ORIGINAL ARTICLE

Adequacy of Labras Software in Planning the Preparation of the Soil for Sugarcane

0000-0002-4109-8775Yoel Betancourt-Rodríguez^{I  *}  , 0000-0001-5520-1836Darién Alonso-Camacho^I  , 0000-0003-4266-7754Jorge Luis Ponce-Salazar^I  , 0000-0002-9881-1869Liván Amado Villavicencio-Ramírez^II 

^IInstituto de Investigaciones de la Caña de Azúcar, Villa Clara (INICA-Villa Clara), Ranchuelo, Villa Clara.

^IIUniversidad Central “Marta Abreu” de las Villas (UCLV), Cuba.

ABSTRACT

The Sugarcane Research Institute worked on the development of the software (SW) LabraS specialized in the planning of soil tillage processes. Evaluating the functional adequacy of the LabraS software in the planning of the sustainable soil preparation for sugarcane cultivation is the objective of this paper. The work was carried out at “Héctor Rodríguez” Base Business Unit (UEB), of the AZCUBA Sugar Group. The evaluation included the 2020-2021 soil preparation campaign, with 2 619.2 ha, concentrated in 57 blocks dedicated to sugarcane. The research conditions were characterized as complex, with a predominance of poorly drained areas (57%), medium and heavy textured soils (89%), and fallows or cane fields with very low agricultural yields without harvest (67%). The results showed an adequate selection of the technological alternatives, their operational variants and tasks by management condition. In addition, the correct application of the ISMACE criteria (integration of knowledge on soil, machinery, crop and working environment) in the algorithm of the LabraS software, for selecting the technologies with the best technological, economic, energetic and environmental impact, generally demonstrated satisfactory results in the validation of the functional adequacy for the planning of sustainable soil preparation.

Key words: Functional Completeness; Functional Correctness; Functional Relevance; Software Testing; Agricultural Planning

INTRODUCTION

Sugar Cane Research Institute (INICA) worked on the development of a computer system, LabraS Software, specialized in planning sustainable processes of soil tillage (^{Betancourt-Rodríguez et al., 2018}; ^{Pérez-Santos, 2018}; ^{Betancourt-Rodríguez et al., 2019a}). The recommendations considered the results of more than 30 years of research in that field in Cuba (^{Gómez et al., 1997}; ^{Crespo et al., 2013}; ^{Gutiérrez et al., 2013}; ^{Oliva et al., 2014}). The platform was the main working tool of the first offer of soil tillage service aimed to the sugarcane producer (^{Betancourt-Rodríguez et al., 2018}).

On the computer systems development, it is important to consider the quality evaluation of the software product. According to the ^{ISO/IEC 25010 (2011)}, it is the way the product satisfies the customer’s requirements and adds value. The quality model defined in that standard includes eight characteristics: Functional suitability, Performance efficiency, Compatibility, Usability, Reliability, Security, Maintainability and Portability, all composed of sub-characteristics that together facilitate the evaluation process by relevant institutions.

According to ^{Blanquicett et al. (2018)}, software testing is a very important process within the software development cycle and it is characterized by its accuracy, reliability and repeatability, being necessary to know what requirements are tested and to check that the product does what the client expects. In that sense, ^{Pauta & Moscoso (2017)} define that in the evaluation of the quality of the software, the slogan of parallel execution of Verification and Validation, also known as V&V, will be applied in the different phases of the life cycle. The complexity of today’s software requires that the test runs parallel to the development, so that errors are timely and can be corrected at low cost (^{Serna et al., 2019}; ^{Marin-Diaz et al., 2020}).

Functional suitability, specifically, represents one of the important characteristics that determines the ability of the SW product to provide functions that meet user’s demand under specified conditions. It is subdivided into three components: Functional completeness, the extent to which the functions cover the tasks and meet the client’s objectives; Functional correctness, ability to provide correct results with the level of accuracy required and Functional relevance, possibility of providing an appropriate set of functions for specific tasks and objectives of users (^{ISO/IEC 25010, 2011}).

