Next Article in Journal
The Impact of Job Satisfaction on the Turnover Intent of Executive Level Central Office Administrators in Texas Public School Districts: A Quantitative Study of Work Related Constructs
Next Article in Special Issue
Processing Image to Geographical Information Systems (PI2GIS)—A Learning Tool for QGIS
Previous Article in Journal
The StarT Project Competition from the Perspective of Mathematics and Academic Literacy
Previous Article in Special Issue
Incorporating Sustainability into Engineering and Chemical Education Using E-Learning
Article Menu

Export Article

Educ. Sci. 2018, 8(2), 68; doi:10.3390/educsci8020068

Solving Power Balance Problems in Single-Traction Tractors Using PTractor Plus 1.1, a Possible Learning Aid for Students of Agricultural Engineering
Department of Engineering, University of Almería, Agrifood Campus of International Excellence (CeiA3), s/n, La Cañada, Almería 04120, Spain
Author to whom correspondence should be addressed.
Received: 4 April 2018 / Accepted: 6 May 2018 / Published: 8 May 2018


Tractors are used to perform jobs that require different types of agricultural tools to be attached to their rear, to their front, or both. These tools may need to be dragged, towed, or suspended above ground, and sometimes require a power supply; this is usually obtained via a hydraulic system or from the tractor’s power take-off system. When tractors have to work with such tools on different types of soils and on different slopes, the need arises to calculate the power the tractor engine will have to produce. In the classroom, this is normally calculated manually with the help of a calculator. This work, however, describes a computer program (written in Delphi and operating under Windows) that rapidly solves the most common types of power balance problems associated with single-traction tractors. The value of this software as a learning aid for students of agricultural engineering is discussed.
improving classroom teaching; simulations; teaching/learning strategies

1. Introduction

The use of new technologies in teaching has, currently, a very important place in the field of education [1]. Thanks to its use, it is possible to extend teaching to students regardless of their level of knowledge, schedule availability, location, or other limitations [2].
Agricultural Engineering university studies are undergoing great changes and improvements, thanks to the use of these technologies. Thus, it has been demonstrated the improvement in the development of agricultural engineering class sessions by using the flipped classroom learning environments (combination of the conventional and virtual teaching) [3]. Other authors proposed the implementation of distance-learning in engineering studies and use it for the benefit of increasing their attractiveness [4].
Among the important advances that have been made in the teaching of engineering and the use of new technologies [5], the following stand out: the tools for simulation of crop growing in agricultural [6], the use of devices to support the crop growing, advising, for example, the need of irrigation or fertilization [7,8], the use of supervisory control and data acquisition systems (SCADA) for the installation design, as drip irrigation systems [9], the use of software of finite element analysis (FEA) for the simulation of structural components of vehicles and machinery [10], the implementation of collaborative computer-aided engineering (CAE) for collaborative tools that allow the integration of different engineering specialties [11], the use of interactive exercises (using software), the use of information in real time, the use, by students, of graphics software for modelling or a virtual reality system for engineering laboratory education [12,13], the combining of attending theory classes with web-based learning and the use of software for solving problems (even in examination situations) [14,15], the use of tools for videoconferences between professors and students, also allowing group classes online [16,17], the use of augmented reality in order to improve the learning results [18,19], and the use of IT tools in the laboratory as a learning aid [20]. In addition, the use of web-based technologies, such as Moodle, as a support to traditional teaching [21,22].
In agricultural engineering, a main aspect is the knowledge of the machinery and equipment used [23]. The tractor is a vehicle widely used in several tasks. The technological development has also contributed to numerous advances in this type of agricultural vehicle [24], such as, for example, improvements in steering systems [25], the development of hybrid tractors [26], the use of algorithms and simulation to improve the suspension systems [27], or the use of a sensor to control the wear of some elements [28].
Therefore, during university studies in this specialty, students must acquire the knowledge for the design and calculation of the main parameters related with the tractors. One of these parameters is the engine power of an agricultural tractor [29]. One way to obtain the torque and power values of a tractor’s engine is through the use of a dynamometer [30]. Also, some software solutions have been developed for these and other types of calculations [31], and mobile applications, which are a complement to the study of tractors [32].
This work describes the development and use of software that students could use to check their manually-produced solutions to problems on the behaviour of single-traction tractors in the field. Tractors are used to perform jobs that require different types of agricultural tools to be attached to their rear, to their front, or both. These tools may need to be dragged, towed, or suspended above ground, and sometimes require a power supply; this is usually obtained via a hydraulic system or from the tractor’s power take-off system [33]. When tractors are working with such tools on different types of soils and on different slopes, the problem arises of calculating the power the tractor engine needs to produce [33,34]. Traditionally, such problems are tackled using the nomenclature described in Table 1 and the equations shown in Figure 1.
This calculation, therefore, demands knowledge of certain physical characteristics of the tractor–tool–soil system, and the resistance to movement caused by the weight supported by each of the tractor’s wheels. The software developed takes all these variables into account, and can determine the power required of the engine under a wide range of work conditions. The value of this software as a learning aid for students of agricultural engineering is discussed.

