A bioeconomic model linking growth, survival, and feed conversion to profitability in farmed Acheta domesticus

Rodrigo Zorrilla González; Erik Neff

doi:10.1163/23524588-bja10365

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A bioeconomic model linking growth, survival, and feed conversion to profitability in farmed Acheta domesticus

In: Journal of Insects as Food and Feed

Authors:

Rodrigo Zorrilla González

Rodrigo Zorrilla González Bravo Farm & Laboratories, 5 poniente 1503, Col San Sebastián, Puebla 72090, Mexico

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https://orcid.org/0000-0002-4035-1521

and

Erik Neff

Erik Neff Fluker’s Cricket Farm, 1333 Plantation Avenue, Port Allen, LA 70767, USA

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https://orcid.org/0000-0002-5521-5629

Online Publication Date:: 23 Jan 2026

Abstract

The rapid expansion of cricket farming highlights the need for practical tools that connect biological performance to economic outcomes. We developed a bioeconomic model for the house cricket (Acheta domesticus) that integrates growth, survivorship, and feed conversion into a profitability framework. Biological parameters were estimated from a published growth and feeding experiment (Morales-Ramos et al., 2018), including exponential decay models for survivorship (mean $R^{2} = 0.76$ ), logistic models for body mass (mean R² = 0.99), and a Gamma generalized linear model with a log link describing feed conversion ratio as a function of age (mean $R^{2} = 0.58$ ). These functions were combined with economic inputs such as feed cost and market price to simulate batch production, and identify the harvest age that maximizes annualized profit per unit area. Using elasticity analyses around low- and high-efficiency farm scenarios, we found that profitability is most sensitive to adult mortality rate, growth rate inflection point, and the slope of the feed conversion rate function. Heatmaps generated from sensitivity analyses revealed that optimal harvest timing shifts depending on biological performance and cost structure, emphasizing the importance of aligning management with system-specific conditions. This framework highlights which traits offer the greatest leverage for improving economic outcomes, supporting both selective breeding targets and short-term management interventions. By providing open R code and transparent documentation, the model is designed for immediate application in research and farm operations. Overall, this work establishes a baseline tool for benchmarking, optimization, and future standardization efforts in the cricket farming industry.

Abstract

Keywords: Acheta domesticus; bioeconomic model; feed conversion ratio; growth rate; profitability optimization; survivorship

1 Introduction

Global food systems face urgent challenges from rising demand and the environmental impacts of conventional livestock production (Carlsson-Kanyama, 1998; Hawkey et al., 2021; Koning et al., 2008). The edible insect industry, while nascent, is rapidly expanding and could mitigate these impacts by providing a more efficient source of protein (Gahukar 2016; van Huis 2020). Crickets, in particular the house cricket Acheta domesticus, offer several advantages in comparison to traditional livestock, including high feed conversion ratios (FCR), reduced greenhouse gas emissions, and lower resource requirements (Halloran et al., 2017; Oonincx et al., 2010). Rapid growth and recognition of environmental benefits position cricket farming as a promising sustainable alternative (Boukid et al., 2022; Caparros Megido et al., 2024; Halloran et al., 2016; Siddiqui et al., 2023). Despite these advantages, challenges remain, including customer acceptability, regulatory uncertainty, and limited standardization (Caparros Megido et al., 2024; Haenssgen et al., 2024; Van Peer et al., 2024; S. Yaemkong et al., 2024). Addressing these barriers requires practical tools that improve efficiency and profitability across scales.

Feed quality and composition are major costs and primary biological constraints determining profitability in cricket farming (Niyonsaba et al., 2021). Studies show diet-driven variation in FCR, survival, and growth, confirming feed as a critical cost center and lever for optimization (Gutiérrez et al., 2020; Oonincx et al., 2015; Suphawadee Yaemkong et al., 2024). To improve profitability and sustainability, regional feeds, food waste, and circular economy-based approaches have attracted considerable attention with mixed results (Jucker et al., 2022; Kuo and Fisher, 2022; Sorjonen et al., 2019). While some byproducts and alternative feed show promising performance, many others cause elevated mortality, poor growth or extended development time, complicating circular economy goals (Lundy and Parrella, 2015; Quek et al., 2020; Vaga et al., 2021). Moreover, suboptimal feed quality interacts with high rearing densities, creating feedback loops of reduced growth and increased cannibalism further exacerbating losses (Gutiérrez et al., 2020; Suphawadee Yaemkong et al., 2024).

