Unveiling the Performance Drivers of Northern Australian Beef Systems: A Time Series Analysis 1990-2022)

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Dissanayake, Nurul Hilmiati, Dianne E. Mayberry, Karen E. Eyre, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7152494/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Improving the productivity and sustainability of beef cattle systems in dry tropical regions requires better understanding of long-run drivers of performance and system adjustment dynamics. This study uses Panel Vector Error Correction Models (PVECM) and 32 years of regional data (1990–2022) to investigate the socio-economic and environmental determinants of beef cattle production and productivity in northern Australia. We hypothesised that reproductive efficiency, labour input, and financial structure are significant long-run drivers of system performance, and that tropical beef systems respond dynamically to shocks over time. The PVECM for beef cattle production revealed positive long-run relationships with branding rate, total labour used, and farm capital, and negative relationships with area operated, non-farm income, liquid assets, and rainfall. The productivity model identified positive effects from non-farm income, livestock contract costs, liquid assets, and rainfall, and a negative effect from business debt. The error correction term for the production model was significant, with strong adjustment observed for total labour used and liquid assets, indicating these variables help rebalance the system after shocks. In the productivity model, the error correction term was also significant, with adjustments evident in branding rate and rainfall, suggesting these variables respond dynamically to deviations in long-run productivity. In conclusion, these findings demonstrate that cattle production systems in tropical environments exhibit both structural persistence and short-run responsiveness. Reproductive efficiency emerged as a central and persistent driver of both production and productivity. The results provide an empirical basis for targeted management interventions to improve resilience, investment efficiency, and long-term profitability. Beef cattle systems Panel Vector Error Correction Model Production Productivity Reproductive performance Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The beef industry is a major economic contributor to northern Australia, encompassing Queensland (QLD), the Northern Territory (NT), and northern Western Australia (WA). This region supports large-scale, extensive grazing systems that span thousands of square kilometres, relying primarily on pasture-based production using native and introduced grasses such as Mitchell grass and Buffel grass (Chilcott et al. 2020 ). In the NT alone, the beef industry contributes over $ 1.3 billion annually (Northern Territory Government 2022 ), while in QLD, it is a cornerstone of rural employment and economic activity (Queensland Government 2022 ). Despite its economic significance, the performance of beef enterprises in northern Australia is highly variable. Periods of low or negative returns are common (Ashton et al. 2016 ), and industry-wide assessments have highlighted concerns about long-term profitability and sustainability (McCosker et al. 2010 ; McLean et al. 2014 ). Benchmarking studies consistently show that the top 25% of producers outperform their peers through higher reproductive rates, lower operating costs, and more efficient labour use (McLean et al. 2023 ; Holmes et al. 2017 ). In contrast to intensive systems, where cattle performance approaches genetic potential and marginal gains dominate, northern beef systems often remain constrained by low-input management and environmental variability. Pasture productivity, particularly during the wet season, remains the key driver of cattle growth, yet has seen little change in recent decades (Chilcott et al. 2020 ). Management and nutritional interventions have the potential to deliver substantial improvements, but their adoption and effects remain unevenly understood at a regional scale. To address this gap, this study investigates the long-term drivers of beef cattle production and total factor productivity (TFP) in northern Australia using a time-series panel econometric approach. We analyse the relationships among biophysical, financial, and management variables over a 32-year period (1990–2022) to better understand what drives performance. We hypothesise that both beef cattle production and productivity are influenced not only by environmental factors such as rainfall, but also by management and financial indicators, particularly reproductive efficiency, labour use, and capital investment. By examining both long-run relationships and short-run dynamics, this study aims to provide insights into the resilience, responsiveness, and leverage points for improving sustainability in northern Australian beef systems. Materials and Methods Data and Data Collection This study used secondary data from Queensland (QLD), the Northern Territory (NT), and Western Australia (WA), covering the period from 1990 to 2022. Data were sourced from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), the Australian Bureau of Statistics (ABS), and the Bureau of Meteorology (BoM), with the latest updates available as of July 2023. Annual beef cattle production was estimated by summing the number of cattle slaughtered (ABS Livestock Products) and live cattle exported (ABS International Merchandise Trade Data Portal) for each state. Total factor productivity (TFP) was used as the main indicator of farm performance, calculated as the ratio of total output to total input (ABARES 2024). Annual rainfall data were obtained from the BoM (BoM 2024) for each corresponding region. Data Analysis To examine the factors influencing beef cattle performance in northern Australia, we applied a Panel Vector Error Correction Model (PVECM). This method is well-suited to time series data that includes both stationary and non-stationary variables and is commonly used to identify both long-run equilibrium relationships and short-run dynamics among multiple interrelated variables (Engle and Granger 1987 ). Variables were first log-transformed to stabilise variance and interpret estimated coefficients as elasticities. Stationarity was tested using the panel version of the augmented Dickey–Fuller test (Costantini and Lupi 2013 ) to determine whether the time series fluctuated around a constant mean or followed a stochastic trend. The optimal number of lags included in the model was selected using the Akaike (AIC) and Hannan–Quinn (HQ) information criteria, which help identify how many past time points should be considered to best capture the short-run relationships between variables without overfitting the model. The Johansen cointegration test (Pedroni 2001 ) was used to detect whether the variables shared a long-run relationship; that is, whether they tended to move together over time despite short-term fluctuations. Estimating such long-run relationships helps identify structural drivers of beef production and productivity, such as labour input, rainfall, or capital investment. To understand how shocks in one variable affect others over time, Impulse Response Functions (IRFs) were used. These estimate how beef cattle production or total factor productivity respond in subsequent years to a sudden change (shock) in one of the explanatory variables. We also applied Forecast Error Variance Decomposition (FEVD) to quantify the relative contribution of each variable to the variability observed in beef production and productivity, helping us assess which factors are most influential over different time horizons (Akinlo and Adejumo 2016 ). Results Panel Vector Error Correction Model for Beef Cattle Production The PVECM results identified one significant long-run cointegration relationship among Y1 and its explanatory variables, based on the Johansen cointegration test (cointegration rank = 1). The estimated equation is as follows: Where Y1 is beef cattle production, and the explanatory variables are defined in Table 1. Model fit statistics indicated good adequacy (AIC = − 3891.4; BIC = − 3596.4; SSR = 16.8). The 5.133 coefficient for LBR indicates that an increase in LBR tends to increase Y1 in the long run. LTL, LCA, and LFBD also positively affected Y1. In contrast, LAO, LNFI, LFLA, and LRF were negatively associated with Y1. LBR and LRF had the highest absolute coefficients, indicating their dominant role in explaining long-run variation in beef cattle production. Table 2 presents the error correction terms (ECTs) and short-run dynamics of the PVECM. The ECT for Y1 was − 0.213 and statistically significant (p < 0.001), indicating that beef cattle production adjusts toward its long-run equilibrium at a relatively fast rate following short-term deviations. Several explanatory variables also showed significant adjustment behaviour. LTL (0.177, p < 0.1), LCA (0.300, p < 0.05), LNFI (0.502, p < 0.05), LFLA (0.431, p < 0.01), and LFBD (0.268, p < 0.1) all exhibited positive and significant ECT coefficients, suggesting gradual convergence toward equilibrium in response to deviations in the long-run relationship. Short-run interactions among variables are summarised in Table 2 . LTL(–1) had a significant positive effect on LBR, suggesting that previous labour use influenced current branding outcomes. LAO was positively affected by LFBD(–1), while LTL was significantly influenced by its own lag and LFBD. In the LCA equation, LFBD(–1) had a significant negative effect. In the short run, LNFI was positively affected by Y1(–1), LAO(–1), LRF(–1), and ES, but negatively affected by its own lag and by LFBD(–1). LFLA responded significantly to its own lag. LFBD was significantly influenced by Y1(–1), indicating feedback from past production to farm business debt. Figure 1 presents the orthogonal Impulse Response Functions (IRFs), which show the effect of a one-standard-deviation shock (i.e. a sudden, unexpected increase) in each explanatory variable on Y1 over a 10-period horizon. These shocks simulate short-term changes in individual drivers, while holding other variables constant, allowing us to assess the dynamic response of beef cattle production to isolated changes in key inputs. Shocks to LBR and LTL produced statistically significant positive responses in Y1, peaking within the first 2–3 periods before gradually stabilising. This indicates short-term gains in beef cattle production following improvements in branding rate and labour use. In contrast, shocks to LRF and LNFI caused negative short-run impacts on Y1, with effects diminishing over time. The response to LRF was initially large and statistically significant, but became negligible after period 5. Shocks to LAO, LFLA, and LFBD showed no statistically significant effect on Y1 across the forecast horizon. The response to LCA was negative and persistent, remaining below baseline across the entire period, though only statistically significant in early periods. Overall, LBR and LTL had the clearest positive influence on Y1, while LRF and LCA showed the strongest negative effects in the short run. Figure 2 shows the Forecast Error Variance Decomposition (FEVD) of Y1 over a 10-period horizon. This analysis estimates the proportion of variation in Y that can be attributed to shocks in each explanatory variable, helping identify the main contributors to forecast uncertainty. The results indicate that nearly all the variability in Y1 is explained by its own shocks, starting just below 100% and stabilising around 95% over time. This suggests that beef cattle production is largely self-driven and exhibits strong autoregressive behaviour. In contrast, shocks from other variables, including LBR, LTL, LCA, LNFI, and LRF, make only minor contributions to the forecast variance, highlighting their limited dynamic influence relative to the momentum within Y1 itself. Panel Vector Error Correction Model for Total Factor Productivity The PVECM for LTFP showed a good model fit (AIC = − 2804.2; BIC = − 2565.1; SSR = 15.52) and identified two cointegrating relationships (cointegration rank = 2). The first, normalised on LTFP (Eq. 2), represents the long-run equilibrium relationship among productivity and its key drivers. The second, normalised on LBR (Eq. 3), reflects the statistical interdependence among the explanatory variables within the system and is not the focus of interpretation. $$\:LTFP=0.251LNFI+0.198LCC+0.189LFLA+1.156LRF-0.308LFBD+\:\epsilon\:\dots\:.