=Paper=
{{Paper
|id=None
|storemode=property
|title=Assistance in making decisions to promote planting and conservation of maize in the state of Puebla of Mexico through answer set programming
|pdfUrl=https://ceur-ws.org/Vol-677/08_LANMR10.pdf
|volume=Vol-677
}}
==Assistance in making decisions to promote planting and conservation of maize in the state of Puebla of Mexico through answer set programming==
https://ceur-ws.org/Vol-677/08_LANMR10.pdf
Assistance in making decisions to promote planting
and conservation of maize in the state of Puebla of
Mexico through Answer Set Programming
Luis A. Montiel1 , Claudia Zepeda2 , Pedro Wesche1 , and Javier A. Cervantes2
1
Universidad de las Américas, CENTIA
2
Benemérita Universidad Autónoma de Puebla
luis.montielmo@udlap.mx
Abstract We present some examples which show how preference ordered
disjunction adds useful features to answer set programming, as well as an in-
teresting example of how it can be useful for modeling a real problem about
agriculture. In this paper, we present a mathematical formalization for the
problem of making decisions to promote planting and conservation of maize
in some geographical zone inside the country of Mexico; we present modeling
semantics that we consider necessary for the mathematical formalization of
this problem using Answer Set Semantics.
Keywords: Agriculture, Ordered Disjunction, Preferences, Answer Set Pro-
gramming, Data Mining.
1 Introduction
At the present time Maize is one of the most important crops around the world, it is
a major source of food for humanity and the livestock sector [5]. Currently it is very
important to support technological development in agriculture in order to increase
yields (production per hectare) of crops that are important to satisfy the food demand
of humanity in the world [20].
A problem present in Mexico is that the total production of maize in the country
is not enough to meet food demand of all Mexican population; Mexico has faced
the need to import foreign corn mainly from U.S. and Canada since it is cheaper to
import corn than to produce national maize in Mexico [20][18].
Given experts recommendations and needs [20][18], and experts works [8][15][14]
we identify that is possible to define The maize fitting zone problem and give a solu-
tion to it in order to support agriculture in the area of corn:
The maize fitting zone problem (MaizeFitZone problem). This problem is defined
by the following tasks and subproblems:
1. To conduct a study on planting and harvesting methodologies employed for corn in a
given geographical area in recent years.
2. To identify biological properties for each variety of native maize in a given geographical
area.
77
3. Given a geographical area for each land zones belonging to it, to identify the attributes
(geographical, climatic, technological, economic, soil, hydrological land use and vegeta-
tion) that define it.
4. To relate this land attributes in function of classes, races and varieties of maize and
their yields produced.
5. To determine which land zones report high or low yields for each variety of corn and to
explain the necessary and sufficient conditions to obtain high yields in each land zone.
6. To produce corn in land zones selecting the best race of maize that fits each land zone to
obtain high yields of production preserving an ecosystem that does not harm nature and
that satifies the nutrimental needs of producers and population of a given geographical
area.
7. To replicate these conditions in land zones with low yields of corn
8. Continuous monitoring of implementation of the model in reality for different classes of
producers by experts, and feedback to the mathematical model with the results. Taking
care not to affect the quality of life for producers who have their sustenance on their own
crops.
Recently work [8][15][14] has been done trying to solve the problem The maize
fitting zone problem. There is great diversity of climatic and geographic attributes, at-
tributes that represent the technologies necessary to grow, control pests and diseases
of maize, and attributes of phenotypes according to breeds and varieties of existing
corn and classes to which they belong. The data type for these attributes are strings,
integers, real numbers, percentages, however in the works we have reviewed percent-
age and numerical attributes are handled through ranges between values in order to
facilitate the categorization of these attributes, because of this it is possible to map,
one to one, quantitative information to qualitative data. Due to the complexity in
the relation between the attributes involved in these problems we require to han-
dle incomplete knowledge. The problems show variables of uncertainty for example
climate and pest or disease control attributes for the corn, and also we require the
use of preferences for example preferences according to the classes of seeds to grow
depending on the environment. Because we are dealing with these attributes and the
type of rules that will arise in these problems, we identified that it is possible to use
Answer Set Programming [10][9] as a means to formalize this problem in an expres-
sive language. We identified the need of using an ASP extension that use preferred
rules [7][4][21] for handling preference conditions.