2. Materials and Methods

A series of algorithms was produced that contemplates the majority of power-requirement situations encountered in the normal use of single-traction tractors. These situations were catalogued by examining the questions given in exercises and examinations to students of agricultural engineering at the University of Almería, Spain, in recent years. These algorithms were incorporated into a program designed to calculate the power required of the tractor engine in different scenarios. The program was written in Delphi [35], a high level language, and compiled to generate a file executable under the Windows operating system. The flowchart of the program is showed in Figure 2, where each step of the diagram is explained below in this section.

2.1. Start-Up Window

On starting the program, a window (Figure 3) appears that allows the user to choose between two options: to begin work on a new problem, or to re-open a saved problem. If desired, the program can be exited at this point by pressing the EXIT button.

2.2. Soil Data

If the “new problem” option is chosen, a new window appears (Figure 4) for the incorporation of data pertaining to the soil over which the tractor must move (maximum specific resistance of the soil and slope). If these data are not required for the solution of the problem, values of 0 are entered. When all data have been entered, the ACCEPT button is pushed.

2.3. Data Pertaining to the Forward and Rear Wheels

Once the soil data are entered, a new window (Figure 5) appears for the introduction of data pertaining to the front wheels of the tractor (forward ballast, the coefficient of rolling resistance of the front wheels, and the front wheel radius). When this has been done, the ACCEPT button is pushed. A new window, similar to the last one, then opens for the rear wheel data to be introduced. The operator then proceeds as above.

2.4. Chassis and Engine Data

Once the above data have all been introduced, a new window (Figure 6) opens to allow the introduction of the chassis and engine data; this includes the distance between the axels, the tractor’s centre of gravity, its weight, the power loss in the hydraulic system, the power reserve of the engine, the mechanical yield of the transmission, power loss in the electrical system, the theoretical velocity of the tractor, the true velocity of the tractor, and coefficient of sliding friction. In the velocity section of the window, two or three of the requested values should be introduced. A value of 0 is introduced if the true value is unknown. The CALCULATE button is then pushed for the unknown value to be determined from the data provided. The ACCEPT button is then pushed. If the values introduced are clearly incorrect (e.g., a true velocity higher than the theoretical velocity has been entered) a message to this effect will appear. Any errors can then be corrected before pushing the ACCEPT button again.

2.5. Main Window

When the above step has been completed, a new window (Figure 7) appears. This allows the machinery (e.g., fertilizer spreader, drill, pulveriser, mould board plough, trailer, etc.) to be connected to the tractor (either at the front or rear) to be introduced.
A desired tool is selected from the options by clicking on it and dragging to the picture of the tractor. The virtual tractor can take a maximum of one suspended or dragged tool and two forward tools; no more can be added, but choices can be changed. To eliminate a tool, all that is required is to drag it from virtual tractor and drop it in the recycle bin. The tool selection options will then become active again. The data for the attached tool can then be introduced by clicking on the “TRACTOR COMPONENTS” dropbox and choosing the tool selected; a data input window will then appear. These data can be modified by performing the process again at any time.

