This study employs a PRISMA-guided systematic evidence map, incorporating a narrative synthesis to delineate the distribution of empirical evidence: specifically, identifying where machine learning (ML) is integrated into the climate adaptation cycle, pinpointing areas of evidence scarcity, and determining where evaluation of maladaptation is absent. Reporting adheres to the PRISMA guidelines (Page et al., 2021), while research questions and eligibility criteria were formulated utilizing a PICoS framework modified specifically for the ML-adaptation domain. The review protocol was established prior to data extraction and encompassed the search strategy, eligibility criteria, screening process, coding framework, quality appraisal, and sensitivity analysis.

The primary search was conducted via the Scopus database, selected for its comprehensive coverage of peer-reviewed literature across the disciplines of agriculture, environmental science, geography, remote sensing, and computer science. The search string was constructed using four conceptual blocks linked by the AND operator: (i) ML/AI, (ii) climate hazards or stressors, (iii) adaptation/livelihoods/food security, and (iv) agrarian populations. An exclusion block was utilized to omit research concerning genomics, plant breeding, pure plant physiology, disease/pest/weed detection, and generic image classification, as these topics lack a direct nexus with climate adaptation. Consequently, the findings should be interpreted as a conservative evidence map of ML studies that explicitly bridge agriculture, climate hazards, adaptation, and agrarian livelihoods.

TITLE-ABS-KEY (

( "machine learning" OR "deep learning" OR "artificial intelligence"

OR "neural network*" OR "random forest" OR "support vector machine*"

OR "gradient boosting" OR "ensemble learning" OR "data-driven model*"

OR LSTM OR "convolutional neural" )

) AND TITLE-ABS-KEY (

( "climate change" OR "climate variability" OR drought OR flood*

OR "rainfall variability" OR "extreme weather" OR "heat stress"

OR "climate risk*" OR "climate hazard*" )

) AND TITLE-ABS-KEY (

( adaptation OR adaptive OR maladaptation OR maladaptive

OR "coping strateg*" OR "adaptation strateg*" OR "adaptation decision*"

OR "adaptive capacity" OR "food secur*" OR livelihood* )

) AND TITLE-ABS-KEY (

( smallholder* OR farmer* OR pastoral* OR agropastoral* OR peasant*

OR "rural communit*" OR "rural household*" OR agrarian

OR "farm household*" OR "rural livelihood*" )

)

AND NOT TITLE-ABS-KEY (

( genom* OR genotyp* OR phenotyp* OR "gene expression" OR transcriptom*

OR proteom* OR "molecular marker*" OR "plant breeding" OR cultivar*

OR "abiotic stress toleran*" OR photosynth* OR germplasm

OR "disease detection" OR "pest detection" OR "leaf disease"

OR "weed detection" OR "image classification" )

)

AND ( LIMIT-TO ( LANGUAGE , "English" ) )

AND ( LIMIT-TO ( DOCTYPE , "ar" ) OR LIMIT-TO ( DOCTYPE , "re" ) )

AND PUBYEAR > 2010

Study eligibility was assessed through three screening layers: topical relevance, climate-adaptation relevance, and Global South relevance. This layered approach mitigates the risk of conflating general precision agriculture studies with climate adaptation studies that are genuinely relevant to livelihoods. The core corpus was restricted to peer-reviewed empirical articles in English published after 2010. Review articles, editorials, commentaries, and studies lacking empirical data were not coded as primary evidence but were utilized for background synthesis and snowballing.

The study selection process and the number of records at each stage are summarized in Figure 1. All records were managed using reference management software (Ouzzani et al., 2016), and deduplication was performed based on DOI, title, and fuzzy title matching. Subsequently, all records passing title-abstract screening were evaluated via full-text screening, with exclusion reasons explicitly recorded: not Global South, not ML/AI, not climate adaptation context, not agriculture/rural livelihoods, precision agriculture without an adaptation dimension, review/non-empirical, or insufficient information.

Data were extracted using a standardized form covering bibliometric attributes, geographic location, livelihood systems, climate hazards, data sources, ML techniques, unit of analysis, evaluated outcomes, and the deepest stage of the adaptation cycle reached. The primary categorization used the principle of the "deepest stage reached," in which each study was placed at the deepest stage genuinely analyzed, rather than merely mentioned. Supplementary multi-label coding was applied to ML techniques, hazard types, data sources, and outcome indicators.

