arxiv: 2604.09938 · v1 · submitted 2026-04-10 · 💻 cs.RO · cs.LG

Recognition: unknown

CableTract: A Co-Designed Cable-Driven Field Robot for Low-Compaction, Off-Grid Capable Agriculture

Ozgur Yilmaz

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:30 UTC · model grok-4.3

classification 💻 cs.RO cs.LG

keywords cable-driven robotagricultural roboticssoil compactionoff-grid energyfield robotdraft force modelingsimulation frameworkimplement carriage

0 comments

The pith

A cable-driven robot keeps its heavy power unit on the headland and rolls only a light carriage across the field, reducing compaction and energy use while supporting off-grid operation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds an integrated simulation framework that connects cable catenary mechanics, drivetrain efficiency, stochastic draft forces fitted to a library of ten co-designed implements, hourly solar-wind-battery modeling across six sites, polygon coverage planning on fifty fields, contact-pressure compaction estimates, discounted cash-flow economics, and global sensitivity analysis. It applies the framework to CableTract, a two-module system in which a stationary Main Unit and fixed Anchor module tension a cable while a lightweight carriage carries the implement. If the models hold, the architecture moves far less mass through the field than a conventional tractor, lowers soil contact pressure, and can run on harvested renewable energy without grid connection. The co-design of the implement library with the cable layout supplies the draft data that lets the other modules show these advantages. The complete code path is prototype-free and open source.

Core claim

CableTract places the heavy winch, motor, battery, and harvester in a stationary Main Unit on the headland, fixes an Anchor module with helical screw piles, and tensions a cable across the strip so that only a lightweight carriage carrying one of ten co-designed implements travels through the field. The chained models predict lower energy consumption, reduced compaction, and feasible off-grid operation when the system is compared against conventional tractor-implement pairs.

What carries the argument

The co-design of the ten-implement library with the cable architecture, which supplies the data for the stochastic draft model that drives the rest of the simulation chain.

If this is right

Only the light carriage enters the field, so contact pressure and resulting soil compaction drop below tractor-wheel levels.
Energy use falls because the heavy Main Unit never traverses the cropped area.
Hourly solar-wind-battery simulation on six sites shows periods of net-positive renewable generation that can support continuous operation without grid power.
The polygon coverage planner on the fifty-field corpus defines operating envelopes for cable length, implement speed, and turn time.
Global sensitivity analysis on twenty inputs ranks which parameters most affect energy, compaction, and cost outcomes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same modeling chain could be reused to compare cable robots against other low-compaction concepts such as wide-track or aerial systems without building hardware first.
If the economics engine shows positive returns, the architecture might suit high-value or fragile soils where conventional traffic is restricted.
Extending the cable to longer strips or adding multiple anchors would require re-running the coverage planner and sensitivity analysis to check whether the energy and compaction gains persist.
Farmers could test the open-source code on their own field polygons to estimate site-specific performance before any investment.

Load-bearing premise

The stochastic draft model and the linked sub-models for cable shape, efficiency, solar-wind harvest, compaction, and economics accurately predict real-world performance even though no physical prototype was ever built or tested.

What would settle it

Measure actual soil compaction depth, energy draw per hectare, and battery state-of-charge over a full growing season on a physical CableTract prototype and compare the values directly to the simulation outputs.

Figures

Figures reproduced from arXiv: 2604.09938 by Ozgur Yilmaz.

**Figure 1.** Figure 1: CableTract operating in a field. The Main Unit (left) houses the winch, drivetrain, battery, PV panel and small wind turbine; the Anchor (right) is a passive helical-pile module that resists the cable reaction. A lightweight implement carriage runs along the tensioned cable across a typical 50 m span. Only the carriage and the cable enter the field — the two heavy modules stay on opposite headlands for the… view at source ↗

**Figure 2.** Figure 2: The two physically separable modules of CableTract. Left: the Main Unit carries the entire active drivetrain (PMSM + winch + drum), the energy stack (9 kWh battery, 15 m2 PV, 600W VAWT) and the controller, and self-anchors with four parallel auger drives during operation (one BLDC drive motor per auger, so all four insert and retract concurrently). Right: the Anchor is a passive sheave block on top of nine… view at source ↗