For sustainable soil management, according to ^{FAO & GTIS (2015)}, it is necessary to use scientific knowledge, local knowledge, proven evidence-based approaches and technologies to increase food supply, provide a valuable tool for climate regulation and safeguard ecosystem services. Likewise, in a broader vision, it proposes to achieve sustainable development in a balanced and integrated way (^{FAO, 2019}).

From the point of view of tillage, when there is a broad knowledge base, it is possible to apply the principles of sustainability if criteria that integrate knowledge of the soil, machinery, cultivation and the environment are implemented. LabraS software (SW) is a good choice because that integration is applied just at the planning time of the agricultural work.

In order to reduce the environmental impact of tillage, some study recommend the development of alternative mechanical management system that reverses, stops or mitigates soil deterioration, decreasing energy consumption and improving the economic results of sugarcane grower (^{Grange et al., 2005}; ^{Tesouro et al., 2019}; ^{Department of Agriculture and Fisheries-Queensland Govemment, 2021}). The use of scarifiers, either for stripe or total soil tillage, is a good choice to transform the scenario towards a balanced environment in sugarcane soil preparation (^{Gómez et al., 1997}; ^{Oliva et al., 2014}; ^{Tesouro et al., 2019}).

Considering the above, the objective of this research is to evaluate the functional suitability of the LabraS software algorithms in the planning sustainable soil preparation for sugarcane.

MATERIALS AND METHODS

The research was carried out in the Base Business Unit (UEB) “Héctor Rodríguez”, belonging to the Sugar Company (EA) Villa Clara, both of AZCUBA Sugar Group. Sugarcane is cultivated in 17,004.98 ha. The genetic soil grouping predominant are Gleysol, with more than 30%; Sialitic brown, Vertisol and Ferralitic occupying between 16 and 19%, according to the genetic classification of 2015 proposed by ^{Hernández et al. (2015)}.

The 2020-2021 soil preparation campaign was planned for 2 619,2 ha, concentrated in 57 blocks dedicated to sugarcane. In the UEB, either by inventory equipment or hiring, there are aggregates (tractor and implement) available to perform the work, although there are implements like C101M and Bayamo that require modification to meet the agronomic requirements for each work (Table 1). The Production Units (UP) do not have the means to carry out the technological process.

TABLE 1 Aggregates available by labors and implements situation 

Labors	Aggregates available	Situation of the implements
		Inventory	Modification	Hiring
Land leveling	YTO 1604 with AF Leveler			X
Break (Discs) and Crossing (Discs)	YTO 1604 with AT-90	X
Raise the strain and break (Arrows)	YTO 1604 with C101M		X
De-Crown and heavy harrow	Komatsu D80 with Heavy Discs Harrow			X
De-Crown and medium harrow	YTO 1604 with GAPCR Discs Harrow	X
Light harrow	YTO 1604 with Rome Discs Harrow			X
Raise the strain and break (Arrows), Break (Arrows) and Crossing (Arrows)	YTO 1604 with Bayamo (Modified)		X
Subsolation	Komatsu D80 with SP 280H			X

LabraS SW owns a set of functions available to the user in order to ensure the functional relevance in the tillage process of sugarcane. The definition of tillage soil limiting factors, soil textures and ground conditions, as fundamental components to select machinery and technology of sustainable soil preparation, came from a functionality established in the Codifier or Nomenclators with specific parameters and algorithms (^{Betancourt-Rodríguez et al., 2019a}).

The algorithms used in LabraS integrate knowledge of the soil, machinery, cultivation and work environment into the same procedure for recommendations, called ISMACE criteria (Soil-Machinery-Crop-Work Environment Integration). That means to evaluate the soil limiting factors to solve by tilling or defining the equipment used in order to avoid tool breakage. Also, they provide results that meet, at any time, the crop´s agronomic requirements and considering the environment in which the recommendations are developed, all seen from the origin of the machinery and the impact on the social and environmental level.

