Contemporary issues in agriculture and natural resource management (AGNR) span a wide spectrum of challenges and scales—from global climate change to resiliency in national and regional food systems to the sustainability of livelihoods of small-holder farmers—all of which may be characterized as complex problems. With rapid development of tools and technologies over the previous half century (e.g., computer simulation), a plethora of disciplines have developed methods to address individual components of these multifaceted, complex problems, oftentimes neglecting unintended consequences to other systems. A systems thinking approach is needed to (1) address these contemporary AGNR issues given their multi- and interdisciplinary aspects; (2) utilize a holistic perspective to accommodate all of the elements of the problem; and (3) include qualitative and quantitative techniques to incorporate “soft” and “hard” elements into the analyses. System dynamics (SD) methodology is uniquely suited to investigate AGNR given their inherently complex behaviors. In this paper, we review applications of SD to AGNR and discuss the potential contributions and roles of SD in addressing emergent problems of the 21st century. We identified numerous SD cases applied to water, soil, food systems, and smallholder issues. More importantly, several case studies are shown illustrating the tradeoffs between short-term and long-term strategies and the pitfalls of relying on quick fixes to AGNR problems (known as “fixes that backfire” and “shifting the burden”, well-known, commonly occurring, systemic structures—or archetypes—observed across numerous management situations [Senge, P.M. The Fifth Discipline, 1st ed.; Doubleday: New York, NY, USA, 1990.]). We conclude that common attempts to alleviate AGNR problems, across continents and regardless of the type of resources involved, have suffered from reliance on short-term management strategies. To effectively address AGNR problems, longer-term thinking and strategies aimed at fundamental solutions will be needed to better identify and minimize the often delayed, and unintended, consequences arising from feedback between management interventions and AGNR systems.

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals. Interest and investment in precision technologies are growing within the livestock sector. Though these technologies offer many promises of increased efficiency and reduced inputs, there is a need to assess the opportunities and challenges associated with precision technology implementation in livestock production systems. In this review, precision livestock measurement and management tools are explained in the context of a logical and iterative five-step process that highlights the need for systems computer modeling to realize anticipated benefits from these technologies and avoid unintended consequences. This review includes key case studies to highlight past challenges and current opportunities within operations that house animals in a central area or building with sufficient infrastructure (confined livestock production systems) and other operation settings that utilize large grasslands that contain far less infrastructure (extensive livestock production systems). The key to precision livestock management success is training the next generation of animal scientists in computer modeling, precision technologies, computer programming, and data science while still being grounded in traditional animal science principles.

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams (“big data”) and the exponential increase in computing power have allowed the appearance of “new” modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine “old” and “new” modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.

Precision Livestock Farming (PLF) has evolved dramatically from basic monitoring systems to sophisticated artificial intelligence (AI)-driven decision support systems that enhance livestock management efficiency, sustainability, and animal welfare. This review examines the technological evolution of PLF since 2017, highlighting significant advancements in sensing technologies, computer vision, and AI. Non-invasive technologies, including red-green-blue and depth cameras, 3D imaging systems, and Internet of Thingsenabled platforms, now capture detailed biometric and behavioral data in real time, while AI algorithms enable early disease detection, optimize feeding strategies, and improve reproductive management. Integrating these technologies with mechanistic models has created hybrid intelligent frameworks that address longstanding challenges in precision nutrition modeling. Future PLF development will likely focus on integrating large language models, adopting federated learning approaches to address data privacy concerns, and democratizing technologies for small-scale producers. Despite technological progress, challenges remain regarding data standardization, connectivity in rural environments, high implementation costs, and ethical considerations around increased animal monitoring. By fostering interdisciplinary collaboration among animal scientists, engineers, computer scientists, and social scientists, PLF can continue to drive sustainable and efficient practices in livestock production while ensuring that technologies complement rather than replace traditional husbandry knowledge.

Perception of freshwater use varies between nations and has led to concerns of how to evaluate water use for sustainable food production. The water footprint of beef cattle (WF_B) is an important metric to determine current levels of freshwater use and to set sustainability goals. However, current WF_Bpublications provide broad WF values with inconsistent units preventing direct comparison of WF_Bmodels. The water footprint assessment (WFA) methodologies use static physio-enviro-managerial equations, rather than dynamic, which limits their ability to estimate cattle water use. This study aimed to advance current WFA methods for WF_Bestimation by formulating the WFA into a system dynamics methodology to adequately characterize the major phases of the beef cattle industry and provide a tool to identify high-leverage solutions for complex water use systems. Texas is one of the largest cattle producing areas in the United States, a significant water user. This geolocation is an ideal template for WF_Bestimation in other regions due to its diverse geography, management-cultures, climate and natural resources. The Texas Beef Water Footprint model comprised seven submodels (cattle population, growth, nutrition, forage, WF_B, supply chain and regional water use; 1432 state variables). Calibration of our model replicated initial WF_Bvalues from an independent study by Chapagain and Hoekstra in 2003 (CH2003). This CH2003v.Texas production scenarios evaluated model parameters and assumptions and estimated a 41–66% WF_Bvariability. The current model provides an insightful tool to improve complex, unsustainable and inefficient water use systems.

Advancements in precision livestock technology have resulted in an unprecedented amount of data being collected on individual animals. Throughout the data analysis chain, many bottlenecks occur, including processing raw sensor data, integrating multiple streams of information, incorporating data into animal growth and nutrition models, developing decision support tools for producers, and training animal science students as data scientists. To realize the promise of precision livestock management technologies, open-source tools and tutorials must be developed to reduce these bottlenecks, which are a direct result of the tremendous time and effort required to create data pipelines from scratch. Open-source programming languages (e.g., R or Python) can provide users with tools to automate many data processing steps for cleaning, aggregating, and integrating data. However, the steps from data collection to training artificial intelligence models and integrating predictions into mathematical models can be tedious for those new to statistical programming, with few examples pertaining to animal science. To address this issue, we outline how open-source code can help overcome many of the bottlenecks that occur in the era of big data and precision livestock technology, with an emphasis on how routine use and publication of open-source code can help facilitate training the next generation of animal scientists. In addition, two case studies are presented with publicly available data and code to demonstrate how open-source tutorials can be utilized to streamline data processing, train machine learning models, integrate with animal nutrition models, and facilitate learning. The National Animal Nutrition Program focuses on providing research-based data on animal performance and feeding strategies. Open-source data and code repositories with examples specific to animal science can help create a reinforcing mechanism aimed at advancing animal science research.