The rapid expansion of cricket farming has spurred global research and innovation, reinforcing its promise (Boukid et al., 2022; Halloran et al., 2016). A central barrier is the shortage of practical bioeconomic models to support operational decisions across scales and technological contexts (Hansen et al., 2025; Nielsen et al., 2014). Related bioeconomic and breeding-oriented models exist for other farmed insects, but not for crickets (Eriksen, 2022; Leipertz et al., 2024; Padmanabha et al., 2020; Zaalberg et al., 2024). Yet, the sector still struggles to translate research into farm-level improvements (Madau et al., 2020). Additionally, inconsistent reporting practices and limited comparability between studies hinder benchmarking, progress tracking and generalization (Van Peer et al., 2024). Thus, common metrics and transparent frameworks are needed for comparability and practical use. Such accessible, transparent, and flexible bioeconomic tools are necessary to transform empirical evidence into widespread, tangible improvements.

Here, we introduce a transparent, adaptable profit model that couples core biological mechanisms (growth, survival, and feed conversion rate) with direct economic optimization. It links profitability to measurable scale-independent parameters and helps farmers identify which factors most influence profitability outcomes and optimal harvest timing. Our model’s elasticity and sensitivity analyses offers actionable guidance for feed evaluation and on-farm decision-making, while identifying priority traits for selective breeding programs, a critical need given the current lack of established breeding protocols for A. domesticus (Eriksson and Picard, 2021; Hansen et al., 2025; Lecocq, 2018). Although developed with A. domesticus data, the framework is species-agnostic and adapts to other farmed crickets by reparameterizing the core functions. We focused on A. domesticus because it is one of the most-farmed species across most regions (Magara et al., 2021; Rowe et al., 2024). Open R code and clear documentation make our approach immediately accessible for research and operations. Ultimately, this framework contributes a practical starting point for future modelling, breeding, farm benchmarking, and industry-wide standardization.

2 Materials and methods

Parameterization of biological traits

All biological parameters were estimated from Morales-Ramos et al., (2018), reporting weekly group averages for Acheta domesticus reared under controlled conditions. Individual-level estimates for body mass and feed consumption were calculated by dividing the reported total mass by the number of surviving individuals at each time point. We excluded a single replicate which did not complete the full series (Temperature 29 °C, Replicate 3). No further cleaning.

Survivorship modelling

Cohort survivorship was modelled using an exponential decay with asymptote:

\begin{matrix} S (t) = (1 - S_{min}) \times exp (- k t) + S_{min} \end{matrix}

where

S_{\min}

is the asymptotic adult survivorship and k is the mortality rate.

Percent survival was calculated per week, replicate, and temperature. We fit groups separately with nonlinear least squares (nlsLM, minpack.lm) with the following parameter bounds: $S_{\min}$ : 0–1; $k \geq 0$ .

Fit quality: Mean parameter values: S_min = 0.164, $k = 0.194$ ; Mean R²: 0.757. Detailed fit diagnostics, replicate-level parameter values, and fitted curves are provided in Table S1 and Figure S1 in the Supplementary material.

Growth modelling

Individual body mass trajectories were modelled with a bounded logistic function:

\begin{matrix} w (t) = \frac{W_{max}}{1 + exp (- r (t - t_{mid}))} \end{matrix}

where

W_{\max}

is the maximum attainable body weight, r is the logistic growth rate, and

t_{mid}

is the inflection point (in weeks).

Fits were per replicate × temperature group using nlsLM, with the parameter ranges: $W_{\max}$ : 0.1–1.0 g; $r = 0.01$ –5; $t_{mid}$ : 0–15 weeks.

Fit quality: Mean parameter values: $W_{\max} =$ 0.366; $r = 0.944$ ; $t_{mid} = 6.71$ ; Mean $R^{2} = 0.995$ . Details provided in Table S2 and Figure S2 in the Supplementary material.

Feed conversion ratio (FCR) modelling

FCR for each weekly interval was calculated as:

\begin{matrix} FCR = \frac{Feed {consumed}_{t}}{Δ Body {mass}_{t}} \end{matrix}

Where Feed consumed_t is individual feed consumed and ΔBody mass_t is individual mass gain between weeks. We excluded intervals with non-positive mass gain, non-positive or non-finite FCR, and missing body mass. For each temperature × replicate, we fitted three candidate models: power-law, exponential and a Gamma generalized linear model (GLM) with log link. Model summaries and observed-vs-predicted plots are in Table S3 and Figures S3–S5 in the Supplementary material.

