The Mediterranean Basin, a key region for temperate fruit and nut production, faces significant challenges due to global warming, which disrupts the synchronization of tree phenological cycles with seasonal changes. This study examines the agroclimatic requirements for flowering in four temperate fruit species (almond, apricot, Japanese plum, and sweet cherry) across multiple Mediterranean locations in Spain, Morocco and Tunisia. Using a standardized experimental protocol and the same chill and heat accumulation models (Dynamic and Growing Degree Hours models, respectively), we characterized the dormancy phases and flowering patterns of 17 cultivars, including six that were evaluated for the first time. The results revealed significant variability in chill and heat accumulation across sites, reflecting differences in climatic conditions. While flowering dates were generally consistent across regions, notable differences were observed in the length of the endo-dormancy phase, with longer endo-dormancy periods not always corresponding to higher chill requirements (CR). We found that the climatic region was more important than the year-to-year temperature variability in determining agroclimatic requirements. A compensatory relationship between chill and heat accumulation was observed, indicating cultivar plasticity to adapt and respond to varying environmental conditions. This adaptive capacity in response to fluctuating levels of chill and heat appears to vary among Prunus species. The findings highlight the importance of considering agroclimatic characteristics of cultivars in orchard design to ensure the resilience of temperate fruit crops to climate change. This study provides critical data for improving chill and heat models and underscores the need for further research into phenological plasticity and adaptation traits. The insights gained are intended to support the sustainability of Mediterranean orchards under current and projected climate scenarios.

Climate change is reshaping the spring phenology of temperate fruit and nut trees worldwide, causing uneven bud break and yield losses across major production regions. Almond trees, including those cultivated in Afghanistan, are susceptible to these shifts. Afghanistan produces over 50 almond varieties, with official exports worth around USD 40 million in 2024. Yet, this value remains below potential due to multiple challenges along the value chain, one of which is the ongoing temperature increase affecting phenology and yields. We projected the flowering dynamics of 51 Afghan almond cultivars using long-term temperature data (1980–2023) and phenological observations (2011–2023). Two historical reference periods (1980 and 2020) and future projections for 2050 and 2085 were simulated under three Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) based on 15 General Circulation Models. Bloom predictions were obtained using the PhenoFlex modeling framework with a recently developed combined-fitting approach. The model accurately reproduced observed bloom patterns, achieving a Root Mean Square Error of Prediction below four days for all cultivars. Compared to 1980, bloom dates advanced by roughly two weeks by 2020. Future projections suggest additional advances of up to 16 days for most cultivars, whereas a few exhibited delays of up to 12 days. Pronounced shifts exceeding seven days were found in about 25

In northwestern Vietnam, agroforestry is promoted to enhance environmental sustainability and support livelihoods, especially in resource-constrained situations. However, farmers adapt agroforestry in diverse ways, and there is still limited structured understanding of how differences in farm resources and configurations shape adoption patterns and support needs. Lack of knowledge about adoption patterns limits the design of cost-effective interventions. To fill this gap, we employed a participatory approach to develop a farm typology that links farm structure, agroforestry adoption patterns and group-specific constraints. Survey data from 101 households were analyzed using archetype analysis to identify distinct farm profiles. These profiles were validated through a series of farmer workshops to explore group-specific constraints and support needs. The archetype analysis identified three distinct agroforestry archetypes, each reflecting unique resource constraints. Archetype 1 and Archetype 2 both represent small-scale farmers with limited agricultural labor and diversified livelihood activities, but they differ in land configuration: Archetype 1 farmers manage more consolidated land and maintain high crop and tree diversity, whereas Archetype 2 farmers operate highly fragmented, small plots with limited potential to diversify. Archetype 3 farmers operate the largest farms among the three groups; they rely on annual crops and show the lowest agroforestry adoption level, with moderate species diversity in their agroforestry systems. Across archetypes, farmers emphasized needs for market access, as well as financial support for production inputs, tree seedlings and irrigation systems, system diversification and technical knowledge. Yet the rationale for these needs differed across groups. Our study is novel in combining data-driven archetype analysis with participatory validation to generate an action-oriented understanding of farm heterogeneity. The results show that support strategies should not only respond to common needs but also to the distinct constraints shaping agroforestry adoption across different farm contexts, highlighting the importance of context-specific, constraint-aware agroforestry policy and extension.