**Figure 3.** Figure 3: One round of the CableTract operating cycle, bird’s-eye view. (a) Forward leg: the Main Unit’s winch reels in cable, the implement carriage moves loaded toward the MU at 1–2.5 km/h, the working tool engages the soil, and the full draft Fdraft is reacted at the Anchor as Freaction. (b) Return leg: the tool retracts, the cable goes slack, and the carriage rolls back to the Anchor end (downhill assist or acti… view at source ↗

**Figure 4.** Figure 4: Why an asymmetric, headland-only architecture wins on compaction. (a) A conventional tractor must drive a boustrophedon (snake) path that covers essentially the entire field area at least once per pass — every square metre of the soil is rolled over by a 1–4 t body. (b) CableTract keeps the Main Unit and the Anchor on opposite headlands and runs only a 250 kg carriage along the cable across the field; over… view at source ↗

**Figure 5.** Figure 5: Catenary sag versus horizontal cable tension for three rope materials at three spans. Synthetic-fibre rope (Dyneema or Spectra) at ≥ 5 kN pretension keeps midspan sag within the ±2 cm depth-control envelope for any span up to 100 m; steel cable of equivalent breaking strength requires ≈25 kN to meet the same tolerance because of its 5× higher self-weight per unit length. What this gives the rest of the pap… view at source ↗

**Figure 6.** Figure 6: Anchor reaction envelope plotted against two per-auger lateral capacity references that span the published range. Dashed red bands (Khand et al. 2024 [4]): 400 N per 30 cm helical pile in loose sand, free-head, strict deflection limit (the per-pile interpretation of the 4-pile raft tests in that paper) — the worst-case bound. Solid blue bands (Magnum Piering 2024 [5], with [6] as a corroborating manual ref… view at source ↗

**Figure 7.** Figure 7: Peak versus continuous motor power across the codesigned implement library at the two drivetrain efficiency presets. 5.4.6 Regenerative return leg A genuine energy advantage of the cable architecture is that the unloaded return leg lifts no implement, so any potential-energy budget (downhill slopes, carriage descent at end of row, elastic 16 [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗

**Figure 8.** Figure 8: ASABE D497.7 [2] draft distributions for the 10-implement CableTract codesigned library, sampled at CableTract operating speeds (1–2.5 km/h). The two vertical lines are the same 9-auger Anchor envelopes plotted in [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗

**Figure 9.** Figure 9: Speed-dependence of draft for three implements that span the full range of D497 coefficient regimes (codesigned planter, narrow chisel, narrow ripper). The CableTract operating window (1–2.5 km/h, green band) and the conventional tractor window (5–9 km/h, grey band) are highlighted. For the narrow ripper, draft grows from 2.42 kN at 2 km/h to 4.11 kN at 8 km/h — a 41 % reduction in draft simply by operatin… view at source ↗

**Figure 10.** Figure 10: Calendar heatmaps of daily decares-covered on harvested energy alone at the six bundled sites [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗

**Figure 11.** Figure 11: Battery SoC time series for each site’s own brightest 7-day window (highest 7-day rolling GHI). Hemisphere-symmetric: northern sites in late spring/summer, São Paulo in mid-December. All six sites run grid-free in their peak week with the codesigned 15 m2 PV + 9 kWh battery. Annual grid backup. The harder question is what happens in the other 51 weeks [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗

**Figure 12.** Figure 12: Annual grid hours required as a function of (panel area, battery capacity) for the best, median, and worst sites by annual GHI. The codesigned reference is overlaid as a white star. Per-site readout. Reading [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗

**Figure 13.** Figure 13: Distribution of best-orientation shape efficiency η across the 50-field corpus, by class. 11Bundled as data/fields/fields.geojson. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_13.png] view at source ↗