2.6. Visualizing the Results

To see the results (Figure 8), the user only needs to do the following:
Press the CALCULATE button in the main window. A window will then appear with all the results.
Clicking on a result will cause a window to appear with the formula used to obtain that results. To move between the different types of result, the user need only click on the different tabs.
To return to the main window, the RETURN button is pushed.

2.7. Saving a Problem

Being able to save a problem means the user need not put in all the data every time he or she wishes to return to it. Saving requires the user to:
Push the SAVE option in the main window.
Choose a file name for the problem and press SAVE in the corresponding window.
If a filename is chosen that already exists, a message appears asking the user if the intention is to overwrite that file. The YES button should be chosen if this is the case, the NO button if it is not. The latter choice will allow the user to provide a new name for the problem to be saved.

2.8. Recovering a Saved Problem

If the program has just been opened, a window will appear asking whether the user would like to re-open a saved problem. If this is the case, the user should press ACCEPT, and choose the problem desired.
If the program is already running and the user wants to access a saved problem, the OPEN button in the main window should be clicked. The directory and appropriate file should be sought, and the problem opened by pressing the OPEN button. This will return the user to the main window, where all the data pertaining to that problem will appear in the correct place.

2.9. Printing Results

To print the results, the user should:
Press the PRINT button in the main window.

2.10. Evaluation of the Software

To evaluate the software developed, a survey has been carried out that includes eleven questions to be answered by a group of students (Table 2). Two questions are about the characteristics of the student and the other nine questions about the use of the software. A total of sixteen students of the Grade in Agricultural Engineering of the University of Almería (Spain) have carried out the survey.
The results of the survey have been statistically evaluated using the XLSTAT2018 software (Addinsoft, Paris, France).

3. Results and Discussion

The program developed, known as PTractor 1.1, quickly performs the tedious tasks required in the calculation of power balances in single-traction tractors. It could be used by engineers to rapidly assess the possibility of a tractor being able to perform a desired task, or to simulate small field trials, such as those performed with single-traction tractors by Evans, Clark & Manor [36], Shibusawa & Sasao [37], Sharma & Pandey [38]. and Raheman & Jha [39]. Perhaps more importantly, however, it has the potential to be used as a learning aid.
Generally, students in our department are first exposed to the basic concepts of the equations required to calculate power balances. They then have to use this new knowledge to work out some general problems. These calculations are performed manually with the aid of a calculator. Each question demands considerable time on the part of the student, who has to repeat the majority of the calculations made if any condition of the problem is changed. However, our students are allowed access to the program described, either individually or in groups. They can then check their calculations and easily modify variables to see what effect this has on the final result—something that should reinforce their understanding of the basic concepts they have been taught.
This type of software-supported learning for the verification of results has been tried in other areas of engineering by other authors [6,11].
Regarding the survey carried out for the students (Table 2), the statistical results obtained are showed in Table 3. Most students who have completed the questionnaires are under 21 (56.25%—T1). This means that almost half (43.75%—T2) are students who have repeated the course. Besides, male students are about twice that female students.
With respect the question about the software, the 93.75% of the users consider that the software is easy to use (“q3”) and fast (“q7”) but, at the same time, consider that it should be improved (“q4”). This may be due to the fact that the program has been used for more than ten years without having been updated with the new Windows environments; however, almost all students find it useful (“q5”).
In question 6, most students think that the software is appropriate to their knowledge; nevertheless, approximately 40% of the students think that it is not appropriate, percentage that coincides with the number of “T2” students. Also, this percentage coincides with the number of students that requires, sometimes, an explanation about the use of the software before using it (“q8”).
Near 70% of the students have found the software easy to navigate (“q9”) and around 80% think that the information is correct and updated (“q10”). All this is positive, but it should not lead the professors to conformism. Improvements would be necessary in the search for educational excellence. Regarding the overall evaluation of the software, the valuation has been medium-high (close to 90%; “q11”).
Finally, as limitations of the software it should be noted that it does not take into account the regenerative auxiliary power of the tractors [40], nor does it solve problems of tractors with double traction. These facts, together with the updating of the interface, advise an update of the PTractor Plus software.