There is now software that efficiently computes ASP [12][19], this is the reason
we believe it is possible to propose a real solution that can be implemented based on
this programming paradigm.
2 Background
In this section we introduce all the necessary terminology and relevant definitions in
order to make this paper self-contained.1
1
We assume that the reader has familiarity with basic concepts of classical logic, logic
programming, answer set semantics, and lattices. For details the reader can refer to [1,6,13].
78
2.1 Extended Logic Programs
We consider extended logic programs which have two kinds of negation, strong nega-
tion ¬ and default negation not. A signature L is a finite set of elements that we call
atoms, where atoms negated by ¬ are called extended atoms. Intuitively, not a is true
whenever there is no reason to believe a, whereas ¬a requires a proof of the negated
atom. In the following we use the concept of atom without paying attention if it is an
extended atom or not. A literal is either an atom a called positive literal, or the nega-
tion of an atom not a called negative literal. Given a set of atoms {a1 , ..., an }, we write
not {a1 , ..., an } to denote the set of atoms {not a1 , ..., not an }. An extended normal
rule (rule, for short) r is a rule of the form a ← b1 , . . . , bm , not bm+1 , . . . , not bm+n
where a and each of the bi are atoms for 1 ≤ i ≤ m + n. If m + n = 0 the rule is an
abbreviation of a ← > such that > is the proposition symbol that always evaluates
to true; the rule is known as a fact and can be denoted just by a. If n = 0 the rule
is an extended definite rule. We denote a rule r by a ← B + , not B − where the set
{b1 , . . . , bm } and the set {bm+1 , . . . , bm+n } are denoted by B+ and B− respectively.
A constraint is a rule of the form ← B + , not B − . We denote by head(r) the head
a of rule r and by body(r) the B + , not B− of the rule r. An extended normal logic
program P is a finite set of extended normal rules and/or constraints. By LP we
denote the signature of P , i.e. the set of atoms that appear in the rules of P . If all
the rules in P are extended definite rules we call the program P extended positive
logic program. In our logic programs we will manage the strong negation ¬ as it is
done in Answer Set Programming (ASP) [1]. Basically, each atom ¬a is replaced by
a new atom symbol a0 which does not appear in the language of the program and
we add the constraint ← a, a0 to the program. For managing the constraints in our
logic programs, we will replace each rule of the form ← B + not B − by a new rule of
the form f ← B + , not B− , not f such that f is a new atom symbol which does not
appear in LP .
2.2 Logic Programs with Ordered Disjunction
Logic programs with ordered disjunction (LPODs) are extended logic programs aug-
mented by an ordered disjunction connector × which allows to express qualitative
preferences in the head of rules [3]. A LPOD is a finite collection of rules of the form
r = c1 × . . . × ck ← b1 , . . . , bm , not bm+1 , . . . , not bm+n where ci (for 1 ≤ i ≤ k) and
each of the bj (for 1 ≤ j ≤ m + n) are atoms. The rule r states that if the body is
satisfied then some ci must be in the answer set, if possible c1 , if not then c2 , and
so on, and at least one of them must be true. Each of the ci represents alternative,
ranked options for problem solutions the user specifies according to a desired order.
If k = 1 then the rule is an extended normal rule. The semantics of LPODs is based
on the following reduction.
Definition 1 (×-reduction). [3] Let r = c1 × . . . × ck ← b1 , . . . , bm , not bm+1 ,
. . . , not bm+n be an ordered disjunction rule and M be a set of atoms. Let P be an
M
LPOD and M be a set of atoms. The ×-reduct r× is defined as
M
r× := {ci ← b1 , . . . , bm |ci ∈ M and M ∩ ({c1 , . . . , ci−1 } ∪ {bm+1 , . . . , bm+n }) = ∅}.
S
The ×-reduct P×M is defined as P×M = r∈P r× M
.
79
Definition 2. [3] Let P be an LPOD and M a set of atoms. Then, M is an answer
set of P if and only if M is a minimal model of P×M . We denote by SEMLP OD (P )
the mapping which assigns to P the set of all answer set of P .
One interesting characteristic of LPODs is that they provide a mean to represent
preferences among answer set by considering the rule satisfaction degree [3].