**Figure 14.** Figure 14: Strip-decomposition plans on three example field shapes (rectangle, L-shape, irregular-concave). Red lines show the per-strip cable lay; green polygons are the field outlines [PITH_FULL_IMAGE:figures/full_fig_p027_14.png] view at source ↗

**Figure 15.** Figure 15: Time budget to fully cover a 5-field 21-ha synthetic farm at the codesigned reference, broken into operation, setup, and inter-field travel. Operating time dominates setup by ∼10× and inter-field travel by >150×, so the system is operation-bound, not setup-bound. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_15.png] view at source ↗

**Figure 16.** Figure 16: Compaction comparison on three field classes (rectangle, L-shape, irregular-concave). Tractor coverage paths in red, carriage strip-midline paths in green. The carriage’s compacted area is essentially an L-shaped frame around the strip pattern; the tractor’s covers the entire field on every pass [PITH_FULL_IMAGE:figures/full_fig_p029_16.png] view at source ↗

**Figure 17.** Figure 17: Monte Carlo throughput envelope versus draft load (1 000 samples). Codesigned reference overlaid as a green star. 5.11.3 Global sensitivity First-order (Sobol first-order sensitivity index (S1)) and total-order (Sobol total-order sensitivity index (ST)) Sobol indices [23] are estimated with the Saltelli sampler [24, 25] at nbase = 256, giving 256 × (2 × 20 + 2) = 10 752 simulator evaluations per Sobol pas… view at source ↗

**Figure 18.** Figure 18: Sobol S1 and ST indices for the top-15 parameters across the four headline outputs. After the Section 5.4.1 decomposition of the lumped drivetrain efficiency, no single parameter accounts for more than 20 % of the variance in throughput. After the decomposition of the lumped drivetrain efficiency, the variance is diffuse. For daily throughput, the five largest contributors — daily solar hours, draft load,… view at source ↗

**Figure 19.** Figure 19: Tornado plot of one-at-a-time NPV-vs-diesel elasticities at the codesigned reference (25 ha/yr, 8 % discount, 15-yr horizon). Baseline NPV €70,300 in the gross-savings frame (see body text for the reconciliation with [PITH_FULL_IMAGE:figures/full_fig_p033_19.png] view at source ↗

**Figure 20.** Figure 20: NPV vs diesel as a function of farm size at three discount rates. NPV is positive across the entire 1–100 ha/yr sweep at every discount rate. NPV is positive across the entire (1–100 ha) sweep at all three discount rates. This is the central economic finding of the paper, and it is the consequence of co-design: at CAPEX parity with a used diesel tractor (35 570 EUR vs 35 000 EUR), every euro saved on dies… view at source ↗

**Figure 21.** Figure 21: Lifecycle CO2 per hectare-year for CableTract, the diesel reference tractor, and a Monarch-class electric tractor (stacked: embodied + fuel/grid). CableTract delivers a 2.2× improvement that does not depend on grid decarbonisation. 5.12 Architectural variants 5.12.1 What this section is for Everything up to this point has analysed a single architecture: the codesigned two-module v1, with one stationary Ma… view at source ↗

**Figure 22.** Figure 22: Architectural variant comparison on the codesigned reference parameter set. Codesigned baseline outlined in black. Lower is better for energy, CAPEX, and payback; higher is better for throughput and surplus power. 5.12.2 Variant 1 — Codesigned baseline This is the v1 architecture analysed throughout the paper: one Main Unit (PMSM + winch + drum + battery + PV/wind harvester) parked at one headland, one An… view at source ↗

**Figure 23.** Figure 23: The CableTract+ variant: a planar cable-robot topology with one Main Unit at each of the four corners of a rectangular field, replacing the v1 single-MU + Anchor pair. At any instant two cables (green, e.g. from the two left corners) actively pull the implement carriage toward themselves while the opposite two (grey, dashed) hold tension at standby. The carriage is now servoed in 2-D between the four corn… view at source ↗

