A team of researchers led by Kashif Khaqan of Federation University Australia, working with colleagues at Adelaide University and partners in industry, has published one of the most comprehensive maps to date of the scientific literature on digital twins in agriculture. Their bibliometric analysis, published in the journal Discover Agriculture, examined 657 Scopus-indexed publications spanning 23 academic disciplines and 87 countries. The headline finding is a striking surge of activity: more than four-fifths of all relevant documents identified in the dataset were published in just the last three years of the study period, with 2025 alone contributing 216 papers, or nearly 33 percent of the total. What was once a niche curiosity has become one of the most dynamic research frontiers in agricultural science.

The study defines a digital twin as a closed-loop system in which real-time data, decision-making, and automated actions continuously interact with and influence a physical asset. This is what separates a true twin from an ordinary computer model. A crop simulation can be run once and left alone; a digital twin is fed constantly by Internet of Things sensors measuring soil properties, microclimates, crop status, and equipment performance, and its outputs flow back into the physical world as irrigation commands, machinery adjustments, or management alerts. The researchers argue that this bidirectional connection gives digital twins a level of fidelity and usefulness that traditional modelling and simulation approaches cannot match, effectively turning a farm into a system that can be rehearsed, predicted, and optimised before decisions are executed in the field.

The numbers behind the analysis reveal where the intellectual centre of gravity currently sits. China leads global output with 118 publications, representing 17.9 percent of the total and attracting 1,129 citations, followed by India with 105 papers and the United States with 66. The United States ranks second in citations with 1,209, while Australia, despite a smaller share of publications, has accumulated the highest total citations at 1,371, suggesting outsized influence per paper. On the institutional stage, Wageningen University & Research in the Netherlands stands out as the single most productive organisation with 21 publications, ahead of China’s Ministry of Agriculture and China Agricultural University. The top five countries together produced more than half of the entire corpus, and 45 of the 87 publishing nations met a threshold of at least five shared collaborations, indicating a genuinely international but unevenly distributed research network.

Perhaps the most revealing part of the study is its keyword co-occurrence analysis, which distilled 4,726 author keywords down to 105 that appeared at least ten times, and then clustered them into five interconnected themes. The first cluster captures the technological backbone: artificial intelligence, big data, cyber-physical systems, real-time monitoring, decision support systems, network security, and predictive maintenance. The second focuses on digital crop management, where machine learning, deep learning, and reinforcement learning allow twins to improve their predictions of crop growth, irrigation needs, and environmental conditions over time, with particular strength in controlled environments such as greenhouses. The third cluster highlights sensing and autonomy: drones, remote sensing, 3D reconstruction, agricultural robots, edge computing, and blockchain. The fourth links the technology to climate-smart agriculture, sustainability, life cycle assessment, food security, and climate change adaptation. The fifth centres on the IoT-driven ecosystem of sensors and connected devices that underpins agriculture 4.0.

Dominance by computer science is both the engine and the weakness of the field. Computer Science accounts for 62.2 percent of publications and Engineering for 48.5 percent, while Agricultural and Biological Sciences contributes just 23.7 percent. The authors read this as a disciplinary imbalance: the machinery of digital twins is being built faster than agronomists and practitioners can apply it. Journal patterns tell a similar story. Computers and Electronics in Agriculture leads the field with 21 articles and 867 citations and a journal impact factor of 8.9, while broader outlets such as Agriculture and Applied Sciences fill out the top tier. Conference papers, at 36 percent of the corpus, slightly outnumber research articles at 32 percent, a signature of a young field still working out its methods in real time.

The gaps are as informative as the clusters. Keywords associated with disease management, climate resilience, agricultural supply chains, and advanced decision-support systems appeared less frequently and linked weakly to the core themes. In other words, most existing digital twins are good at watching and predicting, but poor at deciding. The analysis identifies decision support and disease management as the key research deficits, and the authors call for decision-oriented frameworks that close the loop from sensor data through simulation to a concrete, defensible recommendation for the farmer. On the research side, they argue that twins enable scenario testing without risking crop loss, allow controlled experiments on plant-soil-microbe interactions and pest propagation, and could eventually be aggregated into regional and global networks for supply chain optimisation and food security planning, provided that standardised protocols for data collection, integration, and validation are established.