The Figure 1 presents LabraS SW general scheme for obtaining the reports. It is important to point out that one of the novelties in the established procedures is that the brigade is not only the system to organize the machinery, but the computing section that facilitates the identification of machinery for the specified environment, as part of the ISMACE criteria established.

FIGURE 1 Scheme for data input, processing and report. AR- Algorithms for recommendation. 

The information processing in the AR was established in three stages:

1. Technological Alternative (AT) determination.

In this research, AT means the combination among the components of the factors that make up the technological process. By means of the name of the component and establishing a logical sequence, the name of the alternative is formed, which means that each alternative in its nomenclature contains a condition of agronomic management. Considering the number of factors in soil preparation by ^{Betancourt-Rodríguez et al., (2018)}, 169 possible technological alternatives were defined by the computer platform.

The input of the algorithm is formed by the sugarcane blocks (B) and the technological process (PT). The output is the AT of each block appropriate to the management conditions (C), then being n equal to the total of blocks:

Input: (B, PT)

Output: AT _i

1: for I = 1 to n do

2: C ← GetConditions ( B _I )

3: AT _I ← Match AT I

4: End for

2. Determination of the Appropriate Variant within the AT

Variant (V) contains the sequence of tasks, sorted according to agronomic criteria to meet the requirements of the crop (Figure 2) and they are specific to each alternative, for that reason, the computer platform handles them independently.

In the SW design, the possibility that one or several variants integrate the AT was established, including also the expert criteria to set an order of selection by the user, providing more options and flexibility.

FIGURE 2 Variant for AT in LabraS SW. 

A specific nomenclature is used for the tasks, which constitutes another level of selection that broadens the range of possibilities for the user and facilitates the application of the ISMACE system. An example is that of the work Rotura, which in production conditions uses that single term to refer to everything related to the initial work of plowing the soil. However, in the coding of the work in the LabraS platform there are several terminologies such as Rupture (Discs), for primary tillage with plows and disc harrows and Rupture (Arrows), for primary tillage with chisel plows with bolts or winged plows as they are also known. Considering n as the total number of blocks to process, the algorithm proceeds by the following steps:

Input: (B, AT)

Output: (V _I )

1. For I = 1 to n do

2. Variants ← GetVariants ( B _I, AT _I )

3. If Defined ( ExpertJudgment )

4. Variants ← OrderByExpertJudgment ( variants )

5. Else

6. variants ← OrderByJobsCount ( variants )

7. While Not Complete Variant is Found do

8. Jobs ← GetJobs ( variants _I )

9. V _I ← MatchJobAg g rega tes ( jobs )

10. End While

11. Set (V _I)

12. End for

In the evaluation of the recommended labors, those concerning to the preparation of the field, such as herbicide application and grass cutting, were not considered, because they do not belong directly to the technological process of soil preparation.

3. Selection of an aggregate for each labor that makes up the selected variant.

The aggregate (A) is nothing more than the set formed by the union of the tractor with the agricultural implement. To carry out a labor within the variant, it is possible to find different aggregates in the inventory of machinery that can perform it or that are not always available at the required time. With P being the brigade, the procedure is as follows:

Input: (V, P)

Output: (A _I, jobs _I )

1: jobs ← GetJobs ( V )

2: for I = 1 to jobs .count do

3: a ggregates ← Get Aggregates (jobs _I)

4:

5: If Defined ( ExpertJudgment )

6: a ggregates ← OrderByExpertJudgment (aggregates, ASC)

7: Else

8: a ggregates ← OrderByGenera lCriteria (aggregates, ASC)

9: End If

10:

11: While Aggregates .next do

12: If IsIn ( A _I , P ) And IsAvailable ( A _I )

13: Set ( A _I, jobs _I )

14: Exit While

15: End If

16: End While

17: End for

In the selection of the aggregate as in the variant, the user also has the option of establishing expert criteria, which broadens the range of possibilities by being able to define one of several existing, regardless of the exploitation criteria.