Model fits were assessed by mean $R^{2}$ and the Akaike Information Criterion (AIC; lower values indicate better fit by penalizing model complexity). To avoid overweighting temperatures with more replicates, metrics were computed within each temperature and then macro-averaged across temperatures. Before summarizing, we removed fits with non-finite metrics, required at least three FCR observations per replicate, and when duplicate fits existed for a replicate, retained the lowest-AIC fit. The Gamma/log linear fit was the highest with the second lowest AIC (mean $R^{2} = 0.566$ ; mean AIC = 21.34), followed by the exponential (mean $R^{2} = 0.368$ ; mean AIC = 42.65), and the power (mean $R^{2} = 0.347$ ; mean AIC = 16.489). We therefore chose the Gamma/log linear model for subsequent simulations, balancing explanatory power and simplicity for on-farm use.

All profitability simulations were therefore conducted using the Gamma/log linear FCR model:

\begin{matrix} FCR (t) = exp ({FCR}_{0} + {FCR}_{1} \times t) \end{matrix}

Where ${FCR}_{0}$ is the log-intercept, and ${FCR}_{1}$ is the log-slope and t is age in weeks. Fits were performed per replicate × temperature group using a Gamma/log linear model on the filtered FCR-age data.

Fit quality: ${FCR}_{0} = - 0.936$ ; ${FCR}_{1} = 0.285$ ; Mean $R^{2} = 0.57$ . Details are provided in Table S4 in the Supplementary material.

Parameterization of the bioeconomic model

To assess how core biological traits impact profitability, we parameterized a mechanistic model for A. domesticus using global means from published growth and feeding experiments. Survivorship and growth parameters were fit to data from Morales-Ramos et al., (2018): an exponential decay function to cohort survival (yielding $S_{\min}$ and k), and a bounded logistic model to body mass trajectories (yielding $W_{\max}$ , r and $t_{mid}$ (Tables S1 and 2, Figures S1 and S2 in the Supplementary material). FCR parameters (FCR₀, FCR₁) were estimated by a Gamma/log linear fit of FCR vs time, averaged across all replicates and temperatures (Tables S3 and S4, Figures S3–S5 in the Supplementary material).

To illustrate how biological performance shapes economic outcomes, we defined two contrasting production scenarios – Low- and High-Efficiency – by varying key biological parameters within literature-based ranges, or, where explicit values were unavailable, ±20% around empirical averages (Table 1). Core economic parameters were set to plausible commercial values or literature averages (Table 2). These scenarios are not meant to capture any single farm’s conditions, but rather to demonstrate the sensitivity of profitability to realistic biological variation. All other economic and management constants were held fixed.

$Parameter values used in the profitability model for \textit{Acheta domesticus}$

Table 1

Parameter values used in the profitability model for Acheta domesticus

Citation: Journal of Insects as Food and Feed 2026; 10.1163/23524588-bja10365

Because literature often reports FCR as a single value, we used reported lifetime FCRs (e.g. 2.3 and 10) as targets for the High- and Low-efficiency scenarios. For each scenario, we first adjusted the empirical FCR log-slope (FCR₁) by a fixed proportion around its empirical value (Table 1), then numerically calibrated the log-intercept (FCR₀) by scaling the FCR(t) curve so that the simulated lifetime FCR matched the scenario specific target. This procedure anchors the age-dependent FCR function to observed lifetime averages while allowing scenarios to differ in both in overall efficiency (intercept) and in how rapidly FCR deteriorates with age (slope), without introducing additional functional complexity.

Fit diagnostics and model comparison statistics for survivorship, growth, and FCR models are provided in the Supplementary Material (Tables S1–S4 and Figures S1–S5).

Economic and farm management parameters

Economic and farm management parameters were set to plausible values from the literature or common practice unless noted; baseline simulation values are summarized in Table 2.

Table 2

Baseline economic and farm management parameters used in profitability simulations

Citation: Journal of Insects as Food and Feed 2026; 10.1163/23524588-bja10365

core batch simulation and maintenance modelling

Simulations spanned 15 weeks with 0.1-week time steps. At each step we (i) computed survivorship, (ii) updated mean body weight, (iii) calculated batch biomass (initial cohort × survivorship × mean weight), (iv) computed feed as the greater of growth-based demand (Δbiomass × FCR) or maintenance demand (biomass × maintenance feeding rate), and (v) updated revenue (cumulative biomass × dry mass fraction x price) and feed cost (cumulative feed x unit cost); profit equalled revenue minus feed costs (fixed costs set to zero). To reflect realistic commercial harvest windows, candidate harvest ages for profitability calculations were restricted to 4–15 weeks.