For practice, the pathway to adoption runs through high-value and controlled environments first. The study suggests farmers and agribusinesses begin with greenhouses, orchards, and farms that already have sensor infrastructure, where twins can deliver measurable gains in precision irrigation, nutrient management, yield forecasting, and input-use efficiency. Integration with existing farm management systems, autonomous machinery, and supply chain processes extends the benefits toward traceability and sustainability. But the barriers are real: high implementation costs, technological complexity, a shortage of expertise, interoperability problems between hardware and software platforms, and unresolved questions of data security and privacy. The authors point to pilot deployments, cost-effective sensor networks, user-friendly interfaces, and training programmes as the practical instruments for widening adoption beyond large commercial operations to smallholders.

Policy emerges as the decisive variable. Because digital twins depend on real-time data, sensor networks, and robust connectivity, countries with strong national innovation strategies and Industry 4.0 investment, such as China, dominate output, while many smallholders in lower-income regions cannot afford the underlying infrastructure. The study recommends subsidies, grants, low-interest financing, and shared equipment schemes to prevent diffusion from being confined to industrial-scale farming, alongside interoperability standards developed with industry bodies and universities, and clear regulations on data privacy and ownership to build farmer trust. The analysis has limitations worth noting: it relies on a single database, English-dominated literature, and a deliberately narrow search string pairing digital twin with agriculture, which may exclude adjacent work using terms like smart farming or cyber-physical systems. Yet the trajectory is unmistakable. In a world of tightening water, unstable weather, and rising food demand, the ability to rehearse a bad harvest virtually before it happens may prove one of agriculture’s most consequential tools, and the research community is clearly betting that the twin will soon be as fundamental to a farm as the soil beneath it.

Subject of Research: Bibliometric analysis of digital twin applications in agriculture

Article Title: Exploring digital twins in agriculture with current applications and implications for research, policy and practice

Article References: Khaqan, K., Gautam, P., Paudel, B., Liang, S., Van Duc Long, N., Parvin, M. I., Munigoti, K., Sasanelli, N., Hessel, V., & Sandhu, H. (2026). Exploring digital twins in agriculture with current applications and implications for research, policy and practice. Discover Agriculture, 4(1), Article 278. https://doi.org/10.1007/s44279-026-00760-8

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00760-8

Keywords: digital twins, agriculture, bibliometric analysis, smart farming, Internet of Things, artificial intelligence, precision agriculture, climate-smart agriculture, machine learning, drones, IoT sensors, agricultural policy

The research, published in Smart Agricultural Technology, was led by Sabrina Gulab, Hanan Ishaque, and Guillaume Lhermie, who surveyed 587 commercial crop and livestock producers operating farms larger than 700 acres across the three Prairie provinces. After restricting the analysis to producers who had adopted at least one digital agricultural technology, or DAT, the final analytical sample comprised 392 operations. The team used a binary logit regression model to identify which factors were associated with what they call data-use intensity, defined as the degree to which producers integrate technology-generated data into day-to-day farm management decisions. Respondents were classified as making high use of data or low or no use, and the model estimated how behavioral, psychological, and institutional variables shifted the probability of falling into the high-use category.

The theoretical foundation of the study is an extension of two influential frameworks in technology acceptance research: the Technology Acceptance Model, or TAM, and the Unified Theory of Acceptance and Use of Technology, known as UTAUT. Both frameworks were originally designed to explain why people adopt technology in the first place, emphasizing constructs such as perceived usefulness, effort expectancy, and facilitating conditions. The authors argue that these models fall short in the post-adoption phase, where the relevant question is not whether a farmer expects a tool to perform, but whether the continuous stream of data it generates is perceived as actionable, trustworthy, and valuable for ongoing decisions. They systematically remapped UTAUT constructs to post-adoption equivalents: performance expectancy became the perceived usefulness of data insights, facilitating conditions became data handling support, and social influence was extended to capture emerging norms around data sharing and collective contribution to agricultural data ecosystems.