From the three exploitation criteria defined in the computer program to choose the aggregate (Productivity, ha.day^-1, Specific fuel cost, L.ha^-1 and Cost, pesos.ha^-1), the cost was selected to perform the recommendations.

RESULTS AND DISCUSSION

The distribution of the limiting factor for the tillage mechanization showed a predominance of areas with poor drainage problems with around 57%, followed by the problems of stony (14%), salinity (5%) and effective depth (1%), finding the areas without limitations for tillage at approximately 22% (Figure 3), respect to 2619, 2 ha of soil preparation. In that sense, the recommendation of labor and equipment should be directed to face the limitations and create favorable conditions for the creation of an adequate plantation (^{Crespo et al., 2013}; ^{Oliva et al., 2014}; ^{Betancourt-Rodríguez et al., 2018)}.

From the point of view of soil texture, following the classification given by ^{Betancourt-Rodríguez et al. (2019b)}, those of medium and heavy texture predominated, occupying 2331 ha, leaving the remaining 288 ha on light soils (11%).

FIGURE 3 Percentage of most limiting factors for tillage mechanization. 

The conditions on the ground surface (Figure 4) indicated predominance of fallow or sugarcane of very low crop yields, not harvested areas, representing 67 % (1744 ha), being on demolition the remaining (33%) and those from the rotation shall be null. This situation complicates the planning of soil preparation since the prevailing scenario demands a longer period between labor and total soil preparation time, further aggravated by the possible presence of shrub vegetation that requires robust equipment with greater power, and consequently, the need for hiring with other company.

In all cases, their identification and inclusion within the algorithms creates the conditions for more precise planning, where the most varied and complex conditions find a solution for agronomic management opportunely. It is important to note the need to incorporate area from rotation with other crops by having the ground conditions more suitable for tillage work according to ^{Gutiérrez et al. (2013)}, as well as incorporating other benefits such as the diversification of production.

FIGURE 4 Ground condition in the areas of soil preparation. 

The analysis of the AT recommended (Table 2) showed good relationship with the management conditions, which is shown in that the sum of the total area by AT coincides with the total area of soil preparation of the campaign (2619, 2 ha) and the sum of frequency by AT with the total of processed blocks (57 blocks).

On the other hand, the AT 92, 89 and 83 covering 57% of the soil with poor drainage, correspond with the predominant MLF in the conditions under study. It happens similarly in the demolition conditions present in AT 100.112, 33, 9, 89, 144 covering 876 ha, and in all cases with medium and heavy texture that prevails in the UEB.

TABLE 2 Recommendation of technological alternatives for management conditions 

Number-Alternative Name	Frequency	Area, ha	%
100-Preparation of light soil with stony and / or rockiness in demolition and change of furrow.	4	149	6
112-Preparation of medium or heavy soil with stony and/or rockiness in demolition and change of furrow.	5	97	4
116-Preparation of medium or heavy soil with stony and/or rockiness in fallow or low yield and change of furrow.	5	132	5
128-Preparation of medium or heavy soil with problem of effective depth in fallow or low yield and change of furrow.	1	36	1
33-Preparation of medium and heavy soil without limitations on demolition and furrow changes.	1	167	6
64-Preparation of medium or heavy soil without limitations in fallow or low yield and change of furrow.	1	271	10
9-Preparation of light soil without limitations on demolition and change of furrow.	4	133	5
92-Preparation of medium or heavy soil with poor drainage in fallow or low yield that requires land levelling and change of furrow.	8	1170	45
89-Preparation of medium or heavy soil with poor drainage in demolition that requires land levelling and change of furrow.	1	275	10
83-Preparation of light soil with poor drainage in fallow or low yield that requires land levelling and change of furrow.	6	51	2
144-Preparation of medium or heavy soil with salinity problem in demolition that requires land levelling and change of furrow.	19	55	2
147-Preparation of medium or heavy soil with salinity problem in fallow or low yield that requires land levelling and change of furrow.	2	84	3

Furthermore, AT 112, 116, 64, 92 and 89 were recommended five or more times; being AT 92 the one with the highest frequency with 19 times in the technology charts, therefore, the one that determines with the greatest weight the demand for work and the type of equipment to be used.