\dots\:\dots\:.\left(\text{E}\text{q}\text{u}\text{a}\text{t}\text{i}\text{o}\text{n}\:2\right)$$ $$\:LBR=0.074LNFI+0.003LCC+0.001LFLA+0.188LRF-0.074LFBD+\:\epsilon\:\dots\:\dots\:\dots\:\dots\:.\left(\text{E}\text{q}\text{u}\text{a}\text{t}\text{i}\text{o}\text{n}\:3\right)$$ In the first cointegration relationship (ECT1), significant adjustment was observed in LBR (0.105; p < 0.05) and LRF (0.594; p < 0.01), indicating these variables respond to deviations from the LTFP equilibrium. In the second relationship (ECT2), LBR, LFLA, and LFBD showed strong and significant adjustments, suggesting mechanisms to restore equilibrium when deviations occur in the LBR centred relationship. Short-run dynamics (Table 3 ) revealed additional interactions. In the LNFI equation, significant negative effects were found for LNFI(–1), LRF(–1), and LFBD(–1). LFBD(–1) also had a significant positive influence on LCC. In the LFLA equation, LNFI(–1) and LFLA(–1) were both significant and negative. Exogenous variables AG and ES negatively affected LNFI, with ES also significant at the 10% level. Figure 3 presents the Orthogonal Impulse Response Functions (IRFs) of LTFP to one-standard-deviation shocks in each explanatory variable over a 10-period horizon. These IRFs illustrate how total factor productivity responds dynamically to unexpected changes in the system, while holding other variables constant. A shock to LBR caused an immediate and sustained decline in LTFP, with the negative effect persisting across the entire period, although gradually diminishing. Similarly, shocks to LCC, LFLA, and LFBD resulted in statistically significant short-run reductions in LTFP. These effects were strongest within the first 3–4 periods and remained negative over time, indicating that sudden increases in these inputs are associated with declines in productivity during the short term. In contrast, shocks to LNFI and LRF produced positive and lasting effects on LTFP. The response to LNFI peaked around period 3 and remained above baseline thereafter, indicating that increased non-farm income supports sustained productivity gains. The effect of LRF was immediate and persistent, with the largest gains observed in the first half of the forecast horizon. Overall, the IRFs suggest that LTFP is sensitive to shocks in key financial and climatic variables, with positive responses to LNFI and rainfall, and negative responses to increased branding, contracts, liquid assets, and debt. Figure 4 displays the Forecast Error Variance Decomposition (FEVD) of LTFP over a 10-period horizon, quantifying the relative contribution of each variable’s shock to the forecast variance in LTFP. In the short term, the variance in LTFP is driven almost entirely by its own innovations, accounting for nearly 100% of the forecast error variance at period 1. However, this dominance declines steadily, stabilising around 50% by period 10, as the influence of other variables accumulates. Shocks to LBR emerge as the most influential external driver of LTFP over time, contributing approximately 25% of the variance by period 10. Meanwhile, shocks to LNFI, LCC, LFLA, and LRF each contribute more modest shares, generally under 10%, suggesting a comparatively limited dynamic impact on LTFP. Shocks to LFBD explain only a small fraction of the variance throughout the period. These results highlight the increasing importance of branding rate (LBR) in shaping long-run fluctuations in total factor productivity, while other variables play more minor roles in driving variance over time. Discussion This study set out to investigate the long-run and short-run drivers of beef cattle production and total factor productivity in northern Australia, with a particular focus on the role of reproductive efficiency, labour, capital structure, and environmental variability. Our hypothesis was that key management and financial factors, especially branding rate, labour use, and debt, would exert significant influence on both production and productivity, and that these relationships would manifest through distinct long-term equilibrium paths and short-run adjustment mechanisms. The findings from this paper strongly support this hypothesis. The results highlight the central role of branding rate and labour as production drivers, and the multifactorial, yet structured, influence of financial and climatic variables on long-run productivity and its volatility over time. Long-run Drivers of Beef Cattle Production Our analysis revealed that LBR, LTL, and LCA were key long-run drivers of beef cattle production in northern Australia. The positive coefficient for LBR highlights the central role of reproductive efficiency and early calf survival in determining long-term herd growth. As noted by Holroyd and Fordyce ( 2001 ), a branding rate of at least 80 calves per 100 cows joined is a feasible target in years with average or better rainfall. Improvements in branding, facilitated by better herd management and genetic gains (Cottle and Lewis 2014), can significantly enhance long-term herd size. The importance of labour (LTL) in the long run aligns with the findings of Holmes et al. ( 2017 ) and McLean et al. ( 2018 ), who identified labour efficiency as a key differentiator of top-performing beef operations. The positive role of LCA reflects the importance of investments in land rehabilitation, sustainable grazing, and pasture improvement (O'Reagain et al. 2019 ; Burnett et al. 2020 ), which expand carrying capacity and underpin production growth. Conversely, LAO, LNFI, LFLA, and LRF were negatively associated with beef production in the long run. The negative coefficient for LNFI contrasts with some literature suggesting a positive effect of non-farm income on farm efficiency (Otieno et al. 2012 ; Sirak and Derek 2015 ), and may reflect resource diversion or off-farm prioritisation in certain contexts. Similarly, the negative effect of rainfall (LRF) might relate to extreme variability or stress from extreme weather events, consistent with findings from Allen et al. ( 2023 ) and McGowan ( 2023 ) linking high rainfall to calf loss, disease, and reduced milk yield. The negative long-run effect of LFBD supports concerns raised by Thompson and Martin ( 2012 ) and Bowen and Chudleigh ( 2021 ) regarding the impact of rising debt levels on northern beef operations. High debt may constrain reinvestment or shift focus from productivity to financial survival (Mugera and Nyambane 2015 ). Adjustment Dynamics and Short-run Effects The ECT for beef production (Y1) was negative and statistically significant, indicating a strong tendency for the system to correct deviations from long-run equilibrium. Adjustment was also evident in explanatory variables such as LTL, LCA, LNFI, LFLA, and LFBD, showing that these components respond dynamically to maintain long-run balance. Short-run effects revealed causal interactions between past values of key variables. For instance, LTL(− 1) positively influenced LBR, suggesting that labour inputs support timely and effective branding. LFBD(− 1) influenced LAO, LTL, and LCA, indicating that farm financial status can affect operational and investment decisions. LNFI responded positively to past values of Y1, LAO, and LRF, but negatively to its own lag and LFBD, consistent with the view that off-farm income may be sensitive to farm shocks or financial pressure. The relationship between non-farm income and farm decisions is nuanced; while some studies highlight its benefits for reinvestment (Sheng and Chancellor 2019 ; Rolfe et al. 2019 ), others suggest potential for disinvestment or distraction. Impulse Response of Production The IRFs indicate that shocks to LBR and LTL produce strong and significant increases in beef production. These results support prior studies highlighting the importance of reproductive efficiency and labour availability in extensive systems (Ash et al. 2015 ; Chilcott et al. 2020 ). In contrast, negative responses to LRF and LNFI suggest that unexpected rainfall or shifts in off-farm income can destabilise production, perhaps by affecting herd health, nutrition, or labour allocation (Allen et al. 2023 ; Keli et al. 2008). Shocks to LCA and LFBD produced persistent negative effects, indicating potential inefficiencies or misallocation in the short term. The absence of significant responses to LAO and LFLA suggests that pricing signals and liquidity constraints may not immediately influence production decisions. Variance Decomposition of Production The FEVD showed that Y1 is largely driven by its own past values, especially in the short run. This high level of autoregression reflects the biological inertia and seasonality of beef production systems. The relatively small contribution of other shocks, including LBR and LTL, implies that short-run interventions may have limited impact unless sustained or scaled. This finding is consistent with the view that cattle production is path-dependent and biologically constrained (Godde et al. 2019 ). Long-run Drivers of Productivity Turning to total factor productivity (LTFP), our analysis found positive long-run effects of LNFI and LRF. The beneficial effect of LNFI supports earlier findings by Otieno et al. ( 2012 ) and Sirak and Derek ( 2015 ), who noted the role of off-farm earnings in enhancing farm efficiency through capital reinvestment. Similarly, rainfall contributes to pasture growth and animal nutrition, driving productivity (Scanlan et al. 1994 ; Allen et al. 2023 ). Negative effects were found for LFLA and LFBD, suggesting that increased financial buffers or obligations might correlate with inefficient input use or risk-averse behaviour (Lambert and Bayda 2005 ; Bowen and Chudleigh 2021 ). While financial capital is necessary, its structure and deployment matter. Adjustment and Short-run Causality Adjustment mechanisms were observed in LBR and LRF (ECT1), and in LBR, LFLA, and LFBD (ECT2), underscoring the role of labour, liquidity, and environmental variability in the dynamic balancing of the system. These adjustments reflect the capacity of beef production systems to respond to long-run disequilibrium, particularly through changes in workforce deployment, financial levers, and responses to climatic variability. Similar patterns have been identified by Godde et al. ( 2019 ) and Thompson and Martin ( 2012 ), who noted that labour inputs and rainfall variability are key components shaping adaptive behaviour in northern Australian beef enterprises. Short-run dynamics showed that past values of LNFI, LRF, and LFBD negatively influenced current LNFI, suggesting complex feedbacks between climate, income, and financial stress. Rainfall shocks can directly influence income variability, particularly when producers rely heavily on grazing conditions and must allocate