The literature distinguishes clearly between economic cut‑off grade (COG) used for mine planning/operation and geological cut‑off grade used to report mineral resources. Both are usually called “cut‑off grade”, but they are conceptually and numerically different.

Definitions and Roles

  • Economic cut‑off grade (operational / break‑even / optimum COG)
    Minimum Ni grade at which a tonne of material just covers its full economic cost of mining, processing and selling, typically with the aim of maximizing profit or NPV over the life of mine (Singh, 2025; Asad and Topal, 2011; Githiria and Musingwini, 2019; Sasongko, 2013). It is dynamic and recalculated as prices, costs, recoveries, capacities and discount rate change (Singh, 2025; Asad and Topal, 2011; Githiria and Musingwini, 2019).
  • Resource‑estimation cut‑off grade (geological / reporting COG)
    Grade used when building the block model and declaring mineral resources, to decide which blocks are counted as resource vs. waste under the “reasonable prospects for eventual economic extraction” principle (Jafar, Wahid and Widodo, 2022; Githiria et al., 2024; Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023). It is mostly a technical‑economic assumption at a long‑term “anchor” price and cost, and is often treated as static for a reporting cycle (Singh, 2025; Fadlilah, Isniarno and Guntoro, 2023; Biswas, Sinha and Sen, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023).

Nickel laterite studies commonly adopt a resource COG around 1.4–1.6% Ni for saprolite and 0.5–1.3% Ni for limonite, chosen to reflect what could reasonably be processed given assumed technology and markets, not what is necessarily optimal in any given year (Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023).

How to Calculate Economic Cut‑Off Grade for Nickel

1. Break‑Even Cash‑Flow Concept

At its simplest, break‑even COG equates revenue per tonne of ore to cost per tonne of ore:

Revenue/tonne = Mining + Processing + G&A + Selling costs/tonne

A generic break‑even cut‑off grade formula is:

where:

  • Cmine,Cprocess,CG&AC_{\text{mine}}, C_{\text{process}}, C_{\text{G\&A}}​: Costs per tonne of ore mined and processed (USD/t) (Singh, 2025; Sasongko, 2013; Githiria & Musingwini, 2019)
  • PNi​: Nickel price (USD/t Ni or USD/lb Ni) (Sasongko, 2013; Githiria & Musingwini, 2019)
  • RNi​: Metallurgical recovery (fraction) (Singh, 2025; Sasongko, 2013)

This aligns with the generic expression:

as commonly used for metalliferous deposits (Singh, 2025; Biswas, Sinha & Sen, 2023; Githiria & Musingwini, 2019; Sasongko, 2013).

For NSR‑based cut‑off (relevant for concentrates or matte), the numerator includes all costs to the smelter/refinery, and the denominator is the net smelter return per unit Ni, i.e. payability × price – smelting/refining charges (Singh, 2025; Githiria and Musingwini, 2019).

2. Including Fixed vs Variable Costs and Break‑Even Point

Cost accounting distinguishes fixed and variable costs. At the break‑even point, contribution from production equals fixed cost, which underpins concepts like “marginal cut‑off grade” (Singh, 2025):

  • Break‑even point (BEP) in accounting form:
    BEP = FC / (1 – V/S)
    where (FC) = fixed cost, (V) = total variable cost, (S) = sales revenue (Singh, 2025).
  • Marginal cut‑off grade is the lowest mean grade whose sale value is just sufficient to recover fixed cost at break‑even (Singh, 2025).

In practice, mining COG calculations often treat fixed costs as sunk at the operating stage and focus on short‑run break‑even COG based on variable or avoidable costs; at feasibility and strategic planning stages, total cost (fixed + variable) is included to derive an optimum COG policy that maximizes NPV (Sasongko, 2013; Githiria and Musingwini, 2019; Githiria, Musingwini and Muriuki, 2016).

3. Lane‑Type Optimum Cut‑Off Grade for Nickel

For long‑life open‑pit operations, the industry standard is Lane’s NPV‑maximizing algorithm, later extended and applied in many works (Sasongko, 2013; Biswas, Sinha and Sen, 2023; Githiria and Musingwini, 2019; Githiria, Musingwini and Muriuki, 2016). Key elements:

  • Three capacity‑constrained stages: mining (M), milling (C) and refining/market (R) (Sasongko, 2013; Githiria, Musingwini and Muriuki, 2016).
  • For each stage, a limiting cut‑off grade is derived (when that stage is the bottleneck) and balancing cut‑off grades are derived to balance two stages’ capacities (Sasongko, 2013; Githiria and Musingwini, 2019; Githiria, Musingwini and Muriuki, 2016).
  • Using grade–tonnage curves for the deposit, the algorithm selects the COG that maximizes NPV given capacities, costs, prices and discount rate (Sasongko, 2013; Githiria and Musingwini, 2019; Githiria, Musingwini and Muriuki, 2016).