To map the linkages between agrarian livelihood systems and the climate hazards examined, the entire 310-record corpus was multi-label coded across two axes: nine livelihood systems and seven hazard categories. A tree diagram was utilized as an analytical classification framework to code ML application throughout the climate adaptation cycle. Each study was classified based on the deepest stage reached, ranging from upstream functions (hazard prediction, vulnerability assessment, and decision-making) to downstream functions (implementation monitoring, outcome evaluation, and maladaptation evaluation). This framework enables the review to distinguish between studies that merely predict climate stressors or support adaptation choices from those that genuinely assess whether adaptation is implemented, improves livelihood outcomes, or generates unintended maladaptive consequences.

Maladaptation was coded not merely as an explicit term, but as a level of visibility regarding the negative consequences of adaptation. This framework distinguishes between studies that do not mention maladaptation, studies that only provide a conceptual mention of trade-offs, studies that provide proxies for potential maladaptation, and studies that genuinely evaluate maladaptation empirically. This approach is necessary because maladaptation often emerges as a delayed, relational, and distributional process rather than as a stable, single label.

An ML-adaptation quality appraisal framework was designed to be applied at the full-text stage, assessing ten domains: adaptation relevance, population relevance, hazard specification, data transparency, ground-truth quality, validation design, interpretability, livelihood linkage, justice sensitivity, and maladaptation visibility. Each domain is scored on a scale of 0–3: 0 = absent, 1 = weak/implicit, 2 = adequate, 3 = strong. Scores are not utilized to mechanically exclude studies but serve as a basis for sensitivity analysis and interpretation of the strength of evidence.

Synthesis was conducted narratively and based on evidence mapping. Planned primary outputs include: (i) a heatmap of adaptation cycle stage x region x climate hazard; (ii) a matrix of ML technique x adaptation stage; (iii) a matrix of data source x adaptation stage; (iv) a "downstream visibility index" ranging from 0 to 5, where 0 = hazard only and 5 = maladaptation evaluation; and (v) a summary of geographic, methodological, and outcome gaps. To test the robustness of conclusions, sensitivity analysis will be performed during the full review by repeating primary results after excluding review articles, ambiguous precision-agriculture studies, studies lacking full-text verification, global studies without an explicit focus on the Global South, and studies with low quality scores.

As an additional bibliometric diagnostic, impact per country is calculated using the citations per paper (CPP) indicator—total Scopus citations for a country divided by the number of papers mapped to that country. Study countries were identified from titles, abstracts, and keywords, rather than author affiliation addresses, to ensure the map better represents empirical study locations. Global records, multi-country records without specific locations, or those not geographically identifiable were excluded from the country map. The CPP indicator is used to interpret relative bibliometric influence, not as a direct measure of substantive quality or adaptation effectiveness.

The corpus is dominated by very recent publications: 70 studies were published in 2025 and 53 in 2026, whereas only 14 studies existed prior to 2020. This surge confirms that the application of ML to agricultural climate adaptation in the Global South has exploded in the last two years, making this mapping synthesis particularly timely (Figure 2).

The keyword landscape of the corpus (Figure 3) reinforces the field's orientation. The most prominent terms center on prediction and technique (machine learning, climate change, food security, remote sensing, crop yield, forecasting, drought, and random forest), while terms marking the downstream side of the adaptation cycle, such as "livelihood" and "adaptation strategies," appear significantly smaller. The term "maladaptation" is virtually absent. This keyword map visually confirms the field's shift in attention toward predictive capacity rather than outcome evaluation.

Geographically, evidence is concentrated in a limited number of countries: India (37 studies), China (25), Pakistan (12), and Ethiopia (12) are prominent, followed by Bangladesh, South Africa, Nigeria, and Ghana (Figure 4). Regionally, Africa (65 studies) and South Asia (58 studies) dominate, while Latin America (4 studies) and small island nations are virtually unrepresented Figure 5; 72 studies are global in scope or do not specify a location. This disparity mirrors patterns well established in the climate vulnerability literature (Jakariya et al., 2020; Das, 2026) and simultaneously identifies geographic gaps that necessitate prioritization.