**Figure 24.** Figure 24: CableTract operating envelope on (annual GHI × farm size). (a) Off-grid energy balance: PV harvest minus annual demand (green = energy positive, red = grid needed). The single black contour is the off-grid breakeven (annual surplus = 0); above and to the right of it the system needs grid backup. The six bundled reference sites of Section 5.7.1 are scattered at 25 ha annual operating area. (b) Discounted p… view at source ↗

read the original abstract

Conventional field operations spend most of their energy moving the tractor body, not the implement. Yet feasibility studies for novel agricultural vehicles rarely tie mechanics, energy harvest, draft, field geometry, economics, life-cycle CO2, and uncertainty quantification together on a single reproducible code path. This paper builds such a framework and applies it to CableTract, a two-module cable-driven field robot. A stationary Main Unit (winch + motor + battery + harvester module) (MU) and a lighter Anchor module (held by helical screw piles) tension a cable across a strip while a lightweight implement carriage rolls along it. The heavy bodies stay on the headland; only the carriage enters the field. The carriage runs a 10-implement library co-designed for the cable architecture. This co-design is the paper's central analytical lever. The framework is prototype-free. It chains a catenary cable model, a drivetrain efficiency chain, a stochastic draft model fitted to the co-designed library, an hourly solar + wind + battery simulator on six sites, a polygon coverage planner on a 50-field corpus, a contact-pressure compaction model, a discounted cash-flow economics engine with battery replacement and life-cycle CO2, and a global sensitivity analysis on 20 inputs. An operating-envelope sweep and an architectural-variant comparison close the loop. The full implementation is open source. Applied to the codesigned reference, the framework yields energy, compaction advantages and potential off-grid operation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a simulation-only study of a cable-driven ag robot with an integrated multi-model framework, but zero hardware data to support the claimed energy and compaction gains.

read the letter

The main point is that the paper delivers an open-source simulation pipeline for CableTract, a two-module cable robot that keeps heavy components on the headland while a light carriage works the field, and it applies that pipeline to a co-designed library of 10 implements. The chained models cover catenary geometry, stochastic draft forces, solar-wind-battery dynamics, coverage planning, contact-pressure compaction, and discounted economics with sensitivity analysis on 20 inputs. That level of integration on one reproducible code path is more than most vehicle feasibility papers attempt, and the open-source release is a genuine plus for anyone who wants to inspect or extend the work. The architecture itself is a sensible response to compaction and energy constraints in field operations. The central weakness is the total lack of physical validation. All reported advantages in energy use, soil compaction, and off-grid feasibility come directly from the models, including a stochastic draft model fitted to the same implement library being evaluated. Without calibration against measured draft, tension, or energy data, the quantitative outcomes remain exploratory rather than predictive. The other sub-models draw from external standards, which helps, but the overall claims still rest on untested assumptions. This work is aimed at researchers doing simulation-driven design in agricultural robotics or off-grid systems. A reader interested in unconventional vehicle concepts or multi-domain modeling pipelines could extract useful structure and code, but anyone planning hardware would need to add their own validation layer. It deserves a serious referee because the architecture and the integrated framework are distinct from prior tractor or cable-robot literature, and the reproducibility is explicit. I would send it to peer review with the expectation that reviewers will press for validation plans or clearer bounds on model uncertainty.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces CableTract, a two-module cable-driven field robot (stationary Main Unit with winch/motor/battery/harvester and lighter Anchor module) that tensions a cable across field strips for a lightweight implement carriage. It presents a prototype-free, open-source simulation framework chaining a catenary cable model, drivetrain efficiency chain, stochastic draft model fitted to a co-designed 10-implement library, hourly solar-wind-battery simulator across six sites, polygon coverage planner on a 50-field corpus, contact-pressure compaction model, discounted cash-flow economics with battery replacement and life-cycle CO2, plus global sensitivity analysis on 20 inputs and architectural comparisons. Applied to the co-designed reference, the framework is claimed to demonstrate energy and compaction advantages with potential off-grid operation.