4. Conclusions

The software could be of use to professional engineers when power balances need to be calculated, or for undertaking simulations in the laboratory. However, it may also have great potential as a learning aid for students in real or virtual classrooms.
Also, although the students value, in general, the software positively, a short/medium term update is necessary.

Author Contributions

All authors contributed equally to the manuscript, and have approved the final manuscript.


This research received no external funding.


To Raúl Aroca-Delgado and José-Antonio López-Martínez. Also to Research Plan of the University of Almería.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Mercader, C.; Sallan, J.G. How do university teachers use digital technologies in class? Rev. Docencia Univ. 2017, 15, 257–273. [Google Scholar] [CrossRef]
  2. Palkova, Z.; Vakhtina, E. Innovative learning: From multimedia to virtual worlds. In Proceedings of the 7th International Conference on Education and New Learning Technologies (EDULEARN), Barcelona, Spain, 6–8 July 2015; Gomez-Chova, L., Lopez-Martinez, A., Candel-Torres, I., Eds.; IATED: Valencia, Spain, 2015. [Google Scholar]
  3. Busato, P.; Berruto, R.; Zazueta, F.S.; Silva-Lugo, J. Student performance in conventional and flipped classroom learning environments. Appl. Eng. Agric. 2016, 32, 509–518. [Google Scholar] [CrossRef]
  4. Katzis, K.; Dimopoulos, C.; Meletiou-Mavrotheris, M.; Lasica, I.E. Engineering Attractiveness in the European Educational Environment: Can Distance Education Approaches Make a Difference? Educ. Sci. 2018, 8, 16. [Google Scholar] [CrossRef]
  5. Babich, A.; Mavrommatis, K.T. Teaching of complex technological processes using simulations. Int. J. Eng. Educ. 2009, 25, 209–220. [Google Scholar]
  6. Sanchez, J.A.; Perez, N.; Rodriguez, F.; Guzman, J.L.; Lopez, J.C. Decision support system for controlling the growth of greenhouse pepper crops base on climatic conditions. In Proceedings of the 7th Iberian Congress of Agricultural Engineering and Horticultural Sciences, Madrid, Spain, 26–29 August 2013; Tellez, F.A., Rodriguez, A.M., Sancho, I.M., Robinson, M.V., RuizAltisent, M., Ballesteros, F.R., Hernando, E.C.C., Eds.; The Universidad Politécnica de Madrid: Madrid, Spain, 2014. [Google Scholar]
  7. Perez-Castro, A.; Sanchez-Molina, J.A.; Castilla, M.; Sanchez-Moreno, J.; Moreno-Ubeda, J.C.; Magan, J.J. FertigUAL: A fertigation management app for greenhouse vegetable crops. Agric. Water Manag. 2017, 183, 186–193. [Google Scholar] [CrossRef]
  8. Marti, P.; Royuela, A. Practical sesion on the application of a robust mathematical tool for the calculation of crop water requirements in agricultural engineering. In Proceedings of the 6th International Conference on Education, Research and Innovation (ICERI), Seville, Spain, 18–20 November 2013; Chova, L.G., Martinez, A.L., Torres, I.C., Eds.; IATED: Valencia, Spain, 2013. [Google Scholar]
  9. Molina, J.M.; Ruiz-Canales, A.; Jimenez, M.; Soto, F.; Fernandez-Pacheco, D.G. SCADA Platform Combined with a Scale Model of Trickle Irrigation System for Agricultural Engineering Education. Comput. Appl. Eng. Educ. 2014, 22, 463–473. [Google Scholar] [CrossRef]
  10. Ahmad, F.; Kumar, A.; Kanwar, K.; Patil, P.P. CFX, Static Structural Analysis of Tractor Exhaust System Based on FEA. In Proceedings of the 28th International Conference on CAD/CAM, Robotics and Factories of the Future (CARs and FoF), Kolaghat, India, 6–9 January 2016; Mandal, D.K., Syan, C.S., Eds.; Springer: New Delhi, India, 2016. [Google Scholar]
