domain: sdv sde

text:
Hamza Mohieddinne
email: hamzemohieddin@gmail.com

Andrii Yatskul

Carolina Ugarte

Julien Thibaut

Julien Guidet

Simon Ritz

Trade-off between agronomical and energetical performances during barley sowing varying adjustable parameters in a tractor-tire-tool system

Keywords: seedbed soil conservation, crop seedling and yield, fuel consumption, performance optimization, tractor-tire-tool system, adjustable parameters

Soil compaction on seedbed, resulted from the traffic of a tractor-tire-tool system, alters soil structure and harmfully impacts crop production. Soil conservation during sowing operations and the optimization of energy consumption of tractor-tire-tool system, form the focus of the global agriculture policies and farmers under the energetical and the agroecological transitions.

The present study aims to find a trade-off between the agronomical and the energetical performances, during a sowing operation of spring barley (Hordeum vulgare), varying adjustable parameters (controlled factors) in tractor-tire-tool system (i.e. tractor speed, axle load distribution, inflation pressure and soil working depth). The adjustable parameters have been chosen as to be adjustable by a regular tractor operator. The study was carried out on a silty loam soil (with an average soil gravimetric water content of 23.4%) located in Northern-France.

The used tractor-tire-tool system consisted of a Four-Wheel Drive tractor (140 HP of power) equipped with 480/70 R28 front tires and 580/70 R38 rear tires, and hitched to an air seeder.

Agronomical performance is evaluated via the conservation of soil physical properties, and via the success of the sowing operation. The energetical performance is evaluated via the optimization of energy consumption. The conservation of soil physical properties was assessed via soil bulk density (BD) and air permeability (ka). The success of the sowing operation was assessed via barley seedling, counting the emerged plants at one to two leaves stage, and crop yield. Energy consumption was assessed via the consumption of fuel per hectare. BD and ka were negatively correlated (ρ=-0.56 and p-value<0.001), but ka was significantly more influenced by the controlled factors and their combinations showing higher sensitivity of pore continuity to the structural disturbance than pore space. Crop seedling and yield were slightly influenced by the variation of the controlled factors and their combinations. All these agronomical indicators depended significantly on the combination between the four factors showed as single treatment. Fuel consumption decreased significantly with the equal load distribution and increased with tractor speed and soil working depth solely. Nonetheless, it showed higher dependence on the combinations between the factors. As a conclusion, soil physical properties could be conserved, better barley seedling and yield could be obtained, and energy consumption could be optimized by equally distributing the axle load, accelerating tractor at 14 km/h, working the soil at 10 cm of depth and inflating tires at 140 or 80 kPa of inflation pressure.

Introduction

A successful agricultural operation is the one that prevents soil damage and energy waste and that allows a sustainable consumption agronomical production [START_REF] Botta | Traffic alternatives for harvesting soybean (Glycine max L.): Effect on yields and soil under a direct sowing system[END_REF][START_REF] Naujokienė | Using a Soil Bioregeneration Approach to Reduce Soil Compaction and Financial Costs of Planting Winter Wheat and Rapeseed[END_REF]. For sowing operations, it is essential to have a suitable seedbed for the germination of grains and to obtain the needed crop yield [START_REF] Barnhisel | Coal Surface Mine Reclamation in the Eastern United States: The Revegetation of Disturbed Lands to Hayland/Pasture or Cropland[END_REF][START_REF] John | Principles of Seedbed Preparation for Conservation Seedings (Technical note No. 13[END_REF].

The agriculture machinery and its connected tools increased in weight and size to meet the massive requirement of agricultural operations, from sowing to the harvesting of different crops (Keller and Or, 2022). The barley sowing occurs usually in mildly wet conditions of soil, which has the potential to increase soil damage and energy waste [START_REF] Asinyetogha | Predicting tractor fuel consumption during ridging on a sandy loam soil in a humid tropical climate[END_REF][START_REF] Reintam | Soil compaction effects on soil bulk density and penetration resistance and growth of spring barley (Hordeum vulgare L.)[END_REF]. The traffic of a tractor-tire-tool system upon the soil in such wet soil conditions, applies a mechanical stress to the soil and results in serious soil degradation by compaction which stands behind the degradation of 23% of arable lands [START_REF] Bai | Global Assessment of Land Degradation and Improvement 1[END_REF]Hillel, 2003). The well aerated soil structure undergoes a deformation when the mechanical stress exceeds its bearing capacity, which is a compression of the biopores, shearing of pores' continuity and change of pores' size distribution (Keller et al., 2022). This deformation is translated directly by the increase of soil penetration resistance, bulk density and shear strength [START_REF] Alblas | Impact of traffic-induced compaction of sandy soils on the yield of silage maize in The Netherlands[END_REF][START_REF] Forster | Root traits effect on soil shear strenght to mitigate traffic impacts on soil hydrophysical properties in agroecosystems[END_REF][START_REF] Raper | Agricultural traffic impacts on soil[END_REF]. The compaction results in a reduction of mineralization of organic matters, resource availability for plants, soil permeability, germination ability of grains, and root development and penetrability [START_REF] Forster | Relationships between Root Traits and Soil Physical Properties after Field Traffic from the Perspective of Soil Compaction Mitigation[END_REF][START_REF] Forster | Root traits effect on soil shear strenght to mitigate traffic impacts on soil hydrophysical properties in agroecosystems[END_REF][START_REF] Kuncoro | A study on the effect of compaction on transport properties of soil gas and water I: Relative gas diffusivity, air permeability, and saturated hydraulic conductivity[END_REF].

Topsoil compaction influences soil hydric states leading to reduce tillage performance and the effectiveness of fertilizers amendment, then indirectly it leads to a loss in crop yield and cultivatable areas, then creates a significant cost for farmers and society [START_REF] Batey | Soil compaction and soil managementa review[END_REF][START_REF] Håkansson | Protection of soils from mechanical overloading by establishing limits for stresses caused by heavy vehicles[END_REF]Keller et al., 2022).