The basic profit expression used in these models is (Githiria, Musingwini and Muriuki, 2016):

P = (s – r)Qr – cQc – mQm – fT

where:

  • (s): selling price per tonne of refined nickel
  • (r): refining cost per tonne of product
  • (Qr): tonnes of product refined
  • (c): milling cost per tonne of ore milled
  • (Qc): tonnes of ore milled
  • (m): mining cost per tonne mined
  • (Qm): tonnes mined
  • (f): annual fixed cost
  • (T): time (years)

These quantities (Qm, Qc, Qr) are themselves functions of cut‑off grade and grade–tonnage distribution (Sasongko, 2013; Githiria and Musingwini, 2019; Githiria, Musingwini and Muriuki, 2016). The algorithm:

  1. Reads grade–tonnage distribution by cut‑off intervals (Githiria and Musingwini, 2019; Githiria, Musingwini and Muriuki, 2016).
  2. Inputs costs, capacities, price, recovery, fixed cost, discount rate (Githiria and Musingwini, 2019; Githiria, Musingwini and Muriuki, 2016).
  3. Calculates for each candidate cut‑off grade the annual tonnages and profits.
  4. Iterates cut‑off and NPV over mine life until convergence on an optimum cut‑off policy (a COG each year or period) (Sasongko, 2013; Githiria and Musingwini, 2019; Githiria, Musingwini and Muriuki, 2016).

Nickel‑specific studies apply similar NPV‑based or profit‑based models, sometimes with additional terms for reclamation cost, reclamation revenue and revenue from valuable waste materials, still optimizing COG to maximize total profit or NPV (Sasongko, 2013; Baidowi, Rosyidi and Aisyati, 2021; Cetin and Dalgic, 2025).

4. Stochastic and Dynamic Approaches

Recent work emphasizes that economic cut‑off is inherently dynamic and stochastic:

  • Incorporating grade‑tonnage uncertainty and price volatility via Monte Carlo or machine learning price prediction yields a cut‑off grade policy over LOM rather than a single number, and can significantly increase NPV relative to deterministic approaches (Githiria et al., 2024; Githiria and Musingwini, 2019; Thompson and Barr, 2014).
  • Real‑options modelling under stochastic prices shows optimal cut‑off grades are often lower than traditional deterministic break‑even values, implying more low‑grade material should be considered ore to preserve optionality and reduce resource waste, especially for volatile metals like nickel (Thompson and Barr, 2014).
  • Stochastic extensions of Lane’s algorithm (e.g., NPVMining) simulate multiple grade–tonnage realisations and price distributions, determining an NPV‑maximizing COG path under uncertainty (Githiria and Musingwini, 2019).

Dynamic programming and evolutionary algorithms (e.g., genetic algorithms) have also been applied to guarantee global optimum COG policies, sometimes including depletion rates and rehabilitation cost explicitly (Biswas, Sinha and Sen, 2023; Cetin and Dalgic, 2025; Githiria, Musingwini and Muriuki, 2016).

Net-Present-Value-Calculating-ROI-Using-Net-Present-Value-Method-Factors-to-Consider-in-NPV-Calculations
Figure 1: Net Present Value Calculating ROI Using Net Present Value Method Factors to Consider in-NPV Calculations

How Nickel Resource‑Estimation Cut‑Off Grade Is Chosen

In resource modelling, cut‑off grade serves a classification/filtering role rather than an explicit NPV maximization:

  1. Geostatistical modelling (e.g., ordinary kriging) is used to build a 3D block model of Ni grades (Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023).
  2. A reporting COG is selected so that blocks above this grade are included in tonnage/grade reporting.