Significance. If the sub-models prove accurate, the work offers a reproducible, integrated modeling pipeline for early-stage co-design and evaluation of novel low-compaction agricultural vehicles, incorporating uncertainty quantification and multi-site energy dynamics. The open-source code release is a clear strength that supports scrutiny and extension.

major comments (2)

[Abstract] Abstract: The quantitative claims of energy, compaction advantages, and off-grid feasibility are presented as outcomes of the chained simulation framework, yet the manuscript supplies no physical prototype data, experimental measurements, calibration, baseline comparisons, or error bars for draft forces, cable tension, soil compaction, or energy harvest. This leaves the reported advantages dependent on unvalidated model assumptions.
[Framework description (stochastic draft model)] The stochastic draft model is fitted directly to the same 10-implement co-designed library used for performance predictions in the framework; this introduces circularity that could inflate the apparent advantages of the co-design choices being evaluated.

minor comments (2)

The abstract references a '50-field corpus' and 'six sites' without detailing selection criteria or providing summary statistics; adding a table of site characteristics and field geometries would improve reproducibility.
Notation for the efficiency chain and battery replacement schedule could be clarified with an explicit equation or diagram showing how free parameters (e.g., discount rate) propagate into the discounted cash-flow results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential value of the integrated modeling pipeline and open-source release. We respond point-by-point to the major comments below, acknowledging the simulation-only nature of the work and proposing targeted clarifications.

read point-by-point responses

Referee: [Abstract] Abstract: The quantitative claims of energy, compaction advantages, and off-grid feasibility are presented as outcomes of the chained simulation framework, yet the manuscript supplies no physical prototype data, experimental measurements, calibration, baseline comparisons, or error bars for draft forces, cable tension, soil compaction, or energy harvest. This leaves the reported advantages dependent on unvalidated model assumptions.

Authors: We agree that the manuscript is prototype-free and presents no experimental data, as explicitly noted in the abstract and introduction. The work is framed as an early-stage co-design framework that chains established physical models (catenary cable, contact-pressure compaction) with fitted components for novel elements, enabling reproducible evaluation of energy, compaction, and economics across sites and fields before hardware exists. We will revise the abstract, add an explicit limitations subsection, and expand the discussion to state model assumptions, the absence of calibration data, and the framework's intended role in guiding subsequent prototyping and validation. The open-source code is provided precisely to support such extensions by others. revision: partial
Referee: [Framework description (stochastic draft model)] The stochastic draft model is fitted directly to the same 10-implement co-designed library used for performance predictions in the framework; this introduces circularity that could inflate the apparent advantages of the co-design choices being evaluated.

Authors: The stochastic draft model is fitted to the 10-implement library because these implements are novel and co-designed specifically for the cable architecture, with no independent empirical datasets available. The fitting derives draft-force distributions from the co-design parameters (geometry, mass, operating envelope) to enable system-level predictions of cable tension, energy use, and compaction. While this approach ties the model to the evaluated designs, the framework predictions concern the integrated robot (winch dynamics, coverage planner, battery model, economics) rather than isolated draft re-testing. We acknowledge the risk of optimistic bias and will revise the methods to detail the fitting procedure, add a dedicated paragraph on this limitation, and highlight how the 20-input global sensitivity analysis and architectural-variant comparisons serve as robustness checks. revision: partial

Circularity Check

1 steps flagged

Stochastic draft model fitted to co-designed implement library makes performance predictions partly self-referential

specific steps

fitted input called prediction [Abstract]
"This co-design is the paper's central analytical lever. ... It chains a catenary cable model, a drivetrain efficiency chain, a stochastic draft model fitted to the co-designed library, an hourly solar + wind + battery simulator on six sites, a polygon coverage planner on a 50-field corpus, a contact-pressure compaction model..."