Beyond the absolute number of studies, geographic distribution was also analyzed for bibliometric impact using the Citations per Paper (CPP) indicator. This mapping is critical because publication volume does not necessarily align with the intensity of scientific influence; a country may produce numerous papers with low average citations, whereas countries with smaller publication outputs may generate higher impact per paper.

The CPP map reveals a pattern distinct from the absolute volume map. India remains a primary hub by volume (36 articles in the mapped subset). However, its CPP is relatively moderate (11.5), suggesting that quantitative dominance does not automatically translate to the highest citation intensity per paper. China shows a similar pattern: high volume (24 articles) but a lower CPP (6.3), indicating a broader yet less concentrated influence than in smaller corpora. Conversely, Vietnam stands out with the highest CPP among countries with at least five papers (36.4), followed by Bangladesh (22.8), Ghana (21.3), Ethiopia (17.7), and South Africa (12.8). This pattern indicates that studies that are more contextual and concentrated on specific regions can attain relatively strong bibliometric influence despite lower absolute volumes. Consequently, geographic inequality in the ML-adaptation literature is not merely a question of where studies are produced, but which countries serve as the primary references in shaping the scientific agenda.

The most frequently employed techniques are random forest (96 studies), followed by regression and other ML models (85), deep learning/neural networks (73), boosting/ensemble methods (67), and support vector machines (30) (Figure 7). Regarding climate hazards, the majority of studies are framed within the context of general climate change (192), followed by rainfall variability (76), heat/temperature (70), water and salinity (66), drought (60), and flood (16) (Figure 8). The dominance of drought and water-related hazards over flood is consistent with the focus on rainfed agriculture in arid regions.

As explained in Subsection 2.5, the livelihood–hazard matrix was coded for the entire corpus and is presented in Figures 9 and 10. Since a single study may address multiple livelihood systems and multiple hazards (257 of 310 studies addressed multiple livelihood systems, and 117 addressed multiple hazards), the matrix contains 1,129 study–hazard links rather than a count of individual studies (Figure 9). The distribution is heavily concentrated in two rows: smallholder crop farming (440 links) and food-security/household livelihood (321 links), which together account for approximately two-thirds of all links. Conversely, the systems most exposed to adaptation trade-offs—coastal/delta farming (35), agroforestry/tree-crop systems (20), mixed rural livelihoods (43), and pastoral/agropastoral systems (47) are sparsely represented.

Regarding hazards, one column dominates the landscape: "general climate change / multiple hazards" accounts for 588 of 1,129 links (52.1%), followed by drought (193; 17.1%) and water scarcity (134; 11.9%), while flood (46; 4.1%) and particularly salinity/sea-level stress (18; 1.6%) remain nearly empty. In other words, more than half of the links do not pair a specific livelihood with a specific climate stressor, but rather with a generalized framing of climate change.

Row-wise normalization (Figure 10) sharpens the pattern of specialization for each livelihood system. Although the absolute volume is small, coastal/delta farming is the only system proportionally linked to both salinity/sea-level stress (17.1% of its links) and water scarcity (17.1%); rainfed farming is most inclined toward rainfall variability (28.6%) and drought (23.8%); irrigated farming toward water scarcity (22.9%); and pastoral/agropastoral systems toward drought (23.4%). However, in almost every row, the "general climate change / multiple hazards" category accounts for the largest share (approximately 40% to 70%, except for rainfed farming at 28.6%), indicating that even when specific livelihoods are identified, hazards are often treated in aggregate.

This pattern reinforces the study’s main argument from a different perspective. First, the dominance of the "general climate change / multiple hazards" column indicates a lack of granularity: the literature tends to frame hazards in aggregate rather than linking specific stressors to specific livelihood systems, even though it is precisely at this coupling that adaptation trade-offs and potential maladaptation become evaluable. Second, the two hazards most closely linked to irreversible consequences and vulnerability displacement—salinity/sea-level stress (1.6%) and flood (4.1%)—are the least investigated, meaning that maladaptation pathways typical of coastal and deltaic regions, such as salinity intrusion and land loss, are almost entirely unmapped. Third, livelihood systems where adaptation trade-offs are likely to be sharpest (coastal/delta, pastoral/agropastoral, agroforestry, and mixed livelihoods) are the least well represented. At the same time, evidence accumulates for systems that are relatively easy to measure (smallholder crop farming and household food security indicators). Thus, the livelihood–hazard matrix not only completes the map of "who is studied against what" but also signals that the blind spots in the corpus coincide with the loci where maladaptation is most likely to occur.