The descriptive findings alone are sobering for the digital agriculture industry. Only 40.3 percent of surveyed producers reported high use of data in farm decision-making, while 54 percent reported low use and 6 percent reported no use at all. In other words, a majority of farmers who had already paid for and installed digital technologies were not meaningfully integrating the resulting information into their management choices. Producers operating larger farms showed higher levels of data integration than those on smaller operations, a pattern the authors attribute to economies of scale, where data-driven management produces clearer efficiency gains on complex, resource-intensive enterprises.

The regression results sharpen the picture considerably. Perceived usefulness of data emerged as the single strongest predictor: a one-unit increase in the belief that data helps optimize resources and inputs was associated with a 20.4 percentage point higher probability of high data use. Openness and proactive behavior came next, with roughly a one-standard-deviation increase associated with a 14.9 percentage point rise in the probability of high data use. Producers who actively seek information about new technologies, experiment with unfamiliar tools, and attend workshops appear to sustain engagement with data long after the initial purchase. Altruism also played a meaningful role, with producers comfortable sharing farm data with providers, researchers, companies, and government institutions showing an 8.7 percentage point higher probability of high data use, consistent with the idea that contributing to a shared data ecosystem both reflects and reinforces deeper engagement with one’s own data.

On the negative side, risk perception significantly suppressed data engagement. Producers who felt the risks of digital technologies outweighed their benefits had an 8.1 percentage point lower probability of high data use. Notably, the study uncovered a moderating effect: the negative influence of risk perception was stronger among high adopters who used many technologies, suggesting that farmers with the most at stake operationally and financially are also the most sensitive to concerns about data privacy, system reliability, and technological risk. Lack of digital literacy was another substantial barrier, associated with a 9.8 percentage point lower probability of high data use. Drawing on cognitive load theory, the authors argue that when interpreting complex dashboards and agronomic recommendations exceeds a producer’s cognitive resources, the data simply never makes it into the decision-making process.

Perhaps the most counterintuitive finding concerns social support. Producers who frequently consulted neighbors when they ran into difficulties with their technologies were less likely to use data intensively, with each unit increase in reliance on peer consultation associated with a 4.3 percentage point decrease in the probability of high data use. The authors interpret this as evidence that dependency-oriented support may substitute for independent engagement: farmers with lower technological self-efficacy lean on neighbors rather than developing the autonomous problem-solving skills needed to work with data themselves. This challenges a long-standing assumption in agricultural extension literature that peer networks uniformly accelerate technology uptake, and it suggests that building independent digital competence may matter more than facilitating help-seeking.

Equally striking is what did not predict data use. Concerns around data governance, including privacy, ownership, and commercial exploitation of farm data, showed no statistically significant association with data-use intensity, nor did trust in technology providers or the lack of facilitating conditions such as technical support and system interoperability. These factors dominate the adoption literature and are frequently cited in policy debates, yet in this sample of commercially established producers they did not shape what happened after adoption. The authors propose two explanations: established commercial producers may already operate within sufficiently developed governance arrangements, or governance concerns may function primarily as barriers at the adoption stage rather than during ongoing data integration. Either way, the finding underscores a stage-based view of technology engagement in which different barriers operate at different points along the adoption continuum.

The implications extend beyond farm gates. High-quality, farm-level data are the raw material for the next generation of AI-enabled agriculture, feeding the predictive models and recommendation systems that promise precision forecasting and optimized input use. If most adopters leave their data unexamined, both current returns on technology investment and future innovation pipelines are constrained. The authors argue that policy should therefore prioritize behavioral and cognitive enablers alongside infrastructure spending: strengthening digital literacy, reducing perceived risk through experiential learning, and framing data sharing as a contribution to the collective resilience of the agricultural community. Crop producers, who showed a 29.3 percentage point lower probability of high data use than livestock producers, may deserve particular attention, since livestock systems tend to depend on continuous monitoring that makes data value more immediately visible. As governments pour money into rural broadband and digital agriculture subsidies, this study offers a clear warning: the bottleneck is no longer the technology itself, but the human capacity and motivation to turn its output into decisions.

