Microsoft Word - 1-Agra_39712 1 Bioscience Journal Original Article Biosci. J., Uberlândia, v. 35, n. 1, p. 1-12 , Jan./Feb. 2019 http://dx.doi.org/10.14393/BJ-v35n1a2019-39712 SIMULATION OF THE LENGTH OF SUGARCANE (Saccharum spp.) CROP ROWS IN THE OPERATIONAL MANAGEMENT OF SPRAYING SIMULAÇÃO DO COMPRIMENTO DAS FILEIRAS DE CULTIVO DE CANA-DE- AÇÚCAR (Saccharum spp.) NO GERENCIAMENTO OPERACIONAL DA PULVERIZAÇÃO Neisvaldo Barbosa dos SANTOS 1 1. Professor Assistente B II de Mecânica e Mecanização Agrícola, do Campus Professora Cinobelina Elvas, da Universidade Federal do Piauí - UFPI, Brasil. Doutorando em Engenharia Agrícola (Engenharia de Sistemas Agrícolas) pela Universidade Federal do Ceará - UFC, Brasil. neisvaldo@gmail.com ABSTRACT: Brazil is the world’s largest producer of sugarcane destined to mills and has an average productivity higher than that of other producing countries of this raw material. The application of agricultural control products in sugarcane fields of agroindustries is carried out in extensive cultivation areas with hydraulic self-propelled, trailed, and aerial sprayers. For this, the systematization of cultivation areas, making longer the length of crop rows in the cultivation plots, is necessary to reduce the bedside maneuvers of machinery. This study aimed to assess the impact caused by the length of crop rows on the operational cost of the hydraulic sprayer used for sugarcane management practices. Due to the difficulty in performing this study and meeting the aim under field conditions, a computational model called TratoCana was developed in a spreadsheet and programming language. The model was verified for possible routine errors, validated, and used in the factor analysis and generation of scenarios. The results showed that the length of crop rows positively affects the operational and economic performance of the equipment. KEYWORDS: Agricultural mechanization. Hydraulic sprayers. Planning and management. Bedside maneuver. Computational model. Cost. INTRODUCTION In Brazil, sugarcane planted area for the 2017/2018 season is estimated at 8.74 million hectares, with a total production estimated at 635.60 million tons (CONAB, 2017). The mechanized spraying system of sugarcane mills has its operational and economic performance influenced by the systematization of the planted area. According to Santos and Gadanha Júnior (2016), the plots should be adjusted in a rectangular geometric shape or winding strip in order to increase the length of crop rows and reduce bedside maneuvers of the machinery. Because of this, the length of crop rows is directly related to the operational and economic performance of the machinery. For this, the management of agricultural machinery considers operational and economic performance. The operational performance consists of the variables operating speed, field efficiency, operational field capacity, worked hours, machine- hours, and number of equipment required. On the other hand, economic performance is formed by annual, hourly, and variable fixed costs (BALASTREIRE, 1990; HUNT, 1995; MIALHE, 1974). According to Santos et al. (2015b) apud Williams (2008), in the management for selecting agricultural machines, computational modeling has been adopted because it is a tool that simplifies the development of the proposed idea in to represent the structures and generate scenarios. In addition, according to Santos et al. (2015b) apud Oksanen (2007), a computational model of planning and management of agricultural machinery is developed with the aim of providing acceptable solutions to solve problems. In this context, Hansen et al. (2007) created a model with the aim of determining a standard route for bedside maneuvers in order to obtain the shortest total waiting time to perform them. Santos et al. (2014a) used the computational model “ColheCana” in order to assess field efficiency and cost of the sugarcane mechanized harvesting system. Mercante et al. (2010) elaborated the “software PRAPRAG” in a programming language to observe the cost of self-propelled sprayers. Buckmaster (2003) developed the model “TRACTOR COST” to determine the fuel costs by means of specific consumption of the agricultural tractor. Khoub Bakht et al. (2009), Rashidi and Received: 04/09/17 Accepted: 19/05/18 2 Simulation of the length… SANTOS, N. B. dos Biosci. J., Uberlândia, v. 35, n. 1, p. 1-12 , Jan./Feb. 2019 Ranjbar (2010), and Niari et al. (2012) developed models to analyze the participation of spare parts, salary of operator and mechanic, lubricants, oil