The interaction between topsoil and tire causes a stochastic tire deflection and soil deformation, which wastes up to 55% of the tractor available energy [START_REF] Janulevičius | How to select air pressures in the tires of MFWD (mechanical front-wheel drive) tractor to minimize fuel consumption for the case of reasonable wheel slip[END_REF][START_REF] Pranav | Computer simulation of ballast management for agricultural tractors[END_REF]. The achievable energy optimization is obtained when all the energy from fuel combustion are used in the drawbar work of tractor without soil damage [START_REF] Grisso | PREDICTING TRACTOR FUEL CONSUMPTION[END_REF]. In fact, manufacturers of machinery and tires worked to develop new technologies to improve the vibration process and stress distribution in the soil, and reduce tire defection [START_REF] Kising | Dynamic characteristics of large tyres[END_REF][START_REF] Schjønning | Rules of thumb for minimizing subsoil compaction[END_REF]. Even though new tires could carry heavier load with lower inflation pressure, tire innovation and development could not overcome the heavy impact of machinery traffic in agricultural operations (Comparetti et al., 2019;[START_REF] Ten Damme | The contribution of tyre evolution to the reduction of soil compaction risks[END_REF]. Therefore, practices focusing on the interaction between soil and the tractor-tire-tool system could play a significant role to improve soil-tractor-tire-tool interaction and contribute to mitigate soil compaction and energy waste. The previous studies show that the operator can improve this interaction by some adjustable parameters on its tractor-tire-tool system, where some settings are reviewed, such as the adjustment of tractor speed, tires and ballast, as well as soil tillage methods and working depth [START_REF] Douglas | Soil and crop responses to conventional, reduced ground pressure and zero traffic systems for grass silage production[END_REF][START_REF] Janulevičius | How to select air pressures in the tires of MFWD (mechanical front-wheel drive) tractor to minimize fuel consumption for the case of reasonable wheel slip[END_REF][START_REF] Spagnolo | Fuel consumption of a tractor in function of wear, of ballasting and tire inflation pressure[END_REF][START_REF] Tayel | Effect of plowing conditions on the tractor wheel slippage and fuel consumption in sandy soil[END_REF]. Most of studies focused on tire adjustment as stress transmitter, when the primary focus is to widen the soil-tire contact area which affects the rolling resistance, which in turn has high impact on fuel consumption [START_REF] Taghavifar | Contact area determination of agricultural tractor wheel with soil[END_REF]. There is some available methods that provide wider contact area between soil and tire [START_REF] Raper | Agricultural traffic impacts on soil[END_REF]. Wider contact area can be obtained using dual tires, but it leads to impact a wider surface, then better results can be obtained lowering the inflation pressure of single tires [START_REF] Raper | Agricultural traffic impacts on soil[END_REF][START_REF] Schwanghart | Measurement of contact area, contact pressure and compaction under tires in soft soil[END_REF]. One of the effective methods to widen this contact area is the use of rubber tracks instead of tires, which results in a lower compression but can lead to a higher shear of soil [START_REF] Blunden | Compaction of an earthy sand by rubber tracked and tired vehicles[END_REF][START_REF] Lamandé | Risk assessment of soil compaction in Europe -Rubber tracks or wheels on machinery[END_REF].

Low-corrected inflation pressure can reduce the compaction severity, increase traction efficiency and decrease fuel consumption [START_REF] Antille | Soil Displacement and Soil Bulk Density Changes as Affected by Tire Size[END_REF][START_REF] Battiato | Influence of Tyre Inflation Pressure and Wheel Load on the Traction Performance of a 65 kW MFWD Tractor on a Cohesive Soil[END_REF][START_REF] D L Antille | The Effects of Tyre Size on Soil Deformation and Soil Bulk Density Changes[END_REF][START_REF] Douglas | Soil and crop responses to conventional, reduced ground pressure and zero traffic systems for grass silage production[END_REF][START_REF] Schwanghart | Measurement of contact area, contact pressure and compaction under tires in soft soil[END_REF][START_REF] Udompetaikul | The Effect of Tire Inflation Pressure on Fuel Consumption of an Agricultural Tractor Operating on Paved Roads[END_REF].

However, an over-inflation of tires can result in a ballooning effect, reducing energy waste by the reduction of tire deflection [START_REF] Taghavifar | Multi-criteria optimization model to investigate the energy waste of off-road vehicles utilizing soil bin facility[END_REF]. Ground pressure is the principal element controlling the severity of topsoil compaction [START_REF] Raper | Agricultural traffic impacts on soil[END_REF]. During tractor-pull operations, like sowing, an appropriate adjustment of tire load and pressure is essential to improve tractor traction through the diminution of tire slippage and thereby reduce soil compaction and energy waste [START_REF] Davies | Relative Effects of Tractor Weight and Wheel-Slip in Causing Soil Compaction[END_REF][START_REF] Janulevičius | How to select air pressures in the tires of MFWD (mechanical front-wheel drive) tractor to minimize fuel consumption for the case of reasonable wheel slip[END_REF]. This improvement is usually performed by configuration of load distribution of ballast between rear and front tires [START_REF] Janulevičius | How to select air pressures in the tires of MFWD (mechanical front-wheel drive) tractor to minimize fuel consumption for the case of reasonable wheel slip[END_REF][START_REF] Mamkagh | Effect of Tillage Speed, Depth, Ballast Weight and Tire Inflation Pressure on the Fuel Consumption of the Agricultural Tractor: A Review[END_REF][START_REF] Pranav | Computer simulation of ballast management for agricultural tractors[END_REF]. Wheels are used to be loaded by 55-65% of total axle load in static conditions, and an overloading ballast increases rolling resistance and soil compaction which, also, waste the energy [START_REF] Damanauskas | Influence of Extra Weight and Tire Pressure on Fuel Consumption at Normal Tractor Slippage[END_REF][START_REF] Janulevičius | How to select air pressures in the tires of MFWD (mechanical front-wheel drive) tractor to minimize fuel consumption for the case of reasonable wheel slip[END_REF][START_REF] Pranav | Computer simulation of ballast management for agricultural tractors[END_REF]. Soil compaction occurs under a process of vibration of the mechanical pressure. The increase of tractor speed decreases the hold-time of this process and take up more power at lower slip, and therefore may provoke a lesser compaction to the soil [START_REF] Al Aridhee | Influence of tractor slip on some physical properties of the soil and fuel consumption at varying tillage depths and speed[END_REF][START_REF] Davies | Relative Effects of Tractor Weight and Wheel-Slip in Causing Soil Compaction[END_REF]. On the other hand, consumption of fuel increases with the increase of speed leading to use higher energy for the motion of tractor and decreasing the traction efficiency (Adewoyin and Ajav, 2013;[START_REF] Taghavifar | Multi-criteria optimization model to investigate the energy waste of off-road vehicles utilizing soil bin facility[END_REF]. The optimization of an agricultural operation take into account the duration to perform the operation. Thus, an optimal operation is the one does not waste time also. Accordingly, it should find a compromise between saving time and fuel to a better energetical performance depending on the soil conditions. For instance, an increase of traffic speed could achieve the operation in a reasonable duration and result in an acceptable compaction within a conventional system, when the soil has lower bulk density, but at the same time it could result higher energy waste [START_REF] Stafford | The effect of forward speed on wheel-induced soil compaction: Laboratory simulation and field experiments[END_REF]. Except of the no-till system, the farmers work their soils to prepare the seedbed for the sowing. Therefore, to reduce soil degradation and energy waste, the operator can play on the tillage or soil working depth. Soil tillage or working before sowing softens the compacted soil by decompression and cracking of compacted blocs [START_REF] Parkhomenko | The tillage effect on the change of soil structure[END_REF][START_REF] Zimdahl | Chapter 10 -Methods of Weed Management[END_REF].