Figure 11 maps the intersections between themes within the corpus, rather than merely their frequency. As the coding is multi-label, thematic overlap serves as the most informative indicator of how the field constructs its object of study. Within the climate-hazard theme (panel a), the strongest intersection occurs between drought, water scarcity, and the general climate change frame: drought and water scarcity co-occur in 28 studies. In comparison, drought and the general frame appear in 45 studies.

Conversely, salinity/sea-level stress (6 studies) and flood (21 studies) lie at the periphery of the network and barely intersect with other themes. A similar pattern is repeated in the livelihood theme (panel b): smallholder crop farming and food-security/household livelihood form a very dense core with 196 shared studies, while coastal/delta farming, agroforestry, pastoral, and mixed rural livelihoods attach only thinly to this core. Panel (d) confirms this at the level of cross-coupling: of the twelve strongest livelihood–hazard couplings, almost all center on smallholder farmers, food security, and irrigation paired with drought, water scarcity, or heat stress. Panel (c) demonstrates the structural consequence in the knowledge map. The co-occurrence weight between the technique–hazard prediction cluster and the adaptation–food–decision cluster is 1,360, which exceeds the internal weights of either cluster (1,089 and 1,038). This implies that these two groups have practically merged into a single upstream core, while the yield-forecasting cluster is much looser (internal weight 91). Analytically, thematic overlap functions as a centripetal force: it draws research attention to the predictive center while leaving coastal, salinity, agroforestry, and pastoral themes in the rare periphery. It is precisely in this periphery that adaptation trade-offs are most pronounced, thereby reinforcing the finding that the corpus’s blind spots coincide with the loci where maladaptation is most likely to occur.

The tree diagram in Figure 12 illustrates that the application of machine learning (ML) within the climate adaptation cycle is not confined to hazard prediction; conceptually, it can also be extended to evaluating adaptation consequences. The six themes presented—hazard prediction, vulnerability assessment, decision-making, implementation monitoring, outcome evaluation, and maladaptation evaluation—demonstrate a shift from upstream functions, which are primarily predictive and technical, to downstream functions, which are evaluative and normative. Analytically, this diagram asserts that the capacity of ML to predict drought, flooding, rainfall variability, or vulnerability hotspots is insufficient to substantiate adaptation success, as adaptation effectiveness can only be appraised when implementation, livelihood outcomes, food security, resilience, and potential adverse impacts—such as vulnerability transfer, social exclusion, resource depletion, and technological lock-in—are explicitly evaluated. Consequently, this diagram reinforces the article's central thesis: that the ML-adaptation literature remains robust at stages amenable to measurement and modeling, yet underdeveloped at downstream stages that ultimately determine whether adaptation genuinely reduces vulnerability or degrades into maladaptation.

To address the first research question, each study was coded mutually exclusively based on the deepest stage of the adaptation cycle attained: hazard assessment, vulnerability assessment, adaptation decision-making, implementation monitoring, outcome evaluation, and maladaptation evaluation. The results reveal a sharp disparity Figure 13. Of the 241 studies, 151 (62.7%) terminate at hazard assessment/prediction; 6 (2.5%) reach vulnerability assessment; 82 (34.0%) are situated in adaptation decision-making and planning; no studies (0.0%) evaluate implementation; only 2 (0.8%) evaluate adaptation outcomes; and no studies (0.0%) evaluate maladaptation. Thus, upstream cycle stages (hazard assessment, vulnerability, and decision-making) account for 239 studies (99.2%), while downstream stages (implementation monitoring, outcome evaluation, and maladaptation evaluation) account for only 2 studies (0.8%).