filter, and fuel in the cost of repair and maintenance of agricultural tractors. Rohani et al. (2011) elaborated a model in order to solve problems in the cost with repair and maintenance of tractors. Considering the importance of the length of crop rows in the operational and economic performance of mechanized spraying systems, the aim of this study was to assess the impact caused by it on the operational cost of the hydraulic sprayer for sugarcane management practices. MATERIAL AND METHODS In this study, a model scenario was considered for a mill called Fictitious Mill, with its own area of 22,000 ha. Hydraulic self-propelled, trailed, and aerial sprayers were considered in the control product application system that serves the sugarcane cultivation of this mill. The equipment and economic, technical, and operational characteristics are shown in Table 1. Table 1. Economic, technical, and operational variables of the equipment. Variable Abbreviation Unit Self- propelled sprayer Tractor Sprayer Airplane Initial Value IV US$ 170,350 38,005 37,736 233,154 Rated Power Power kW/CV 147/200 74/100 - 238/324 Number of Spray Tips NST Number 56 - 49 42 Spacing between Tips ST cm 50 - 50 35.71 Total Tank Volume TTV L 3,000 - 3,000 950 Operating Speed OS km h−1 9.0 9.0 222.0 Bedside Maneuver Speed BMS km h−1 5.0 5.0 - Refueling Speed RS km h−1 20.0 20.0 - Transfer Speed from the Flight Track to the Field TSFTF km h−1 - - 280.0 To meet the objective, a computational model named TratoCana Version 2.0, which meets the basic characteristics of mechanized spraying for sugarcane cultivation, was developed. This model is based on a flowchart (Figure 1) constructed according to the symbology proposed by Oakland (2007). TratoCana Version 2.0 was developed in an Excel® spreadsheet and in Visual Basic® programming language. The model begins its operation (1)2 with the input of data from the crop (2), such as the area to be sprayed. Item (3) is the climate data input: total number of days to perform spraying, working day, relative air humidity, air temperature, and wind speed. Crop and climate data result in operational rhythm (4). 2 Numbers in parentheses refer to the flowchart of Figure 1. Data input (5) refers to technical/operational characteristics of terrestrial spraying: number of tips, spacing between tips, tip flow rate, refueling time, average distance between field and refueling pump, average length of cultivation stripes, operating speed, bedside maneuver speed, refueling speed, total volume of the sprayer tank, field efficiency, and others. Data input (6) refers to technical/operational characteristics of aerial spraying: number of tips, spacing between tips, tip flow rate, refueling time, average distance between field and refueling pump, average distance between flight track and field, average length of cultivation stripes, operating speed, transfer speed from the flight track to the field, effective strip width, time of each return curve, time in the ground between each flight, aerial application rate, total volume of the sprayer tank, field efficiency, and others. The operational rhythm, associated with technical/operational characteristics of spraying, determines the operational performance of sprayer3, tractor plus sprayer, and airplane (7): available time, operational field capacity, application volume, total application flow rate, total time of displacement and refueling, total traveled distance, machine-hours, and number of equipment required. 3 When the text refers only to sprayer it is because it is self- propelled type equipment. The results of operational performance, associated with the data of economic performance of machines (8): initial and final value, useful life in 3 Simulation of the length… SANTOS, N. B. dos Biosci. J., Uberlândia, v. 35, n. 1, p. 1-12 , Jan./Feb. 2019 years and hours, interest per year, housing, insurance and fees, fuel consumption, repair and maintenance factor, and others make it possible to calculate the economic performance (9), which refers to the cost per hour, area, and liter. The results of the model (10) allow the user to assess the operational and economic performance of the mechanized spraying and decide (11) on viability (12) or not. In case the spraying is not feasible for the user (13) or the user choose to assess another scenario, new data should be inserted. The numbers in parentheses refer to the flowchart of Figure 1. Figure 1. General flow chart of the computational model. Agroclimatic factors The factor climate of the mill