Nickel laterite case studies:

  • At PT Vale Indonesia, applying a 1.4% Ni COG for saprolite yields about 89.8 Mt of resource at an average 1.83% Ni (Husain, Bakri and Firdaus, 2023).
  • Another project uses >1.4% Ni as COG for saprolite and 0.5–1.3% Ni for limonite; block modelling with these COGs gives 670,838 t of resource at 2.90% Ni (Nawir et al., 2023).
  • A laterite project in Sulawesi sets minimum COG on the assumption of a 1:1 blending ratio with a 1% correction to reach ~1.8% product Ni, leading to classification of low, medium and high‑grade ore domains for resource reporting (Fadlilah, Isniarno and Guntoro, 2023).
  • In Region X, a blending study indicates that ore with ≥1.6% Ni can be economically extracted and blended to achieve a 1.8–2.0% Ni product, so 1.6% Ni is adopted as the practical COG in that area (Jafar, Wahid and Widodo, 2022).

These studies show the COG used in resource estimation is usually:

  • Anchored to product specifications (e.g., 1.8% Ni ore) and practical blending strategies (Jafar, Wahid and Widodo, 2022; Fadlilah, Isniarno and Guntoro, 2023).
  • Chosen to ensure that reported resources have reasonable prospects for economic extraction, but not rigorously optimized for NPV (Jafar, Wahid and Widodo, 2022; Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023).
  • Used to define lithological/grade domains (low-, medium‑, high‑grade) for selective mining (Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023).
Geostatistical Estimation Parameters
Figure 2: Geostatistical Estimation Parameters

Key Differences Between Economic and Resource‑Estimation Cut‑Off Grades

Focus and Objective

  • Economic COG (operational / feasibility):
    • Aim: maximize NPV or profit over the mine life (Sasongko, 2013; Asad and Topal, 2011; Biswas, Sinha and Sen, 2023; Githiria and Musingwini, 2019).
    • Directly linked to cash‑flow modelling, capacities, discount rate, risk, reclamation obligations (Sasongko, 2013; Githiria and Musingwini, 2019; Baidowi, Rosyidi and Aisyati, 2021; Cetin and Dalgic, 2025).
    • Explicitly accounts for time value of money, allowing higher COG early and lower COG later as NPV declines (Sasongko, 2013; Asad and Topal, 2011; Githiria and Musingwini, 2019).
  • Resource‑estimation COG:
    • Aim: support geological reporting, not optimize financial value (Jafar, Wahid and Widodo, 2022; Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023).
    • Must satisfy “reasonable prospects of eventual economic extraction” at a notional long‑term price and cost set (Fadlilah, Isniarno and Guntoro, 2023; Biswas, Sinha and Sen, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023).
    • Used to define resources, not reserves; reserve estimation later applies more rigorous economic COGs.

Static vs Dynamic

  • Economic COG is dynamic:
    • Recommended to be updated frequently as prices, costs, recovery, grade distribution, and capacities change (Singh, 2025; Sasongko, 2013; Githiria and Musingwini, 2019; Githiria et al., 2024).
    • Optimal policies vary year by year; higher COG early, lower later (Sasongko, 2013; Asad and Topal, 2011; Githiria and Musingwini, 2019).
    • Stochastic approaches explicitly model distributions for price and grade (Githiria et al., 2024; Githiria and Musingwini, 2019; Thompson and Barr, 2014; Cetin and Dalgic, 2025).
  • Resource COG is often static or infrequently changed:
    • Selected at scoping/pre‑feasibility based on “typical” or conservative parameters and kept fixed over a reporting period (Singh, 2025; Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023).
    • Changes mainly when long‑term price outlook or major processing options change, which triggers re‑estimation.

Parameter Treatment

  • Economic COG uses:
    • Detailed, often stage‑specific costs: mining, processing, refining, G&A, reclamation, waste removal, plus any stockpiling or by‑product credits (Sasongko, 2013; Githiria and Musingwini, 2019; Baidowi, Rosyidi and Aisyati, 2021; Cetin and Dalgic, 2025; Singh, 2025).
    • Capacity constraints (M, C, R) and grade–tonnage distribution, sometimes multiple ore types and products (Sasongko, 2013; Githiria, Musingwini and Muriuki, 2016; Muttaqin, Ciptomulyono and Siswanto, 2025).
    • Financial parameters: discount rate, interest rate, escalation, sometimes hedging and optionality (Sasongko, 2013; Thompson and Barr, 2014; Githiria and Musingwini, 2019; Cetin and Dalgic, 2025).
  • Resource COG uses:
    • Representative operating cost and recovery values, but often simplified (e.g., average cost per tonne, single recovery) (Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023).
    • No explicit capacity constraint or scheduling; time value of money is rarely included.
    • Geostatistical considerations dominate (kriging, variogram, search radius, RKSD, etc.) with COG applied as a filter afterwards (Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023).