off-farm income to offset drought-related losses (Godde et al. 2019 ). Similarly, rising debt levels increase financial vulnerability, which can constrain household spending and reduce disposable income (Thompson and Martin 2012 ). The positive effect of LFBD(− 1) on LCC may reflect increased operational liquidity made possible through credit access, even if this creates long-term inefficiencies. Access to borrowed capital can allow producers to maintain short-term cash flow and working capital, consistent with findings by Mugera and Nyambane ( 2015 ) and Bowen and Chudleigh ( 2021 ), who argue that debt may temporarily alleviate financial pressure but can reduce technical efficiency and increase business fragility. Finally, the sensitivity of LFLA to both its own lag and LNFI highlights the intertwined nature of financial variables in beef enterprises. Liquid asset levels are likely influenced by past income decisions, savings behaviour, and the need to maintain flexibility in uncertain operating environments. This aligns with studies by Sirak and Derek ( 2015 ) and Rolfe et al. ( 2019 ), who found that off-farm income and diversification strategies influence investment in liquid assets and working capital, especially among small to medium-sized livestock operations. Impulse Response of Productivity The IRFs demonstrated that LTFP responds positively to shocks in LNFI and LRF, confirming the importance of financial resilience and favourable environmental conditions. The responses were strong and persistent, highlighting their relevance for long-term investment strategies. Negative effects from LBR, LCC, LFLA, and LFBD indicate that abrupt increases in these variables may reduce short-term productivity, possibly due to input misallocation, overcapitalisation, or climate-induced inefficiencies. These findings support earlier concerns about the adverse effects of high rainfall and financial stress on productivity (Chepkwony et al. 2020 ; McGowan 2023 ). Variance Decomposition of Productivity Initially, LTFP was almost entirely explained by its own shocks, reflecting the high degree of persistence and autoregressive behaviour in productivity performance. However, as the forecast horizon lengthened, shocks to LBR emerged as a key driver, explaining around 25% of forecast variance by period 10. This highlights the growing influence of reproductive and management performance on long-term productivity. Previous studies have shown that reproductive efficiency, as captured by branding rate, is a major determinant of productivity growth in northern beef systems (Holroyd and Fordyce 2001 ; Cottle and Lewis 2014), particularly under extensive conditions where calf output per breeder is a central driver of system performance (McCosker et al. 2023 ). Other variables, including LNFI, LCC, LFLA, and LRF, made smaller contributions to the variance decomposition, yet their presence reinforces the multifactorial nature of productivity. As found by Rolfe et al. ( 2019 ) and Sirak and Derek ( 2015 ), financial diversification through non-farm income can buffer production systems and enable reinvestment, while environmental factors like rainfall can both constrain and enhance productivity depending on timing and severity (McGowan 2023 ; Allen et al. 2023 ). While LTFP retains strong internal momentum, its long-run variability is increasingly shaped by interactions among key management, financial, and environmental variables. This supports calls for integrated strategies that improve reproductive output, enhance financial resilience, and respond to climatic variability, critical elements for sustainable productivity gains in northern Australian beef systems (Gleeson et al. 2012 ; Thompson and Martin 2012 ). Overall, the findings suggest that while beef production in northern Australia is strongly shaped by biological momentum and path dependency, strategic improvements in reproductive performance and labour management offer clear pathways for enhancing system performance. Likewise, enhancing productivity will require a more nuanced approach that balances capital availability with financial discipline and resilience to climate variability. These results underscore the importance of integrated management strategies that not only improve herd-level outcomes (e.g. Branding rate) but also enhance financial efficiency and adaptability. By aligning policy, extension, and investment priorities with these key leverage points, the northern Australian beef industry can make meaningful progress toward sustainable productivity gains and long-term viability. Conclusion Beef production in northern Australia, and across dry tropical systems more broadly, continues to exhibit marked variability in productivity and profitability, with many producers operating below sustainable thresholds. Understanding which factors consistently influence beef system performance, amid environmental, financial, and operational complexity, remains a core challenge. This study provides one of the first system-level, dynamic assessments of beef cattle performance in northern Australia using panel vector error correction modelling. By examining the interactions between reproductive efficiency, labour, financial indicators, and environmental variability across a 32-year period, we offer new evidence that long-term productivity and production outcomes are shaped not by isolated variables but by persistent interactions among key management and contextual drivers. Our findings confirm the central, strategic importance of reproductive performance (e.g., Branding rate), the dynamic role of labour and land capital, and the compounding impact of debt structure on both short-term adjustments and long-term productivity growth. Importantly, the modelling approach identifies not only which factors matter, but how and when they matter, clarifying the relative timing, persistence, and influence of shocks within the production system. For producers and advisors, these insights can inform more targeted investment in reproductive management, labour planning, and financial resilience. For policymakers, the results underscore the importance of supporting adoption pathways that strengthen long-run system performance, particularly under growing climate and market volatility. Looking ahead, this framework can be extended to other production zones or used to integrate behavioural, adoption, and policy dimensions. By linking bioeconomic and financial models with empirical time series analysis, future work can help translate these insights into practical strategies for a more sustainable and resilient beef industry. Declarations Conflict of Interest. The authors declare that there is no conflict of interest regarding the publication of this paper. Funding. All authors confirm that the preparation of this manuscript was carried out without any external funding. Author Contributions. All authors contributed to the conception and design of the study. Sampath Priya Dissanayake and Luis Prada e Silva carried out the data collection and data analysis. The first draft of the manuscript was written by Sampath Priya Dissanayake, and all authors provided feedback on previous versions. All authors read and approved the final manuscript. Data Availability. All data associated with this manuscript can be made available upon reasonable request. 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Anim Prod Sci 63:1–2. https://doi.org/10.1071/ANv63n4_FO McLean I, Holmes P, Counsell D (2014) The northern beef report 2013. Northern beef situation analysis. Meat and Livestock Australia, North Sydney. McLean I, Holmes P, Wellington M, Herley J, Medway M (2018) Pastoral Company Benchmarking Project 2012–2017. MLA Final Report P.PSH.0718. Meat and Livestock Australia, North Sydney. McLean IA, Wellington MJ, Holmes PR, Bertram JD, McGowan MR (2023) The Australian Beef Report 2023. Bush Agribusiness Pty Ltd: Toowoomba Mugera AW, Nyambane GG (2015) Impact of debt structure on production efficiency and financial performance of Broadacre farms in Western Australia. Aust J Agric Resour Econ 59(2):208-224. https://doi.org/10.1111/1467-8489.12075 Northern Territory Government (2022) Northern Territory Primary Industry Snapshot. https://business.nt.gov.au/__data/assets/pdf_file/0007/1033496/nt-primary-industry-snapshot.pdf. Accessed 15 February 2024 O'Reagain P, Bushell J, Holloway C (2019) Innovations in sustainable grazing management for extensive beef enterprises in northern Australia. Anim Prod Sci 59(11):1935-1943. Otieno DJ, Hubbard L, Ruto E (2012) Determinants of technical efficiency in beef cattle production in Kenya. Paper presented at the International Association of Agricultural. Economists (IAAE) Triennial Conference, Foz do Iguacu, Brazil, August. Pedroni P (2001) Fully modified OLS for heterogeneous cointegrated panels. In Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited Queensland Government (2022) Queensland Agricultural Land Use Mapping. https://data.qld.gov.au/dataset/queensland-agricultural-land-use-mapping. Accessed 8 February 2024 Rolfe J, Windle J, Whitten S (2019) Off-farm income and farm capital productivity in Australian broadacre agriculture. Aust J Agric Resour Econ 63(3):705-723. Scanlan JC, McKeon GM, Day KA, Mott JJ, Hinton AW (1994) Estimating safe carrying capacities in extensive cattle grazing properties within tropical semi-arid woodlands of north-eastern Australia. The Rangel J 16:64–76. doi:10.1071/RJ9940064 Sheng Y, Chancellor W (2019) Exploring the relationship between farm size and productivity: evidence from the Australian grains industry. Food Policy 84:196-204. Sirak B, Derek B (2015) Determinants of Profit Efficiency among Smallholder Beef Producers in Botswana. Int Food Agribus Manage Rev 18 (3). Thompson T, Martin P (2012) Australian beef: Financial performance of beef cattle producing farms, 2009–10 to 2011–12. CC BY 3.0. https://www.mla.com.au/globalassets/mla-corporate/prices--markets/documents/trends--analysis/abares-farm-survey/financial-performance-of-beef-farms-2009-10-to-2011-12.pdf. Accessed 12 March 2024 Tables Table 1 Variables used in the panel vector error correction models. Endogenous variable Abbreviation Beef cattle production Y1 Total factor productivity LTFP Branding rate (%) LBR Area operated (ha) LAO Total labour used (weeks) LTL Capital at July 01 ($) LCA Total non-farm income ($) LNFI Cost of livestock contracts ($) LCC Farm business debt ($) LFBD Total annual rainfall (mm) LRF Farm liquid assets ($) LFLA Exogenous variable Abbreviation Economic shocks ES Age of the owner/manager (years) AG Table 2 Estimated coefficients of the panel vector error correction model for beef cattle production Equation ECT Intercept Y1(-1) LBR(-1) LAO(-1) LTL(-1) LCA(-1) LNFI(-1) LFLA(-1) LRF(-1) LFBD(-1) ES_1 Y1 -0.213* 0.402* 0.040 -0.172 -0.019 0.741 0.056 0.012 -0.020 0.023 -0.295 -0.007 LBR 0.032 -0.055 -0.007 -0.171 -0.042 0.205* 0.007 0.004 -0.005 0.008 -0.026 -0.005 LAO 0.165 -0.298 0.156 -0.144 0.128 -0.229 -0.362 -0.049 -0.129 -0.066 0.355* -0.023 LTL 0.177*** -0.322*** 0.063 -0.159 0.076 -0.372* -0.089 -0.046 -0.038 -0.065 0.191*** -0.017 LCA 0.300** -0.551** 0.282 -0.153 0.211 -0.210 -0.337 -0.091 -0.090 -0.134 0.433** 0.009 LNFI -0.502*** 0.969*** 0.767** -0.555 0.893** 0.001 -0.059 -0.375*** 0.006 0.579** -0.461* -0.093* LFLA 0.292* -0.546* -0.273 0.626 -0.101 -0.705 0.145 -0.203* -0.511*** -0.166 0.205 0.019 LRF -0.165 0.311 0.231 -1.395* 0.130 0.127 0.024 -0.005 0.031 -0.237 -0.031 -0.016 LFBD 0.453*** -0.837*** 0.549* 0.102 0.255 -0.574 0.101 -0.158 -0.110 -0.169 0.185 -0.008 Note: (*) Significant at 1%, (**) Significant at 5%, and (***) Significant at 10% Table 3 Estimated coefficients of the panel vector error correction model for total factor productivity. Equation ECT1 ECT2 Intercept LTFP(-1) LBR(-1) LNFI(-1) LCC(-1) LFLA(-1) LRF(-1) LFBD(-1) AG ES LTFP -0.047 -0.388 0.443 -0.158 0.126 -0.031 0.027 -0.024 -0.049 0.012 9.3e-05 -0.0215 LBR 0.105** -0.613*** 1.036*** -0.105 0.136 -0.001 0.013 0.018 0.023 0.002 0.001 -0.001 LNFI -0.048 3.360** -4.156* 0.456 -1.220 -0.339*** 0.175 -0.045 0.538** -0.427** -0.010* -0.103* LCC 0.061 -1.636 2.235 -0.391 0.026 -0.072 -0.228 -0.122 0.024 0.420* 0.003 0.003 LFLA 0.366 -2.335** 3.627* -0.419 1.310 -0.174* 0.186 -0.471*** 0.023 0.001 0.007 0.019 LRF 0.594*** -0.356 1.843 -0.367 -0.405 0.048 0.019 0.085 -0.080 -0.008 3.6e-05 -0.021 LFBD 0.357 -2.412* 3.638* -0.626 1.221 -0.077 -0.019 0.039 0.024 0.012 0.009* -0.001 Note: (*) Significant at 1%, (**) Significant at 5%, and (***) Significant at 10% Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7152494","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533437189,"identity":"055777bd-4b8b-47a7-9045-ac39a7180129","order_by":0,"name":"Sampath P. 