Rising heat, resilient markets: What AI reveals about livestock economics

Rising temperatures and intensifying humidity levels are placing growing pressure on livestock systems worldwide, with heat stress increasingly linked to reduced productivity and animal welfare concerns. However, whether these environmental shocks directly translate into commercial instability remains a key question, particularly in emerging agricultural markets where granular production data are limited.

A new study, titled Artificial Intelligence Modeling of Climate-Driven Variability in Livestock-Related Sales Using Satellite-Derived Bioclimatic Indices, and published in the journal Agriculture, explores whether widely used climatic stress metrics, particularly the Temperature Humidity Index, can predict fluctuations in weekly livestock-related sales when analyzed through both classical econometric models and machine learning techniques.

Climate stress meets commercial data

Heat stress is widely recognized as a critical risk factor in livestock systems. Rising temperatures and humidity levels can reduce feed intake, slow growth, impair reproduction, and increase mortality. The Temperature Humidity Index, commonly known as THI, has long served as a benchmark indicator of thermal stress in cattle and other livestock species. Yet while physiological impacts are well documented, the downstream commercial consequences remain less understood, particularly in regions where farm-level monitoring data are limited.

The authors test whether satellite-derived THI could serve as a macro-level proxy for economic stress in livestock markets. The researchers integrated weekly commercial sales data from two livestock-related retail branches in Samborondón, Ecuador, spanning March 2020 to February 2025, with meteorological variables retrieved from the NASA POWER platform. Air temperature and relative humidity data were used to compute weekly THI values, offering a standardized measure of heat stress exposure.

To refine their analysis, the team constructed two additional climatic indicators. The first, a basal temperature index, represented a four-week moving average of THI to capture accumulated or chronic thermal load. The second, a thermal anomaly index, measured deviations of weekly THI from its recent baseline, capturing abrupt climatic shocks. These metrics allowed the researchers to distinguish between sustained heat exposure and short-term anomalies.

The economic variable under study was weekly Net Sales, reflecting aggregated retail performance of meat products. The authors emphasized that Net Sales represent commercial outcomes rather than direct production metrics, meaning that the relationship between climate stress and retail performance may be mediated by supply chains, inventory systems, and market dynamics.

Testing linear and non-linear AI models

To examine the relationship between climate variability and sales, the researchers deployed a hybrid modeling framework. Classical econometric time-series models were paired with machine learning techniques to test whether climatic indicators meaningfully improve forecasting performance.

The first approach involved ARIMAX models, which extend traditional autoregressive integrated moving average models by incorporating exogenous variables. In this case, lagged climatic indicators were introduced to determine whether they improve predictive accuracy beyond the inherent persistence of the sales series itself. Baseline comparisons included simple persistence forecasts and ARIMA models without climate inputs.

In parallel, the team trained a Random Forest model, a non-linear machine learning algorithm capable of capturing complex interactions and delayed effects. The Random Forest incorporated lagged climatic and economic features to evaluate whether hidden patterns between heat stress indicators and sales might emerge outside linear assumptions.

Principal Component Analysis was also applied to explore whether climatic variables and commercial performance share common variance structures or operate as largely independent systems.

The results delivered a nuanced picture. Climatic variables, particularly THI and the basal temperature index, dominated the environmental variance component in the data. However, Net Sales loaded separately in principal component space, suggesting that retail performance and climatic variability function as largely independent dimensions at the weekly scale analyzed.

Correlation analysis reinforced this conclusion. While climatic persistence was strong, meaning warm weeks tended to cluster together, correlations between lagged THI values and Net Sales were weak. In other words, even sustained heat exposure did not translate into proportional or immediate changes in weekly meat sales.

The ARIMAX models reflected this limited linkage. Including climatic variables produced only modest improvements over autoregressive baselines, and explanatory power remained relatively low. Sales dynamics appeared driven primarily by internal market inertia and temporal structure rather than direct climatic shocks.

The Random Forest model achieved substantially stronger predictive performance, with a high coefficient of determination on the test set. However, the authors caution that this performance largely reflects the algorithm's ability to model non-linear sales persistence and autoregressive features rather than strong climate-driven effects. Climatic indicators functioned as secondary inputs rather than dominant drivers.

Buffering mechanisms in livestock supply chains

Regional thermal stress, as captured by satellite-derived THI, does not directly or synchronously drive short-term fluctuations in aggregated livestock-related sales. Commercial systems appear buffered by logistical resilience, inventory management, and distribution adjustments.

This finding does not contradict established biological research showing that heat stress impairs livestock productivity. Rather, it highlights the importance of scale and mediation. Animal-level physiological stress does not necessarily translate immediately into retail-level economic variability. Supply chains can absorb production shocks through storage, redistribution, or pricing adjustments, masking short-term climatic signals in aggregated sales data.

The researchers emphasize that retail performance reflects a complex interplay of production cycles, market demand, transportation logistics, and economic behavior. Climatic forcing may exert delayed or indirect effects that require longer time horizons or finer-grained data to detect.

From a digital agriculture perspective, the study underscores the role of artificial intelligence in disentangling weak or indirect signals within complex systems. Even when climatic effects are limited, satellite-derived bioclimatic indices provide valuable contextual information. In data-scarce regions, gridded climate products offer a scalable alternative to expensive on-farm monitoring networks.

However, the authors caution against overinterpreting satellite-derived indices as direct proxies for animal-level stress. Gridded climatic averages may obscure microclimatic variations within barns, pastures, or housing systems. Without integrating farm-level measurements, predictive models risk conflating environmental exposure with biological response.

The research also demonstrates the practical value of hybrid modeling approaches. Linear econometric models offer interpretability and transparency but may miss complex interactions. Machine learning models provide flexibility and strong predictive power but can obscure causal relationships. Combining both frameworks allows researchers to compare explanatory depth with predictive accuracy.

The study's methodology is designed for transferability. By relying on publicly available satellite data and standard commercial transaction records, the framework can be applied across livestock supply chains in developing regions. As climate change intensifies thermal variability worldwide, scalable monitoring and forecasting tools will become increasingly important for agricultural resilience.

Climate stress does not automatically produce immediate retail volatility. Policymakers and agribusiness leaders seeking early warning systems must recognize that commercial outcomes are mediated through layered systems of buffering and adaptation.

Future research directions outlined in the study include incorporating on-farm microclimate measurements, animal-level productivity metrics, and longer time horizons to capture delayed effects. Integrating feed availability, disease outbreaks, and economic indicators could further refine predictive accuracy.