Evidence answers the title question directly: at present, ML cannot "see" maladaptation. However, beneath that 0% figure lies a problem more fundamental than mere scarcity, namely the intrinsic difficulty of operationalizing maladaptation as an object that machines can learn. Recent conceptual literature asserts that maladaptation is not a binary category easily labeled, but a process that is contextual, delayed, and relational: an adaptation action is only revealed as maladaptive when it displaces vulnerability to another group, locks in a detrimental development trajectory, or erodes future adaptive capacity (Rouzaneh et al., 2024). This definition—which is constantly being "redefined"—implies that the maladaptation label depends on value frameworks, timescales, and who is harmed —dimensions not captured by the supervised learning paradigm, which demands stable and measurable labels.

Here, the corpus findings gain theoretical significance. Studies linking resilience, vulnerability, and adaptation indicate that adaptation outcomes can only be assessed longitudinally relative to livelihood trajectories (Hung, 2024), while concrete cases reveal how interventions that appear adaptive can become maladaptive when the time horizon is extended (Scott et al., 2024). ML, which excels in mapping static patterns from abundant data, is not structurally designed to capture such counterintuitive dynamics. Most illuminating are studies on climate services: archetype analyses show that even data-driven information systems can produce maladaptation when they reduce adaptation decisions to predictive outputs without accounting for social context (Biella et al., 2024). These findings extend the argument: it is not only climate services that are at risk, but the entire wave of ML applications we mapped, as 99.2% of them terminate before the very stage that determines whether adaptation succeeds or backslides into harm. In other words, ML's inability to "see" maladaptation is not an oversight that can be corrected with more studies, but rather a fundamental incompatibility between the relational-temporal nature of maladaptation and the predictive paradigm that dominates this field.

The prediction–evaluation asymmetry shown in Figure 13 reflects a broader critique of AI epistemology: the tendency to optimize what is easily measured rather than what is most important. Hazard prediction is a supervised learning problem well-suited to abundant, standardized, and relatively clearly labeled satellite imagery and climate data. Conversely, evaluating implementation, livelihood outcomes, and maladaptation demands longitudinal socio-economic data, causal tracing, and normative judgments about who benefits and who bears the costs. Therefore, the roughly 120:1 ratio between upstream evidence (239 studies) and downstream evidence (2 studies) is not just a numerical disparity but an indicator that the ML research culture is more comfortable producing predictions than verifying the consequences of adaptation.

The implication is that claims that ML "supports adaptation" must be read with caution. A model can predict drought, map harvest risks, or recommend irrigation. However, without evidence of implementation and outcomes, such studies cannot demonstrate that adaptation genuinely occurs, or that vulnerability is reduced. The vacuum at the implementation stage (n = 0) is significant: it severs the attribution chain linking the information the model produces to changes on the ground. The vacuum in outcome evaluation (n = 2) weakens claims of social benefit, because adaptation success must ultimately be measured through yield stability, income, food security, water security, or household adaptive capacity, not merely by the model’s prediction accuracy.

Critical AI literature supports and deepens this reading. Studies on AI for sustainable development warn that data-driven technologies tend to pursue efficiency and measurable accuracy while ignoring dimensions of justice and context that are harder to quantify (Bachmann et al., 2022; Wilson et al., 2022). Calls for "human-centered" AI arise precisely because systems optimized for technical metrics often fail to serve deeper social goals (Mhlanga, 2022). In the agricultural domain, surveys of AI, digital agriculture, and decision-support systems show a similar pattern: technology is discussed primarily as an instrument for optimization, recommendation, and efficiency improvement, not as a tool for evaluating socio-ecological consequences (Talaviya et al., 2020; Zhai et al., 2020; Parra-López et al., 2024). Even discourse on AI sustainability itself is largely focused on computational footprints and energy efficiency (Verdecchia et al., 2023; Richie, 2022), rather than on the distributive consequences of its application. Our findings provide systematic, corpus-scale evidence for critiques that have hitherto been largely theoretical: the ML-adaptation field not only has the potential for epistemic bias but measurably allocates most of its attention to the upstream of prediction and abandons the evaluation side.