was defined by the number of working days unsuitable for spraying (NWDUS) according to the methodological proposal of Santos (2017). The proposal considers agroclimatic parameters such as relative air humidity (RAH), wind speed (WS), and air temperature (AT). Mean values of agroclimatic parameters of the mill were considered in order to meet this proposal (Table 2). These data refer to Rio Largo, AL, for the year of 2014, and were gathered from the Agrometeorological Station of the Center of Agrarian Sciences of the Federal University of Alagoas (CECA/UFAL). The number of working days unsuitable for spraying (NWDUS) was considered in the available time (AT), according to the methodology of Mialhe (1974). The calculation of the available time (AT) was obtained by summing the number of Sundays and holidays (NSH) and number of working days unsuitable for spraying (NWDUS) then subtracting the total number of days (TN) and the values associated to the working hours (WH). 4 Simulation of the length… SANTOS, N. B. dos Biosci. J., Uberlândia, v. 35, n. 1, p. 1-12 , Jan./Feb. 2019 Table 2. Mean values of agroclimatic parameters. Parameter Abbreviation Unit Months of Control Product Application Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. Air Temperature AT °C 26.15 26.05 26.75 27.70 25.70 24.50 23.95 23.35 24.65 24.65 26.25 25.60 Relative Air Humidity RAH % 65.65 67.60 67.40 68.91 71.75 69.20 67.35 69.95 70.60 69.05 63.95 66.10 Wind Speed WS Km h−1 7.20 6.12 6.12 5.40 5.40 6.12 6.12 6.12 6.48 6.84 6.84 6.84 Source: CECA/UFAL Operational performance The operational performance of the set sprayer plus tractor and sprayer was based on the proposals of Mialhe (1974) and Santos et al. (2014b). These proposals define the number of equipment necessary to carry out control product application in the sugarcane cultivation of the mill. The number of machines (NM) was calculated by the ratio between the operational rhythm (OR) and operational field capacity (OFC) of the equipment. The operational rhythm (OR) was calculated by the ratio between the area to be sprayed (AS) and the available time to perform the agricultural operation (AT). The operational field capacity (OFC) was calculated by the association between the total boom width (TBW), operating speed (OS), and field efficiency (FE). The spray solution volume to be applied is in accordance with the proposals of Gadanha Júnior (2000) and Matuo et al. (2010). Application volume (AV) was calculated by the ratio between tip flow rate (TF), spacing between tips (ST) and operating speed (OS). The total application rate (TAR) was defined by the association between the application volume (AV) and operational field capacity (OFC). The travel time for refueling (TTR) is the round trip time to the field. The travel time was calculated by the ratio between the average distance between field and refueling pump (ADFRP) and refueling speed (RS). The total travel and refueling time (TTRT) refers to the time spent to travel to the control product tank, refueling in the tank, and the return travel to the field. The total time was calculated by the sum of the travel time for refueling (TTR) and refueling time (RT). The number of refueling (NR) was defined by the ratio of the application volume (AV), area to be sprayed (AS), and total tank volume (TTV) of the equipment. The bedside maneuver distance (MD) was determined by the maneuver turning radius (MTR). The bedside maneuver time (MT) was calculated by the ratio between the maneuver distance (MD) and maneuver speed (MS). The number of bedside maneuvers (NBM) was defined by the ratio between the area to be sprayed (AS), total boom width (TBW), and average length of cultivation stripes (ALCS). The operational performance of the airplane was also calculated according to the proposal of Mialhe (1974) and Santos et al. (2014b) to define the number of equipment required, as described for the sprayer and set tractor plus sprayer. The operational field capacity (OFC) of the airplane is in accordance with the adjusted proposal of Araújo (2009). It is calculated by the association of total volume of the sprayer tank (TVST), application volume (AV), distance from the flight track to the field (DFTF), travel speed from the flight track to the field (TSFTF), effective strip width (ESW), time of each return curve (TRC), average length of crop rows (ALCR), and time in the ground between each flight (TGF). Economic performance of the sprayer The total cost of the sprayer (TCS) was determined by the association between