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19:35:55","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119352,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7152494/v1/2a96a4d5382f9ff91ebc6b50.html"},{"id":95054521,"identity":"02ab5430-75d4-4f46-8b77-d4ee15c15e6b","added_by":"auto","created_at":"2025-11-03 19:35:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104930,"visible":true,"origin":"","legend":"\u003cp\u003eOrthogonal impulse response functions of log-transformed total beef cattle production (Y1) to shocks in each endogenous variable over 10 periods. Black solid lines: response estimates; dashed lines: 95% confidence intervals; red line: zero baseline.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7152494/v1/15ac906d230be04eb567bad6.png"},{"id":95054522,"identity":"a50f681d-d139-4218-87e9-af5c995f8844","added_by":"auto","created_at":"2025-11-03 19:35:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61726,"visible":true,"origin":"","legend":"\u003cp\u003eForecast error variance decomposition of log-transformed beef cattle production (Y1) over a 10-period horizon. A y-axis break is used to improve visibility of lower-contributing shocks.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7152494/v1/2e2073bd4c13b5479d2a1070.png"},{"id":95054524,"identity":"f1105cb9-32cf-4bd2-9155-ee26affb30d9","added_by":"auto","created_at":"2025-11-03 19:35:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80002,"visible":true,"origin":"","legend":"\u003cp\u003eOrthogonal impulse response functions of log-transformed total factor productivity (LTFP) to shocks in each endogenous variable over 10 periods. Black solid lines: response estimates; dashed lines: 95% confidence intervals; red line: zero baseline.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7152494/v1/c4da6688a9ae3596bf709fbe.png"},{"id":95054525,"identity":"5e349b0e-16cb-4b06-b382-7fd1dce4653e","added_by":"auto","created_at":"2025-11-03 19:35:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57110,"visible":true,"origin":"","legend":"\u003cp\u003eForecast error variance decomposition of log-transformed total factor productivity (LTFP) over a 10-period horizon\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7152494/v1/fd8eca1abcde0cd50411dd2d.png"},{"id":99791994,"identity":"2d1c18d9-3025-4718-aa77-0fcc84f9be32","added_by":"auto","created_at":"2026-01-08 13:12:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1076047,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7152494/v1/2b56f2cd-cf2b-4bb2-b271-df62cdbcfa10.pdf"}],"financialInterests":"","formattedTitle":"Unveiling the Performance Drivers of Northern Australian Beef Systems: A Time Series Analysis 1990-2022)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe beef industry is a major economic contributor to northern Australia, encompassing Queensland (QLD), the Northern Territory (NT), and northern Western Australia (WA). This region supports large-scale, extensive grazing systems that span thousands of square kilometres, relying primarily on pasture-based production using native and introduced grasses such as Mitchell grass and Buffel grass (Chilcott et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the NT alone, the beef industry contributes over \u003cspan\u003e$\u003c/span\u003e1.3\u0026nbsp;billion annually (Northern Territory Government \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while in QLD, it is a cornerstone of rural employment and economic activity (Queensland Government \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite its economic significance, the performance of beef enterprises in northern Australia is highly variable. Periods of low or negative returns are common (Ashton et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and industry-wide assessments have highlighted concerns about long-term profitability and sustainability (McCosker et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; McLean et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Benchmarking studies consistently show that the top 25% of producers outperform their peers through higher reproductive rates, lower operating costs, and more efficient labour use (McLean et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Holmes et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast to intensive systems, where cattle performance approaches genetic potential and marginal gains dominate, northern beef systems often remain constrained by low-input management and environmental variability. Pasture productivity, particularly during the wet season, remains the key driver of cattle growth, yet has seen little change in recent decades (Chilcott et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Management and nutritional interventions have the potential to deliver substantial improvements, but their adoption and effects remain unevenly understood at a regional scale.\u003c/p\u003e\u003cp\u003eTo address this gap, this study investigates the long-term drivers of beef cattle production and total factor productivity (TFP) in northern Australia using a time-series panel econometric approach. We analyse the relationships among biophysical, financial, and management variables over a 32-year period (1990\u0026ndash;2022) to better understand what drives performance. We hypothesise that both beef cattle production and productivity are influenced not only by environmental factors such as rainfall, but also by management and financial indicators, particularly reproductive efficiency, labour use, and capital investment. By examining both long-run relationships and short-run dynamics, this study aims to provide insights into the resilience, responsiveness, and leverage points for improving sustainability in northern Australian beef systems.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eData and Data Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study used secondary data from Queensland (QLD), the Northern Territory (NT), and Western Australia (WA), covering the period from 1990 to 2022. Data were sourced from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), the Australian Bureau of Statistics (ABS), and the Bureau of Meteorology (BoM), with the latest updates available as of July 2023.\u003c/p\u003e\u003cp\u003eAnnual beef cattle production was estimated by summing the number of cattle slaughtered (ABS Livestock Products) and live cattle exported (ABS International Merchandise Trade Data Portal) for each state. Total factor productivity (TFP) was used as the main indicator of farm performance, calculated as the ratio of total output to total input (ABARES 2024). Annual rainfall data were obtained from the BoM (BoM 2024) for each corresponding region.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eTo examine the factors influencing beef cattle performance in northern Australia, we applied a Panel Vector Error Correction Model (PVECM). This method is well-suited to time series data that includes both stationary and non-stationary variables and is commonly used to identify both long-run equilibrium relationships and short-run dynamics among multiple interrelated variables (Engle and Granger \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eVariables were first log-transformed to stabilise variance and interpret estimated coefficients as elasticities. Stationarity was tested using the panel version of the augmented Dickey\u0026ndash;Fuller test (Costantini and Lupi \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) to determine whether the time series fluctuated around a constant mean or followed a stochastic trend. The optimal number of lags included in the model was selected using the Akaike (AIC) and Hannan\u0026ndash;Quinn (HQ) information criteria, which help identify how many past time points should be considered to best capture the short-run relationships between variables without overfitting the model. The Johansen cointegration test (Pedroni \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) was used to detect whether the variables shared a long-run relationship; that is, whether they tended to move together over time despite short-term fluctuations. Estimating such long-run relationships helps identify structural drivers of beef production and productivity, such as labour input, rainfall, or capital investment.\u003c/p\u003e\u003cp\u003eTo understand how shocks in one variable affect others over time, Impulse Response Functions (IRFs) were used. These estimate how beef cattle production or total factor productivity respond in subsequent years to a sudden change (shock) in one of the explanatory variables. We also applied Forecast Error Variance Decomposition (FEVD) to quantify the relative contribution of each variable to the variability observed in beef production and productivity, helping us assess which factors are most influential over different time horizons (Akinlo and Adejumo \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePanel Vector Error Correction Model for Beef Cattle Production\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PVECM results identified one significant long-run cointegration relationship among Y1 and its explanatory variables, based on the Johansen cointegration test (cointegration rank\u0026thinsp;=\u0026thinsp;1). The estimated equation is as follows:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg 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\" width=\"667\" height=\"73\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere Y1 is beef cattle production, and the explanatory variables are defined in Table\u0026nbsp;1. Model fit statistics indicated good adequacy (AIC = \u0026minus;\u0026thinsp;3891.4; BIC = \u0026minus;\u0026thinsp;3596.4; SSR\u0026thinsp;=\u0026thinsp;16.8).\u003c/p\u003e\n\u003cp\u003eThe 5.133 coefficient for LBR indicates that an increase in LBR tends to increase Y1 in the long run. LTL, LCA, and LFBD also positively affected Y1. In contrast, LAO, LNFI, LFLA, and LRF were negatively associated with Y1. LBR and LRF had the highest absolute coefficients, indicating their dominant role in explaining long-run variation in beef cattle production.