is started, as was the case with plant dynamics. o When a domain is complex but its implementation is relatively well controlled, a wiki web server is a possible solution to explain and disseminate knowledge. o When the difficulty factor has a high rating, a pilot plot is used to identify good practices over a long period and for their dissemination through farmer meetings.

-The level of knowledge in a domain between agricultural advisors and researchers is sometimes different. Consequently, the ratings can be different. In our analysis of ratings, we distinguish agricultural advisors and researchers. For a researcher, information is rare when there is a limited number of scientific publications about the topic. For an agricultural advisor, information is rare when they cannot be found in practical cases. This point can explain some differences in ratings. In that case, the knowledge transfer is possible. For instance, concerning the carbon footprint issue, courses can be created to transfer knowledge related to carbon footprint. Knowledge transfer can also be produced by technical actions. In this case technical skills can be transferred to researchers. It can be for example the case for the methods used in practice for varied plant prairie harvesting and storage.

TRACO -a decision support tool for knowledge transfer

The methodical tool TRACO allows choosing the best approaches. TRACO offers 16 knowledge transfer methods (Club de gestion des connaissances, 2009), classified into four types:

-"Classroom" training courses where the trainer and the trainees are physically in the same room, -Methods mixing practical and theoretical knowledge such as block-release training, -Knowledge transfer media such as knowledge server software dedicated to knowledge dissemination, -knowledge networks in which people can share and enrich their practices (such as communities of practices). four main types:

-The deadlines / urgency of knowledge transfer and the contextual information, -The nature of knowledge, -The source, i.e., the holders of the knowledge, -The target, i.e., the receivers of these knowledge

For each of the 16 transfer methods proposed by TRACO, each 28 criteria is evaluated according to a colour code:

-The "red" colour is used when the method is not recommended, -"Yellow" if the method is partially adequate, -"Green" when the method is adequate, -The "White" colour means that the criterion has no influence on the method analysis.

We take an example of the analysis of the classroom training course method for the factor of knowledge integration difficulty for one person. As shown in Figure 2, a classroom training course is appropriate when knowledge can easily be integrated in the practices.

We have 28 criteria to be applied to each of the 16 knowledge transfer systems. Consequently, we study 448 results to determine the most appropriate actions. Of course, the systems that have the greatest number of "green" colours will be preferred for the studied organization. In the French project called TATABOX (funded by the Research National Agency -ANR) on the agro-ecological transition, we experimented TRACO to determine the most appropriate knowledge transfer tools to use between an agricultural cooperative called Qualisol and its farmers. Qualisol is a pioneer cooperative in agro-ecology in the Southwest of France. The development of grain legumes (beans, lentils, chickpeas, etc.) completely complies with the objectives of Qualisol and this agro-ecological transition project. In addition to their nutritional qualities, grain legumes have also very good agronomic properties. Their cultivation provides nitrogen to the soil. The decrease in the use of fertilizers reduces health and environmental impacts of nitrogen inputs (Projets ANR LEGITIMES ET TATABOX, Ecole d'ingénieurs de PURPAN et al. 2015;[START_REF] Soulignac | Sillage » et « Sillage Télédéclaration » : des logiciels pour le contrôle réglementaire des épandages de matières fertilisantes en agriculture (mai 2015)[END_REF]. The authors of [START_REF] Magrini | La transition vers des systèmes agroalimentaires durables : quelle place et qualification pour les légumineuses à graines ? La Grande Transformation de l'Agriculture, 20 ans après[END_REF] show that the development of grain legumes implies technological problems. The solutions are the definition of actions of knowledge capitalization and dissemination conducted by research, teaching and advisory organisations. To transfer knowledge on grain legumes, the Qualisol cooperative needs to use the most appropriate tools. Purpan engineering school students (France) conducted a survey of the Qualisol chief agronomist. The result is shown in Figure 3. Over the 16 transfer methods, TRACO allows us to highlight independent learning, but also "classroom" training courses, knowledge networks and workshops. Our knowledge in the agricultural environment allows us to go further in the analysis of the approaches to develop. One of the methods proposed by TRACO is the use of knowledge networks. Their goal is to promote learning. This aspect has been described by [START_REF] Wenger | Communities of practice : learning, meaning and identy New York[END_REF]. It is highly developed in agriculture within the Local Professional Groups (LPGs) [START_REF] Darré | La production de connaissance dans les groupes locaux d'agriculteurs. L'innovation en agriculture : Questions de méthodes et terrains d'observation[END_REF]. LPGs allow grouping together farmers who are geographically close and who have the same cultural practices. highlighted by the TRACO method. LPGs have tacit knowledge based on their own experience. There are two additional advantages related to the use of "classroom" training courses for LPGs. First, feedbacks about the same crops can be exchanged between LPGs located in different small agricultural areas. Common knowledge can be capitalized. Second, during classroom trainings, the animator can provide important scientific knowledge. This exchange can result in the writing of technical reports produced by both empirical and scientific knowledge. Each report constitutes a common base of knowledge that can be adapted by LPGs depending on local soil and climatic conditions.