Subject of Research: Post-adoption data use behavior among commercial crop and livestock producers using digital agricultural technologies in Western Canada.

Article Title: Beyond adoption: Understanding data use behavior in digital agriculture

Article References: Gulab, S., Ishaque, H., & Lhermie, G. (2026). Beyond adoption: Understanding data use behavior in digital agriculture. Smart Agricultural Technology, 15, Article 102546. https://doi.org/10.1016/j.atech.2026.102546

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102546

Keywords: digital agriculture, data use behavior, precision agriculture, technology adoption, UTAUT, data governance, digital literacy, farm decision-making, risk perception, altruism, Western Canada, agricultural policy

The research, published in the journal Discover Agriculture, examined the sources of total factor productivity (TFP) and profit growth in Beninese cotton production between 2000 and 2018. A team led by Idelphonse O. Saliou of the University of Abomey-Calavi assembled an impressive dataset: 482 cotton producers surveyed across three major agroecological zones—the central region around Savalou, the northern district of Banikoara, and the western Atacora region around Cobly. To qualify for the study, producers had to be at least 40 years old with a minimum of 19 consecutive years of farming experience, ensuring that each participant could reconstruct nearly two decades of production history.

Reconstructing nineteen years of farm records in a setting where most smallholders keep no formal accounts is a formidable challenge. The researchers used retrospective recall techniques, dividing the study period into four sub-periods aligned with successive presidential regimes—a practical aid to memory in a country where agricultural policy shifts with political transitions. Producers identified years of high and low performance within each sub-period, then supplied production details for the remaining years. Where available, farmers supplemented their recollections with accounting books, input purchase invoices, and labor contracts. The final estimation sample included 5,577 observations, reduced from a theoretical maximum of 9,158 by recall limitations, missing variables, and strict data-cleaning procedures that the authors argue should not introduce systematic bias under standard missing-data assumptions.

Methodologically, the study employed a parametric translog cost frontier approach, a flexible second-order approximation of the true cost function that imposes few prior restrictions on the underlying technology. The framework, following the decomposition methods of Kumbhakar and colleagues, separates productivity growth into three distinct components: technical efficiency change, which measures how close farmers operate to the best-practice frontier; technological change, which captures shifts in that frontier itself; and scale effects, which reflect economies or diseconomies of expanding production. Input prices for fertilizer, labor, and a Laspeyres index of other inputs—including seeds, insecticides, herbicides, and animal or mechanical traction—were normalized by land prices to satisfy linear homogeneity constraints. Crucially, the model accounted for unobserved heterogeneity among farms, distinguishing persistent inefficiency rooted in structural conditions from time-varying inefficiency that fluctuates year to year.

The headline result is modest but meaningful: TFP in Beninese cotton production grew by an average of 1.26 percent per year between 2000 and 2018. Decomposition reveals that this growth was powered almost entirely by technological progress, which advanced at 2.81 percent annually, complemented by a modest gain in technical efficiency of 0.24 percent per year. The technological momentum reflects concrete changes in the field: continuous varietal improvement programs that guarantee quality seed to producers, the gradual replacement of hand tools with animal traction and mechanical power, and intensified use of mineral fertilizers and pesticides for weed control and plant health. The technical efficiency gains, meanwhile, are credited in part to Benin’s dense extension network—nearly all cotton farmers belong to Village Cotton Producers Cooperatives, and extension agents are evaluated on the basis of cotton production performance.

But there is a troubling counterweight. The scale component exerted a negative effect of 1.83 percent per year on TFP, indicating that Beninese cotton farms are operating under decreasing returns to scale. When farmers increased the use of all inputs, output rose proportionally less, driving up unit costs for each additional kilogram of cotton harvested. In plain terms, farms are growing beyond their most efficient size, and unexploited economies of scale represent a pool of unrealized productivity. The authors note similar findings in Chinese agriculture after reforms, and contrast them with European and Finnish dairy farms where scale effects contributed positively to productivity—evidence that lower average costs could be achieved by producing at more optimal scales.