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the error correction terms (ECTs) and short-run dynamics of the PVECM. The ECT for Y1 was \u0026minus;\u0026thinsp;0.213 and statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that beef cattle production adjusts toward its long-run equilibrium at a relatively fast rate following short-term deviations. Several explanatory variables also showed significant adjustment behaviour. LTL (0.177, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1), LCA (0.300, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), LNFI (0.502, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), LFLA (0.431, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and LFBD (0.268, p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) all exhibited positive and significant ECT coefficients, suggesting gradual convergence toward equilibrium in response to deviations in the long-run relationship.\u003c/p\u003e\n\u003cp\u003eShort-run interactions among variables are summarised in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. LTL(\u0026ndash;1) had a significant positive effect on LBR, suggesting that previous labour use influenced current branding outcomes. LAO was positively affected by LFBD(\u0026ndash;1), while LTL was significantly influenced by its own lag and LFBD. In the LCA equation, LFBD(\u0026ndash;1) had a significant negative effect. In the short run, LNFI was positively affected by Y1(\u0026ndash;1), LAO(\u0026ndash;1), LRF(\u0026ndash;1), and ES, but negatively affected by its own lag and by LFBD(\u0026ndash;1). LFLA responded significantly to its own lag. LFBD was significantly influenced by Y1(\u0026ndash;1), indicating feedback from past production to farm business debt.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the orthogonal Impulse Response Functions (IRFs), which show the effect of a one-standard-deviation shock (i.e. a sudden, unexpected increase) in each explanatory variable on Y1 over a 10-period horizon. These shocks simulate short-term changes in individual drivers, while holding other variables constant, allowing us to assess the dynamic response of beef cattle production to isolated changes in key inputs.\u003c/p\u003e\n\u003cp\u003eShocks to LBR and LTL produced statistically significant positive responses in Y1, peaking within the first 2\u0026ndash;3 periods before gradually stabilising. This indicates short-term gains in beef cattle production following improvements in branding rate and labour use. In contrast, shocks to LRF and LNFI caused negative short-run impacts on Y1, with effects diminishing over time. The response to LRF was initially large and statistically significant, but became negligible after period 5. Shocks to LAO, LFLA, and LFBD showed no statistically significant effect on Y1 across the forecast horizon. The response to LCA was negative and persistent, remaining below baseline across the entire period, though only statistically significant in early periods. Overall, LBR and LTL had the clearest positive influence on Y1, while LRF and LCA showed the strongest negative effects in the short run.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Forecast Error Variance Decomposition (FEVD) of Y1 over a 10-period horizon. This analysis estimates the proportion of variation in Y that can be attributed to shocks in each explanatory variable, helping identify the main contributors to forecast uncertainty. The results indicate that nearly all the variability in Y1 is explained by its own shocks, starting just below 100% and stabilising around 95% over time. This suggests that beef cattle production is largely self-driven and exhibits strong autoregressive behaviour. In contrast, shocks from other variables, including LBR, LTL, LCA, LNFI, and LRF, make only minor contributions to the forecast variance, highlighting their limited dynamic influence relative to the momentum within Y1 itself.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel Vector Error Correction Model for Total Factor Productivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PVECM for LTFP showed a good model fit (AIC = \u0026minus;\u0026thinsp;2804.2; BIC = \u0026minus;\u0026thinsp;2565.1; SSR\u0026thinsp;=\u0026thinsp;15.52) and identified two cointegrating relationships (cointegration rank\u0026thinsp;=\u0026thinsp;2). The first, normalised on LTFP (Eq.\u0026nbsp;2), represents the long-run equilibrium relationship among productivity and its key drivers. The second, normalised on LBR (Eq.\u0026nbsp;3), reflects the statistical interdependence among the explanatory variables within the system and is not the focus of interpretation.\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:LTFP=0.251LNFI+0.198LCC+0.189LFLA+1.156LRF-0.308LFBD+\\:\\epsilon\\:\\dots\\:.\\dots\\:\\dots\\:.\\left(\\text{E}\\text{q}\\text{u}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:2\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:LBR=0.074LNFI+0.003LCC+0.001LFLA+0.188LRF-0.074LFBD+\\:\\epsilon\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:.\\left(\\text{E}\\text{q}\\text{u}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:3\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the first cointegration relationship (ECT1), significant adjustment was observed in LBR (0.105; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and LRF (0.594; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating these variables respond to deviations from the LTFP equilibrium. In the second relationship (ECT2), LBR, LFLA, and LFBD showed strong and significant adjustments, suggesting mechanisms to restore equilibrium when deviations occur in the LBR centred relationship.\u003c/p\u003e\n\u003cp\u003eShort-run dynamics (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed additional interactions. In the LNFI equation, significant negative effects were found for LNFI(\u0026ndash;1), LRF(\u0026ndash;1), and LFBD(\u0026ndash;1). LFBD(\u0026ndash;1) also had a significant positive influence on LCC. In the LFLA equation, LNFI(\u0026ndash;1) and LFLA(\u0026ndash;1) were both significant and negative. Exogenous variables AG and ES negatively affected LNFI, with ES also significant at the 10% level.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the Orthogonal Impulse Response Functions (IRFs) of LTFP to one-standard-deviation shocks in each explanatory variable over a 10-period horizon. These IRFs illustrate how total factor productivity responds dynamically to unexpected changes in the system, while holding other variables constant. A shock to LBR caused an immediate and sustained decline in LTFP, with the negative effect persisting across the entire period, although gradually diminishing. Similarly, shocks to LCC, LFLA, and LFBD resulted in statistically significant short-run reductions in LTFP. These effects were strongest within the first 3\u0026ndash;4 periods and remained negative over time, indicating that sudden increases in these inputs are associated with declines in productivity during the short term.\u003c/p\u003e\n\u003cp\u003eIn contrast, shocks to LNFI and LRF produced positive and lasting effects on LTFP. The response to LNFI peaked around period 3 and remained above baseline thereafter, indicating that increased non-farm income supports sustained productivity gains. The effect of LRF was immediate and persistent, with the largest gains observed in the first half of the forecast horizon. Overall, the IRFs suggest that LTFP is sensitive to shocks in key financial and climatic variables, with positive responses to LNFI and rainfall, and negative responses to increased branding, contracts, liquid assets, and debt.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e displays the Forecast Error Variance Decomposition (FEVD) of LTFP over a 10-period horizon, quantifying the relative contribution of each variable\u0026rsquo;s shock to the forecast variance in LTFP. In the short term, the variance in LTFP is driven almost entirely by its own innovations, accounting for nearly 100% of the forecast error variance at period 1. However, this dominance declines steadily, stabilising around 50% by period 10, as the influence of other variables accumulates.\u003c/p\u003e\n\u003cp\u003eShocks to LBR emerge as the most influential external driver of LTFP over time, contributing approximately 25% of the variance by period 10. Meanwhile, shocks to LNFI, LCC, LFLA, and LRF each contribute more modest shares, generally under 10%, suggesting a comparatively limited dynamic impact on LTFP. Shocks to LFBD explain only a small fraction of the variance throughout the period. These results highlight the increasing importance of branding rate (LBR) in shaping long-run fluctuations in total factor productivity, while other variables play more minor roles in driving variance over time.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study set out to investigate the long-run and short-run drivers of beef cattle production and total factor productivity in northern Australia, with a particular focus on the role of reproductive efficiency, labour, capital structure, and environmental variability. Our hypothesis was that key management and financial factors, especially branding rate, labour use, and debt, would exert significant influence on both production and productivity, and that these relationships would manifest through distinct long-term equilibrium paths and short-run adjustment mechanisms. The findings from this paper strongly support this hypothesis. The results highlight the central role of branding rate and labour as production drivers, and the multifactorial, yet structured, influence of financial and climatic variables on long-run productivity and its volatility over time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLong-run Drivers of Beef Cattle Production\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur analysis revealed that LBR, LTL, and LCA were key long-run drivers of beef cattle production in northern Australia. The positive coefficient for LBR highlights the central role of reproductive efficiency and early calf survival in determining long-term herd growth. As noted by Holroyd and Fordyce (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), a branding rate of at least 80 calves per 100 cows joined is a feasible target in years with average or better rainfall. Improvements in branding, facilitated by better herd management and genetic gains (Cottle and Lewis 2014), can significantly enhance long-term herd size.\u003c/p\u003e\u003cp\u003eThe importance of labour (LTL) in the long run aligns with the findings of Holmes et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and McLean et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who identified labour efficiency as a key differentiator of top-performing beef operations. The positive role of LCA reflects the importance of investments in land rehabilitation, sustainable grazing, and pasture improvement (O'Reagain et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Burnett et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which expand carrying capacity and underpin production growth.\u003c/p\u003e\u003cp\u003eConversely, LAO, LNFI, LFLA, and LRF were negatively associated with beef production in the long run. The negative coefficient for LNFI contrasts with some literature suggesting a positive effect of non-farm income on farm efficiency (Otieno et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sirak and Derek \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and may reflect resource diversion or off-farm prioritisation in certain contexts. Similarly, the negative effect of rainfall (LRF) might relate to extreme variability or stress from extreme weather events, consistent with findings from Allen et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and McGowan (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) linking high rainfall to calf loss, disease, and reduced milk yield. The negative long-run effect of LFBD supports concerns raised by Thompson and Martin (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Bowen and Chudleigh (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) regarding the impact of rising debt levels on northern beef operations. High debt may constrain reinvestment or shift focus from productivity to financial survival (Mugera and Nyambane \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAdjustment Dynamics and Short-run Effects\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe ECT for beef production (Y1) was negative and statistically significant, indicating a strong tendency for the system to correct deviations from long-run equilibrium. Adjustment was also evident in explanatory variables such as LTL, LCA, LNFI, LFLA, and LFBD, showing that these components respond dynamically to maintain long-run balance.