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Can combine headers and travel speeds affect the quality of soybean harvesting operations?/Plataformas de corte e velocidades podem afetar a qualidade da operacao de colheita de soja?

INTRODUCTION

Harvesting must be high-quality and efficient to ensure that investments during production are converted into profit, because this is the final agricultural operation in the field, and is conducted over a short period of time. Production losses often occur during this process (Loureiro Junior et al., 2014), and they may be affected by several characteristics of the harvester and the field conditions (Toledo et al., 2008).

Methods for monitoring losses in harvesting operations may generate data that help to reduce production losses (Chioderoli et al., 2012). Of these methods, statistical process control (SPC) has been used to monitor agricultural processes in mechanical soybean harvesting (Chioderoli et al., 2012; Compagnon et al., 2012; Loureiro Junior et al., 2014; Paixao et al., 2017), peanut sowing (Santos et al., 2017), and mechanical sugarcane fertilization (Carneiro et al., 2017).

However, few studies have applied SPC to monitor mechanical soybean harvesting losses as a function of combine header type because they may account for 80-85% of losses (Cunha & Zandbergen, 2007).

The area under soybean production has been increasing in Rondonia, and soybean is considered one of the main agricultural products of the state. Therefore, the quality of agricultural operations must be controlled to ensure increased efficiency and profitability. Because the use of the combine header and the harvesting speed may affect the quantity of operational losses, the aim of this study was to assess the quality of mechanical soybean harvesting in the Southern Cone of Rondonia, performed using two types of combine header and two speeds, using cutting height and harvesting losses as quality indicators.

MATERIAL AND METHODS

The experiment was conducted in the municipality of Cabixi, RO in an agricultural area located at the geographical coordinates 13[degrees] 27' S and 60[degrees] 41' W, during the 2015/16 harvest. The average altitude was 208 m, and the climate was defined as Am, according to the Koppen climate classification (Alvares et al., 2013). The soybean cultivar P98Y12 was planted in the experimental area and generated a mean crop yield of 3.781 kg ha-1.

Two Massey Ferguson combine harvesters, model MF 9790, with an axial flow threshing system, 261 kW (355 hp) engine power rating, and 10.570 L grain tank capacity, were used to monitor mechanical soybean harvesting losses. The combine harvesters were equipped with a 10.7 m (35 feet) powerflex draper header and a 9.1 m (30 feet) screw conveyor (auger). Two travel speeds were used, V1: 6 km [h.sup.-1] and V2: 8 km [h.sup.-1].

The study was designed according to the basic premises of SPC. Samples were collected over time at two points in the field, 100 m apart, at each passage of the combine harvester header, totaling 80 points (40 points for each machine). The following quality indicators were assessed in the field: cutting height, combine header, internal and total losses.

Four circular frames (A, B, C, and D), totaling an area of 1.0 [m.sup.2], were used to determine the harvesting losses. The circular frames were dropped to the ground immediately after the passage of the combine harvester header, positioned along the width of the combine harvester. Grains and pods found in the circular frames were collected; those found under the frame were considered header losses, and those found over the frame were considered internal losses. Total losses were calculated by adding the previous losses, disregarding natural losses, which were assessed at 10 points in an area of 2 [m.sup.2]. The effective cutting height was measured using a ruler.

The results were analyzed by descriptive statistics for general data analyses, and normality was assessed using the Ryan-Joiner test. Individuals and moving range control charts were used to assess the quality of the soybean harvest.

The mean losses in each circular frame (A, B, C, and D) were presented separately in the individual control charts for combine header and internal losses to assess the distribution of losses along the width of the combine header.

Specific upper and lower limits of 0 and 1.5%, respectively, were set in the total losses chart, based on a tolerable level of losses of 60 kg [ha.sup.-1] (EMBRAPA, 2017) and on the mean crop yield of the study area.

RESULTS AND DISCUSSION

Standard deviation (SD) and coefficient of variation (CV; Table 1) analysis shows the data dispersion or variability. According to the classification by Gomes & Garcia (2002), the CV of the present study was high, possibly due to the effects of harvester, climate, plant, and soil factors, which cause high variability during data collection. High CV values have been observed by other researchers (Campos et al., 2005; Holtz & Reis, 2013; Silva et al., 2013; Loureiro Junior et al., 2014; Cassia et al., 2015) and suggest the need for a classification that considers the specificities of the agricultural area, particularly with respect to agricultural mechanization.