Discussion

The study of the application in agriculture of these two tools, CKF and TRACO, allows us to define a new method for crop management:

-For a given crop, the CKF tool highlights the lacks in knowledge about crop management, -A collection of empirical knowledge from the best practitioners produces first technical report drafts, -The "classroom" training course combines these empirical knowledge sources in order to produce more generic reports about crop management, -Exchanges between LGP members (and with the animator) can provide technical solutions to various unsolved problems identified by CKF, -Finally, in case of persistent lacks of knowledge, more information can be provided by researchers, -If no solution can be provided by researchers, new research action plan must be started.

This new method must be validated in use cases.

Conclusion

Conceptual approaches -particularly the CKF method -have demonstrated that these tools can be used to integrate the spatial and temporal dimensions of agricultural production. The implementation criteria show that these tools can be used for explicit knowledge -those that people can write -and for tacit knowledge that must be transmitted by learning. These two types of knowledge are both important in agriculture. If knowledge management tools (such as the tools proposed by the French knowledge management) seem to be relevant to manage knowledge in agriculture, users need time to learn to use these tools. For some people, the use of these tools may seem a tedious and time-consuming task. To correctly analyse the survey results, it is very important to take into account the different professional categories and also the soil, climate and economic context.

In France, farming advice service is mainly provided by agricultural cooperatives and agribusiness stakeholders. They provide technical recommendations (e.g., technical reports) to their members. These reports are based on the results produced by agricultural knowledge management system, but their writing is based on a top-down approach, from research results to farmers. Thanks to the tools proposed by the French knowledge management, we have developed an alternative approach. Our method involves all the stakeholder types in the agriculture [START_REF] Nagel | Knowledge flows in agriculture ; Linking Research, Extension and the farmer[END_REF], from farmers to researchers. This approach is based on the middle-up-down knowledge management proposed in [START_REF] Nonaka | A dynamic theory of organizational knowledge creation[END_REF]. This organizational method improve the communication between the "hierarchy" and the professional stakeholders. It facilitates the creation of tacit knowledge and their external communication, whereas the "hierarchy" tends to combine knowledge and learning.

In our opinion, methods such as proposed in this paper, are important for a sustainable agriculture that must take into account environmental impacts. Research institutes have an important role in knowledge creation. Farmers and Local Professional Groups produces tacit knowledge. The challenge is to adapt this model to middle-up-down management, a model that seems relevant to agricultural knowledge management. Agricultural cooperatives could help to structure the operational knowledge in this new paradigm of agro-ecology.

This paper presents some results produced in the TATABOX project, funded by the "Agence National de la Recherche" (AGROBIOSPHERE 2013). Its contents do not represent any official position of the National Research Agency and is fully the responsibility of the authors.

Figure 1 .

 1 Figure 1. Methodology for knowledge management (by the French knowledge management group -"club de gestion des connaissances" -http://www.clubgc.asso.fr)

Figure 2 .Figure 3 .

 23 Figure 2. One example of a ranking proposed for the criterion « knowledge usability for one person » and for the method « classroom training course »

Table 1 :

 1

	N°	Topics	Knowledge fields
	1	Development strategy of the forage system	Varied flora prairie destruction
	2	Technical action	Fertilizers
	3	Development strategy of the forage system	Socio-economical factor
	4	Development strategy of the forage system	Role of varied flora prairie in crop rotations
	5	Technical action	Control of bioagressor and disease
	6	Development strategy of the forage system	Pedoclimatic knowledge
	7	Development strategy of the forage system	Animal needs
	8	Technical action	Harvesting and storage method
	9	Environment	Water
	10	Technical action	Agricultural seeding
	11	Plant dynamics	Dry matter
	12	Environment	Landscape
	13	Technical action	Control of bioagressor and weed
	14	Technical action	Control of bioagressor and pest
	15	Technical action	Location
	16	Environment	Carbon footprint
	17	Plant dynamics	Quality
	18	Environment	Biodiversity
	19	Development strategy of the forage system	Floristic composition of varied flora prairie
	20	Development strategy of the forage system	Forage system adaptation
	21	Development strategy of the forage system	Role of varied flora prairie in forage system
	22	Plant dynamics	Diversity
	23	Plant dynamics	Sustainability

Table 1 . Criticality knowledge ranking related to the management of varied plant prairie (5-10 year plant) in organic agriculture

 1

  These groups can produce practical knowledge during meetings. The soil and climate variability justifies the creation of LPGs; in agro-ecology, knowledge in crop management is particularly appropriate for a given soil and climate context. The construction of local knowledge is very important. In agro-ecology, soil and climate are two very important parameters. Only LPGs can correctly build local knowledge. The joint use of farmers' meetings and "classroom" training courses is relevant, as