The study also traced how productivity translated, or failed to translate, into the bottom line. Farm profits grew by an average of 1.65 percent per year over the period, but the sources of that growth were largely external rather than internal. Rising cotton prices, which climbed at 3.03 percent annually, were the dominant driver, aided by Benin’s price stabilization mechanism that guarantees a minimum income for producers and shields them from world market volatility. Output quantity growth of 0.89 percent per year and the TFP gains also helped. Working against these gains, input prices rose at 2.91 percent per year, steadily eroding profitability—a pattern the authors note mirrors Kumbhakar and Lien’s findings in Norwegian dairy farming. Profit growth was strongest between 2011 and 2015, driven by favorable price movements, while the earliest sub-period saw profits squeezed by input cost inflation.

One finding stands out for its starkness: the estimated overall cost efficiency of Beninese cotton farms is only about 23.5 percent, combining persistent efficiency of 28 percent with time-varying efficiency of 83.8 percent. Actual production costs remain substantially above the minimum attainable frontier. The authors caution that this does not simply reflect poor management. Rather, the persistent inefficiency component likely captures structural and systemic constraints beyond individual farmers’ control: poor rural infrastructure, high transportation and input transaction costs, imperfect access to mechanization services, climate variability, credit market imperfections, and institutional rigidities. Similar low-efficiency findings across West Africa support this interpretation, with prior research showing that institutional environments—particularly access to credit, inputs, and marketing channels—significantly shape producer performance.

The researchers confirmed both of their formal hypotheses: technical efficiency gains contributed positively to TFP growth, and rising cotton prices positively influenced farm profits. Yet they are candid about the study’s limitations. Retrospective data collection risks recall bias and measurement error; the sample of experienced, older producers may overestimate efficiency; the cost frontier framework does not address potential endogeneity, including the possibility that government-set cotton prices are not truly exogenous; and the assumption of full allocative efficiency—that farmers use optimal input combinations—may not hold in practice. Future work using profit frontier models could test how sensitive these conclusions are to those assumptions.

For policymakers, the recommendations are clear. The authors call for strengthening producer capacities through training, promoting technological innovations such as mechanization and pest-resistant varieties, and implementing incentive-compatible price policies that support optimal input use. The stagnation of TFP growth in the final sub-period of the study, driven by losses in technical efficiency and negative scale effects, serves as a warning that past gains are not guaranteed to persist. With nearly three million livelihoods tethered to the cotton plant, Benin’s experience offers a broader lesson for agricultural development across West Africa: productivity growth is possible even under structural constraints, but converting it into durable farmer prosperity requires tackling the institutions, infrastructure, and input markets that determine whether efficiency gains reach the farm gate.

Subject of Research: Total factor productivity and profit growth in cotton production in Benin, West Africa

Article Title: Total factor productivity and profit growth in cotton production in Benin, West Africa

Article References: Total factor productivity and profit growth in cotton production in Benin, West Africa. (n.d.). https://doi.org/10.1007/s44279-026-00749-3

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00749-3

Keywords: Benin, cotton production, total factor productivity, technical efficiency, profit growth, cost frontier, agricultural economics, West Africa, technological change, economies of scale, farm profitability, agricultural policy

Strategic foresight differs fundamentally from conventional forecasting. Where forecasting extrapolates present trends forward and assumes a broadly stable environment, foresight deliberately constructs multiple plausible futures, each shaped by different combinations of driving forces. These can include climate extremes, energy price swings, geopolitical disruption, trade fragmentation, technological breakthroughs and shifts in consumer demand. By developing scenarios that span this possibility space, researchers and policymakers can stress-test transition strategies before committing scarce public and private resources, identifying which elements of a climate-neutral farming pathway are robust across many futures and which are fragile bets on a single expected outcome.