Because Δbiomass × FCR alone predicted zero food intake once growth ceases, we incorporated a maintenance feeding function. Maintenance began when (1) weekly mean-weight gain fell below 2% of the cohort’s maximum observed weekly gain and (2) mean weight exceeded 95% of the cycle’s maximum, both sustained for >2 consecutive time points. Thresholds were detected programmatically per simulation.

Default box area and stocking density reflect typical small-scale commercial bins, but are adjustable as needed for other system sizes. Fixed costs were set to zero in all simulations.

Profit calculation and annualization

Batch profit equalled cumulative revenue (harvestable dry biomass x market price) minus cumulative feed costs. To compare harvest ages and scales, profit was normalized by floor area and annualized (profit/m² per year).

\begin{aligned} Annualized profit \\ = \frac{Batch profit (USD, dry mass)}{Box floor area (m^{2})} \\ \times \frac{52}{Harvest age (weeks)} \end{aligned}

Optimal harvest timing was the time point maximizing annualized profit per m² in each scenario.

Elasticity analysis

To identify influential biological parameters, we ran an elasticity analysis with two illustrative scenarios (Low- and High-efficiency). Parameters included

S_{\min}

, k, r,

W_{\max}

t_{mid}

, FCR₀ and FCR_t. Elasticities were computed by perturbing each parameter ±20% around its scenario baseline, holding all others constant, and recording the change in annualized dry-mass profit per m². For each parameter, elasticity was the mean proportional profit change per 1% parameter changed (averaged over ± perturbations):

\begin{aligned} Elasticity = & \frac{1}{2} (\frac{Δ {Profit}_{+} ∕ {Profit}_{0}}{Δ {Parameter}_{+} ∕ {Parameter}_{0}} \\ + \frac{Δ {Profit}_{-} ∕ {Profit}_{0}}{Δ {Parameter}_{-} ∕ {Parameter}_{0}}) \end{aligned}

where ΔProfit± is the change in profit after increasing or decreasing the parameter by 20%, and Profit₀ and Parameter₀ are the baseline values.

Elasticity results are visualized using tornado plots, which highlight the most influential parameters under each scenario. This approach is intended to help prioritize experimental, breeding, or management interventions by identifying high-leverage biological traits within the current modeling framework.

Sensitivity and heatmap analyses

We explored interactions between key biological and economic parameters, harvest timing and annualized profit using two-dimensional sensitivity analysis and heatmaps. For each focal parameter, we varied its value across a representative range while holding others at empirically derived baselines. For each combination of focal value and harvest age (restricted to a window of 4–15 weeks), we simulated batch production, computed revenue and feed costs and calculated annualized profit per m², enabling comparison of relative importance and tradeoffs.

3 Results

Elasticity of annualized profit to core model parameters

Elasticity analysis demonstrates that biological parameters with the strongest influence on annualized profit per m² vary by production context (Figure 1). In the low-efficiency scenario (Figure 1A), profit is most sensitive to the growth inflection point ( $t_{mid}$ ), intrinsic growth rate (r), and the slope of the FCR-age curve (FCR₁). In the high-efficiency scenario (Figure 1), the growth inflection point ( $t_{mid}$ ), maximum body weight ( $W_{\max}$ ) and asymptotic adult survivorship (S_min) are the most sensitive parameters. Overall, this analysis stresses the need for scenario-specific optimization: the most effective targets for intervention depend on each farm’s individual performance characteristics.

$Tornado plots showing the elasticity of annualized profit (per square meter) to key model parameters for a low-efficiency scenario (red) and a high-efficiency scenario (cyan). Elasticity values represent the percent change in profit per 1% change in each parameter, averaged over $\pm $20% perturbations. Positive values indicate that increasing the parameter raises profit; negative values indicate that increasing the parameter reduces profit. Numeric labels show elasticity estimates. Parameters are: $S_{\mathrm{min} }$ (asymptotic adult survivorship), $k $ (mortality decay rate), $r $ (logistic growth rate), $W_{\mathrm{max} }$ (asymptotic body weight), $t_{\mathrm{mid} }$ (age at logistic growth inflection), FCR\tsub{0} (log-intercept of the FCR--age relationship) and FCR\tsub{1} (log-slope of the FCR--age relationship) Parameter sets are defined in Tables 1 and 2.$

Figure 1

Tornado plots showing the elasticity of annualized profit (per square meter) to key model parameters for a low-efficiency scenario (red) and a high-efficiency scenario (cyan). Elasticity values represent the percent change in profit per 1% change in each parameter, averaged over ±20% perturbations. Positive values indicate that increasing the parameter raises profit; negative values indicate that increasing the parameter reduces profit. Numeric labels show elasticity estimates. Parameters are: $S_{\min}$ (asymptotic adult survivorship), k (mortality decay rate), r (logistic growth rate), $W_{\max}$ (asymptotic body weight), $t_{mid}$ (age at logistic growth inflection), FCR₀ (log-intercept of the FCR–age relationship) and FCR₁ (log-slope of the FCR–age relationship) Parameter sets are defined in Tables 1 and 2.