\u003c/p\u003e\u003cp\u003eShort-run effects revealed causal interactions between past values of key variables. For instance, LTL(\u0026minus;\u0026thinsp;1) positively influenced LBR, suggesting that labour inputs support timely and effective branding. LFBD(\u0026minus;\u0026thinsp;1) influenced LAO, LTL, and LCA, indicating that farm financial status can affect operational and investment decisions. LNFI responded positively to past values of Y1, LAO, and LRF, but negatively to its own lag and LFBD, consistent with the view that off-farm income may be sensitive to farm shocks or financial pressure. The relationship between non-farm income and farm decisions is nuanced; while some studies highlight its benefits for reinvestment (Sheng and Chancellor \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rolfe et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), others suggest potential for disinvestment or distraction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImpulse Response of Production\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe IRFs indicate that shocks to LBR and LTL produce strong and significant increases in beef production. These results support prior studies highlighting the importance of reproductive efficiency and labour availability in extensive systems (Ash et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chilcott et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, negative responses to LRF and LNFI suggest that unexpected rainfall or shifts in off-farm income can destabilise production, perhaps by affecting herd health, nutrition, or labour allocation (Allen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Keli et al. 2008).\u003c/p\u003e\u003cp\u003eShocks to LCA and LFBD produced persistent negative effects, indicating potential inefficiencies or misallocation in the short term. The absence of significant responses to LAO and LFLA suggests that pricing signals and liquidity constraints may not immediately influence production decisions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariance Decomposition of Production\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe FEVD showed that Y1 is largely driven by its own past values, especially in the short run. This high level of autoregression reflects the biological inertia and seasonality of beef production systems. The relatively small contribution of other shocks, including LBR and LTL, implies that short-run interventions may have limited impact unless sustained or scaled. This finding is consistent with the view that cattle production is path-dependent and biologically constrained (Godde et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eLong-run Drivers of Productivity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTurning to total factor productivity (LTFP), our analysis found positive long-run effects of LNFI and LRF. The beneficial effect of LNFI supports earlier findings by Otieno et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Sirak and Derek (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), who noted the role of off-farm earnings in enhancing farm efficiency through capital reinvestment. Similarly, rainfall contributes to pasture growth and animal nutrition, driving productivity (Scanlan et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Allen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNegative effects were found for LFLA and LFBD, suggesting that increased financial buffers or obligations might correlate with inefficient input use or risk-averse behaviour (Lambert and Bayda \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Bowen and Chudleigh \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While financial capital is necessary, its structure and deployment matter.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAdjustment and Short-run Causality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAdjustment mechanisms were observed in LBR and LRF (ECT1), and in LBR, LFLA, and LFBD (ECT2), underscoring the role of labour, liquidity, and environmental variability in the dynamic balancing of the system. These adjustments reflect the capacity of beef production systems to respond to long-run disequilibrium, particularly through changes in workforce deployment, financial levers, and responses to climatic variability. Similar patterns have been identified by Godde et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Thompson and Martin (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who noted that labour inputs and rainfall variability are key components shaping adaptive behaviour in northern Australian beef enterprises.\u003c/p\u003e\u003cp\u003eShort-run dynamics showed that past values of LNFI, LRF, and LFBD negatively influenced current LNFI, suggesting complex feedbacks between climate, income, and financial stress. Rainfall shocks can directly influence income variability, particularly when producers rely heavily on grazing conditions and must allocate off-farm income to offset drought-related losses (Godde et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, rising debt levels increase financial vulnerability, which can constrain household spending and reduce disposable income (Thompson and Martin \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe positive effect of LFBD(\u0026minus;\u0026thinsp;1) on LCC may reflect increased operational liquidity made possible through credit access, even if this creates long-term inefficiencies. Access to borrowed capital can allow producers to maintain short-term cash flow and working capital, consistent with findings by Mugera and Nyambane (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Bowen and Chudleigh (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who argue that debt may temporarily alleviate financial pressure but can reduce technical efficiency and increase business fragility.\u003c/p\u003e\u003cp\u003eFinally, the sensitivity of LFLA to both its own lag and LNFI highlights the intertwined nature of financial variables in beef enterprises. Liquid asset levels are likely influenced by past income decisions, savings behaviour, and the need to maintain flexibility in uncertain operating environments. This aligns with studies by Sirak and Derek (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Rolfe et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who found that off-farm income and diversification strategies influence investment in liquid assets and working capital, especially among small to medium-sized livestock operations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImpulse Response of Productivity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe IRFs demonstrated that LTFP responds positively to shocks in LNFI and LRF, confirming the importance of financial resilience and favourable environmental conditions. The responses were strong and persistent, highlighting their relevance for long-term investment strategies. Negative effects from LBR, LCC, LFLA, and LFBD indicate that abrupt increases in these variables may reduce short-term productivity, possibly due to input misallocation, overcapitalisation, or climate-induced inefficiencies. These findings support earlier concerns about the adverse effects of high rainfall and financial stress on productivity (Chepkwony et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; McGowan \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariance Decomposition of Productivity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInitially, LTFP was almost entirely explained by its own shocks, reflecting the high degree of persistence and autoregressive behaviour in productivity performance. However, as the forecast horizon lengthened, shocks to LBR emerged as a key driver, explaining around 25% of forecast variance by period 10. This highlights the growing influence of reproductive and management performance on long-term productivity. Previous studies have shown that reproductive efficiency, as captured by branding rate, is a major determinant of productivity growth in northern beef systems (Holroyd and Fordyce \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Cottle and Lewis 2014), particularly under extensive conditions where calf output per breeder is a central driver of system performance (McCosker et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOther variables, including LNFI, LCC, LFLA, and LRF, made smaller contributions to the variance decomposition, yet their presence reinforces the multifactorial nature of productivity. As found by Rolfe et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Sirak and Derek (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), financial diversification through non-farm income can buffer production systems and enable reinvestment, while environmental factors like rainfall can both constrain and enhance productivity depending on timing and severity (McGowan \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Allen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile LTFP retains strong internal momentum, its long-run variability is increasingly shaped by interactions among key management, financial, and environmental variables. This supports calls for integrated strategies that improve reproductive output, enhance financial resilience, and respond to climatic variability, critical elements for sustainable productivity gains in northern Australian beef systems (Gleeson et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Thompson and Martin \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, the findings suggest that while beef production in northern Australia is strongly shaped by biological momentum and path dependency, strategic improvements in reproductive performance and labour management offer clear pathways for enhancing system performance. Likewise, enhancing productivity will require a more nuanced approach that balances capital availability with financial discipline and resilience to climate variability. These results underscore the importance of integrated management strategies that not only improve herd-level outcomes (e.g. Branding rate) but also enhance financial efficiency and adaptability. By aligning policy, extension, and investment priorities with these key leverage points, the northern Australian beef industry can make meaningful progress toward sustainable productivity gains and long-term viability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBeef production in northern Australia, and across dry tropical systems more broadly, continues to exhibit marked variability in productivity and profitability, with many producers operating below sustainable thresholds. Understanding which factors consistently influence beef system performance, amid environmental, financial, and operational complexity, remains a core challenge.