Since 2023, Agroforestry (AF) is recognised as eligible for direct payments in Germany under the Common Agricultural Policy in the form of annual financial support (Eco Schemes) and regionally variable investment support. However, AF adoption remains low, indicating that Germany is not reaching its AF targets. We investigate whether current and proposed funding schemes sufficiently increase the comparative advantage of AF over arable cropping to improve its financial attractiveness for German farmers. Using Decision Analysis (DA) and probabilistic modelling, we assess the plot-scale economic performance of a silvoarable system integrating apple trees with arable crops. We use Monte Carlo simulations to compute the Net Present Value of the decision to implement the AF system across 10 funding scenarios, including region-specific schemes and one proposed by the German AF Association (DeFAF). Using Value of Information (VoI) analysis, we identify key uncertainties and assess the informative value of our model across various simulation lengths. Our results show a minimal effect of all current funding schemes across simulation lengths. Only the DeFAF-suggested scheme makes AF the preferable option in intermediate time frames (10 years), while the baseline scenario remains preferable in all other scenarios. Over longer periods (15–20 years), AF becomes economically viable in all scenarios, although outcome uncertainty increases. VoI analysis identifies expected apple yield and market prices as decision-relevant uncertainties. Our findings highlight the importance of time-sensitive, outcome-driven support measures that account for farmers’ economic constraints. We show that DA can support funding evaluation, by informing effective targeted support schemes for land-use transitions.

Late spring frosts repeatedly cause significant yield reductions and economic losses for apple producers. Observations from recent decades show a trend toward earlier apple flowering and an increasing risk of frost damage in Germany. This raises the question of how frost risk will develop in the future. To address this question, we used weather data and crowd-sourced phenological observations from 1993 to 2022 to train the phenology prediction model PhenoFlex. Based on predicted phenological stages and critical temperature thresholds, we estimated the probability of frost damage for the future periods 2050 and 2085 under four climate scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5). For the timing of phenological stages preceding bloom, we estimated stage-specific shares of the trees’ heat requirements that had to be fulfilled. We assessed the probability of frost damage at 3350 locations across Germany and interpolated the results to produce frost damage risk maps for Germany.The forecast results indicate that the trend toward earlier apple blossom will continue across all climate scenarios. However, we detected regional differences in the effect on frost risk around mid-century. Toward the end of the century, the probability of frost damage decreases in most locations compared to 1993–2022, with greater reductions under more severe climate scenarios. Despite these changes, late spring frost will remain a challenge for apple production in Germany throughout the 21st century. The maps we generated can assist farmers in deciding on investments in frost protection measures, and they can provide information to policy-makers aiming to develop support measures for the fruit sector.

This study presents a holistic decision analysis framework for optimizing school garden interventions in public and private schools across Vietnam. We evaluate the effects of such interventions on child health, biodiversity, and economic outcomes, analyzing five investment scenarios, from passive gardens to STEM-focused programs. Using food environment and food system frameworks, we mapped impact pathways to identify trade-offs, synergies, risks, and uncertainties. Monte Carlo simulations, sensitivity analysis, Pareto optimization, and Expected Value of Perfect Information (EVPI) guided the evaluation of decision options and key success factors. Results indicate that passive gardens consistently provide greater biodiversity and health benefits, while STEM gardens face financial and operational barriers, particularly in public schools. Key drivers of success include garden-related school events, teacher training, and community support. Findings underscore the financial constraints in public schools compared to private institutions, revealing critical trade-offs among economic, biodiversity, and health outcomes, and highlighting the need for targeted investments to mitigate these uncertainties.

Climatic factors strongly influence the phenology of olive trees, with flowering time responding sensitively to temperature variations. This study investigated the effects of inter-annual temperature variability on olive phenology in a mountainous Mediterranean region of Morocco. Experiments were conducted over two contrasting seasons (2020-2021 and 2021-2022) on four cultivars (Picholine Marocaine, Haouzia, Dahbia and Arbequina) in Khenifra. Forcing tests were performed to determine endodormancy release dates and to estimate chill and heat requirements. Throughout the dormancy period, fresh flower bud weights were recorded before and after a 7-day forcing period at weekly intervals and bud water content was monitored. The climatic requirements of each cultivar represent a major determinant of adaptability under variable seasonal conditions. The results revealed clear inter-cultivar differences in endodormancy and ecodormancy durations, thermal requirements and flowering dates. Arbequina exhibited the earliest dormancy release with relatively low chill requirements, whereas Picholine Marocaine and Dahbia flowered later and required higher chill accumulation. In all cultivars, bud growth activity increased near the time of dormancy release, indicated by water content exceeding 30 %, with only minor genotypic variation in the transition between dormancy phases. Across both seasons, flowering occurred after heat accumulation ranging from 6774 to 8051 Growing Degree Hours (GDH). These findings suggest that co-planting Picholine Marocaine and Dahbia may improve cross-pollination and enhance yield potential due to their similar flowering responses to seasonal temperature patterns. Overall, this research provides valuable insights for cultivar selection and orchard management undervariable climatic conditions in Mediterranean environments.