Primary tillage is one of the effective practices to prepare the seedbed increasing structural macro-porosity and breaking up compacted soil, but a significant soil compaction could be generated following a secondary tillage over a freshly tilled soil [START_REF] Botta | Seedbed compaction produced by traffic on four tillage regimes in the rolling Pampas of Argentina[END_REF][START_REF] Zimdahl | Chapter 10 -Methods of Weed Management[END_REF]. However, it is one of the highest energy consuming between the agricultural operations [START_REF] Badalíková | Influence of Soil Tillage on Soil Compaction[END_REF][START_REF] Moitzi | Energy consumption in cultivating and ploughing with traction improvement system and consideration of the rear furrow wheel-load in ploughing[END_REF]. The effect of tillage, and its different methods and practices, is vastly arguable, but it depends always on soil and climatic conditions [START_REF] Badalíková | Influence of Soil Tillage on Soil Compaction[END_REF][START_REF] Bogunovic | Tillage management impacts on soil compaction, erosion and crop yield in Stagnosols (Croatia)[END_REF]. For example, an increase of tillage depth can positively affect crop yield but negatively affect soil structure, as well as it requires higher energy supply [START_REF] Arvidsson | Working depth in non-inversion tillage-Effects on soil physical properties and crop yield in Swedish field experiments[END_REF]. Even though, the conventional tillage using mouldboard ploughing reduces soil bulk density and results in a higher crop yield, it can increase compaction and erosion risk and decrease soil bioactivity [START_REF] Arvidsson | Working depth in non-inversion tillage-Effects on soil physical properties and crop yield in Swedish field experiments[END_REF][START_REF] Badalíková | Influence of Soil Tillage on Soil Compaction[END_REF][START_REF] Bogunovic | Tillage management impacts on soil compaction, erosion and crop yield in Stagnosols (Croatia)[END_REF].

To the best of our knowledge, the combination between all the adjustable settings to reduce soil compaction and optimize energy consumption is not documented. These settings are usually setup together, as single combination, but the previous researches focus on the closest settings only, such as tire inflation pressure and ballasting or tire inflation pressure and tractor speed etc. Energetic and agronomic performance are rarely treated as a single target of a successful agricultural operation. The objective of the present study is to examine the possibility of conciliating the optimization of energetic performance, via the reduction of fuel consumption, and the optimization of agronomic performance, via the conservation of soil physical properties on seedbed and a best crop production. The aim of this study is to find the best combinations consisted of adjustable parameters (i.e. tire inflation pressure, tractor speed, axle load distribution and soil working depth) to present the trade-off between the energetical and agronomical performances.

Materials and methods

Study site and experimental design

The study was conducted on an agriculture parcel of 1.5 ha of flat surface, located in Milly-sur-Thérain, Northern-France (49°29'48.6"N 2°01'10.3"E). The climate is sub-oceanic with average of annual precipitation of 749 mm and average annual temperature of 10.8 °C (Climate-Data.org). The soil is a Luvisol (IUSS Working Group WRB, 2015) of silty loam texture, with an average of clay content of 167 g/Kg, an average of organic matter content of 17.5 g/Kg, an average of cation exchange capacity (CEC) of 8.93 cmol+/Kg and an average of pH (in distilled water) of 7.9. The parcel is used for cereal cultivation: before the barley (Hordeum vulgare), the parcel was cultivated in wheat (Triticum turgidum L. var. durum). The two cereal crops were separated by Brassicaceae cover crop of white mustard (Sinapis alba). The parcel was stubbled then soil was tilled up to 30 cm and shallow tilled up to 5 cm just before sowing.

The used tractor-tire-tool system consisted of a Four-Wheel Drive tractor of 140 HP (Massey-Ferguson 6714 S) equipped by Michelin Multibib 480/70 R28 and 580/70 R38 tires (front and rear tires respectively), and hitched to a towed mini-till air-seeder fitted with a disc harrow concave scalloped discs (Kuhn Espro 3000). The adjustable parameters chosen as controlled factor were: tractor speed (three levels: 10, 12 and 14 Km/h), inflation pressure (three levels: 80, 100 and 140 kPa equivalent to 0.8, 1 and 1.4 bar), soil working depth (two levels: 5 and 10 cm) and the static axle load distribution (two levels: 60%/40% and 50%/50% on rear and front axles respectively). The total load of the whole system within unequal axle load distribution was 9.3 Mg and within the equal one was 9.28 Mg.

The combination between all controlled factors established an experimental design consisting of 36 treatments enumerated from T01 to T36 (see table AII-I in Appendix II). Each treatment was used to sow one strip of 60 m of length and 3 m of width. As shown in figure 1, Treatment strips were randomized in three blocks horizontally. A buffer zone of 20 m of width was settled between the blocks and served as setting adjustment zone (adjustment of parameters' level).

Each parallel treatment strips were spaced about 20 cm apart to prevent any possible sowing overlap. Three additional lines of 3 m of width were also settled between treatment strips: the first one is non-sowed line serving as control soil and the others served as traffic path of the sprayer (later intervention in the cultivation process) to protect the treatment strips from any additional traffic after sowing (c.f. figure 1). Sowing took place late February and soil was near field capacity during sowing (average soil gravimetric water content = 23.4% and average matric potential = -14.9 kPa).

Energetic data acquisition

Energy consumption was characterized by the specific fuel consumption per worked hectare.

Absolute fuel consumption was provided by the volumetric differential flow meter Aquametro Contoil DFM8D installed in the supply and return lines of tractor's engine. Tractor pull was determined using a dynamometric frame, equipped with two three-axial force transducers SIXAXES FX2.3 (0.8 to 1.5 mV V -1 ). A part of data such as an engine's RPM (Rotation Per Minute), tractor real speed and Global Position System (GPS) data were provided through CAN (controller area network) bus network (CAN 2.0A, 500 kb s-1). Data were recorded every 10 ms with a universal acquisition unit (SIRIUSi 8xSTG+, DEWESoft) on the same time scale.

All necessary computing and conversions were made through the post-processing software (Devesoft X) provided by DEWESoft. The straightforward during trails was controlled by an autosteering option of Massey Ferguson Auto-Guide 3000 coupled with an RTK receiver (Trimble AG-382) installed in the tractor cab and used position protocol J1939 with a precision of 2.5 cm. In each treatment, mechanical data recording was started once the target value of the ground speed is reached and motor regime was stabilized. In average, motor regime stabilized after about 20 m from the beginning of the sowing on a treatment strip.

Soil properties and crop data acquisition

Sampling was conducted on the zone where the motor regime is stabilized during sowing. To insure a uniform sampling, the sampling was performed following the direction of sowing and on the traffic trail of the right tires. Two sampling transects were selected, the first one at 30 m of length, and the second one at 45 m. On each transect, three repetitions, spaced 1 m apart, were sampled (i.e. six sampling repetition per treatment). Soil was sampled between 3-7 cm of depth (on the seedbed) using a metallic cylinder of 100 cm 3 of volume (inner diameter 5 cm, height 5.1 cm). Soil sampling took place the next day after the sowing and soil was near field capacity (same conditions of soil during the operation). The sampled cylinders were used to measure air permeability (ka in µm²) using the method of [START_REF] Iversen | AIR AND WATER PERMEABILITY IN DIFFERENTLY TEXTURED SOILS AT TWO MEASUREMENT SCALES[END_REF] with a pressure gradient of 5 hPa. Then, soil samples were oven-dried at 105 °C for 48 hours to determine bulk density (BD in g/cm 3 ) using core method (ISO 11272). Barley emergence was assessed counting the emerged barley plants at one to two leaves' stage on a linear meter for two successive sowing arrays. Barley harvest was carried out late July and three repetitions were sampled on a 5 m of length and 3 m of width of each treatment, then barley grains were weighted and yield crop was calculated.