\u003c/p\u003e\u003cp\u003eThis study provides one of the first system-level, dynamic assessments of beef cattle performance in northern Australia using panel vector error correction modelling. By examining the interactions between reproductive efficiency, labour, financial indicators, and environmental variability across a 32-year period, we offer new evidence that long-term productivity and production outcomes are shaped not by isolated variables but by persistent interactions among key management and contextual drivers.\u003c/p\u003e\u003cp\u003eOur findings confirm the central, strategic importance of reproductive performance (e.g., Branding rate), the dynamic role of labour and land capital, and the compounding impact of debt structure on both short-term adjustments and long-term productivity growth. Importantly, the modelling approach identifies not only which factors matter, but how and when they matter, clarifying the relative timing, persistence, and influence of shocks within the production system. For producers and advisors, these insights can inform more targeted investment in reproductive management, labour planning, and financial resilience. For policymakers, the results underscore the importance of supporting adoption pathways that strengthen long-run system performance, particularly under growing climate and market volatility.\u003c/p\u003e\u003cp\u003eLooking ahead, this framework can be extended to other production zones or used to integrate behavioural, adoption, and policy dimensions. By linking bioeconomic and financial models with empirical time series analysis, future work can help translate these insights into practical strategies for a more sustainable and resilient beef industry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest.\u003c/strong\u003e\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding.\u003c/h2\u003e\u003cp\u003eAll authors confirm that the preparation of this manuscript was carried out without any external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contributions.\u003c/h2\u003e\u003cp\u003eAll authors contributed to the conception and design of the study. Sampath Priya Dissanayake and Luis Prada e Silva carried out the data collection and data analysis. The first draft of the manuscript was written by Sampath Priya Dissanayake, and all authors provided feedback on previous versions. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability.\u003c/h2\u003e\u003cp\u003eAll data associated with this manuscript can be made available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkinlo AE, Adejumo OO (2016) Determinants of Total Factor Productivity Growth in Nigeria, 1970\u0026ndash;2009. Glob Bus Rev. https://doi.org/10.1177/0972150915619801 \u003c/li\u003e\n\u003cli\u003eAllen LR, Barnes TS, Fordyce G, McCosker KD, McGowan MR (2023) Reproductive performance of northern Australia beef herds. 8. Impact of rainfall and wild dog control on percentage fetal and calf loss. Anim Prod Sci 63 (4):388-394.\u003c/li\u003e\n\u003cli\u003eAsh A, Hunt L, McDonald C, Scanlan J, Bell L, Cowley R, Watson I, McIvor J, MacLeod N (2015) Boosting the productivity and profitability of northern Australian beef enterprises: exploring innovation options using simulation modelling and systems analysis. Agric Syst 139:50-65. https://doi.org/10.1016/j.agsy.2015.06.001 \u003c/li\u003e\n\u003cli\u003eAshton D, Oliver M, Valle H (2016) Australian beef-Financial performance of Australian farms, 2013-14 to 2015-16. Research Report 16.10. Australian Bureau of Agricultural and Resource Economics and Sciences. \u003c/li\u003e\n\u003cli\u003eAustralian Bureau of Agricultural and Resource Economics and Sciences (2024) Australian Agricultural Productivity-Broadacre and Dairy Estimates. https://www.agriculture.gov.au/abares/research-topics/productivity/agricultural-productivity-estimates#download-data. Accessed 05 October 2024\u003c/li\u003e\n\u003cli\u003eBowen MK, Chudleigh F (2021) An economic framework to evaluate alternative management strategies for beef enterprises in northern Australia. Anim Prod Sci 61:271-281. https://doi.org/10.1071/AN20125\u003c/li\u003e\n\u003cli\u003eBureau of Meteorology (2024) Rainfall: Mean monthly, seasonal and annual rainfall data for Australia (base climatological datasets). http://www.bom.gov.au/metadata/19115/ANZCW0503900351. Accessed 17 January 2024\u003c/li\u003e\n\u003cli\u003eBurnett F, Norton J, Hogan A. (2020) The economic benefits of investing in water infrastructure on northern Australian cattle properties. Aust J Agric Resour Econ 64(4):1003-1023.\u003c/li\u003e\n\u003cli\u003eChepkwony R, Castagna C, Heitk\u0026ouml;nig I, van Bommel S, van Langevelde F (2020) Associations between monthly rainfall and mortality in cattle due to East Coast fever, anaplasmosis and babesiosis. Parasitol 147:1743\u0026ndash;1751. doi:10.1017/S0031182020001638 \u003c/li\u003e\n\u003cli\u003eChilcott C, Ash A, Lehnert S, Stokes C, Charmley E, Collins K, Pavey C, Macintosh A, Simpson A, Berglas R, White E, Amity M (2020) Northern Australia beef situation analysis. A report to the Cooperative Research Centre for Developing Northern Australia. CRCNA, Townsville, Australia.\u003c/li\u003e\n\u003cli\u003eCostantini M, Lupi C (2013) A simple panel-cadf test for unit roots. Oxford Bulletin of Economics and Statistics 75(2):276\u0026ndash;296.\u003c/li\u003e\n\u003cli\u003eCottle DJ, Lewis K (2014) Beef cattle production and trade. Published by CSIRO, 150, Oxford Street, (PO Box 1139), Collingwood, VIC 3066 Australia.\u003c/li\u003e\n\u003cli\u003eEngle RF, Granger CWJ (1987) Cointegration and error correction: representation, estimation, and testing. Econometrica 55(2): 251\u0026ndash;276.\u003c/li\u003e\n\u003cli\u003eGleeson T, Martin P, Mifsud C (2012) Northern Australian beef industry: assessment of risks and opportunities. ABARES report to client prepared for the Northern Australia Ministerial Forum, Canberra.\u003c/li\u003e\n\u003cli\u003eGodde C, Dizyee K, Ash A, Thornton P, Sloat L, Roura E, Henderson B, Herrero M (2019) Climate change and variability impacts on grazing herds: Insights from a system dynamics approach for semi-arid Australian rangelands. Global Change Biol 25(9):3091-3109. https://doi.org/10.1111/gcb.14669\u003c/li\u003e\n\u003cli\u003eHolmes PR, McLean I, \u0026amp; Banks R (2017) Australian beef report. Bush AgriBusiness, Toowoomba. http://www.bushagri.com.au/abr\u003c/li\u003e\n\u003cli\u003eHolroyd RG, Fordyce G (2001) Cost-effective strategies from improved fertility in extensive and semi-extensive management conditions in northern Australia. Proceedings of the International Symposium on Animal Reproduction (Cordoba, Argentina) 4: 39\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eKeil A, Zeller M, Wida A, Sanim B, Birner R (2008) What determines farmers\u0026rsquo; resilience towards ENSO-related drought? An empirical assessment in Central Sulawesi, Indonesia. Clim Change 86:291\u0026ndash;307. https://doi.org/10.1007/s10584-007-9326-4\u003c/li\u003e\n\u003cli\u003eLambert DK, Bayda VV (2005) The Impacts of Farm Financial Structure on Production Efficiency. J Agric Appl Econ 37(1): 277\u0026ndash;289. doi:10.1017/S1074070800007252 \u003c/li\u003e\n\u003cli\u003eMcCosker KD, Smith DR, Burns BM, Fordyce G, O\u0026rsquo;Rourke PK, McGowan MR (2023) Reproductive performance of northern Australian beef herds. 3. Descriptive analysis of major factors affecting reproductive performance. Anim Prod Sci 63:320-331. \u003c/li\u003e\n\u003cli\u003eMcCosker T, McClean D, Holmes P (2010) Northern beef situation analysis 2009. Final report to Meat and Livestock Australia, B.NBP.0518. Meat and Livestock Australia, North Sydney. https://www.mla.com.au/contentassets/da7806510806481493a1a85c05ef8571/nbp.0518_final_report.pdf \u003c/li\u003e\n\u003cli\u003eMcGowan MR (2023) Foreword: Reproductive performance of northern Australia beef herds. Anim Prod Sci 63:1\u0026ndash;2. https://doi.org/10.1071/ANv63n4_FO\u003c/li\u003e\n\u003cli\u003eMcLean I, Holmes P, Counsell D (2014) The northern beef report 2013. Northern beef situation analysis. Meat and Livestock Australia, North Sydney.\u003c/li\u003e\n\u003cli\u003eMcLean I, Holmes P, Wellington M, Herley J, Medway M (2018) Pastoral Company Benchmarking Project 2012\u0026ndash;2017. MLA Final Report P.PSH.0718. Meat and Livestock Australia, North Sydney.\u003c/li\u003e\n\u003cli\u003eMcLean IA, Wellington MJ, Holmes PR, Bertram JD, McGowan MR (2023) The Australian Beef Report 2023. Bush Agribusiness Pty Ltd: Toowoomba\u003c/li\u003e\n\u003cli\u003eMugera AW, Nyambane GG (2015) Impact of debt structure on production efficiency and financial performance of Broadacre farms in Western Australia. Aust J Agric Resour Econ 59(2):208-224. https://doi.org/10.1111/1467-8489.12075\u003c/li\u003e\n\u003cli\u003eNorthern Territory Government (2022) Northern Territory Primary Industry Snapshot. https://business.nt.gov.au/__data/assets/pdf_file/0007/1033496/nt-primary-industry-snapshot.pdf. Accessed 15 February 2024\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Reagain P, Bushell J, Holloway C (2019) Innovations in sustainable grazing management for extensive beef enterprises in northern Australia. Anim Prod Sci 59(11):1935-1943.\u003c/li\u003e\n\u003cli\u003eOtieno DJ, Hubbard L, Ruto E (2012) Determinants of technical efficiency in beef cattle production in Kenya. Paper presented at the International Association of Agricultural. Economists (IAAE) Triennial Conference, Foz do Iguacu, Brazil, August.\u003c/li\u003e\n\u003cli\u003ePedroni P (2001) Fully modified OLS for heterogeneous cointegrated panels. In Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited \u003c/li\u003e\n\u003cli\u003eQueensland Government (2022) Queensland Agricultural Land Use Mapping. https://data.qld.gov.au/dataset/queensland-agricultural-land-use-mapping. Accessed 8 February 2024\u003c/li\u003e\n\u003cli\u003eRolfe J, Windle J, Whitten S (2019) Off-farm income and farm capital productivity in Australian broadacre agriculture. Aust J Agric Resour Econ 63(3):705-723.\u003c/li\u003e\n\u003cli\u003eScanlan JC, McKeon GM, Day KA, Mott JJ, Hinton AW (1994) Estimating safe carrying capacities in extensive cattle grazing properties within tropical semi-arid woodlands of north-eastern Australia. The Rangel J 16:64\u0026ndash;76. doi:10.1071/RJ9940064 \u003c/li\u003e\n\u003cli\u003eSheng Y, Chancellor W (2019) Exploring the relationship between farm size and productivity: evidence from the Australian grains industry. Food Policy 84:196-204.\u003c/li\u003e\n\u003cli\u003eSirak B, Derek B (2015) Determinants of Profit Efficiency among Smallholder Beef Producers in Botswana. Int Food Agribus Manage Rev 18 (3).\u003c/li\u003e\n\u003cli\u003eThompson T, Martin P (2012) Australian beef: Financial performance of beef cattle producing farms, 2009\u0026ndash;10 to 2011\u0026ndash;12. CC BY 3.0. https://www.mla.com.au/globalassets/mla-corporate/prices--markets/documents/trends--analysis/abares-farm-survey/financial-performance-of-beef-farms-2009-10-to-2011-12.pdf. Accessed 12 March 2024 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Variables used in the panel vector error correction models.