The core insight of the research is that resilience and foresight are inseparable concerns for agricultural transformation. Farming sits at the intersection of ecological, economic and social systems, each with its own thresholds and feedback loops. A transition strategy that reduces greenhouse gas emissions on paper may nevertheless prove brittle if it depends on uninterrupted supply chains, stable subsidy regimes or benign weather. Strategic foresight provides a systematic way to expose these dependencies, revealing how plausible disruptions, from drought sequences to fertilizer market shocks, could interact with the transition process itself and either accelerate, slow or reverse progress toward climate neutrality.

Technically, the foresight approach typically proceeds through a sequence of steps. Analysts first scan for driving forces, categorizing them by their certainty and their potential impact on the system. The most consequential and most uncertain forces become the axes of scenario construction, producing a small set of internally coherent future worlds. Within each world, the dynamics of agricultural transition are explored: how farmers might adopt practices such as reduced tillage, cover cropping, improved nutrient management, agroforestry, precision fertilization or renewable-energy integration, and how those adoption patterns respond to the economic and institutional conditions of each scenario. The resilience of the transition is then assessed by comparing outcomes across scenarios and locating the points of common vulnerability.

One of the most important contributions of this framing is its treatment of time. Climate neutrality is usually expressed as a target date, but the journey toward that date is uneven and path-dependent. Early choices, such as which practices receive public support or which supply chains are reorganized first, can lock in certain configurations and foreclose others. Foresight makes these lock-in risks visible. It can show, for example, that a transition strategy optimized for a future of high carbon prices and stable trade may collapse under a future of price volatility and protectionism, whereas a more diversified strategy, combining multiple mitigation practices and revenue streams, retains functionality across both worlds.

The volatility emphasis is particularly timely. Recent years have confronted agriculture with a compound stress test: pandemic-era supply disruptions, energy and fertilizer price spikes linked to geopolitical conflict, recurrent droughts and floods, and shifting trade relationships. Each of these events strained farm businesses and policy frameworks alike. A transition to climate neutrality adds new layers of dependence, on carbon accounting systems, on emerging markets for low-emission products, and on technologies still moving down their cost curves. The research underscores that planning for the transition without accounting for such volatility would be a category error, because volatility is not an aberration but a defining feature of the operating environment.

Resilience, in this context, is unpacked rather than assumed. The analysis draws on the established conceptual vocabulary of resilience research, distinguishing the capacity of farming systems to absorb shocks, to adapt their structures and practices in response, and, where necessary, to transform into fundamentally new configurations. Applied to the climate-neutral transition, these capacities imply different design principles. Absorbency favors buffers such as financial reserves, diversified rotations and soil organic matter that cushions drought. Adaptability favors flexible policy instruments, learning networks among farmers, and monitoring systems that detect stress early. Transformability favors institutional space for experimentation, so that if climate or market conditions shift beyond what incremental change can handle, the sector can reorganize rather than collapse.

Strategic foresight also changes who is involved in planning. Because scenarios are built from assumptions about driving forces, the process benefits from the participation of a wide range of actors: farmers whose livelihoods embody the practical constraints, scientists who model biophysical processes, industry actors who control supply chains, and policymakers who set incentives. Participatory foresight exercises generate a shared vocabulary for discussing uncertain futures, which can reduce polarization and help stakeholders commit to transition strategies even when they disagree about which future is most likely. The research suggests this shared understanding is itself a resilience asset, enabling faster and more coordinated responses when real-world shocks arrive.

The implications for policy design are concrete. Strategies emerging from foresight-informed analysis tend to favor portfolios over silver bullets, combining emissions-reduction measures with adaptation measures and explicit contingency planning. They favor reversible and modular interventions, which can be scaled up or down as conditions change, over irreversible commitments whose value depends on a single forecast. They favor investment in information infrastructure, including monitoring, scenario updating and early-warning capacity, so that plans can be revised as evidence accumulates. And they favor attention to distributional consequences, because a transition that concentrates risk on vulnerable farms or regions is unlikely to sustain the social support it needs through a decade of turbulence.