Statistical analyses

The relations between the controlled factors (i.e. tractor speed, inflation pressure, soil working depth and axle load distribution) as explanatory variables and the normally distributed response variables (i.e. BD, count of emerged plant, crop yield and fuel consumption) were examined applying ordinary least square regression (OLS). The relations between the controlled factors as explanatory variables and the non-normally distributed response variable (i.e. ka) were examined applying generalized linear model regression (GLM) using Poisson family. The acquisition of tractor speed simultaneously with the energetic data acquisition offers the use of the values of tractor speed as continuous explanatory factor to analyze the energetic response variables, in which these values are close or equal to the three levels of tractor speed in a stabilized motor regime. The interaction between the explanatory variables was considered in the examination of the regressions. Then, the treatment was applied as explanatory variable of the different response studied variables to examine its effect as single body factor. Analysis of variance (ANOVA) was applied on the regression models to test the effect of the explanatory variables and their interactions (in case of interaction) on the response variables. Multiple mean comparison using post-hoc Duncan test following ANOVA analysis of variance was performed to compare between the means of the different response variables with respect to the treatment.

Furthermore, means of BD per treatment was compared to the bulk density threshold for soil compaction of 1.4 g/cm 3 (USDA Natural Resources Conservation Service) to assess whether the treatment induced soil damage or not. Mean count of emerged barley was compared to the count should be obtained with the germination rate of the sowed variety (51 plants/m) to assess the effect of treatment on the success of the germination and seedling. Mean crop yield per treatment was compared to the 2021 average crop yield of spring barley in Northern France (Hauts-de-France state), which is equal to 6.32 t/ha [START_REF] Arvalis | Moisson 2021 des céréales à paille pertubée par la météo -Arvalis[END_REF], to assess the effect of treatment on the success of the operation. A principle component analysis (PCA) was performed to reveal the correlation between the agronomical performance indicators. Fuel consumption means were also compared referring to the median of the fuel consumption, and the treatments show significantly equal or lower consumption than this median are considered as showing optimal consumption. To better explain the variation of fuel consumption as energetic response variable, tractor pull (expressed in KN) was analyzed in the same way to discuss the effect of controlled factors on energy consumption and traction efficiency. Then a Granger-Causality test was performed to test the possible forecasting of fuel consumption by tractor pull.

All statistical analyses were performed using R software environment (R. Core Development Team, 2020).

Results

Effect of controlled factors on soil properties

The regression of BD as function of the controlled factors shows that BD increased significantly with the combination of the unequal axle load distribution, tractor speed and soil working depth (see table AI-I in appendix I). ka increased significantly with: i) soil working depth, ii) inflation pressure, iii) the combination of unequal axle load distribution, tractor speed and soil working depth, iv) the combination of unequal axle load distribution, tractor speed and inflation pressure, v) the combination of unequal axle load distribution, soil working depth and inflation pressure, vi) and the combination of tractor speed, soil working depth and inflation pressure. ka decreased significantly with: i) the combination of unequal axle load distribution and soil working depth, ii) the combination of tractor speed and soil working depth, iii) the combination of unequal axle load distribution and inflation pressure, iv) the combination of soil working depth and inflation pressure, and v) the combination of the four factors together (see table AI-II in appendix I).

Noteworthy, the coefficient of determination (R²) for the regression models of BD and ka in function to the controlled factors were 0.06 and 0.25 respectively. On the other hand, when the combination of the four factors together (treatment) was used as single factor, then R² increased to be 0.49 and 0.55 for BD and ka respectively with a p-value < 0.001.

Effect of controlled factors on the emergence of barley and crop yield

The count of emerged plant decreased significantly with the unequal axle load distribution, the inflation pressure, and the combination of unequal axle load distribution, soil working depth and inflation pressure (table AI-III, appendix I). Likewise, crop yield decreased significantly with the combination of unequal load distribution, soil working depth and inflation pressure (table AI-IV, appendix I). Noteworthy, R² for the regression models of the count of emerged plant and the crop yield as function of the controlled factors were 0.46 and 0.19 respectively.

On the other hand, when the combination of the four factors together (treatment) was used as single factor, then R² increased to 0.57 and 0.49 for the count of emerged plant and the crop yield respectively with a p-value < 0.001 and p-value = 0.008 respectively.

Effect of controlled factors on fuel consumption

Fuel consumption increased significantly with: i) unequal axle load distribution, ii) soil working depth, iii) tractor speed, iv) the combination of soil working depth and tractor speed, v) the combination of unequal axle load distribution, tractor speed and soil working depth, vi) the combination of unequal axle load distribution, tractor speed and inflation pressure, vii) the combination of unequal axle load distribution, soil working depth and inflation pressure, and viii) the combination of tractor speed, soil working depth and inflation pressure. Fuel consumption decreased significantly with the combination of unequal axle load distribution and inflation pressure, and ii) the combination of tractor speed and inflation pressure (table AI-V in appendix AI). Noteworthy, R² of the regression model for fuel consumption as function of the controlled factors was 0.50 and it increased to 0.56 when the combination of the four factors together (treatment) was used as single factor. Considering the non-significant effect of inflation pressure solely on fuel consumption, Figure 3 shows the fuel consumption as function of the continuous tractor speed and categorized with respect to soil working depth and axle load distribution. Irrespectively to the axle load distribution, fuel consumption was significantly higher with the highest soil working depth, but the difference became non-significant when tractor accelerate between 13 and 14 km/h (figure 3). Likewise, fuel consumption was higher with the unequal axle load distribution (figure 3). Figure 3 shows that fuel consumption was significantly equal using unequal axle load distribution combined to the 5 cm of soil working depth or the equal axle load distribution combined to the 10 cm of soil working depth, independently from the speed of tractor. On the other hand, it shows that the combination between the equal axle load distribution and the 5 cm of soil working depth demonstrated the lowest fuel consumption. A slight decrease of fuel consumption with the continuous tractor speed was also found (figure 3).

Trade-off between fuel consumption, soil conservation and barley production

Table 1 shows the means of the different studied indicators and the results of their comparison.