  11. Sancibrian, R.; Llata, J.R.; Sarabia, E.G.; Torre-Ferrero, C.; Blanco, J.M.; San-Jose, J.T. Industry of the future: Implementation of collaborative CAE tools in industrial engineering degrees. In Proceedings of the 11th International Conference on Technology, Educarion and Development (INTED), Valencia, Spain, 6–8 March 2017; Chova, L.G., Martinez, A.L., Torres, I.C., Eds.; IATED: Valencia, Spain, 2017. [Google Scholar]
  12. Hashemipour, M.; Manesh, H.F.; Bal, M. A modular virtual reality system for engineering laboratory education. Comput. Appl. Eng. Educ. 2011, 19, 305–314. [Google Scholar] [CrossRef]
  13. Potkonjak, V.; Gardner, M.; Callaghan, V.; Mattila, P.; Guetl, C.; Petrovic, V.M.; Jovanovic, K. Virtual laboratories for education in science, technology, and engineering: A review. Comput. Educ. 2016, 95, 309–327. [Google Scholar] [CrossRef]
  14. Reimann, P.; Bull, S.; Halb, W.; Johnson, M. Design of a computer-assisted assessment system for classroom formative assessment. In Proceedings of the 14th International conference on Interactive Collaborative Learning (ICL), Piestany, Slovakia, 21–23 September 2011. [Google Scholar]
  15. Reimann, P.; Kickmeier-Rust, M.; Albert, D. Problem solving learning environments and assessment: A knowledge space theory approach. Comput. Educ. 2013, 64, 183–193. [Google Scholar] [CrossRef]
  16. Fita, A.; Monserrat, J.F.; Molto, G.; Mestre, E.M.; Rodriguez-Burruezo, A. Use of Synchronous e-Learning at University Degrees. Comput. Appl. Eng. Educ. 2016, 24, 982–993. [Google Scholar] [CrossRef]
  17. Gilarranz, C.; Olivares, J.; Munoz, M.A.; Lazaro, J.M.; Ramos-Paul, P. Collaborative tutorial groups in virtual environments with Moodle as a support. In Proceedings of the 7th Iberian Congress of Agricultural Engineering and Horticultural Sciences, Madrid, Spain, 26–29 August 2013; Tellez, F.A., Rodriguez, A.M., Sancho, I.M., Robinson, M.V., RuitzAltisent, M., Ballesteros, F.R., Hernando, E.C.C., Eds.; The Universidad Politécnica de Madrid: Madrid, Spain, 2014. [Google Scholar]
  18. Lytridis, C.; Tsinakos, A.; Kazanidis, I. ARTutor—An Augmented Reality Platform for Interactive Distance Learning. Educ. Sci. 2018, 8, 6. [Google Scholar] [CrossRef]
  19. Wei, X.D.; Weng, D.D.; Liu, Y.; Wang, Y.T. Teaching based on augmented reality for a technical creative design course. Comput. Educ. 2015, 81, 221–234. [Google Scholar] [CrossRef]
  20. Fabregas, E.; Farias, G.; Dormido-Canto, S.; Esquembre, F. Developing a remote laboratory for engineering education. Comput. Educ. 2011, 57, 1686–1697. [Google Scholar] [CrossRef]
  21. Djouad, T.; Mille, A. Observing and Understanding an On-Line Learning Activity: A Model-Based Approach for Activity Indicator Engineering. Technol. Knowl. Learn. 2018, 23, 41–64. [Google Scholar] [CrossRef]
  22. Rio, C.J.; Pastor, M.E.M.; Calle, R.C.; Robaina, N.F. Academic Performance in Higher Education and its Association to Active Participation in the Moodle Platform. Estudios Sobre Educ. 2018, 34, 177–198. [Google Scholar] [CrossRef]
  23. Kic, P. The course transport, handling and manipulation machinery in engineering education. In Proceedings of the 13th International Scientific Conference on Engineering for Rural Development, Jelgava, Latvia, 29–30 May 2014; Osadcuks, V., Ed.; Latvia University of Agriculture: Jelgava, Latvia, 2014. [Google Scholar]