The coefficients of skewness were moderate to high, according to Tavares et al. (2015), for all study variables except for cutting height with a draper header at a speed of 8 km [h.sup.-1]. Most quality indicators showed positive asymmetry (to the right), thus indicating data concentration at the lowest values. This type of asymmetry is interesting in the case of harvesting losses, because it indicates that, despite the asymmetry, the losses are concentrated in values close to and/or lower than the mean and are therefore acceptable.

Control chart analysis showed that the mean effective cutting height ranged from 8.15 to 9.06 cm (Figure 1), which is within the limits considered ideal for mechanical harvesting by cutting below the insertion of the first pod, which, in the present study, had a mean height of 10.5 cm. This mean height was lower than that observed by Ormond et al. (2015) at 10.84 cm, who used a combine header with a lower technological level (5 m, rigid header), demonstrating that the type of header used may affect the cutting height and, therefore, the operational quality.

This indicator indicated better process quality when using the draper header because, at both speeds, the draper header showed lower variability and higher stability. The screw conveyor presented the highest variability at a speed of 8 km [h.sup.-1], due to special causes. Soil irregularities occurred during the experiment, which complicated the monitoring of the combine header. In addition, the higher speed (8 km [h.sup.-1]), which requires a faster response time of the header sensors to monitor the terrain, may have contributed to this process instability, thereby adversely affecting the operational quality and increasing the level of losses.

Compagnon et al. (2012) reported that a deficiency in cutting height favored an increase in losses during mechanical soybean harvest. Toledo et al. (2008) stated that the cutting height of the header may directly affect mechanical harvesting losses.

The lowest average values of combine header losses and process variability were assessed when using the Draper header (Figure 2). The mean screw conveyor losses were 35% higher than the mean Draper header losses at a speed of 6 km [h.sup.-1] (V1) and 51% higher at a speed of 8 km [h.sup.-1] (V2). Therefore, the Draper header provides higher stability and quality to the harvesting process even when operating at higher speeds.

The higher quality of the Draper header was also observed by Gobbi et al. (2014), who noted that the Draper header decreased losses by 40% compared with the screw conveyor. According to Nietiedt et al. (2011), this decrease in harvesting losses may be explained by the absence of friction between the pods and the screw conveyor in Draper headers. This confirms that the harvesting quality of Draper headers is higher than that of screw conveyors.

The draper header also presented lower mean internal losses and process variability at both travel speeds compared with the screw conveyor (Figure 3). The best process quality was assessed at V1 (6 km [h.sup.-1]), with lower mean losses and without special causes.

Although an outlier was observed with the draper header at V2 (8 km [h.sup.-1]; observation number 16), this had no effect on process quality, because a lower SD was observed in this chart, and therefore, a shorter distance between the mean and upper control limits.

The better process quality observed with the Draper header, in terms of internal losses, may be attributed to the higher feed rate uniformity of this header. This results in lower mass flow fluctuations, which are processed by internal mechanisms and therefore, maintain better tracking conditions, causing lower loss variability (Gobbi et al., 2014).

Thus, as shown for internal losses, an outlier was also observed for total losses (Figure 4A, B), which may be explained as described above.

The total losses ranged from 1.8 to 4.4%, exceeding the upper control limit of 1.5%. This limit was set based on the tolerable level of losses, which, according to EMBRAPA (2017), is up to 60 kg [ha.sup.-1]. Considering this result, harvesting operations urgently require interventions to reduce losses and to improve the process.

This improvement may be achieved by reducing header losses because they account for 70-53% of the total losses from screw conveyor and draper headers, respectively. The higher losses observed with the screw conveyor reflect the higher variability in total losses when using this type of combine header (Figure 4A), which had points with high losses (higher than 5%) at both V1 and V2. Although header losses had a stronger impact, corrective measures to reduce losses in internal mechanisms cannot be disregarded because the mean internal losses were also higher than the specific limit of 1.5%.

The mean header losses assessed in each circular frame (Figure 5A) were higher and more variable in frame C with the screw conveyor, at both speeds. Outliers occurred in most frames when using this header, thus indicating the occurrence of special causes, which may be due to the way the material cut is transported to the center of the header. The contact between pods and the screw conveyor (auger) often causes grains to fall outside the header. In the present study, a large number of weeds was detected in the harvested area, which overloaded the material transported by the header, thereby causing packing owing to the way the material is transported by the screw conveyor. Transport of material by this type of header was not continuous, which may have caused the high variability in the distribution of losses.