Six treatments (T05, T12, T26, T27, T29 and T32) show an average of ka significantly lower than the average ka of the control soil (9.46 µm²). There was no treatment showing an average BD significantly higher than the control soil (1.39 g/cm 3 ), but 10 treatments (T12, T13, T19, T26, T27, T29, T31, T32, T33, T36) showed an average BD higher than the threshold of compaction for a silty loam soil (1.4 g/cm 3 ). A total of 19 treatments (T01, T02, T07, T13, T17, T18, T20, T21, T24, T25, T27, T28, T29, T30, T31, T32, T33, T35, T36) showed an average count of emerged plant lower than the average of emerged plants expected at the germination rate of the used variety of barley (51 emerged plant/m). For the reason of an operational problem, we could not have the count of emerged plant for T19, T22, T23 and T34. A total of 11 treatments (T02, T05, T06, T07, T08, T17, T24, T25, T30, T31 and T34) showed an average crop yield lower than the average of barley yield in Northern France in 2021 (6.32 t/ha). For the reason of an operational problem, the yield of T19, T22 and T23 are missed. Nine treatments (T09, T17, T24, T26, T27, T28, T33, T35 and T36) show an average fuel consumption significantly higher than the median of the global consumption (7.46 l/ha). For a reason of a technical problem during the registration of energetical data, the consumption of T05 was missed. All the remaining treatments could be considered as performant for the specific studied indicator. The common performant treatments between all the indicators are considered as the expected trade-off between the soil conservation, barley production and fuel consumption. Seven treatments (T03, T04, T10, T11, T14, T15, and T16) showed this trade-off (see table AII-I in appendix II for the details of treatment combinations).

Discussion

Influence of adjustable parameters on the agronomical performance

The agronomical performance is evaluated with regard to the conservation of soil physical properties and the success of the sowing operation. BD and ka were negatively correlated (ρ=-0.56 and p-value<0.001) but they did not show similar response. The impact of the controlled factors and their combinations was higher on ka than BD. Indeed, air permeability is reported to be highly sensitive to macroporosity and pore continuity, and the change in pore continuity exceed the change in pore volume [START_REF] Chen | Effects of compaction and cover crops on soil least limiting water range and air permeability[END_REF][START_REF] Dörner | Direction-dependent behaviour of hydraulic and mechanical properties in structured soils under conventional and conservation tillage[END_REF]. The present results agree with Tuli et al. (2005) showing the greatest effect of structural disturbance on air permeability. The increase of ka with the increase soil working depth indicates that a deeper soil cultivation work can improve soil porosity and their continuity. Contrarily to what was expected, the increase of inflation pressure did not lead to neither decrease ka via the decrease of soil-tire contact area, nor showing significant increase of BD. This could be attributed to probable beneficial effect of soil compaction after deep tillage of the soil, under which aggregate stability, as determinant factor of soil permeability, can be degraded [START_REF] Cavalieri | Long-term effects of no-tillage on dynamic soil physical properties in a Rhodic Ferrasol in Paraná, Brazil[END_REF]Tuli et al., 2005). Both, BD and ka were influenced by the combinations of the controlled factors, and the augmentation of R² when the treatment was applied as single body factor may explain that the four adjustable parameters interact between them and act to explain the variation in the soil properties. This support our hypothesis that to reduce soil compaction on seedbed, a tractor-tire-tool system should be adjusted as an entire system rather to treat each parameter in solo. For instance, the reduction of soil structural deformation via adjustments on inflation pressure is well documented (see as example: [START_REF] Douglas | Soil and crop responses to conventional, reduced ground pressure and zero traffic systems for grass silage production[END_REF][START_REF] Keller | A Model for the Prediction of the Contact Area and the Distribution of Vertical Stress below Agricultural Tyres from Readily Available Tyre Parameters[END_REF][START_REF] Raper | Agricultural traffic impacts on soil[END_REF][START_REF] Schjønning | Modelling effects of tyre inflation pressure on the stress distribution near the soil-tyre interface[END_REF][START_REF] Schwanghart | Measurement of contact area, contact pressure and compaction under tires in soft soil[END_REF], but rarely the distribution of axle load between rear and front axles was reported in the considered researches. Our findings show that the distribution of axle load between rear and front axle is an important factor to reduce soil deformation, though its effect appears when it is coupled with adjustments of other factors.

As an example, the increase of BD with the combination of unequal axle load distribution, tractor speed and soil working depth, can be explained by the combined effect of the higher load on the rear axle and the deeper soil work on the traction which may increase slip and increase soil compaction [START_REF] Janulevičius | How to select air pressures in the tires of MFWD (mechanical front-wheel drive) tractor to minimize fuel consumption for the case of reasonable wheel slip[END_REF]. This result is consistent with Çarman (1994) who found that tire load has higher effect than tractor forward velocity on soil cone index, shear strength and bulk density. The increase of ka with the previous combination may be justified by an increase of soil microporosity [START_REF] Dickerson | Soil Compaction After Tree-Length Skidding in Northern Mississippi[END_REF]) increasing air diffusion.

Even though, the increase of inflation pressure showed a positive effect on ka, it decreased when inflation pressure joined the combination mentioned above. This suggests that the obtained improvement of pore continuity and connectivity can be disadvantaged by the decrease of soiltire contact area and related closer to the inflation pressure than the other factors.

The controlled factors affected the crop yield and seedling of barley. Their effect appeared clearly when they were analyzed as a single factor (i.e. treatment) by the increase of R² of the regression models for the count of emerged plant and crop yield. The two models showed that about 50% of the variation of crop yield and emergence of barley is explained by the treatment as a single factor. However, there was no one evident effect of the controlled factors and their combinations on these two indicators. The principle component analysis (PCA) showed a positive correlation between crop yield and air permeability, as well as a negative correlation between emergence of barley and bulk density (figure 4). Consequently, the compression of soil porosity impedes barley seedling, but the increase of air permeability benefits the growth of barley leading to a greater yield. Thus, the impact of the controlled factors on soil physical properties leads to influence indirectly plant seedling and crop yield. A fully sufficient agronomical performance could be obtained without introducing damage on soil physical properties disturbing soil aeration to obtain a favorable seedling and crop yield [START_REF] Tang | A study on the air permeability as affected by compression of three French soils[END_REF]. The treatments identified as agronomically performing are the ones showing higher ka, lowest BD, higher crop yield and higher count of emergence plant. The fully agronomical performing treatments are T03, T04, T09, T10, T11, T14, T15, T16 (see table AII-I in appendix II for the details of the treatment combinations). We remark that the agronomically performing treatments belong to the equal distributed axle load. This result probably demonstrates better distribution of stress on the soil when the load is equally distributed between rear and front axle, leading to a lesser disturbance of soil structure, and therefore, improving agronomical production.

Influence of adjustable parameters on the energetical performance

Our findings show the decrease of fuel consumption when the axle load was equally distributed.

They are consistent with previous studies showing the increase of fuel consumption with the increase of tillage depth (named as soil working depth in the present study) and tractor speed [START_REF] Mamkagh | Effect of Tillage Speed, Depth, Ballast Weight and Tire Inflation Pressure on the Fuel Consumption of the Agricultural Tractor: A Review[END_REF]. Even though, there was no significant effect of inflation pressure solely on fuel consumption. Inflation pressure has the power to significantly decrease fuel consumption when it is combined to tractor speed or unequal axle load distribution. In contrast, when soil working depth is added to these two combinations, fuel consumption increases significantly.