  24. Daroczi, M.; Toth, R.; Molnar, C. The influence of Information Technology on Agricultural Machinery. In Proceedings of the 7th International Scientific Conference on Managerial Trends in the Development of Enterprises in Globalization Era (ICoM), Nitra, Slovakia, 1–2 June 2017; Kosiciarova, I., Kadekova, Z., Eds.; Slovak University of Agriculture: Nitra, Slovak, 2017. [Google Scholar]
  25. Yin, C.Q.; Sun, Q.; Wu, J.; Liu, C.Q.; Gao, J. Development of Electrohydraulic Steering Control System for Tractor Automatic Navigation. J. Electr. Comput. Eng. 2018, 2018, 5617253. [Google Scholar] [CrossRef]
  26. Lee, D.H.; Kim, Y.J.; Choi, C.H.; Chung, S.O.; Inoue, E.; Okayasu, T. Development of a Parallel Hybrid System for Agricultural Tractors. J. Fac. Agric. Kyushu Univ. 2017, 62, 137–144. [Google Scholar]
  27. Sim, K.; Lee, H.; Yoon, J.W.; Choi, C.; Hwang, S.H. Effectiveness evaluation of hydro-pneumatic and semi-active cab suspensión for the improvement of ride confort of agricultural tractors. J. Terramechanics 2017, 69, 23–32. [Google Scholar] [CrossRef]
  28. Castagnetti, D.; Bertacchini, A.; Spaggiari, A.; Lesnjanin, A.; Larcher, L.; Dragoni, E.; Arduini, M. A novel ball joint wear sensor for low-cost structural health monitoring of off-highway vehicles. Mech. Ind. 2015, 16, 507. [Google Scholar] [CrossRef]
  29. Rencin, L.; Polcar, A. Determination of the tractor engine power in the field conditions. In Proceedings of the 23rd International PhD Students Conference (MendelNet), Brno, Czech Republic, 9–10 November 2016; Polak, O., Cerkal, R., Belcredi, N.B., Horky, P., Vacek, P., Eds.; Mendel Univerzity in Brno: Brno, Czech Republic, 2016. [Google Scholar]
  30. De Farias, M.S.; Schlosser, J.F.; Estrada, J.S.; Frantz, U.G.; Rodriguez, F.A. Evaluation of new agricultural tractors engines by using a portable dynamometer. Cienc. Rural 2016, 46, 820–824. [Google Scholar] [CrossRef]
  31. Pereira-Marin, C.A.; Perez-Mendez, A.; Marin-Darias, D.; Gonzalez-Cueto, O. ExploMaq, software for energetic and economic evaluating of farming machine. Rev. Cienc. Técnicas Agropecu. 2015, 24, 72–76. [Google Scholar]
  32. Santos, F.L.; de Queiroz, D.M. Simtrac-an application for simulation of traction efficiency of agricultural tractors with front wheel assist. Acta Sci.-Technol. 2016, 38, 423–430. [Google Scholar] [CrossRef]
  33. March-Andreu, V.; Lozano-Terrazas, J.L. Mecanización Agraria. Maquinaria Agrícola y forestal; Llorens: Valencia, Spain, 2014; p. 318. ISBN 978-84-616-8005-4. [Google Scholar]
  34. Callejon-Ferre, A.J.; Lopez-Martinez, J.A.; Lopez-Perez, A.I. Manual de Ejercicios y Cuestiones de Clase de la Asignatura de Motores y Máquinas Agrícolas Adaptado al Espacio Europeo de Educación Superior; Universidad de Almería: Almería, Spain, 2008; pp. 1–190. ISBN 978-84-691-8107-2. [Google Scholar]
  35. Matcho, J. Using Delphi 2, Special Edition ed; Prentice Hall: Bergen, NJ, USA, 1996. [Google Scholar]
  36. Evans, M.D.; Clark, R.L.; Manor, G. An improved traction model for ballast selection. Trans. ASAE 1991, 34, 773–780. [Google Scholar] [CrossRef]
  37. Shibusawa, S.; Sasao, A. Traction data analysis with the traction prediction equation. J. Terramechanics 1996, 33, 21–28. [Google Scholar] [CrossRef]
  38. Sharma, A.K.; Pandey, K.P. Traction data analysis in reference to a unique zero condition. J. Terramechanics 1998, 35, 179–188. [Google Scholar] [CrossRef]