All of the above shows an adequate functional correction of LabraS SW in the determination of AT by management condition, also demonstrating satisfactory results of the recommendation algorithms in the first stage.

For the selection of the labor and the aggregates, 29 variants (V) were processed in the 12 recommended AT, with possibilities to choose from one to five (Table 3). The sums of the frequency of recommendation match with the number of blocks (57). Further selection was not always in the first variant, but according to the procedures established in the ISMACE criteria, as it observes in the AT 144 with three variants, only V3 was recommended, or also in the AT 89 with five possibilities, the choice was concentrated on V4 and V5.

TABLE 3 AT variants and frequency of recommendation 

AT Number	AT Variants	Variants recommendation frequency
		Variant 1	Variant 2	Variant 3	Variant 4	Variant 5
100	1	4
112	1	5
116	2		5
128	2	1
144	3			1
147	2	1
33	3		4
64	2		8
83	1	1
89	5				2	4
92	4		2	17
9	3			2

The use of the expert criteria (CE) in the selection of variants as an option to expand the range of possibilities for the user was validated on AT 92 where variant 3 is recommended 17 times for having CE equal to 1. The machinery availability as a factor to be considered in the algorithms is demonstrated in the same AT, where variant 2 is recommended twice, as a result of that equipment were used at the same time. This part demonstrated, as established in the ISO Standard ^{ISO/IEC 25010 (2011)}, the fulfillment of the three subcharacteristics of the functional suitability of LabraS SW: completeness, correctness and functional relevance.

The variants, although independent, cannot be seen isolated from the labor and aggregates, in this sense and in order to achieve greater understanding, in Tables 4 and 5, the AT-Variant-Labor relationship is shown, where the labors recommended by AT can be identified, but without maintaining the sequence of the technology chart.

TABLE 4 Technological alternatives (AT), variants (V) and labor relationship in primary tillage 

AT	De-Crown				RSB (Arrows)		Break (Discs)			Break (Arrows)			Crossing (Discs)			Crossing (Arrows)				Subsolation
	V1	V2	V3	V4	V3	V5	V1	V2	V3	V1	V2	V3	V1	V2	V3	V1	V2	V3	V5	V1	V2	V3	V4
100							4						4
112	5						5						5
116																					5
128										1						1
144					1													1
147										1						1
33		4						4									4
64																					8
83																				1
89				2		4								2					2				2
92			17					2	17								2	17
9									2						2

For soil preparation, 11 labors were recommended. The De-Crown was aimed for medium and heavy textured soils on demolition, where a soil mound was formed on the surface that restricted the movement of the equipment, perpendicular or oblique with respect to the direction of the furrows. In such cases, where complex conditions limited the use of the middle disc harrow, such as the presence of shrubby vegetation, only heavy disc harrow was recommended.

Scarifier was recommended for total and localized primary tillage in proper conditions, as De-crown and Break (Arrows), Break (Arrows) and Cross (Arrows) (AT 144, 89, 147, 128, 33, 92), which satisfies algorithms for choosing which has fewer operating costs. It is also convenient to point out that collaterally it incorporates technological, energy and environmental benefits in minimum tillage of soil (^{Gómez et al., 1997}; ^{Crespo et al., 2013}; ^{Oliva et al., 2014}); ^{Tesouro et al., 2019}) and follows the principles established by the FAO in soil tillage (^{FAO & GTIS, 2015; FAO, 2019}).