The slight decrease of fuel consumption with the increase of tractor speed, shown in figure 3, is induced by the effect of the combination of tractor speed, soil working depth and axle load distribution. This combination showed a significant effect on decreasing fuel consumption when soil working depth decreases and the axle load is equally distributed. Thus, an evident higher effect of the working depth and axle load distribution compared to tractor speed on fuel consumption is found. To better explain the variation of fuel consumption, tractor pull was also examined through the same data analyses performed for fuel consumption. Regarding the variation of tractor pull in function of the continuous speed of tractor and categorized with respect to the soil working depth and axle load distribution, we observed almost a similar relationship compared to fuel consumption, but inversely related to the tractor speed (see figure AI-1 in appendix I). Fuel consumption and total tractor pull were correlated (ρ = 0.47, p-value < 0.001), and these two variables depend on the variation of the controlled factors and their combinations. Otherwise, the factors and their combinations that influenced significantly fuel consumption showed significant influences on tractor pull. Running a Granger-Causality test to investigate whether tractor pull is useful for forecasting fuel consumption or not, we found that tractor pull significantly Granger-causes fuel consumption (F-value = 32.1, p-value < 0.001).

For instance, the lower tractor pull was found with the combination showed the lower fuel consumption (see figure 3 and figure AI-1 in appendix I).

Similarly to what we found studying the agronomical performance for the tractor-tire-tool system, the augmentation of R² when we applied the treatment as single body factor may explain that the four adjustable parameters interact between them and act as a combination to explain the variation of the energetical performance. The energetical performance must compromise between efficient tractor pull (i.e. higher tractor pull) and the lower fuel consumption. Means of tractor pull were compared using Duncan post-hoc test following ANOVA analysis of variance, then they were compared referring to the median of tractor pull (13 KN). When a tractor pull is significantly equal or higher than this median, the tractor power of the treatment is considered efficient. Several treatments showed an efficient tractor pull and lower fuel consumption, including the treatments showed high fuel consumption. The energetically performant treatments are those showed optimal fuel consumption and efficient tractor pull.

Consequently, a total of 17 treatments (T01, T02, T04, T06, T07, T08, T16, T17, T18, T20, T22, T23, T25, T29, T31, T32, T33) are considered as energetically performant.

Trade-off between the agronomical performance and the energetical performance

Even though, the agronomical performance and the energetical performance could not be correlated, the adjustable parameters on a tractor-tire-tool system influence them both. The studied adjustable parameters on a tractor-tire-tool system (i.e. tractor speed, inflation pressure, ale load distribution and soil working depth), shown as treatment, to affect, both, the energetical and the agronomical performances. Thus, the adjustment on a tractor-tire-tool system should consider all these four factors as a combination to optimize the performance energetically and agronomically. A trade-off between the optimization of these performances could be obtained considering the common treatments as energetically and agronomically performant in the present paper. This trade-off can be obtained with T04 and T16, so applying an equal axle load distribution, 10 cm of soil working depth, and accelerating tractor speed at 14 km/h combined to 80 kPa or 140 kPa of inflation pressure.

Conclusion

The present paper presents the first study showing the combined effect of adjustable parameters, in a tractor-tire-tool system, on soil conservation, agronomical production, and energy optimization. Our study showed that the combination between tractor speed, axle load distribution, soil working depth and inflation pressure could be adjusted in a simple way by the operator to optimize the energetical and agronomical performances. We found that pore continuity is more sensitive than pore space to the variation of the adjustable parameters in tractor-tire-tool system. Fuel consumption depends on the variation of the adjustable parameters and their combinations. The agronomical performance, revealed by barley successful seedling, barley yield, and the conservation of physical soil properties on seedbed, as well as the energetical performance, revealed by the optimization of fuel consumption, depend on the combination of the studied adjustable parameters shown as treatment. Equally distributing the axle load on the rear and the front axle may lead to better agronomical and energetical performances. The trade-off between an agronomical performance and an energetical performance, for a tractor-tire-tool system carrying out a sowing operation of spring barley on a silty loam Luvisol with an average of gravimetric water content of 23.4% , can be obtained, equally distributing the axle load distribution, working the soil at 10 cm of depth, and accelerating tractor speed at 14 km/h combined to 80 kPa or 140 kPa of inflation pressure. We

Appendix II

Figure 1 :

 1 Figure 1: Schema of the experimental design showing the treatment strips enumerated from T01 to T36 and their distribution on field. Each treatment is defined in the schema starting by axel load distribution (%), inflation pressure (kPa), soil working depth (cm), and tractor speed (km/h). The dashed arrows indicate the direction of sowing.

Figure 2 :

 2 Figure 2: Energetical data acquisition setup. a. general view of the tractor-seeder assembly. b. volumetric differential flow meter, c. dynamometric frame, d. three-axial force transducer, e. GPS-RTK receiver, f. universal acquisition unit.

Figure 3 :

 3 Figure 3: Variation of fuel consumption in function to the continuous tractor speed and categorized with respect to soil working depth (line type) and axel load distribution (line color).

Figure 4 :

 4 Figure 4: Principal Component Analysis on the agronomic performance indicators

Figure

  Figure AI-I: Variation of tractor pull (KN) in function to the continuous tractor speed and categorized with respect to soil working depth (line type) and axle load distribution (line color).

Table 1 :

 1 Mean of the studied indicators (i.e. air permeability, bulk density, count of emerged plant, crop yield, fuel consumption and traction power) and their groups resulted from post-hoc Duncan mean comparison analyses following one-way analysis of variance (ANOVA). Means that are not in same group of letters are significantly different at a level of p-value ≤ 0.05. NA means No Available and corresponds to the missed data. (See Table AII-1 in Appendix II for the details of the treatments).