Definition 3. [3] Let M be an answer set of an LPOD P . Then M satisfies the rule
r = c1 × . . . × ck ← b1 , . . . , bm , not bm+1 . . . , not bm+n :
– to degree 1 if bj 6∈ M for some j (1 ≤ j ≤ m), or bi ∈ M for some i (m + 1 ≤ i ≤ m + n),
– to degree j (1 ≤ j ≤ k) if all bl ∈ M (1 ≤ l ≤ m), bi 6∈ M (m + 1 ≤ i ≤ m + n), and
j = min{r | cr ∈ M, 1 ≤ r ≤ k}.
The degrees can be viewed as penalties: the higher the degree the less satisfied we
are. If the body of a rule is not satisfied, then there is no reason to be dissatisfied
and the best possible degree 1 is obtained [3]. The satisfaction degree of an answer
set M w.r.t. a rule, denoted by degM (r), provides a ranking of the answer set of an
LPOD, and a preference order on the answer set can be obtained using some proposed
combination strategies [3].
3 Important computational aspects for MaizeFitZone
problem
In order to show that it is possible to model MaizeFitZone problem througth An-
swer Set Programming we need to focus our attention in subproblems (5) and (6).
Subproblem (5) presents the problem of determining for a geographical zone its agri-
cultural production potential. Subproblem (6) presents the problem of locating seeds
candidates who best fit the needs of farmers based on the agricultural production
potential of the geographical zone where they are located. Subproblems (5) and (6)
describes the use of an integrated database MaizeBioGeoClimAgriTechDB obtained
from subproblems (1) to (4). Subproblems (7) and (8) describes the solution in real
life once the mathematical model of the main problem has been implemented.
MaizeFitZone problem (1) to (4). We identify that it is possible to link Geo-
graphical, Weather and Agricultural techonology databases in order to cross informa-
tion on the different corn seeds (Creole or Improved), their yields in each municipality
and locality, and the relevant attributes (climatic, soil, etc.) involved to obtain certain
level of production (low, medium, high, very high for example). Let us call this linked
information GeoClimAgriTechDB.
From GeoClimAgriTechDB it is possible relate land attributes in function of maize
classes and their yields produced, however to relate land attributes in function of
races and varieties of maize and their yields produced it is necessary to integrate
GeoClimAgriTechDB database with Biological properties of maize database, so it is
possible to know in a general level the candidates of races and varieties of maize seeds
that could be presented in the land zone and the probability of appearing in it. Let
us call to this integrated database MaizeBioGeoClimAgriTechDB.
To see more detalied information about the MaizeBioGeoClimAgriTechDB database
see technical report[16].
80
MaizeFitZone problem (5). Recent work addresses this problem [8][15][14].
The computer systems developed by INIFAP Puebla [8] and Chiapas [15][14] cal-
culate agricultural production potential in given geographic areas, that is, the level
of fitness for a plant to be cultivated successfully. The Puebla system provides the
potential for various crops (monocultures), however there is no documentation about
the mathematical model employed. The Chiapas system provides the potential for
varieties of native and improved maize seeds (also monocultures) and uses climatic
and soil information; documentation about the mathematical model employed exists
[15][14].
In order to discern from classes(Creole or Improved), races and varieties of corn
seeds yields produced for each municipality and locality the relevant attributes in-
volved to obtain certain level of production (low, medium, high, very high for exam-
ple) and to explain the necessary and sufficient conditions to obtain high yields in
each land zone from MaizeBioGeoClimAgriTechDB, it is possible to run datamining
algorithms to discover patterns in the information such as C4.5 [17] or ID3 [2], expose
these patterns to agronomists experts to discern interesting patterns.
There exists efficient software that implements data mining algorithms such as
ID3 and C4.5 that it is easy to use such as WEKA software [11].
Let us call to the combination of the subproblems MaizeFitZone problem (4) and
MaizeFitZone problem (5) the maize zone production potential problem.
MaizeFitZone problem (6). Once the potential of agricultural production (the
level of fitness for a plant to be cultivated successfully) has been computed, in the
Chiapas System [15][14] a suggestion of improved seeds candidates who best fit the
needs of the user is given, this computed from a set of geographical, climatic, and
technological attributes values given as input by the user.
Once a solution is given for maize zone production potential problem and based
on this solution, given a geographical zone, let us call maizes best fitting zone
problem to the problem of selecting the races and varieties of maize to be planted
that fits each land zone to obtain high yields of production preserving an ecosystem
that does not harm nature and that satifies the nutrimental needs of producers and
population of a given geographical area.