  39. Raheman, H.; Jha, S.K. Wheel slip measurement in 2WD tractor. J. Terramechanics 2007, 44, 89–94. [Google Scholar] [CrossRef]
  40. Huang, Y.J.; Khajepour, A.; Zhu, T.J.; Ding, H.T. A Supervisory Energy-Saving Controller for a Novel Anti-Idling System of Service Vehicles. IEEE/ASME Trans. Mechatron. 2017, 22, 1037–1046. [Google Scholar] [CrossRef]
Figure 1. Power balance for a single-traction tractor.
Figure 1. Power balance for a single-traction tractor.
Education 08 00068 g001
Figure 2. Program flowchart.
Figure 2. Program flowchart.
Education 08 00068 g002
Figure 3. Start-up window.
Figure 3. Start-up window.
Education 08 00068 g003
Figure 4. Soil data input window.
Figure 4. Soil data input window.
Education 08 00068 g004
Figure 5. Tractor wheel data input window; (a) Front wheel; (b) Rear wheel.
Figure 5. Tractor wheel data input window; (a) Front wheel; (b) Rear wheel.
Education 08 00068 g005
Figure 6. Chassis and engine data input window.
Figure 6. Chassis and engine data input window.
Education 08 00068 g006
Figure 7. Main window.
Figure 7. Main window.
Education 08 00068 g007
Figure 8. Results windows; (a) Calculated forces; (b) Force diagram; (c) Power scheme.
Figure 8. Results windows; (a) Calculated forces; (b) Force diagram; (c) Power scheme.
Education 08 00068 g008aEducation 08 00068 g008b
Table 1. Nomenclature used.
Table 1. Nomenclature used.
NeEffective power of the enginepPressure in the hydraulic system
NTPower transmittedqHydraulic flow
ηmMechanical yield of the transmissionηgOverall pump performance
Ns.e.Power consumed by the electrical systemTMoment of the power take-off system
NhPower consumed by the hydraulic systemωVelocity of power take-off
Nt.d.f.Power supplied by the power take-off systemUPeripheral forces in drive wheels
NUUsable powerRResistance to movement
NρPower losses in movementVtTheoretical velocity
NαPower associated with the slopeVRTrue velocity
NaPower associated with accelerationPTCross-sectional weight of the system
NσPower lost due to slidingFiInertia of acceleration
NZPower transferred to the towing barZForce associated with the towing bar
ηbBar yield (Z)
Table 2. Qualitative variables collected for each student.
Table 2. Qualitative variables collected for each student.
1. SexMaleMal
2. Age≤20 years oldT1
≥21 years oldT2
3. Is the interface easy to use?Yesq3y
Ever q3e
4. Would it improve the interface (titles, windows, menus, icons, buttons or figures)?Yesq4y
Ever q4e
5. Do you find useful the software to solve problems of power balances in tractors?Yesq5y
Ever q5e
6. Is the level of knowledge required to use the software in accordance with the academic level?Yesq6y
Ever q6e
7. Is the speed of response of the software correct? (user interaction and program)Yesq7y
Ever q7e
8. Do you think necessary previous explanations of the teacher in the classroom for the use of the software?Yesq8y
Ever q8e
9. Is it easy to navigate the program?Yesq9y
Ever q9e
10. Is the information correct and updated?Yesq10y
Ever q10e
11. Rate the software PTractor Plus 1.1. from 1 to 10:<5L
Table 3. Frequencies and modes for the qualitative variables by category.
Table 3. Frequencies and modes for the qualitative variables by category.
AgeT1 *956.25
Mal *1168.75
q3y *1593.75
q4y *743.75
q5y *1593.75
q6y *1062.50
q7y *1593.75
q8q8e *743.75
q9y *1168.75
q10y *1381.25
q11H *743.75
M *743.75
* Mode.

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Educ. Sci. EISSN 2227-7102 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top