On the other hand, the scarifier was also avoided in the areas affected by rocks and stones (AT 100, 112 and 116), thereby, the occurrence of damage that could completely invalidate the implement. So the integration soil-machinery in the algorithms and the adequate functional correction of the SW was demonstrated, not only to solve the limitations of the soil for the development of the crop, but also in the appropriate selection of equipment.

TABLE 5 Alternative technology (AT), variants (V) and labor relationship in secondary operation 

AT	Heavy discs harrow				Median discs harrow					Land leveling					Light discs harrow
	V1	V2	V3	V4	V1	V2	V3	V4	V5	V1	V2	V3	V4	V5	V1	V2	V3	V4	V5
100					4										4
112					5										5
116		5				5										5
128					1										1
144							1					1				1
147					1					1					1
33						4										4
64		8				8										8
83					1					1					1
89								2	4				2	4				2	4
92						2	17				2	17				2	17
9							2										2

For the areas with rockiness and stony problems, Break (Discs) and Crossing (Discs) (AT 100 and 112) were recommended, which agrees with what was suggested by ^{Crespo et al. (2013)}; ^{Oliva et al. (2014)}; ^{Betancourt-Rodríguez et al. (2018)}. It was also proposed on soils without limitations (AT 9 and 89) because the scarifiers were not available on the starting date. An example of that is in the AT 89 with five variants, where variant that combines both types of equipment (scarifier and those with discs) was recommended, also due to the availability of the implements on the specified date established in the algorithms.

The other part of the tools to meet the technological chart come from hiring, whose timely identification facilitates work planning itself, being in this case, implements for subsolation, heavy disc harrow and enlistment labors (Land leveling and Light disc harrow) carried out by the TRANZMEC company. This result demonstrates the application of the ISMACE criterion in the algorithms for planning, specifically regarding the brigade as a computational environment for linking the recommendations with the work environment.

The heavy harrow complemented the work with subsoilers for fallow conditions or low yield block due to the presence of shrub vegetation (AT 116, 64, 83), or in medium textured soils with poor internal drainage (AT 89). The land leveling is associated with the need identified by the grower; in this sense, a preponderance was observed in the AT 92, because it contains the predominant handling condition. Median and light harrow are recommended in all conditions to satisfy the requirement of achieving an adequate proportion of soil aggregates to ensure good furrowing and covering of seed.

The total area for the planned work indicated that the scarifier was recommended only in 11% of the area (Figure 5), low level compared to the height potential of the area (85%). That situation was due to the predominance of fallow land conditions or low yields not harvested (67%).

FIGURE 5 Total area by labor. 

It is important to remark that the balance did not include those with stony or rockiness problems; on the other hand, Break (Disks) and subsolation were proposed. In the case of subsolation, two passes were planned, representing 1209 ha. It is important to specify that scarification was not conceived in the UEB Technical-Economic Plan, which shows the potentiality of LabraS SW in the proper planning of the soil preparation labor.

Median discs harrow is the predominant according to the level of work, proposed in two passes and occupying 88% of the area, the remaining is labored with heavy discs harrow directed for De-crown in the most complex conditions by the existence of shrubby vegetation such as Dichrostachys cinerea (Marabú), Albizia procera (Algarrobillo) and Leucaena leucocephala (Leucaena). The light disc harrow was recommended at least in one pass to the end of the preparation, representing 100% of the area, and land leveling with two passes for the demanded area, which justifies its application in around 2800 hectares.

It is important to point out that since the entire area is in conditions of furrow change and with a predominance of fallow or low yield, there are limitations to recommend localized tillage technology (LL), leaving total preparation with and without turning up soil the prism for being the most suitable.

The satisfactory result of LabraS SW in the completeness, correctness and functional relevance in the planning of the sustainable soil preparation for sugarcane is due to the performing of parallel development testing on each built-in functionality, coinciding with what was proposed by ^{Serna et al. (2019)} and ^{Marin-Diaz et al. (2020)}.