4 Computational modeling Answer Set Programming
approach for maize fitting zone problem
This section determines the knowledge that is required to provide a knowledge mod-
eling, and using the model of recent work [15][14] we show that it is possible to model
the knowledge that is required to provide the knowledge modeling of MaizeFitZone
problem.
4.1 Knowledge Sources
We define the maize agricultural knowledge to cultivate maize plants as the union
of the following knowledge:
1. Agronomists experts’ knowledge.
81
2. The knowledge generated by centuries-old traditions and experience of the rural
poor
3. Patterns and knowledge discovered from MaizeBioGeoClimAgriTechDB database
using knowledge discovering techinques such as data mining.
in order to generate a mathematical model to solve MaizeFitZone problem effi-
ciently and accurately.
Now we present the argumentation that shows that it is possible to model Maize-
FitZone problem using Answer Sets programming using extended semantics handling
preferences in disjunctive rules.
To model Agronomist experts’ knowledge and experience of the rural poor rep-
resent the same problem since experience of the rural poor can be structured in the
same way Agronomist experts’ knowledge have been structured.
It is possible to provide a mathematical model for maizes best fitting zone problem
using Answer Sets[10,9] based on work and documentation form INIFAP Chiapas
[15,14] to show how it is possible to model Agronomist experts’ knowledge.
It is possible to run datamining algorithms to discover patterns from the infor-
mation contained in MaizeBioGeoClimAgriTechDB such as C4.5 [17] or ID3 [2], and
from this kind of algorithms is possible to generate a logic program, for instance an
ASP program for example from a classifictation tree to generate in a direct way, at
least an ordered disjunctive logic program.
In order to model maize fit problem first part of this problem (maize zone pro-
duction potential problem)is modeled from natural language to ASP rules, and the
second part, maizes best fitting zone problem is modeled using a C4.5 tree obtained
from implicit knowledge in [15][14] in order to generate a preferred ordered disjunctive
program.
4.2 Modeling of maize zone production potential problem: Answer Sets
approach
The works [15][14] present the scientific and technological agronomist experts’ knowl-
edge on how is determined for a given geographical zone if the potential of production
in order to cultivate native maize seeds is very good, good, intermediate or low.
As input of the problem, part of extensional data base (this knowledge is included
in MaizeBioGeoClimAgriTechDB knowledge base), we have the following facts:
– From processing and analysis of climate we have the data:
• precipitation(Zone, Day, V alue).
• evaporation(Zone, Day, V alue).
• temperature(Zone, Day, V alue).
• precipitationByYear(Zone, Y ear, V alue).
• evaporationByYear(Zone, Y ear, V alue).
• temperatureByYear(Zone, Y ear, V alue).
– From generation and classification of images (geographical data):
• zone(Zone).
• locality(Locality).
• localityInZone(Zone, Locality).
• terrainSlope(Zone, V alue).
• soilDepth(Zone, V alue).
• soilTexture(Zone, V ale).
– From user input attributes for a locality:
• organicMatter(L, V alue).
• pending(L, V alue).
82
• rainfedQuality(L, V alue).
• altitude(L, V alue).
• cultureCycle(L, V alue).
• moistureRegime(L, V alue).
• soilTexture(Zone, V ale).
Where value is a cualitative value or an integer in a range inside [0, 1000]
The rules that gives insigth to model this problem are based in this expert
knowledge[15][14]:
potentialProductivity(Z, veryGood) ← zone(Z), growingSeasonPeriod(Z, middle),
soilDepthCentimeter(Z, veryDeep), not abnormal(Z).
potentialProductivity(Z, good) ← zone(Z), growingSeasonPeriod(Z, long),
(soilDepthCentimeter(Z, shallow)∨
soilDepthCentimeter(Z, veryDeep)),
not abnormal(Z).
potentialProductivity(Z, intermediate) ← zone(Z), growingSeasonPeriod(Z, middle),
soilDepthCentimeter(Z, shallow),
not abnormal(Z).
(1)
potentialProductivity(Z, intermediate) ← zone(Z), growingSeasonPeriod(Z, short),
soilDepthCentimeter(Z, veryDeep),
not abnormal(Z).
potentialProductivity(Z, low) ← zone(Z), (growingSeasonPeriod(Z, short)∨
soilDepthCentimeter(Z, shallow)
∨abnormal(Z)).
abnormal(Z) ← zone(Z), erosion(Z).
abnormal(Z) ← zone(Z), naturalResourcesDegradation(Z).
abnormal(Z) ← zone(Z), droughtChance(Z).