The study also acknowledges the limits of foresight. Scenarios are not predictions, and there is a persistent risk that decision-makers treat the most comfortable scenario as the default. Foresight works best when it is iterative, revisited as conditions change, and when its outputs are explicitly linked to decision processes rather than filed away as reports. Maintaining that discipline requires institutional commitment, but the payoff, the authors argue, is a climate-neutral farming transition that is not merely planned but genuinely robust, one that can bend under pressure without breaking and can seize unexpected opportunities as the global environment continues to shift.

Beyond the immediate design of transition strategies, the foresight perspective carries implications for how agricultural research itself is organized. Much of agronomic science is built around optimizing individual practices under relatively controlled conditions, yet the resilience questions raised here concern combinations of practices interacting with turbulent external conditions. A scenario-based framing suggests value in research portfolios that evaluate practices not only for their average performance but for their performance under stress, including how cover cropping, nutrient management and energy integration behave when input prices, labor availability or weather patterns deviate sharply from historical norms.

The connection between soil processes and transition resilience deserves particular attention. Practices such as reduced tillage, diversified rotations and organic matter accumulation are frequently promoted for their mitigation benefits, but they also function as biophysical buffers. Soils with greater organic content hold more water during dry periods and recover more quickly from extreme rainfall, which means the same interventions that reduce emissions can simultaneously dampen the impact of climate shocks on yields. This dual character complicates simple cost-benefit accounting, because a practice that appears marginal when valued only for carbon may be clearly worthwhile once its risk-reduction role is included, a point that scenario analysis is well suited to surface.

Economic heterogeneity across the farming sector is another dimension that foresight exercises tend to expose. Farms differ enormously in size, capital access, tenure arrangements and exposure to international markets, so a transition pathway that is robust for a well-capitalized arable operation may be fragile for a small mixed farm carrying debt. When scenarios are populated with this heterogeneity rather than a representative average farm, the analysis can identify which policy instruments, such as targeted credit, insurance design or transition payments, determine whether the whole sector moves together or whether vulnerable segments fall behind and undermine collective targets.

The temporal structure of shocks also matters in ways that single-scenario planning obscures. Sequences of stressful years, rather than isolated extreme events, can deplete the financial and biological buffers that farms rely on, pushing systems past thresholds that individual disturbances would not. Foresight methods that explicitly model event sequences, including back-to-back droughts or coincident market and weather disruptions, therefore provide a more demanding and more informative resilience test than average-condition analysis, and they align closely with the absorb-adapt-transform vocabulary the study employs.

Finally, the iterative character of foresight connects naturally to emerging monitoring capacity in agriculture. Satellite observation, farm-level data platforms and improved biophysical models make it increasingly feasible to track indicators of transition health, such as adoption rates, soil carbon trends and input dependencies, and to compare them against scenario assumptions. When such signals diverge from the future world a strategy was designed for, that divergence becomes an early trigger for revision rather than a crisis discovered late. In this sense, foresight is less a one-time planning exercise than an ongoing navigation discipline, one that treats the climate-neutral transition as a course to be continuously corrected through volatile conditions rather than a route to be plotted once and followed regardless of weather.

Subject of Research: Using strategic foresight methods to assess the resilience of climate-neutral agricultural transition pathways under global volatility

Article Title: Strategic foresight provides insight into the resilience of climate-neutral farming transitions in a volatile world

Article References: Styles, D., Henn, D., Duffy, C., Black, K., & Martinez-Arce, A. (2026). Strategic foresight provides insight into the resilience of climate-neutral farming transitions in a volatile world. npj Sustainable Agriculture, 4(1), Article 73. https://doi.org/10.1038/s44264-026-00185-2

Image Credits: AI Generated

DOI: 10.1038/s44264-026-00185-2

Keywords: strategic foresight, climate-neutral agriculture, farming transitions, resilience, scenario analysis, sustainable agriculture, agricultural policy, volatility, food systems, climate mitigation, adaptive capacity, agroecology