All the procedures presented are the bases to conform the technological charts block by block, such as it was presented in the sugarcane plantation process by ^{Betancourt-Rodríguez et al. (2019b)}, and represent a choice to transform the scenario towards a balanced environment in sugarcane soil preparation.

CONCLUSIONS

• The research conditions were characterized by the existence of a high complexity for soil preparation, where predominated areas with poor drainage (57%), soils of medium and heavy texture (89%) and the fallow land or sugarcane of very low agricultural yield without harvesting (67%).

• The technological alternatives, their variants, tasks and operations, were selected appropriately by management condition. The ISMACE criteria in the algorithm of the LabraS software allowed the selection of the technologies with better economic, energy and environmental impact, also taking in to account the role of machinery respect to its ownership, based on:

• To recommend the scarifiers satisfactorily at the break (11%) and the crossing (57%) operation, although very low compared to the potential of the area, close to 85%, due to the complex surface conditions.

• To direct properly the use of heavy tracked equipment, such as the Komatsu tractor with the heavy harrow and the SP 280 subsoilers, towards woody plant areas regardless of non-UEB machinery.

• To recommend the use of traditional means such as discs plows and harrows in areas with stony and rocky problems, regardless of whether they do not have better exploitation criteria, to avoid breakage in the scarifiers.

• The LabraS Software showed satisfactory results in the validation of the functional suitability for the planning sustainable soil preparation in the specified circumstances

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Received: October 18, 2022; Accepted: March 13, 2023

^*Author for correspondence: Dr.C. Yoel Betancourt-Rodríguez, e-mail: [e-mail]; [e-mail]

Yoel Betancourt-Rodríguez, Dr.C., Ing. Mec., Investigador titular, Instituto de Investigaciones de la Caña de Azúcar Villa Clara (INICA-Villa Clara). Autopista nacional km 246, Ranchuelo, Villa Clara. Profesor Titular adjunto de la Universidad Central “Marta Abreu” de las Villas (UCLV), Cuba, e-mail: [e-mail]; [e-mail] ORCID ID: https://orcid.org/0000-0002-4109-8775)

Darién Alonso-Camacho, Ingeniero en Ciencias Informáticas, Especialista. Instituto de Investigaciones de la Caña de Azúcar Villa Clara (INICA-Villa Clara), Cuba, e-mail: [e-mail] ORCID ID: https://orcid.org/0000-0001-5520-1836

Jorge Luis Ponce-Salaza, Ingeniero Agropecuario, Especialista. Instituto de Investigaciones de la Caña de Azúcar (INICA), Estación Territorial de Investigaciones de la Caña de Azúcar (ETICA Centro-Villa Clara), Cuba, e-mail: [e-mail] (ORCID ID: https://orcid.org/0000-0003-4266-7754).

Liván Amado Villavicencio-Ramírez, Estudiante graduado de Ingeniería Agrícola en la Universidad Central “Marta Abreu” de las Villas (UCLV, Cuba, e-mail: [e-mail] ORCID ID: https://orcid.org/0000-0002-9881-1869

Conceptualization: Yoel Betancourt Rodríguez. Data curation: Yoel Betancourt Rodríguez, Formal Analysis: Yoel Betancourt Rodríguez. Investigation: Yoel Betancourt Rodríguez, Darién Alonso Camacho, Jorge Luis Ponce Salazar, Liván Amado Villavicencio Ramírez. Methodology: Yoel Betancourt Rodríguez. Software: Yoel Betancourt Rodríguez, Darién Alonso Camacho. Supervision: Yoel Betancourt Rodríguez. Validation: Yoel Betancourt Rodríguez, Jorge Luis Ponce Salazar, Liván Amado Villavicencio Ramírez. Writing – original draft: Yoel Betancourt Rodríguez, Darién Alonso Camacho. Writing – review & editing: Yoel Betancourt Rodríguez, Darién Alonso Camacho.

The authors of this work declare no conflict of interests.

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