the operational cost of the sprayer (OCS) and the area to be sprayed (AS). The operational cost of the sprayer (OCS) was defined as the ratio between the hourly cost of the sprayer (HCS) and the operational field capacity (OFC). The operational application cost of the sprayer (OACS) was determined by the ratio between the hourly cost of the sprayer (HCS) and the total application rate (TAR). The hourly cost of the sprayer (HCS) was calculated by the sum of the hourly fixed cost of the sprayer (HFCS) and the variable cost of the sprayer (VCS). The hourly fixed cost of the sprayer (HFCS) was calculated according to the methodology proposed by ASABE (2011), being defined by the 5 Simulation of the length… SANTOS, N. B. dos Biosci. J., Uberlândia, v. 35, n. 1, p. 1-12 , Jan./Feb. 2019 ratio between annual fixed cost (AFC) and the number of hours worked per year (NHWY). The variable cost of the sprayer (VCS) was defined by the sum of the cost with fuel (CF) and the cost with repair and maintenance (CRM). The calculation of sprayer fuel consumption was adapted from Banchi et al. (2008). The adaptation occurred by the adoption of average consumption values per motor power range of agricultural tractors. The calculation of the cost with repair and maintenance (CRM) and factor of repair and maintenance (FRM) of the sprayer are in accordance with ASABE (2011). Economic performance of the set tractor plus sprayer The total (TCS), operational (OCS), operational application (OACS), and hourly (HCS) cost of the set4 were calculated in the same way as that of the sprayer. The hourly fixed cost of the set (HFCS) was calculated according to the methodology proposed by ASABE (2011), as the calculation of the hourly fixed cost of the sprayer. The variable cost of the set (VCS) was determined by the sum of the cost with the tractor fuel (CTF) and with repair and machine maintenance (CRM). The calculation of the tractor fuel consumption considered the average values of consumption by motor power range of agricultural tractors proposed by Banchi et al. (2008). The calculation of the cost with repair and maintenance (CRM) and factor of repair and maintenance (FRM) of the set are in accordance with ASABE (2011). Economic performance of the airplane The total (TCA), operational (OCA), operational application (OACA), and hourly (HCA) cost of the airplane were calculated in the same way as for the sprayer and set. The hourly fixed cost of the airplane (HFCA) was calculated according to the methodology proposed by ASABE (2011), as the calculation of the hourly fixed cost of the sprayer and set. The variable cost of the airplane (VCA) was determined by the sum of the cost with the airplane fuel (CAF) and with repair and maintenance (CRM). For the airplane fuel consumption, an 4 When only the word “set” is mentioned, it is a tractor and sprayer (trailed or mounted). average value was considered according to the best power to be used and a higher working regime, according to EMBRAER/NEIVA (2012). The calculation of the cost with repair and maintenance (CRM) of the airplane is in accordance with ASABE (2011). The factor of repair and maintenance (FRM) is in accordance with the data provided by PBA AVIATION (2012). Validation TratoCana Version 2.0 was validated by comparing simulation results with raw (primary) data obtained in the field and data from the bibliography (secondary). The sensitivity and consistency analysis of the computational model was performed by the cost. RESULTS AND DISCUSSION The average values of agroclimatic parameters of the mill, which is related to the agroclimatic conditions of Rio Largo, AL, in 2014, resulted in a number of working days unsuitable for spraying (NWDUS) of 257 days and available time (AT) of 2,583 hours. Figure 2 shows the data and input variables of the computational model in the spreadsheet. Figure 3 shows the data and output variables of the computational model in the spreadsheet. Figure 4 shows the data and input variables of the computational model in programming language. Figure 5 shows the data and output variables of the computational model in programming language. 6 Simulation of the length… SANTOS, N. B. dos Biosci. J., Uberlândia, v. 35, n. 1, p. 1-12 , Jan./Feb. 2019 Jan. Fev. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. Relative Air Humidity RAH % 65.65 67.60 67.40 68.91 71.75 69.20 67.35 69.95 70.60 69.05 63.95 66.10 Air Temperature AT C° 26.15 26.05 26.75 27.70 25.70 24.50 23.95 23.35 24.65 24.65 26.25 25.60 Wind Speed WS Km h -1 7.20 6.12 6.12 5.40 5.40 6.12 6.12 6.12 6.48 6.84 6.84 6.84 Σ % Number of Days ND Number 31 28 31 30 31 30 31 31 30 31 30 31 365 - Number of Sundays and Holidays NSH Number 0 0 0 0 0 0 0 0 0 0 0 0 0 - Number of Working Days Unsuitable for Spraying NWDUS Number 24 20 22 21 20 20 21 21 21 22 23 23 257 71 Working Hours WH h 24 24 24 24 24 24 24 24 24 24 24 24 24 - Available Time AT h 178.9 195.3 209.4 219.6 257.5 229.3 232.3 250.1 226.2 217.1 172.8 194.6 2,583 - Abbreviation Unit Mark the Month of Application by Pressing the Corresponding Button P la n n in g -C li m at e Output Months of Control Product Application Parameter Fev. Apr. May June July Aug. Sept. Oct. Nov.Mar.Jan. Dec. Uncheck Months Tractor Sprayer Airplane AT h AS ha ASP t ha -1 PTS US$ t -1 IV US$ 38,005 37,736 233,154 FV Decimal 0.20 0.15 0.40 ULY Year 10 8 20 ULH h 12,000 6,000 10,000 i Decimal 0.1350 0.1350 0.0350 HIF Decimal 0.02 0.02 0.04 FRM Decimal 1.00 0.70 0.98 FP US$ L -1 0.81 CF L h -1 - SPEED km h -1 5.0 20.0 222.0 FE Decimal - MP CV 324 NT Number 42 ST cm 35.71 TFR L min -1 2.00 RT min 15 ADFRP/ADFTF m/km 10 ALCR m BMTR m - TSFTF km h -1 280 ESW m 15 TRC s 40 TGF min 15 AAR L ha -1 15 TTV L 950 Time of each Return Curve Time in the Ground between each Flight Aerial Application Rate Total Tank Volume 20 0.32 E co no m ic 170,350 0.20 0.78 0.50 9.0 100 Climate Initial Value Final Value Variable Housing, Insurance, and Fees Factor of Repair and Maintenance Fuel Price (Diesel/Álcohol) Consumption Factor Operating/Maneuver/Refueling/Airplane Operating Speed Useful Life in Years Useful Life in Hours Interest per Year Available Time 22,000Sprayed Area Average Distance between Field and Refueling Pump/Average Distance between Flight Track and Field Number of Tips Spacing between Tips Tip Flow Rate Refueling Time 200 Transfer Speed from Flight Track to the Field Average Length of Crop Rows Motor Power of the Self-propelled Sprayer/Agricultural Tractor/Airplane 49 50 Item Abbreviation 0.1350 Unit 10 10,000 C ro p Average Sugarcane Productivity - Self-propelled Sprayer 1,500 0.8 80.00 19.01 50 0.32 500 56 0.8 P L A N N IN G T ec hn ic al C ha ra ct er is ti cs Bedside Maneuver Turning Radius 7.0 8.0 - - - - - - Price of the Ton of Sugarcane Field Efficiency 0.02 0.70 0.50 3,000 3,000 - - - - 2,583 Effective Strip Width Insert the Data of the Months of Pesticide Application Estimated Consumption (L h)Av erage Consumption Figure 2. Data and input variables of the computational model in spreadsheet. 7 Simulation of the length… SANTOS, N. B. dos Biosci. J., Uberlândia, v. 35, n. 1, p. 1-12 , Jan./Feb. 2019 Tractor Sprayer Airplane AT Day OR ha h -1 SP t TBW m 15.00 EFC ha h -1 332.96 OFC ha h -1 7.10 AV L ha -1 15.14 TAR L h -1 107 TTR min 4.29 TTRT min 19.29 NR Number 351 BMD m - BMT s - NBM Number 9,779 WH h 1,097 1,097 2,788 Machine Hours MH h year -1 1,097 1,097 1,394 Number of Machines NM Number 1 1 2 TDTF km 14,668 AFC US$ year -1 6,879 7,693 22,033 HFC US$ h -1 6.27 7.01 15.81 US$ year -1 3,040 4,009 6,995 US$ h -1 2.77 3.65 5.02 US$ year -1 3,078 2,929 5,712 US$ h -1 2.81 2.67 4.10 US$ year -1 760 755 9,326 US$ h -1 0.69 0.69 6.69 L ha -1 13.87 L h -1 98.40 CF US$ h -1 79.70 CRMH US$ h -1 3.17 4.40 22.85 CRMY US$ year -1 3,475 4,830 31,850 HC US$ h -1 14.10 11.41 118.36 OC US$ ha -1 16.68 AOC US$ L -1 1.10 TC US$ 367,003 GIM US$ NIM US$ 33,090,597 8,980 Annual Fixed Cost Hourly Fixed Cost Fuel Consumption Gross Income of the Mill Net Income of the Mill FC 0.07 33,457,600 33,396,524 33,425,777 5.98 4.66 1.45 0.03 31,823 13,798 0.73 11.53 0.34 11.92Cost with Repair and Maintenance per Hour Cost with Repair and Maintenance per Year Hourly Cost 948 61,076 HIF 3,407 3.59 R E S U L T S E co n om ic P er fo rm an ce O p er at io na l P er fo rm an ce Bedside Maneuver Time Bedside Maneuver Distance 16 21.99 Operational Cost Application Operational Cost Total Cost 2.78 14.55 14.78 14.37 AIT Total Boom Width Depreciation Annual Interest Housing, Insurance, and Fees Cost with Fuel Total Distance Traveled in the Field 948 1 7,857 30,833 32.51 DPA 13,628 11,308 55.97 Self-propelled Sprayer Sigla AbbreviationItem Variable Travel Time for Refueling 108 8.52 - 24.50 17.64 42.67 3.00 25.20 22.05 Available Time Operational Rhythm Effective Field Capacity Operational Field Capacity Application Volume Total Application Rate Number of Refueling Total Travel and Refueling Time Worked Hours A g ro n om y 1,760,000 753 23.00 860 Number of Bedside Maneuvers Sugarcane Production 313 28.00 20.16 42.67 5,238 5,986 313 18 25.13 Figure 3. Data and output variables of the computational model in spreadsheet. 