Citation: Journal of Insects as Food and Feed 2026; 10.1163/23524588-bja10365

Given their magnitude, and relative importance to each scenario, we focus the remainder of the results section on $S_{\min}$ , $t_{mid}$ and FCR₁. Additional parameters appear in the Supplementary Material (Figures S6–S11). While these scenarios combine empirical, literature and illustrative values, this approach demonstrates the value of elasticity mapping for guiding idiosyncratic intervention priorities in diverse production settings. It is important to note that elasticities are local to each stylized scenario and assume parameters vary independently; in practice, management changes may affect several parameters simultaneously.

Parameter-wise heatmaps: interactions between biological traits, harvest timing, and annual profit

To visualize how core biological parameters modulate profitability, we generated heatmaps of annualized profit across harvest age and focal parameter values, holding all other parameters at their empirically estimated global means (see Tables 1 & 2). For each trait, panel A shows the predicted trajectory for illustrative values from low and high efficiency scenarios, while panel B shows the profit landscape across the full parameter range and harvest ages.

Adult survivorship ( $S_{\min}$ ): Increasing asymptotic adult survivorship consistently raises annual profit, as more of the initial cohort reach harvest (Figure 2A, B). The effect is most pronounced when survivorship is low, with diminishing returns as $S_{\min}$ approaches 1. Higher $S_{\min}$ values also shift the optimal harvest time later, permitting additional growth without a steep penalty in cumulative mortality.

$Effects of adult survivorship ($S_{\mathrm{min} }$) on population dynamics and annual profit. (A) Survivorship trajectories over a 15-week production cycle for two representative values of asymptotic adult survivorship ($S_{\mathrm{min} } =0.1$, red; $S_{\mathrm{min} } =0.7$, cyan). Higher $S_{\mathrm{min} }$ values sustain a larger proportion of the initial cohort through to harvest, while low $S_{\mathrm{min} }$ leads to rapid population decline. (B) Heatmap showing the effect of $S_{\mathrm{min} }$ and harvest age on annualized profit per square meter. Increasing $S_{\mathrm{min} }$ consistently raises profitability, with diminishing returns as survivorship approaches one. Contour lines indicate profit isoclines.$

Figure 2

Effects of adult survivorship ( $S_{\min}$ ) on population dynamics and annual profit. (A) Survivorship trajectories over a 15-week production cycle for two representative values of asymptotic adult survivorship ( $S_{\min} = 0.1$ , red; $S_{\min} = 0.7$ , cyan). Higher $S_{\min}$ values sustain a larger proportion of the initial cohort through to harvest, while low $S_{\min}$ leads to rapid population decline. (B) Heatmap showing the effect of $S_{\min}$ and harvest age on annualized profit per square meter. Increasing $S_{\min}$ consistently raises profitability, with diminishing returns as survivorship approaches one. Contour lines indicate profit isoclines.

Citation: Journal of Insects as Food and Feed 2026; 10.1163/23524588-bja10365

Growth inflection point ( $t_{mid}$ ): Decreasing the growth inflection point ( $t_{mid}$ ) strongly increases annual profit by shifting rapid growth to earlier stages, when survivorship is higher (Figure 3A, B). Earlier $t_{mid}$ values produce heavier crickets sooner and allow profitable harvest at younger ages, which leads to more frequent harvests per year.