For example the rule:
potentialProductivity(Z, veryGood) ← zone(Z), growingSeasonPeriod(Z, middle),
soilDepthCentimeter(Z, veryDeep), (2)
not abnormal(Z).
Can be read as: the agricultural potential of production for a given zone Z is very
good if there is evidence that there exists in the zone Z a middle growing season
perdiod and very deep soil depth, and there is no evidence that in zone Z there exists
a risk of erosion, and there is no evidence that in zone Z there exists natural resources
degradation risk, and there is no evidence that in zone Z there exists drought risk.
For the following rule:
potentialProductivity(Z, low) ← zone(Z),
(growingSeasonDays(Z, short) ∨
(3)
soilDepthCentimeter(Z, shallow)
∨ abnormal(Z)).
It can be shown that c ← l1 , . . . , lm , (lm+1 ∨ . . . ∨ lm+n ) where li are literals for
n, m ≥ 0 can be replaced by the rules contained in the set {c ← l1 , . . . , lm , lm+j |1 ≤
j ≤ n}.
If we have the predicate growingSeasonDays(Z, LengthDays) it is possible to
create a rule that discern the value for growingSeasonPeriod(Z, value) from this
predicate. For example:
growingSeasonPeriod(Z, long) ← Zone(Z),
growingSeasonDays(Z, LengthDays), (4)
LengthDays ≥ 146.
For the sentence [15][14]:
83
We determined the growing season (duration, start and end), which is the number of days
during the year in which there is availability of water or moisture and a suitable temperature
for crop development.
we propose the rule:
growingSeasonDays(Z, LDaysGSD) ← LDaysGSD := max{LengthDays|Zone(Z),
growingSeason(Z, StartDay, F inishDay),
LengthDays := F inishDay − StartDay,
not waterAvailabilityInadequate(Z, (5)
StartDay, F inishDay),
not temperatureInadequate(Z,
StartDay, F inishDay)}.
where the last rule can be read as: the growing season days length for a given zone
Z is the maximum growing season days period length for a zone Z from the starting
day to the finishing day of the period, in which for this zone Z and this period, there
is no evidence that there exists water availability inadequate, and there is no evidence
that there exists temperature innadequate.
Equation (5) can be easily translated to the following rules:
setConditions(Z, LengthDays) ← Zone(Z),
growingSeason(Z, StartDay, F inishDay),
F inishDay := LengthDays + StartDay,
not waterAvailabilityInadequate(Z, StartDay, F inishDay),
not temperatureInadequate(Z, StartDay, F inishDay). (6)
betterSetConditions(Z, LnDays) ← setConditions(Z, LnDays),
setConditions(Z, Y ), LnDays < Y.
growingSeasonDays(Z, LnDays) ← setConditions(Z, LnDays),
not betterSetConditions(Z, LnDays).
The condition about water availability inadequate is present for a period of time
[StartDay, F inishDay] and a zone Z if there exist evidence that for zone Z water
resources are inadequate, or for zone Z in the period [StartDay, F inishDay] the
humidity is inadequate. So the following rule is generated:
waterAvailabilityInadequate(Z, StartDay, F inishDay) ← Zone(Z),
(waterResourscesInadequate(Z) ∨
humidityInadecuate(Z, StartDay, F inishDay)).
(7)
The definition of temperature inadequate given for a zone Z for a period of time
[StartDay, F inishDay] is defined using the given temperature for every day in the
given zone and period. The temperature for a given day and zone is part of extensional
data base.
Humidity inadecuate given for a zone Z for a period of time [StartDay, F inishDay]
is present if for the zone Z and period of time [StartDay, F inishDay] there exist ex-
cess of humidity or deficit of humidity. This gives the following rule:
humidityInadecuate(Z, StartDay, F inishDay) ← Zone(Z),
(excessHumidity(Z,
StartDay, F inishDay) ∨ (8)
deficitHumidity(Z,
StartDay, F inishDay)).