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"598\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndogenous variable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeef cattle production\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eY1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal factor productivity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLTFP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBranding rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLBR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArea operated (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLAO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal labour used (weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLTL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCapital at July 01 ($)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal non-farm income ($)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCost of livestock contracts ($)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFarm business debt ($)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLFBD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal annual rainfall (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLRF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFarm liquid assets ($)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLFLA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExogenous variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEconomic shocks\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge of the owner/manager (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Estimated coefficients of the panel vector error correction model for beef cattle production\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEquation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eY1(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLBR(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAO(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLTL(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLCA(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLNFI(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLFLA(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRF(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLFBD(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eES_1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eY1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.213*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.402*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.295\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLBR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.042\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.205*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.165\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.298\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.144\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.128\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.229\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.362\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.049\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.129\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.066\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.355*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.177***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.322***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.063\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.159\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.076\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.372*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.089\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.065\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.191***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.017\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.300**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.551**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.210\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.337\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.091\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.090\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.134\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.433**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLNFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.502***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.969***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.767**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.555\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.893**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.059\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.375***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.579**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.461*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.093*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLFLA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.292*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.546*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.273\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.626\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.101\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.705\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.145\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.203*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.511***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.019\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.165\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.311\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.231\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-1.395*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.130\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.127\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.024\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.031\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.016\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLFBD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.453***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.837***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.549*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.102\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.255\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.574\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.101\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.158\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.110\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.169\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.185\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: (*) Significant at 1%, (**) Significant at 5%, and (***) Significant at 10%\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Estimated coefficients of the panel vector error correction model for total factor productivity.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEquation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLTFP(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLBR(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLNFI(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLCC(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLFLA(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRF(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLFBD(-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eLTFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e9.3e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.0215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eLBR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.105**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.613***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.036***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eLNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.360**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-4.156*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-1.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.339***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.538**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.427**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.103*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eLCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.420*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eLFLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-2.335**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.627*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.174*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.471***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eLRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.594***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e3.6e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eLFBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-2.412*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.638*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: (*) Significant at 1%, (**) Significant at 5%, and (***) Significant at 10%\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Beef cattle systems, Panel Vector Error Correction Model, Production, Productivity, Reproductive performance","lastPublishedDoi":"10.21203/rs.3.rs-7152494/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7152494/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImproving the productivity and sustainability of beef cattle systems in dry tropical regions requires better understanding of long-run drivers of performance and system adjustment dynamics. This study uses Panel Vector Error Correction Models (PVECM) and 32 years of regional data (1990\u0026ndash;2022) to investigate the socio-economic and environmental determinants of beef cattle production and productivity in northern Australia. We hypothesised that reproductive efficiency, labour input, and financial structure are significant long-run drivers of system performance, and that tropical beef systems respond dynamically to shocks over time. The PVECM for beef cattle production revealed positive long-run relationships with branding rate, total labour used, and farm capital, and negative relationships with area operated, non-farm income, liquid assets, and rainfall. The productivity model identified positive effects from non-farm income, livestock contract costs, liquid assets, and rainfall, and a negative effect from business debt. The error correction term for the production model was significant, with strong adjustment observed for total labour used and liquid assets, indicating these variables help rebalance the system after shocks. In the productivity model, the error correction term was also significant, with adjustments evident in branding rate and rainfall, suggesting these variables respond dynamically to deviations in long-run productivity. In conclusion, these findings demonstrate that cattle production systems in tropical environments exhibit both structural persistence and short-run responsiveness. Reproductive efficiency emerged as a central and persistent driver of both production and productivity. The results provide an empirical basis for targeted management interventions to improve resilience, investment efficiency, and long-term profitability.\u003c/p\u003e","manuscriptTitle":"Unveiling the Performance Drivers of Northern Australian Beef Systems: A Time Series Analysis 1990-2022)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 19:35:50","doi":"10.21203/rs.3.rs-7152494/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8227da3-96aa-4dbd-8173-a83833a40815","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:28:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 19:35:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7152494","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7152494","identity":"rs-7152494","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}