Draper header losses were more uniform (Figure 5A). No outliers were found, thus showing process stability, and variability was lowest at V2. The results suggest that this type of header, even when working under adverse conditions, such as the presence of weeds and at high speeds, increases process stability and reduces losses, thereby improving the operational quality.

The internal losses in each circular frame (Figure 5B) varied with the combine header used. The highest header losses of the screw conveyor were concentrated in the center of the combine harvester, frames B and C, with 73% at V1 and 81% at V2. The draper header losses were distributed more evenly across the combine harvester width, that is, in the four circular frames (A, B, C, and D).

Data collection using circular frames to assess harvesting losses makes it possible to assess the distribution of grain losses across combine harvesters and to indirectly assess stalk distribution. This is because the behavior of both will be similar, thereby enabling losses and function to be monitored in order to adequately adjust the controls with the ultimate aim of improving the operational quality.

CONCLUSIONS

1. The draper header showed better harvesting quality with lower mean losses and process variability.

2. Travel speed had little effect on operational quality based on the quality indicators assessed.

3. The level of harvesting losses was higher than the acceptable limit, indicating the need for improved processes.

DOI: http://dx.doi.org/10.1590/1807-1929/agriambi.v22n10p732-738

LITERATURE CITED

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Campos, M. A. O.; Silva, R. P. da; Carvalho Filho, A.; Mesquita, H. C. B.; Zabani, S. Perdas na colheita mecanizada de soja no estado de Minas Gerais. Engenharia Agricola, v.25, p.207-213, 2005. https:// doi.org/10.1590/S0100-69162005000100023

Carneiro, F. M.; Furlani, C. E. A.; Ormond, A. T. S.; Kazama, E. H.; Silva, R. P. da. Mechanized fertilization: Individual application of nitrogen, phosphorus and potassium in sugarcane. Revista Ciencia Agronomica, v.48, p.278-287, 2017. https://doi.org/10.5935/1806-6690.20170032

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EMBRAPA--Empresa Brasileira de Pesquisa Agropecuaria. Perdas na colheita de soja. 2017. Disponivel em: <http://blogs.canalrural. com.br/embrapasoja/2017/01/27/perdas-na-colheita-de-soja/>. Acesso em: Jun. 2017.

Gobbi, F. T.; Zandonadi, R. S.; Pinto, F. de A. Desempenho de colhedoras de graos utilizando plataforma de corte com condutor helicoidal e esteira transportadora. In: Congresso Brasileiro de Engenharia Agricola, 43, 2014, Campo Grande. Anais ... Campo Grande: SBEA, 2014.

Gomes, F. P.; Garcia, C. H. Estatistica aplicada a experimentos agronomicos e florestais: Exposicao com exemplos e orientacoes para uso de aplicativos. Piracicaba: FEALQ, 2002. 309p.

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Paixao, C. S. S.; Chrispin, C. P.; Silva, R. P.; Girio, L. A. S.; Voltarelli, M. A. Physical and physiological quality of soybean seeds at three speeds of the harvester. Revista Brasileira de Engenharia Agricola e Ambiental, v.21, p.214-218, 2017. https://doi.org/10.1590/1807-1929/agriambi.v21n3p214-218

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Toledo, A. de; Tabile, R. A.; Silva, R. P. da; Furlani, C. E. A.; Magalhaes, S. C.; Costa, B. O. Caracterizacao das perdas e distribuicao de cobertura vegetal em colheita mecanizada de soja. Engenharia Agricola, v.28, p.710-719, 2008. https://doi.org/10.1590/S0100-69162008000400011

Patricia C. de Menezes (1), Rouverson P. da Silva (2), Franciele M. Carneiro (2), Lucas A. da S. Girio (2), Mailson F. de Oliveira (2) & Murilo A. Voltarelli (3)

(1) Instituto Federal de Educacao, Ciencia e Tecnologia de Rondonia. Colorado do Oeste, RO. E-mail: patricia.menezes@ifro.edu.br (Corresponding author)--ORCID: 0000-0002-5570-0405