$Effects of growth inflection points ($t_{\mathrm{mid} }$) on population dynamics and annual profit. (A) Growth trajectories over a 15-week production cycle for two growth inflection points ($t_{\mathrm{mid} } = 8.05$, red; $t_{\mathrm{mid}} =5.47$, cyan). Earlier $t_{\mathrm{mid} }$ values shift rapid growth to younger ages, yielding heavier animals sooner. (B) Heatmap showing the effect of $t_{\mathrm{mid} }$ and harvest age on annualized profit per m\tsup{2} per year. Profit is highest when growth inflection occurs early and harvest ages are within an intermediate window; delayed $t_{\mathrm{mid}}$ or late harvesting times both reduce profitability. Contour lines indicate profit isoclines.$

Figure 3

Effects of growth inflection points ( $t_{mid}$ ) on population dynamics and annual profit. (A) Growth trajectories over a 15-week production cycle for two growth inflection points ( $t_{mid} = 8.05$ , red; $t_{mid} = 5.47$ , cyan). Earlier $t_{mid}$ values shift rapid growth to younger ages, yielding heavier animals sooner. (B) Heatmap showing the effect of $t_{mid}$ and harvest age on annualized profit per m² per year. Profit is highest when growth inflection occurs early and harvest ages are within an intermediate window; delayed $t_{mid}$ or late harvesting times both reduce profitability. Contour lines indicate profit isoclines.

Citation: Journal of Insects as Food and Feed 2026; 10.1163/23524588-bja10365

Feed Conversion Slope (FCR_t): Decreasing the FCR log-slope (FCR_t) improves profitability, because a flatter FCR(t) curve keeps feed conversion efficient for longer as crickets age (Figure 4A, B). As FCR_t increases, FCR deteriorates rapidly at later ages, and profit drops across all harvest ages, with late harvests becoming especially unprofitable.

$Effects of feed conversion slope (FCR\tsub{t}) on FCR trajectory and annual profit. (A) Linear FCR(t) trajectories for two representative log-slopes (FCR\tsub{t} $=$ 0.313, red; FCR\tsub{t} $=$ 0.228, cyan). Higher FCR\tsub{1 } values cause FCR to rise more steeply with age (B) Heatmap showing the effect of FCR\tsub{t} and harvest age on annualized profit per m\tsup{2} dry per year. Increasing FCR\tsub{t} reduces profitability, with the largest impacts observed at higher values and longer rearing intervals. Contour lines indicate profit isoclines.$

Figure 4

Effects of feed conversion slope (FCR_t) on FCR trajectory and annual profit. (A) Linear FCR(t) trajectories for two representative log-slopes (FCR_t = 0.313, red; FCR_t = 0.228, cyan). Higher FCR₁ values cause FCR to rise more steeply with age (B) Heatmap showing the effect of FCR_t and harvest age on annualized profit per m² dry per year. Increasing FCR_t reduces profitability, with the largest impacts observed at higher values and longer rearing intervals. Contour lines indicate profit isoclines.

Citation: Journal of Insects as Food and Feed 2026; 10.1163/23524588-bja10365

Complete heatmaps for the remaining parameters market price, feed cost, mortality rate (k), growth rate (r), maximum body weight ( $W_{\max}$ ), and FCR intercept (FCR₀) are provided in the Supplementary Material (Figures S6–S11).

Summary of the main findings

These results identify survivorship, growth inflection point, and FCR slope as influential levers for profitability in A. domesticus systems. Targeted improvements in core traits via selective breeding, management or environmental optimization substantially increase economic returns. While we quantify each parameter’s effect, the Discussion details optimization strategies, practical considerations and trade-offs.

This model captures the essential biological drivers of profitability; however, it simplifies real-world systems by holding prices, environmental conditions, and management practices constant. Accordingly, this framework is a transparent starting point for future, more detailed models that integrate ecological interactions, evolutionary consequences, farm-level data, environmental variability, and broader economic risks.

4 Discussion

Model contributions

To our knowledge, this is the first Acheta domesticus framework that links batch-level growth, survivorship, and feed intake directly to profit optimization using operationally measurable inputs. Our approach empowers producers to benchmark, optimize, and adapt decisions using accessible, real-time biological and economic data, supported by open-source, customizable R code. The scenario specific-elasticities order trait priorities by scenario (Figure 1). In addition, the parameter-wise heatmaps translate trait variation into actionable harvest timing (Figures 2B, 3B, 4B).

The framework’s strength lies in its simplicity: average body weight, feed consumption, and cohort survivorship are practical to measure, making on-farm application accessible regardless of scale. Our analysis suggests that the parameters exerting the strongest profit response vary depending on the efficiency scenario (Figure 1). Although ecological factors like density, temperature, and feed composition can affect profitability (Booth and Kiddell, 2007; Mahavidanage et al., 2023; Takacs et al., 2023), in this framework their effects enter through measurable parameters (eg., adult survivorship and the survivorship decay rate) so the model remains informative without explicit covariates and is designed to accommodate them in future extensions. Tracking body weight is straightforward with subsampling, feed use can be measured with routine adjustments (Morales-Ramos et al., 2018), and while non-invasive survivorship measurement remains a challenge, well established population measurement techniques such as mark-recapture and new solutions like smart sensors hold promise as technology advances (Petit and Valiere, 2006; Tjandrata and Liawatimena, 2025). For farmers selling crickets at multiple life stages, stage-specific survivorship can be approximated by comparing realized harvest mass with mass expected from the initial number stocked and average individual weight at that stage; tracking this ratio over time could develop an actionable survivorship curve. Finally, to improve comparability and decision relevance, we encourage reporting FCR as a function of body mass or age (rather than only lifetime averages) and studying how this trajectory shifts with diet, density, and temperature.