	Treatment	ka (µm²)	BD (g/cm 3 )	Count of emerged	Crop yield (t/ha)	Fuel consumption	Tractor pull (KN)
				plant		(l/ha)
	Control	9.46 klmnop	1.39 abcdef	NA	NA	NA	NA
	T01	7.74 nopqr	1.26 gh	50.83 abcdef	6.378 abcdef	5.88 s	12.69 ij
	T02	12.36 jkl	1.37 bcdef	49.83 abcdefg	5.964 cdef	7.15 lmno	12.69 ij
	T03	5.12 klmnop	1.32 efg	53.5 abc	6.747 abcdef	7.46 hi	11.56 klm
	T04	7.95 mnopqr	1.37 bcdef	54 ab	6.782 abcdef	7.05 pqr	14.19 fgh
	T05	5.72 rst	1.39 abcdef	56.5 a	6.133 bcdef	NA	NA
	T06	8.27 pqrs	1.33 cdefg	52.83 abcde	6.169 bcdef	7.62 ijkl	13.59 ghi
	T07	15.88 hi	1.36 cdef	46.5 bcdefgh	6.262 abcdef	7.43 o	14.31 efgh
	T08	21.11 f	1.25 gh	52.83 abcde	6.129 bcdef	7.75 klmno	15.15 bcde
	T09	13.71 ijk	1.31 efg	54 ab	7.729 a	8.16 cde	14.14 fgh
	T10	8.93 klmno	1.38 abcdef	52.67 abcde	7.049 abcd	6.23 rs	11.56 klm
	T11	24.13 e	1.21 h	55 a	6.373 abcdef	7.09 jklmn	11.19 mno
	T12	7.15 qrs	1.41 abcdef	53.5 abc	6.644 abcdef	7.03 op	11.44 lmn
	T13	8.93 lmnopq	1.41 abcdef	50.33 abcdefg	6.076 bcdef	5.7 rs	12.18 jkl
	T14	40.89 a	1.3 fg	52 abcde	7.609 ab	6.28 pqr	10.47 o
	T15	12.84 klmn	1.37 bcdef	51 abcde	6.911 abcdef	6.59 qr	10.58 no
	T16	8.71 nopqr	1.35 cdefg	53.33 abcd	6.72 abcdef	7.59 jklmn	13.36 hi
	T17	7.75 klmno	1.37 bcdef	49.67 abcdefg	6.036 cdef	7.89 a	14.24 efgh
	T18	12.35 klmno	1.38 abcdef	46.67 bcdefgh	6.533 abcdef	7.61 mno	14.23 efgh
	T19	37.49 b	1.41 abcde	NA	NA	6.99 hij	12.39 jk
	T20	20.46 fg	1.33 defg	49.33 abcdefg	6.56 abcdef	7.38 jklmn	12.79 ij
	T21	3.85 t	1.36 cdef	50.33 abcdefg	6.711 abcdef	6.86 pq	12.36 jk
	T22	10.35 jkl	1.39 abcdef	NA	NA	7.03 no	14.45 defg
	T23	9.03 opqr	1.36 cdef	NA	NA	8.52 hij	14.84 cdef
	T24	17.05 gh	1.36 bcdef	40 hij	5.431 ef	8.85 def	14.84 cdef
	T25	11.64 klm	1.38 abcdef	40 hij	5.409 f	8.14 gh	15.78 b
	T26	7.4 qrs	1.4 abcdef	48.67 abcdefg	7.391 abc	8.52 fg	15.31 bcd
	T27	7.63 qrs	1.48 a	44.83 efghi	7.204 abc	8.87 bc	14.15 fgh
	T28	17 hij	1.34 cdefg	46 bcdefgh	7.413 abc	7.71 gh	12.66 ij
	T29	4.08 st	1.44 abc	36 j	7.347 abc	7.75 ijk	13.41 hi
	T30	15.78 d	1.17 h	37.67 ij	6.218 abcdef	7.89 ijklm	11.41 lmn
	T31	9.65 mnopqr	1.41 abcde	45.5 cdefghi	5.667 def	7.36 ijk	13 ij
	T32	7.21 qrs	1.4 abcdef	37.83 ij	6.573 abcdef	6.69 op	12.82 ij
	T33	9.58 mnopqr	1.43 abcd	40.83 hij	6.373 abcdef	8.58 bcd	12.8 ij
	T34	10.64 klmnop	1.39 abcdef	NA	5.569 def	6.09 rs	10.43 o
	T35	32.26 c	1.32 efg	39 hij	7.436 abc	8.43 b	16.93 a
	T36	36.39 b	1.47 ab	42.83 ghij	6.942 abcde	8.71 efg	15.54 bc

Table AI -

 AI I: Descriptive of the regression model of soil bulk density as response variable to the controlled factors as explanatory variables and their combinations and the descriptive statistics of its analysis of variance (ANOVA). Significance is required at level of p-value ≤0.05

		Regression model	One-way ANOVA
	Factor	coefficient estimate	t value	F value	p-value
	Intercept	-2420.1585	-1.104
	axle load distribution	47.56	1.2	0.4341	0.51088
	tractor speed	194.6304	1.076	1.1436	0.28642
	soil working depth	259.5075	0.945	1.0241	0.31299
	inflation pressure	1362.7166	0.687	0.1555	0.69385
	axle load distribution*tractor speed	-3.7847	-1.156	0.6287	0.42895
	axle load distribution*soil working depth	-4.9872	-1.001	0.0363	0.84906
	tractor speed*soil working depth	-19.4962	-0.862	1.2033	0.27423
	axle load distribution*inflation pressure	-26.6938	-0.744	0.6164	0.43348
	tractor speed*inflation pressure	-105.3601	-0.642	0.7889	0.37571
	soil working depth*inflation pressure	-131.7564	-0.532	0.046	0.83038
	axle load distribution*tractor speed*soil working depth	0.3747	0.912	3.9665	0.04802
	axle load distribution*tractor speed*inflation pressure	2.0643	0.695	0.6699	0.41422
	axle load distribution*soil working depth*inflation pressure	2.5349	0.564	0.4857	0.48683
	tractor speed*inflation pressure*soil working depth	9.1423	0.447	0.0908	0.7635
	axle load distribution*tractor speed*soil working depth*inflation pressure	-0.1769	-0.447	0.2271	0.63429

Table AI -

 AI II: Descriptive of the regression model of air permeability (ka) as response variable to the controlled factors as explanatory variables and their combinations and the descriptive statistics of its analysis of variance (ANOVA). Significance is required at level of pvalue ≤0.05.

		Regression model	One-way ANOVA
	Factor	coefficient estimate	z value	F value	p-value
	Intercept	-71.127679	-2.976
	axle load distribution	1.469154	3.386	3.3941	0.06
	tractor speed	8.058658	1.095	0.0591	0.8
	soil working depth	6.322077	2.042	6.9021	0.008
	inflation pressure	84.263647	3.778	9.0476	0.003
	axle load distribution*tractor speed	-0.158306	-4.428	0.0015	0.97
	axle load distribution*soil working depth	-0.120532	-2.158	8.3178	0.003
	tractor speed*soil working depth	-0.721726	-2.842	14.6787	0.0001
	axle load distribution*inflation pressure	-1.668115	-4.135	38.1062	< 0.001
	tractor speed*inflation pressure	-8.616875	-4.717	54.8486	< 0.001
	soil working depth*inflation pressure	-8.083682	-2.784	29.5376	< 0.001
	axle load distribution*tractor speed*soil working depth	0.013822	3.014	13.6397	< 0.001
	axle load distribution*tractor speed*inflation pressure	0.169227	5.129	23.782	< 0.001
	axle load distribution*soil working depth*inflation pressure	0.154501	2.955	31.2684	< 0.001
	tractor speed*inflation pressure*soil working depth	0.828213	3.488	6.1723	0.01
	axle load distribution*tractor speed*soil working depth*inflation pressure	-0.015923	-3.724	13.9243	< 0.001

Table AI -

 AI III: Descriptive of the regression model of the count of emerged plant as response variable to the controlled factors as explanatory variables and their combinations and the descriptive statistics of its analysis of variance (ANOVA).