The evaluation blind spot is not merely a methodological issue; it has direct implications for adaptation justice. When ML guides "when and where hazards arrive" without assessing "who benefits or suffers from the response," the technology risks reinforcing the technocratic side of adaptation while closing off distributive and procedural questions. Adaptation justice literature shows that interventions appearing technically neutral can produce displacement and dispossession: nature-based solutions, for example, can trigger resource grabbing if justice is not integrated from the outset (Anguelovski et al., 2023), and access to ecosystem services is often systematically inequitable (Nazmul Haque et al., 2024). These mechanisms—transferring vulnerability from one group to another—constitute the essence of maladaptation, and this is precisely what remains invisible to all studies in our corpus, as none of the 241 studies reached the maladaptation evaluation stage.

This disparity operates across scales. Studies of adaptation funding reveal multi-scalar inequities, in which resources flow to interventions that are easy to measure and report rather than to those most needed by vulnerable communities (Venner et al., 2024). When ML systems prioritize "measurable" hazard prediction, there is a real risk that the same allocation logic is reproduced algorithmically. Conversely, transformative adaptation literature emphasizes the importance of building community capacity and local agency (Ziervogel et al., 2022), as well as recognizing societal resistance as a legitimate form of adaptation (Mills-Novoa et al., 2025), dimensions that are inherently difficult to reduce to model features. Empirical examples from coastal Bangladesh, where salinity erodes household food security, confirm that livelihood outcomes ultimately determine adaptation success (Lam et al., 2022)—exactly the metric absent from the corpus. Our findings thus challenge the solutionist narrative that ML automatically strengthens resilience: without the capacity to evaluate outcomes, such claims (Zhang, 2026) remain largely untested and potentially mask inequality rather than reducing it.

The livelihood–hazard matrix (Figures 9–10) systematically strengthens this concern: coastal/delta systems and salinity hazards—the exact context of the aforementioned Bangladesh case—are among the least studied in the corpus, meaning the maladaptation pathways most damaging to livelihoods are the least "visible" to the ML literature. Bibliometric impact dimensions deepen the reading of geographic justice. The CPP map shows that centers of volume and centers of influence do not always coincide: India and China dominate in terms of the number of papers, but citation intensity per paper is higher in smaller countries such as Vietnam, Bangladesh, Ghana, and Ethiopia. Analytically, this suggests two points. First, the Global South ML-adaptation agenda is not solely driven by countries with the largest publication volumes; studies that provide strong empirical context, use rare datasets, or make clear methodological contributions can be relatively more influential. Second, CPP should not be read as an indicator of adaptation success, as citations are influenced by publication age, journal reputation, open access, and the size of the research community. However, the misalignment between volume and CPP reinforces this study’s key message: ML-adaptation evidence is not only spatially unequal but also unequal in how it forms knowledge authority and research priorities.

Beyond the aggregate distribution in Figure 13, the knowledge structure of the field can be read directly from the corpus keyword bibliometrics. Three complementary visualizations—the co-occurrence network Figure 14, temporal evolution overlay Figure 15, and concentration density map Figure 16 triangulate the location of the actual knowledge gap, thereby guiding the most urgent future research directions. The most striking finding is straightforward: of the 55 keyword nodes in this map, not a single node signifies maladaptation, livelihood, outcome, welfare, equity/justice, gender, vulnerability, or evaluation. The vocabulary of the downstream adaptation cycle is literally absent from the field’s semantic map, a qualitative confirmation of the evidence collapse shown quantitatively in Figure 13.

The co-occurrence network Figure 14 is organized into three tightly interconnected clusters, all of which are situated upstream: (i) the technique–hazard prediction core (ML, random forest, remote sensing, support vector machine, artificial neural network, drought, soil moisture); (ii) the adaptation–food–decision cluster (climate change, food security, AI, precision agriculture, irrigation, decision making, adaptive management, smallholder farmers, decision support systems); and (iii) the yield-forecasting cluster (crop yield, forecasting, yield prediction, deep learning, wheat). The strongest links, namely climate change–ML (weight 60), climate change–food security (49), climate change–crops (39), and ML–food security (34), all reside within the nexus of prediction and food supply. Crucially, there are no nodes for maladaptation, livelihood, outcome, welfare, income, equity, gender, or evaluation. Thus, the knowledge gap is not merely quantitative (few studies), but semantic-structural: the field does not even have a lexicon for naming downstream outcomes. Future research directions should introduce and standardize a downstream lexicon (livelihood outcome, vulnerability transfer, adaptation outcome, distributional effect) so that outcomes and maladaptation become categories that can be named, coded, and eventually learned by machines.