The stochastic draft model is fitted directly to the 10-implement library that was co-designed for the cable architecture. The framework then applies this fitted model to predict the energy, compaction, and off-grid advantages of the identical architecture, so the claimed benefits incorporate the design choices embedded in the fit rather than arising from independent validation data.

full rationale

The paper's central quantitative claims rest on a chained simulation framework whose load-bearing draft sub-model is explicitly fitted to the same 10-implement library that was co-designed for the CableTract architecture. This creates a fitted-input-called-prediction pattern: the model parameters encode the design choices being evaluated, so reported energy, compaction, and off-grid advantages are partly defined by construction rather than by independent data. Other sub-models (catenary, solar-wind-battery, coverage planner) are drawn from external standards and do not exhibit the same reduction. No self-citation chains, uniqueness theorems, or ansatzes are load-bearing. The overall circularity is therefore partial (score 5) rather than total.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 1 invented entities

The framework rests on standard engineering models for cables and renewables plus a fitted draft model whose parameters are chosen to match the new implement library; no new physical entities are postulated beyond the proposed robot architecture itself.

free parameters (2)

stochastic draft model parameters
Fitted to the co-designed 10-implement library to generate force distributions used in energy and compaction calculations.
battery replacement schedule and discount rate
Chosen within the discounted cash-flow economics engine.

axioms (2)

standard math Catenary equation governs cable shape and tension under self-weight and carriage load
Invoked for the cable model between Main Unit and Anchor.
domain assumption Hourly solar and wind resource data from six sites are representative of operating conditions
Used in the renewable energy simulator.

invented entities (1)

CableTract two-module cable-driven architecture with 10-implement co-designed library no independent evidence
purpose: Achieve low-compaction field operations while enabling off-grid power
The robot configuration and implement set are introduced by the paper.

pith-pipeline@v0.9.0 · 5569 in / 1630 out tokens · 37232 ms · 2026-05-10T16:30:51.566602+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

28 extracted references · 5 canonical work pages

[1]

Orlando and A

R. Orlando and A. Zoffoli,Agricultural traction system with cable and hoists, U.S. Patent 8763714 B2, July 2014

2014
[2]

Joseph, MI, March 2011 (R2015)

American Society of Agricultural and Biological Engineers,ASABE Standard D497.7: Agri- cultural Machinery Management Data, ASABE, St. Joseph, MI, March 2011 (R2015). 49

2011
[3]

Joseph, MI, 2006 (R2020)

American Society of Agricultural and Biological Engineers,ASABE Engineering Practice EP496.3: Agricultural Machinery Management, ASABE, St. Joseph, MI, 2006 (R2020)

2006
[4]

Lateral capacity of group helical piles in sand: an experimental and numer- ical study for sustainable infrastructures,

M.M.H. Khand, “Lateral capacity of group helical piles in sand: an experimental and numer- ical study for sustainable infrastructures,”International Journal of Geotechnical Engineering, vol. 18, nos. 7–10, pp. 728–742, 2024. doi:10.1080/19386362.2024.2406372

work page doi:10.1080/19386362.2024.2406372 2024
[5]

magnumpiering.com

Magnum Piering, Inc.,Magnum Helical Pile Allowable Lateral Shaft Capacity (lbs) — Fixed- Head Condition, product datasheet, October 2024 revision. magnumpiering.com

2024
[6]

Hubbell Power Systems / CHANCE Foundation Solutions,CHANCE Technical Design Manual, 5th ed., Hubbell Power Systems, Centralia, MO, 2024, 337pp

2024
[7]

Irvine,Cable Structures, MIT Press, Cambridge, MA, 1981

H.M. Irvine,Cable Structures, MIT Press, Cambridge, MA, 1981

1981
[8]

Feyrer,Wire Ropes: Tension, Endurance, Reliability, 2nd ed., Springer, Berlin Heidelberg,

K. Feyrer,Wire Ropes: Tension, Endurance, Reliability, 2nd ed., Springer, Berlin Heidelberg,
[9]

doi:10.1007/978-3-642-54996-0

work page doi:10.1007/978-3-642-54996-0
[10]

Modelling of power and energy requirements for tillage implements operating in Serdang sandy clay loam, Malaysia,

A.F. Kheiralla, A. Yahya, M. Zohadie, and W. Ishak, “Modelling of power and energy requirements for tillage implements operating in Serdang sandy clay loam, Malaysia,”Soil and Tillage Research, vol. 78, no. 1, pp. 21–34, 2004