Numerical Implications

Empirical studies show that:

  • Optimal economic COG can be significantly lower than traditional deterministic break‑even or reporting COGs when price volatility, uncertainty and optionality are considered (Thompson and Barr, 2014; Githiria et al., 2024; Githiria and Musingwini, 2019).
  • Deposits with certain grade‑distribution characteristics (e.g., lower shape and scale factors of lognormal distribution) are highly sensitive to metal price and discount rate; small COG changes can have large NPV impacts (Balci and Kumral, 2024).
  • Using a high, static COG for resources (e.g., 1.6–1.8% Ni) may under‑represent potentially economic low‑grade ore that could be profitably mined under a dynamic, NPV‑based COG policy or with blending (Balci and Kumral, 2024; Jafar, Wahid and Widodo, 2022; Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023).

Illustrative Comparison for Nickel Projects

Conceptual Roles of Different COGs

AspectEconomic (Operational) COGResource‑Estimation COG
Main objectiveMaximize NPV/profit over LOMDemonstrate reasonable prospects and report tonnage/grade
Time dimensionDynamic policy (varies by year/phase)Usually static for a report cycle
InputsDetailed costs, capacities, prices, recovery, discount rate, reclamationRepresentative long‑term price, average cost & recovery, geostatistics
MethodLane algorithm, dynamic programming, stochastic/ML optimizationKriging/block modelling then apply COG as filter
Typical nickel valuesMay justify COGs < reporting COG, especially later in LOM or under high volatilityOften ~1.4–1.6% Ni for saprolite, 0.5–1.3% Ni for limonite

Figure 3: Contrasting Operational and Geological Cut-off Grades for Nickel Projects

Why the Distinction Matters in Practice

  1. Misinterpretation of Resource Size
    Investors reading a resource estimate often overlook that “cut‑off grade” there is essentially a geological filter, not the optimized economic cut‑off. This can lead to misunderstanding of what portion of the deposit can actually be mined profitably under realistic schedules and cash‑flow constraints (Singh, 2025; Biswas, Sinha and Sen, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023; Sasongko, 2013).
  2. Resource Conservation vs. Short‑Term Profit
    Static reporting COGs may favour higher‑grade, smaller resources, while dynamic economic COGs, especially under stochastic or real‑options frameworks, suggest lower COGs and greater utilization of low‑grade material, reducing waste and improving overall resource efficiency (Singh, 2025; Thompson and Barr, 2014; Githiria et al., 2024; Githiria and Musingwini, 2019; Biswas, Sinha and Sen, 2023).
  3. Nickel‑Specific Operational Realities
    Lateritic nickel mining typically relies on selective mining and blending of low, medium and high‑grade ores to meet smelter or HPAL feed specs (e.g., 1.8% Ni) (Jafar, Wahid and Widodo, 2022; Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023). Resource COG is often set close to the minimum Ni grade that can contribute to blending; economic COG is then dynamically adjusted to decide, at each stage, which low‑grade blocks are worth mining and stockpiling vs. leaving as waste (Jafar, Wahid and Widodo, 2022; Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Githiria and Musingwini, 2019; Muttaqin, Ciptomulyono and Siswanto, 2025).
  4. Environmental and Reclamation Considerations
    New optimization models include reclamation cost and reclamation revenue as explicit terms, showing that environmental obligations can raise the economic cut‑off if they significantly increase lifecycle cost, or lower it if reclaimed land generates revenue, further distancing economic COG from simplistic reporting COGs (Cetin and Dalgic, 2025; Baidowi, Rosyidi and Aisyati, 2021; Biswas, Sinha and Sen, 2023).

Summary

  • Economic cut‑off grade for nickel is a techno‑economic decision variable—often computed via break‑even formulas and refined using NPV‑maximizing algorithms that account for costs, prices, recovery, capacities, time value and uncertainty (Singh, 2025; Sasongko, 2013; Asad and Topal, 2011; Biswas, Sinha and Sen, 2023; Githiria and Musingwini, 2019; Thompson and Barr, 2014; Githiria et al., 2024).
  • Resource‑estimation cut‑off grade for nickel is a geological reporting threshold, typically set using simpler, long‑term economic assumptions and applied post‑kriging to classify resource blocks (e.g., ≥1.4–1.6% Ni) (Jafar, Wahid and Widodo, 2022; Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023; Biswas, Sinha and Sen, 2023).
  • Economic COG is dynamic, project‑specific and schedule‑dependent, whereas resource COG is static and reporting‑oriented. They serve different purposes and will usually produce different numerical values and different implied “ore” tonnages for the same nickel deposit (Singh, 2025; Sasongko, 2013; Biswas, Sinha and Sen, 2023; Githiria and Musingwini, 2019; Fadlilah, Isniarno and Guntoro, 2023; Husain, Bakri and Firdaus, 2023; Nawir et al., 2023; Thompson and Barr, 2014; Githiria et al., 2024).