By meeting the sector’s urgent need for transparent, adaptable, and standardized tools (Caparros Megido et al., 2024; Van Peer et al., 2024), our model provides a practical platform for farm management and hypothesis testing. Its modular design supports individual producer empowerment, establishment of selective breeding programs, and bioeconomic model development, paving the way for a data-driven, standardized future in cricket farming (Eriksson and Picard, 2021; Hansen et al., 2025; Lecocq, 2018; Nielsen et al., 2014). Consistent with analysis, selective breeding can prioritize traits with consistently positive elasticities (eg., higher adult survivorship, and lower survivorship decay rate), while management can tune harvest timing along the profit isoclines visualized in the heatmaps (Figures 2B, 3B, 4B).

Biological and Operational “levers”

A variety of rearing strategies and farm management techniques are available to optimize growth and survivorship in Acheta domesticus, drawing on a robust and rapidly expanding research base (Kuo and Fisher, 2022; Morales-Ramos et al., 2024; Sengendo et al., 2025; Van Peer et al., 2024; Zafar et al., 2024). Seminal and modern reviews provide practical guidelines for colony maintenance, feeding, and environmental management in commercial and laboratory settings (Clifford and Woodring, 1990; Morales-Ramos et al., 2024; Patton, 1978). However, effectiveness depends interacting factors (e.g. feed formulation, rearing density, and temperature control) many of which are still being optimized for large-scale production (Kuo and Fisher, 2022; Mahavidanage et al., 2023; Morales-Ramos et al., 2020; Takacs et al., 2023). The following sections briefly touch on the most impactful, evidence-based levers for managing growth and survivorship in farmed house crickets, as well as key processing considerations, with an emphasis on scalable, actionable practices.

Growth rate and maximum body size in A. domesticus can be manipulated through a variety of biological and management levers. Selective breeding (Ryder and Siva-Jothy, 2001; Tennis, 1985) and inadvertent selective pressures imposed by routine husbandry (Lecocq, 2018; Olzer et al., 2019) can yield larger, faster-growing crickets over multiple generations, though such approaches may trade-off with immunity (Bascuñán-Garcı́a et al., 2010). Feed composition is becoming a well-established driver: protein, phosphorus, and other nutrients influence growth rate, final size, and condition. Recent studies show both macronutrients (protein, carbohydrate, lipid) and micronutrients (phosphorus, sterol, manganese, vitamin C) can boost biomass gain (Gutiérrez et al., 2020; Morales-Ramos et al., 2020; Muzzatti et al., 2024; Visanuvimol and Bertram, 2011; Suphawadee Yaemkong et al., 2024). Research into regional and alternative feeds (byproducts, municipal food waste, and crop residues) has produced mixed results: some options, such as maize grain distillers, support good growth, while others cause stunted development or high mortality (Jucker et al., 2022; Lundy and Parrella, 2015; Oloo et al., 2020; Oonincx et al., 2015; Quek et al., 2020; Van Peer et al., 2021). Temperature management is another accessible lever; higher rearing temperatures accelerate growth but can reduce final size and in other edible crickets, longevity (Booth and Kiddell, 2007; Kong et al., 2025; Morales-Ramos et al., 2018). Together, optimizing these parameters is a direct, well-supported route to increased profitability.