84
For the sentence [15][14]: We estimated the water balance resulting from dividing the
precipitation between the ETP, as support for determining the growing seasons and periods
of excess and water deficit.
waterBalance(Z, Day, V alue) ← Zone(Z),
potentialEvapotranspiration(Z, Day, P ET V alue),
(9)
precipitation(Z, Day, P recipitationV alue),
P recipitationV alue := V alue ∗ P ET V alue.
Rule defined in (9) there is a division that can be rewritten in terms of multipli-
cation, in order to simulate the multiplication in answer set, multiplication can be
replaced by a predicate multiplication(A,B,AB) in the extensional data base where
the third argument is the result of multiplicate the first two arguments.
Definition of predicates growingSeason(Zone, StartDay, F inishDay),
excessHumidity(Zone, StartDay, F inishDay) and
deficitHumidity(Zone, StartDay, F inishDay) depends on the definition of
waterBalance(Zone, Day, V alue) (rule (9)). Works [8][15][14] does not provide the
way how they are related, however we believe that is easy to construct the definition
of these predicates if they are defined as an statistical function.
For the sentence [15][14]:
For each year level and estimated daily potential evapotranspiration (ETP) from the
evaporation data multiplication by a constant factor of 0.75. The ETP is multiplied by the
constant factor of 0.5. We identified periods of moisture surplus or deficit (duration, start
and end).
We model the rule:
potentialEvapotranspiration(Z, Day, V alue) ← Zone(Z)
evaporation(Z, Day, EvaV alue), (10)
V alue := EvaV alue ∗ 0.75 ∗ 0.5.
In (10) there is a multiplication of rational numbers, since 0.75 · 0.5 = 0.375, it
is possible to handle rational numbers knowing the range value of evaporation for
every day in a similar way that in rule (9) and mapping each rational number to an
integer proportional value. Potential evapotranspiration for a year is defined in terms
of potential evapotranspiration of a day by a statistical function.
For the sentences [15][14]:
– If the slope is less than 15 percent to prevent soil erosion and degradation of natural
resources in general.
– If there are no droughts lasting over 45 days.
– If the probability of occurrence of a drought is not greater than 66 percent.
the following rules are generated:
soilErosion(Z) ← zone(Z)
terrainSlopePercent(Z, S), S > 15.
(11)
naturalResourcesDegradation(Z) ← zone(Z)
terrainSlopePercent(Z, S), S > 15.
droughtChance(Z) ← zone(Z)
longerDroughtLengthDays(Z, S), L > 45 (12)
droughtProbabilityPercent(Z, E), E > 66.
Rules in (11) are defined in terms of terrain slope, however this definition can
be extended with more predicates related with this definition. In (12) definition of
longerDroughtLengthDays can be declared in a similar way than in rule (6).
85
The predicate droughtProbabilityPercent can be defined as the top value of
the range probability for the number of years a drought may occur for a given zone
Z. For example:
droughtProbabilityPercent(Z, 45) ← zone(Z),
(13)
numberOfYearsDroughtMayOccur(Z, 4).
4.3 Modeling of maizetech fit zone problem using Answer Set
In [15][14] once is determined for a given geographical zone the potential of produc-
tion in order to cultivate native maize seeds, given some diagnosis data by the user
(culture cycle, moisture regime, altitude, rainfed quality, pending, organic matter,
soil texture), the system computes what seeds are more appropriate to plant in the
geographical zone. Since in [15][14] is not clearly stated how this part is modeled, we
evaluated of possible chances from the system by manual inspection and using data
mining techniques (C4.5 algorithm) a decision tree, from this tree we can build rules
as the following one:
seed(v537c) × seed(v538c) × seed(hv521c) ← locality(L),
organicMatter(L, OrgM atP ercent),
OrgM atP ercent > 3,
pending(L, P enP ercent),
P enP ercent < 5, (14)
rainfedQuality(L, highRiskOf Drought),
altitude(L, AltV alue),
AltV alue < 1200,
cultureCycle(L, springSummer).
Note that there is a preference depending of the class of maize seed.
5 Conclusions
In this work we provide ASP modeling for AgriFitZone problem. We propose to use
an ASP approach using preferences. Once necessary and sufficient conditions have
been determined to give solution for AgriFitZone, it is possible to provide assistance
to farmers in making decisions to promote planting and conservation of maize through
the implementation of the mathematical model (in this case using ASP) according
to the classes of producers, environmental conditions and geographical location. In
future work we plan to research about planning on agricultural cultivation of maize
using possibilistic logic programs.
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