References

Singh, N., 2025. Cut-off Grade-A Concept. International Journal For Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2025.v07i05.56528

Balci, M., & Kumral, M., 2024. Impacts of Grade Distribution and Economies of Scale on Cut-off Grade and Capacity Planning. Mining, Metallurgy & Exploration, 41, pp. 1171 – 1193. https://doi.org/10.1007/s42461-024-00982-8

Jafar, N., Wahid, H., & Widodo, S., 2022. Classification of Ni Levels for Determination Cut-Off Grade in Region X. Journal of Geology and Exploration. https://doi.org/10.58227/jge.v1i1.3

Thompson, M., & Barr, D., 2014. Cut-off grade: A real options analysis. Resources Policy, 42, pp. 83-92. https://doi.org/10.1016/j.resourpol.2014.10.003

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Fadlilah, M., Isniarno, N., & Guntoro, D., 2023. Pemodelan dan Estimasi Sumberdaya Nikel Laterit Berdasarkan Kandungan Ni dan Fe dengan Menggunakan Metode Kriging di PT Putra Perkasa Abadi, Kecamatan Langgikima, Kabupaten Konawe Utara, Provinsi Sulawesi Tenggara. Bandung Conference Series: Mining Engineering. https://doi.org/10.29313/bcsme.v3i2.8834

Sasongko, W., 2013. Kajian Kadar Batas Optimum (Orytimum Cut-off Grade) pada Penambangan Nikel Laterit dengan Penjualan dalam Bentuk Material Bijih Mentah. **.

Asad, M., & Topal, E., 2011. Net present value maximization model for optimum cut-off grade policy of open pit mining operations. Journal of The South African Institute of Mining and Metallurgy, 111, pp. 741-750.

Biswas, P., Sinha, R., & Sen, P., 2023. A review of state-of-the-art techniques for the determination of the optimum cut-off grade of a metalliferous deposit with a bibliometric mapping in a surface mine planning context. Resources Policy. https://doi.org/10.1016/j.resourpol.2023.103543

Husain, J., Bakri, H., & Firdaus, A., 2023. Estimation of Laterite Nickel Resources Using Ordinary Krigging Method at PT Vale Indonesia Nuha District East Luwu South Sulawesi Province. Journal of Geology and Exploration. https://doi.org/10.58227/jge.v2i2.113

Githiria, J., & Musingwini, C., 2019. A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value. Journal of the Southern African Institute of Mining and Metallurgy. https://doi.org/10.17159/2411-9717/2019/v119n3a1

Githiria, J., Musingwini, C., & Muriuki, J., 2016. Development of a computer-aided application using Lane’s algorithm to optimize cut-off grade. Journal of The South African Institute of Mining and Metallurgy, 116, pp. 1027-1035. https://doi.org/10.17159/2411-9717/2016/v116n11a4

Cetin, E., & Dalgic, A., 2025. The use of dynamic programming in optimisation of cut-off grades and different depletion rates together with rehabilitation cost. International Journal of Mining, Reclamation and Environment, 39, pp. 459 – 470. https://doi.org/10.1080/17480930.2025.2450340

Nawir, A., Thamsi, A., Sanjaya, H., & Aswadi, M., 2023. Resources Estimation of Laterite Nickel Using Ordinary Kriging Method at PT Mahkota Semesta Nikelindo District Wita Pond Morowali District. International Journal of Applied Sciences and Smart Technologies. https://doi.org/10.24071/ijasst.v5i2.6939

Muttaqin, B., Ciptomulyono, U., & Siswanto, N., 2025. Optimizing cut-off grades under stochastic price: A model for open-pit lateritic nickel mining with multiple products. Resources Policy. https://doi.org/10.1016/j.resourpol.2025.105630

Baidowi, N., Rosyidi, C., & Aisyati, A., 2021. A Cut-Off Grade Optimization Model in Open Pit Mining Considering Reclamation Cost and Revenue. IOP Conference Series: Materials Science and Engineering, 1096. https://doi.org/10.1088/1757-899x/1096/1/012021