Figure 15 shows the topic evolution overlay. The overlay colors nodes based on the average publication year, thereby highlighting the field’s leading edge. The most current front (2025–2026) is dominated by agricultural machinery (avg. 2025.6), smart agriculture (2025.4), sustainable agriculture (2025.3), learning systems (2025.1), and deep learning (2024.7), all of which remain technique- and production-oriented. This means that even the newest wave reproduces the upstream orientation; the field is not self-correcting toward outcome evaluation. The only faint social turn is the appearance of "smallholder farmers" (2024.8) and "climate adaptation" (2024.1) in the more recent range, but these are not accompanied by any outcome or maladaptation vocabulary. Analytically, the prediction–evaluation gap is widening, not narrowing: more sophisticated models (deep learning, learning systems) deepen predictive capacity without extending to downstream consequences. Future research directions should intentionally steer the growing frontier, particularly the deep learning and learning systems wave, toward causal and outcome-based longitudinal tasks, for instance, through funding calls and datasets that incentivize outcome evaluation rather than merely predictive accuracy.

The density map Figure 16 displays a single hotspot centered on climate change–ML–crops–food security–remote sensing—the nodes with the highest frequency and degree (climate change: 146; ML: 118; food security: 77). The field’s intellectual gravity is locked into the prediction–technique–hazard nexus, while social, evaluative, and place-specific vocabulary are in the cold periphery or absent entirely. This concentration confirms that what is monopolized upstream is not only the number of studies but also research attention. Future research directions should shift analytical attention from the dense predictive core toward the sparse periphery, where questions of livelihood, justice, and maladaptation must be built—a redistribution of research attention that complements the geographic coverage redistribution discussed in Sub-section 4.3.

These three visual readings unify a single diagnosis: the knowledge gap in ML-adaptation literature is structural (downstream vocabulary is absent), temporal (even the latest frontier remains upstream), and attentional (intellectual gravity is locked into prediction). Proceeding from this diagnosis, we formulate six research directions to shift ML from the upstream of prediction to the downstream of evaluation.

First, methodologically, the field needs to adopt causal and counterfactual inference frameworks, rather than mere predictive correlation, to ask whether an adaptation strategy genuinely improves livelihoods or, conversely, causes harm. Second, data integration needs to be strengthened: blending remote sensing with longitudinal socio-economic data, farmer knowledge, and local knowledge can bridge the gap between biophysical prediction and livelihood evaluation (Iticha & Husen, 2019; Alvarez et al., 2026). Studies combining artificial and collective intelligence for irrigation and drought adaptation also suggest potential for more deliberative hybrid designs (Polo-Murcia et al., 2026). Third, data infrastructure is a prerequisite: without FAIR, longitudinal data linked to well-being indicators, outcomes, and maladaptation will remain difficult for machines to learn (Hu et al., 2023). Fourth, the scope of outcomes must be expanded to food-security crises, diet, and livelihood consequences, not just yield or hazard signals (Busker et al., 2024; Khan et al., 2025).

Fifth, transparency and governance. Because adaptation decisions concern the equitable allocation of resources, ML systems at the downstream stage require greater interpretability and accountability than purely predictive models. The development of explainable AI (Yenkikar et al., 2025) and principles for AI governance (Sousa-Pinto et al., 2025) provide a foundation. However, they need to be adapted specifically for the context of just adaptation. Sixth, empirical coverage must be expanded to include underrepresented regions: Latin America, small island nations, agroforestry landscapes, and dryland livelihoods, where trade-offs are often sharpest (Kmoch et al., 2024; Imbach et al., 2017). Reviews of adaptation implementation in response to extreme heat show that the evidence base for implementation and evaluation lags behind that for prediction (Turek-Hankins et al., 2021). Making maladaptation "visible" to machines, therefore, is not merely a technical challenge, but a socio-technical project that simultaneously demands new data, methods, and governance.