Survivorship is strongly affected by diet quality; most alternative or byproduct feeds lead to reduced survival unless carefully formulated to meet cricket nutrition needs (Jucker et al., 2022; Lundy and Parrella, 2015; Oonincx et al., 2015; Patton, 1967; Quek et al., 2020; Sorjonen et al., 2019; Vaga et al., 2020, 2021). Although cricket nutrition is not yet fully understood Morales-Ramos et al., (2020) demonstrates that crickets self-select a diet with average macronutrient ratios of approximately 6:30:63 lipids:protein:carbohydrates. High rearing densities increase mortality and can exacerbate cannibalism, disease transmission, and developmental delays, making density management critical (Gutiérrez et al., 2020; Mahavidanage et al., 2023). Environmental conditions, particularly temperature, also interact with density: higher densities can increase pathogen abundance, but optimal temperature management can help offset some of these risks (Takacs et al., 2023). Persistent high-density environments may unintentionally select for more aggressive, competitive behavioural phenotypes (Olzer et al., 2019), compounding survivorship challenges. Exploring hybridization with wild or low-density lines (selected for favourable traits such as size or growth) may mitigate these unintended effects and improve long-term viability. While increased survivorship offers both biological and economic gains, interventions must be tailored to each operation to balance trade-offs among density, disease, cannibalism, and resource efficiency.

Processing and feed formulation affect nutritional composition, safety, and functional properties of cricket-derived products. Diets influence not just growth but also the chemical composition and micronutrient profiles; both macronutrient and fatty acid content respond to substrate and feed type (Morales-Ramos et al., 2020; Oonincx and Finke, 2021; Riekkinen et al., 2022; Ververis et al., 2022; Suphawadee Yaemkong et al., 2024). Processing steps such as drying, grinding, and protein extraction alter nutritional value, microbial load, and techno-functional properties (solubility, emulsification, gelling, water and oil binding), affecting product quality, usefulness, and shelf life (Bawa et al., 2020; Brena-Melendez et al., 2024, 2025; Gravel and Doyen, 2020; Meyer-Rochow et al., 2021; Pan et al., 2022; Pellerin and Doyen, 2024; Ribeiro et al., 2019). Furthermore, cricket flour made from whole insects versus separate body parts may slightly alter protein content and functional behavior (Brena-Melendez et al., 2024, 2025). Feed may also influence virome richness and viral load (Cholleti et al., 2022). The interplay of diet, processing, and extraction methods impact both nutritional and safety profiles; for risk assessments and health hazard profiles, see (Garofalo et al., 2019; Marzoli et al., 2024; Siddiqui et al., 2023). For in-depth reviews of how nutritional and technofunctional properties can be optimized through feed and processing, see (Gravel and Doyen, 2020; Meyer-Rochow et al., 2021; Oonincx and Finke, 2021; Pan et al., 2022).

5 Conclusion

Cricket farming continues to expand globally as a promising solution for sustainable protein production, yet faces persistent operational, economic, and standardization challenges. By providing a transparent, practical framework that couples key biological drivers to economic outcomes, our model addresses sector-wide barriers: limited standardization, difficulty benchmarking across production systems, and the challenges of translating empirical research into actionable, on-farm improvements. This approach supports ongoing innovation in feed, management, and breeding, and lays the groundwork for industry-wide adoption of common metrics and transparent benchmarking. As the field advances, broad application and iterative refinement of tools like this will be essential for realizing the full ecological and economic potential of crickets as food and feed. Our framework supports empirical research and operational practice, offering a foundation for sustainable growth in both science and industry.

Corresponding author; e-mail: rozg@bravofl.com

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Title:: A bioeconomic model linking growth, survival, and feed conversion to profitability in farmed Acheta domesticus

Article Type:: Research Article

DOI:: https://doi.org/10.1163/23524588-bja10365

Language:: English

Pages:: 1–14

Keywords:: Acheta domesticus; bioeconomic model; feed conversion ratio; growth rate; profitability optimization; survivorship

In:: Journal of Insects as Food and Feed

In:: Advance Articles

Received:: 27 Aug 2025

Accepted:: 12 Jan 2026

Publisher:: Wageningen Academic

E-ISSN:: 2352-4588

Subjects:: General, Life Sciences, Food & Health, Animal & Veterinary, Entomology, Biology

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Table 1

Parameter values used in the profitability model for Acheta domesticus

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Table 2

Baseline economic and farm management parameters used in profitability simulations

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Figure 1

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Figure 2

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Figure 3

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Figure 4

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A bioeconomic model linking growth, survival, and feed conversion to profitability in farmed Acheta domesticus

In: Journal of Insects as Food and Feed

Authors:

Rodrigo Zorrilla González

https://orcid.org/0000-0002-4035-1521

and

Erik Neff

Erik Neff Fluker’s Cricket Farm, 1333 Plantation Avenue, Port Allen, LA 70767, USA

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https://orcid.org/0000-0002-5521-5629

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Online Publication Date:: 23 Jan 2026

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Abstract

Keywords: Acheta domesticus; bioeconomic model; feed conversion ratio; growth rate; profitability optimization; survivorship

1 Introduction