(2) Universidade Estadual Paulista/Faculdade de Ciencias Agrarias e Veterinarias/Departamento de Engenharia Rural. Jaboticabal, SP. E-mail: rouverson@gmail.com--ORCID: 0000-0001-8852-2548; franmorlin1@gmail.com--ORCID: 0000-0003-0117-7468; lucas_girio@hotmail.com ORCID: 0000-0002-2383-9779; mailsonagronomia@gmail.com--ORCID: 0000-0003-4771-0424

(3) Universidade Federal de Sao Carlos/Centro de Ciencias da Natureza/Campus Lagoa do Sino. Buri, SP. E-mail: voltarelli.ufscar@gmail.com--ORCID: 0000-0002-3774-1705

Ref. 187040-Received 25 Oct, 2017 * Accepted 27 Jun, 2018 * Published 26 Aug, 2018

Caption: Figure 1. Individual (A) and moving range (B) control charts for cutting height as a function of combine header and speed

Caption: Figure 2. Individual (A) and moving range (B) control charts for header losses as a function of combine header and speed

Caption: Figure 3. Individual (A) and moving range (B) control charts for internal losses as a function of combine header and speed

Caption: Figure 4. Individual (A) and moving range (B) control charts for total losses as a function of combine header and speed

Caption: Figure 5. Individual control charts for header (A) and internal (B) losses as a function of the type of combine header and speed in circular frames A, B, C, and D
Table 1. Descriptive statistics for cutting height, header, and
internal and total losses as a function of combine header and
travel speed

Quality               Header              S           M     Med
indicators                         (km [h.sup.-1])

Header losses (%)

Header losses     Screw conveyor          I          3.43   3.05
                                         II          2.17   1.83

                  Draper header           I          1.82   1.30
                                         II          1.23   0.99
Internal losses (%)

Internal losses   Screw conveyor          I          0.95   0.60
                                         II          1.48   1.13

                  Draper header           I          0.82   0.73
                                         II          1.91   0.59
Total losses (%)

Total losses      Screw conveyor          I          4.38   3.59
                                         II          3.65   3.19

                  Draper header           I          2.64   2.28
                                         II          3.15   1.50
Cutting height (cm)

Cutting height    Screw conveyor          I          9.08   8.61
                                         II          9.77   9.33

                  Draper header           I          8.78   8.42
                                         II          8.15   8.08

Quality             CV     [sigma]    Cs      Ck      RJ         P
indicators

Header losses (%)

Header losses     77.98     2.70     1.32    1.11    0.919   <0.01 (A)
                  68.15     1.48     1.42    1.97    0.932    0.02 (A)

                   69.59    1.27     0.92    -0.59   0.926   <0.01 (A)
                  105.06    1.30     3.38    13.17   0.760   <0.01 (A)

Internal losses (%)

Internal losses   88.69     0.84     1.55    1.48    0.882   <0.01 (A)
                  62.08     0.92     1.19    1.75    0.943    0.04 (A)

                   63.22    0.52     1.65    3.39    0.921   <0.01 (A)
                  181.43    3.47     2.97    9.28    0.709   <0.01 (A)
Total losses (%)

Total losses      68.70     3.01     1.18    1.02    0.936    0.02 (A)
                  54.26     1.98     1.55    2.76    0.911   <0.01 (A)

                  51.89     1.37     0.95    0.39    0.961   >0.10 (N)
                  122.40    3.85     2.53    7.03    0.789   <0.01 (A)

Cutting height (cm)

Cutting height    22.14     2.01     2.51    8.25    0.860   <0.01 (A)
                  29.89     2.92     1.45    2.10    0.928   <0.01 (A)

                  14.23     1.25      1.07    0.91   0.954    0.07 (A)
                  12.14     0.99     -0.02   -0.23   0.987   >0.10 (N)

S - Speed; M - Arithmetic mean of the sample; Med - Median;
CV - Coefficient of variation; [sigma] - Standard deviation;
Cs - Coefficient of skewness; Ck - Coefficient of kurtosis;
RJ - Ryan-Joiner test for normality; P - Probability value according to
the Ryan-Joiner test (N Normal distribution; A Non-normal distribution
with p < 0.10)
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Author:Menezes, Patricia C. de; Silva, Rouverson P. da; Carneiro, Franciele M.; Girio, Lucas A. da S.; Oliv
Publication:Revista Brasileira de Engenharia Agricola e Ambiental
Date:Oct 1, 2018
Words:3405
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