2004
[11]

Siemens and W

J.C. Siemens and W. Bowers,Machinery Management: How to Select Machinery to Fit the Real Needs of Farm Managers, Farm Business Management series, Deere & Company, Moline, IL, 1999

1999
[12]

The absorption of radiation in solar stills,

P.I. Cooper, “The absorption of radiation in solar stills,”Solar Energy, vol. 12, no. 3, pp. 333–346, 1969

1969
[13]

Insolation in relation to cloudiness and cloud density,

B. Haurwitz, “Insolation in relation to cloudiness and cloud density,”Journal of Meteorology, vol. 2, no. 3, pp. 154–166, 1945

1945
[14]

Justus,Winds and Wind System Performance, Franklin Institute Press, Philadelphia, PA, 1978

C.G. Justus,Winds and Wind System Performance, Franklin Institute Press, Philadelphia, PA, 1978

1978
[15]

Druckverteilung im Boden und Bodenverformung unter Schlepperreifen,

W. Söhne, “Druckverteilung im Boden und Bodenverformung unter Schlepperreifen,”Grund- lagen der Landtechnik, no. 5, pp. 49–63, 1953

1953
[16]

Challenges in the development of analytical soil compaction models,

T. Keller and M. Lamandé, “Challenges in the development of analytical soil compaction models,”Soil and Tillage Research, vol. 111, no. 1, pp. 54–64, 2010

2010
[17]

Hammond and C.I

G.P. Hammond and C.I. Jones,Inventory of Carbon and Energy (ICE), version 3.0, University of Bath / Circular Ecology, Bath, UK, 2019

2019
[18]

Emilsson and L

E. Emilsson and L. Dahllöf,Lithium-Ion Vehicle Battery Production: Status 2019 on Energy Use, CO2 Emissions, Use of Metals, Products’ Environmental Footprint, and Recycling, IVL Swedish Environmental Research Institute, Report C444, Stockholm, 2019

2019
[19]

Fraunhofer Institute for Solar Energy Systems ISE,Photovoltaics Report, Freiburg, Germany, updated February 2022

2022
[20]

Comparative environmen- tal life cycle assessment of conventional and electric vehicles,

T.R. Hawkins, B. Singh, G. Majeau-Bettez, and A.H. Strømman, “Comparative environmen- tal life cycle assessment of conventional and electric vehicles,”Journal of Industrial Ecology, vol. 17, no. 1, pp. 53–64, 2013

2013
[21]

UK Department for Environment, Food & Rural Affairs,UK Government GHG Conversion Factors for Company Reporting — 2023, DEFRA, London, 2023

2023
[22]

European Environment Agency,Greenhouse Gas Emission Intensity of Electricity Generation in Europe, EEA, Copenhagen, 2023

2023
[23]

BloombergNEF,Lithium-ion Battery Price Survey 2024, Bloomberg New Energy Finance, December 2024. 50

2024
[24]

Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates,

I.M. Sobol’, “Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates,”Mathematics and Computers in Simulation, vol. 55, nos. 1–3, pp. 271–280,
[25]

doi:10.1016/S0378-4754(00)00270-6

work page doi:10.1016/s0378-4754(00)00270-6
[26]

Saltelli, M

A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola,Global Sensitivity Analysis: The Primer, John Wiley & Sons, Chichester, UK, 2008

2008
[27]

SALib: an open-source Python library for sensitivity analysis,

J. Herman and W. Usher, “SALib: an open-source Python library for sensitivity analysis,” Journal of Open Source Software, vol. 2, no. 9, p. 97, 2017. doi:10.21105/joss.00097

work page doi:10.21105/joss.00097 2017
[28]

Socio-Env

T. Iwanaga, W. Usher, and J. Herman, “Toward SALib 2.0: advancing the accessibility and interpretability of global sensitivity analyses,”Socio-Environmental Systems Modelling, vol. 4, p. 18155, 2022. doi:10.18174/sesmo.18155. 51

work page doi:10.18174/sesmo.18155 2022