			Significance is required at level of p-
	value ≤0.05.
		Regression model	One-way ANOVA
	Factor	coefficient estimate	t value	F value	p-value
	Intercept	1096.29412	2.289
	axle load distribution	-20.66779	-2.373	1198.8177 < 0.001
	tractor speed	-67.23739	-1.69	0.0021	0.96
	soil working depth	-783.98693	-1.804	7.9275	0.005
	inflation pressure	-126.67311	-2.03	0.5992	0.44
	axle load distribution*tractor speed	1.34237	1.853	0.2026	0.65
	axle load distribution*soil working depth	15.64402	1.982	0.4774	0.49
	tractor speed*soil working depth	49.94223	1.386	1.9705	0.16
	axle load distribution*inflation pressure	2.46372	2.158	0.176	0.66
	tractor speed*inflation pressure	8.53557	1.635	0.6332	0.43
	soil working depth*inflation pressure	100.26564	1.785	0.8358	0.36
	axle load distribution*tractor speed*soil working depth	-1.01015	-1.542	0.1815	0.67
	axle load distribution*tractor speed*inflation pressure	-0.16788	-1.753	2.3595	0.12
	axle load distribution*soil working depth*inflation pressure	-1.95777	-1.911	11.7516	< 0.001
	tractor speed*inflation pressure*soil working depth	-6.39916	-1.366	1.404	0.24
	axle load distribution*tractor speed*soil working depth*inflation pressure	0.12673	1.48	2.1907	0.14

Table AI -

 AI IV: Descriptive of the regression model of crop yield as response variable to the controlled factors as explanatory variables and their combinations and the descriptive statistics of its analysis of variance (ANOVA). Significance is required at level of p-value ≤0.05.

		Regression model	One-way ANOVA
	Factor	coefficient estimate	t value	F value	p-value
	Intercept	298.56431	0.275
	axle load distribution	-4.48587	-0.222	0.0127	0.91
	tractor speed	5.50072	0.059	1.9101	0.17
	soil working depth	-68.03469	-0.74	0.6039	0.44
	inflation pressure	101.85032	0.096	2.1096	0.15
	axle load distribution*tractor speed	-0.09509	-0.054	0.2229	0.64
	axle load distribution*soil working depth	1.44673	0.541	0.003	0.96
	tractor speed*soil working depth	2.59027	0.207	1.0844	0.30
	axle load distribution*inflation pressure	-1.98357	-0.1	0.0176	0.89
	tractor speed*inflation pressure	-33.18263	-0.359	3.2114	0.07
	soil working depth*inflation pressure	23.39295	0.166	0.7032	0.40
	axle load distribution*tractor speed*soil working depth	-0.06162	-0.262	1.9576	0.17
	axle load distribution*tractor speed*inflation pressure	0.63508	0.718	1.8725	0.17
	axle load distribution*soil working depth*inflation pressure	-0.55876	-0.21	5.4257	0.02
	tractor speed*inflation pressure*soil working depth	1.13126	0.09	0.2011	0.65
	axle load distribution*tractor speed*soil working depth*inflation pressure	-0.01301	-0.054	0.003	0.96

Table AI -

 AI V: Descriptive of the regression model of fuel consumption as response variable to the controlled factors as explanatory variables and their combinations and the descriptive statistics of its analysis of variance (ANOVA). Significance is required at level of p-value ≤0.05.

		Regression model	One-way ANOVA
	Factor	coefficient estimate	t value	F value	p-value
	Intercept	-78.4	44.69
	axle load distribution	1.669	0.7983	104.1911	< 0.001
	tractor speed	4.319	3.902	158.4399	< 0.001
	soil working depth	2.941	6.368	104.857	< 0.001
	inflation pressure	91.64	43.49	1.6555	0.198885
	axle load distribution*tractor speed	-91.45	0.06965	0.7545	0.385527
	axle load distribution*soil working depth	-0.06271	0.1124	1.7052	0.192282
	tractor speed*soil working depth	-0.05395	0.5676	6.7989	0.009428
	axle load distribution*inflation pressure	-1.774	0.7658	19.8829	< 0.001
	tractor speed*inflation pressure	-5.52	3.834	4.2082	0.040813
	soil working depth*inflation pressure	-4.75	6.517	2.4127	0.121065
	axle load distribution*tractor speed*soil working depth	0.000265	0.01002	15.7643	< 0.001
	axle load distribution*tractor speed*inflation pressure	0.11	0.06772	5.7153	0.017233
	axle load distribution*soil working depth*inflation pressure	0.102	0.1143	22.386	< 0.001
	tractor speed*inflation pressure*soil working depth	0.148	0.5868	3.9397	0.047773
	axle load distribution*tractor speed*soil working depth*inflation pressure	0.00428	0.01029	0.1733	0.67739

Table AII -

 AII I: the configuration of the combinations between the adjustable parameters (controlled factors).

	Treatment	Axel load distribution (rear axle/front	Tractor speed (km/h)	Inflation pressure	Soil working depth
		axle)		(kPa)	(cm)
	T01	50%/50%	14	140	5
	T02	50%/50%	12	140	5
	T03	50%/50%	10	140	5
	T04	50%/50%	14	140	10
	T05	50%/50%	12	140	10
	T06	50%/50%	10	140	10
	T07	50%/50%	14	100	10
	T08	50%/50%	12	100	10
	T09	50%/50%	10	100	10
	T10	50%/50%	14	100	5
	T11	50%/50%	12	100	5
	T12	50%/50%	10	100	5
	T13	50%/50%	14	80	5
	T14	50%/50%	12	80	5
	T15	50%/50%	10	80	5
	T16	50%/50%	14	80	10
	T17	50%/50%	12	80	10
	T18	50%/50%	10	80	10
	T19	60%/40%	14	140	5
	T20	60%/40%	12	140	5
	T21	60%/40%	10	140	5
	T22	60%/40%	14	140	10
	T23	60%/40%	12	140	10
	T24	60%/40%	10	140	10
	T25	60%/40%	14	100	10
	T26	60%/40%	12	100	10
	T27	60%/40%	10	100	10
	T28	60%/40%	14	100	5
	T29	60%/40%	12	100	5
	T30	60%/40%	10	100	5
	T31	60%/40%	14	80	5
	T32	60%/40%	12	80	5
	T33	60%/40%	10	80	5
	T34	60%/40%	14	80	10
	T35	60%/40%	12	80	10
	T36	60%/40%	10	80	10

state that the present work shows the results of an experiment conducted on one site with specific soil conditions and over one season of an agricultural year. Therefore, as perspectives, complementary studies will take place over other season (some of them are conducted) and on other conditions, which they will support the finding presented in this manuscript.

Acknowledgements

This work has been supported by the Chair in Agricultural Machinery and New Technologies, backed by the Polytechnical Institute UniLaSalle with financial support from Michelin Corporate Foundation, AGCO Massey-Ferguson, Kuhn, the Hauts-de-France Regional Council and the European Regional Development Fund (ERDF). The used agriculture parcel was rented from EARL Daveaux Benoit represented by Mr. Kevin Daveaux. The authors are grateful to Mr. Guillaume Depaux and Mr. Erwan Verité, research engineer and laboratory technician, respectively, at Massey-Fergusson, for their help conducting the experiments. The authors thank the reviewers for their thoughtful reviews.

Conflict of interest

The authors declare no conflict of interest.

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