8 Simulation of the length… SANTOS, N. B. dos Biosci. J., Uberlândia, v. 35, n. 1, p. 1-12 , Jan./Feb. 2019 Figure 4. Data and input variables of the computational model in programming language. Figure 5. Data and output variables of the computational model in programming language. 9 Simulation of the length… SANTOS, N. B. dos Biosci. J., Uberlândia, v. 35, n. 1, p. 1-12 , Jan./Feb. 2019 According to the results of a model scenario, the average length of crop rows influenced the operational cost of the sprayer (Figure 6). Figure 6. Operational cost of the sprayer and relative variation of the operational cost as a function of the average length of crop rows. Having as reference a length of 100 m, the variation in the operational cost in 500 m had a reduction of -20.75% and 15,714 bedside maneuvers, while in 1,000 m, it was of -22.49% and 7,857 maneuvers. For a length of 1,500 m, the variation in cost was -23.04%, with 5,238 maneuvers, while in 2,000 m, it was reduced in -23.31%, being necessary to perform 3,929 maneuvers. The average length of crop rows has a share in the operational cost of the set (Figure 7). Figure 7. Operational cost of the set and relative variation of the operational cost as a function of the average length of crop rows. Having as reference a length of 100 m, the variation in the operational cost in 500 m had a 10 Simulation of the length… SANTOS, N. B. dos Biosci. J., Uberlândia, v. 35, n. 1, p. 1-12 , Jan./Feb. 2019 reduction of -22.21%, requiring 17,959 bedside maneuvers, while in 1,000 m, this variation was -23.94%, with 8,980 maneuvers. For a length of 1,500 m, the variation in cost resulted in a reduction of -24.49%, with 5,986 maneuvers, while for 2,000 m, the variation was reduced by -24.76%, being required 4,490 maneuvers. The average length of crop rows interferes with the operational cost of the airplane (Figure 8). Figure 8. Operational cost of the airplane and relative variation of the operational cost as a function of the average length of crop rows. Having as reference a length of 100 m, the change in the operational cost in 500 m was reduced by -75.25%, with 29,337 bedside maneuvers, while in 1,000 m, this variation was reduced by -84.65%, being required 14,668 maneuvers. For a length of 1,500 m, the variation in cost resulted in a reduction of -87.50%, with 9,779 maneuvers to be performed, while in 2,000 m, this variation was reduced by -88.93%, with 7,334 maneuvers. According to the model scenario (Figures 6, 7, and 8), an average length of crop rows of 1,000, 1,500, and 2,000 m resulted in a reduction in the number of bedside maneuvers by 50.00, 66.67, and 75.00%, respectively. The increase in the average length of crop rows results in a polynomial reduction of the operational cost. However, this increase in length has a strong positive impact (variation) on the operational cost. According to Santos et al. (2015a), this is due to an increase in the productive time, worked time, and worked hours of machines. Therefore, according to Santos and Gadanha Júnior (2016), plots should be systematized with a rectangular geometric shape or a winding strip to obtain a long length of crop rows and reduce the number of maneuvers. CONCLUSIONS An increased length of crop rows is advantageous because it reduces bedside maneuvers and the operational cost of the machine. The mills must carry out the systematization of their areas to enable mechanized operations. RESUMO: O Brasil é o maior produtor mundial de cana-de-açúcar destinada às usinas e tem produtividade média, superior aos demais países produtores dessa matéria-prima. A aplicação de defensivos agrícolas em canaviais das agroindústrias é realizada em extensas áreas de cultivo, com pulverizadores hidráulicos autopropelidos, tratorizados e aéreo. Para tanto é necessário sistematizar as áreas de cultivo, tornando os talhões com longo comprimento de fileiras de cultivo, a fim de diminuir as manobras de cabeceira das máquinas. O objetivo do trabalho é avaliar o impacto causado pelo comprimento das fileiras de cultivo no custo operacional de pulverizadores hidráulicos para os tratos culturais de cana-de-açúcar. Pela dificuldade que existe em realizar o trabalho e atender o objetivo nas condições de campo, optou-se em desenvolver um modelo computacional denominado TratoCana, em planilha eletrônica e em linguagem de programação. O modelo foi 11 Simulation of the length… SANTOS, N. B. dos Biosci. 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