Accurately predicting future events under novel environmental conditions is a central challenge in modeling, especially when no validation data are available. While model transferability is often discussed through the concept of a "forecast horizon," we expand this framework by introducing the concept of "validity domains." These consider not only the extrapolation distance from the calibration data but also the absolute position of calibration and application conditions along an environmental gradient. Using phenological observations from Japanese Yoshino cherry (Prunus × yedoensis) across a climate gradient in Japan, we calibrated process-based and machine learning models for each of 48 locations and validated them with data from all other locations. Interpolating model performance metrics yielded a continuous synthetic surface of predictive accuracy across the full observed temperature range, from which we delineated model-specific validity domains and assessed how transferability depends on both model type and calibration environment. Our findings show that process-based models retain broader validity when calibrated in colder environments but degrade in warmer settings. In contrast, machine learning models exhibit narrower but more consistent validity across the gradient. These systematic differences reveal that the location of calibration and the structure of the model fundamentally shape its reliability under new conditions. By identifying where prediction errors remain below a context-specific validity threshold, our approach provides a robust framework for assessing model applicability under shifting climate conditions. Mapping validity domains offers practical guidance for model selection and allows quantifying how far models can be pushed before their predictions become unreliable.

Spring frosts remain a major threat to apple (Malus domestica) production in temperate regions. We assessed historical cultivar-specific frost exposure timing in the Lake Constance fruit-growing region in southwestern Germany. Using continuous phenology records for 26 cultivars with ≥25 years of observations collected at the ‘Kompetenzzentrum Obstbau Bodensee’ (KOB) experimental station between 1963 and 2024, we linked phenology data to local temperature data and quantified frost exposure during and after flowering (accumulated frost hours below 0 °C and below −2.2 °C). In addition, we screened ∼800 traditional cultivars (2021–2025) to identify late-flowering genotypes preserved in a regional genetic resource collection. Across cultivars, frost events during or after bloom occurred frequently, with high variability in intensity and timing. The historical record revealed three major prolonged frost events during bloom (1974, 1981, 2017), which coincided with pronounced regional yield losses, with yields dropping to 47%, 38%, and 45% of the long-term mean. Full bloom advanced by ∼4 days per decade; in 2015–2024, bloom occurred 19.2 days earlier than in 1963–1972. Although cultivar-specific differences in full bloom within the modern cultivar set were comparatively small (6.6 days between the earliest and latest cultivars), bloom timing strongly explained variation in frost exposure frequency (R² = 0.64 at 0 °C; R² = 0.42 at −2.2 °C). A 5.5-day difference in bloom timing (Gravensteiner vs. Golden Delicious) translated into a threefold difference in frost-hour exposure. Screening of traditional germplasm identified 22 consistently late-flowering cultivars that remained several BBCH stages behind Golden Delicious, implying substantially improved stage-dependent frost tolerance. Overall, modern commercial cultivars show limited phenological diversity, whereas traditional germplasm offers a much wider range for breeding late-flowering genotypes to reduce spring frost exposure and thereby enhance yield stability.

Phenological datasets for temperate fruit trees are often short, fragmented and geographically restricted, which hampers the development of cultivar-specific spring phenology models. To address this, we propose a novel calibration approach (“combined-fitting”), which pools observations from several cultivars of the same species, distinguishing between shared and cultivar-specific parameters. This method requires fewer observations per cultivar and allows jointly analyzing cultivars of the same species. We evaluate combined-fitting using the PhenoFlex framework, comparing it to a baseline model and to models that are fitted only with data for single cultivars (“cultivar-fit”). Our analysis is based on flowering data from nine almond, six apricot and six sweet cherry cultivars across Mediterranean (Spain, Morocco, Tunisia) and German climates. The combined-fit model failed to achieve higher prediction accuracy compared to the cultivar-fit and the baseline approach, as evidenced by similar root mean square errors across the data splits and calibration dataset sizes. When comparing the estimated parameters of the chill and heat accumulation submodels, we observed a large variation among cultivars of the same species in the cultivar-fit models. In contrast and by design, the combined-fit yielded only one parameter set for cultivars of the same species. Our findings demonstrate that integrating data from multiple cultivars can yield spring phenology models with high accuracy. Even though the combined-fit approach did not outperform the cultivar-fit approach, combined-fitting offers a practical solution for spring phenology modeling with limited datasets and facilitates comparison across cultivars of the same species.

Biennial bearing is one of the major challenges in the commercial production of apples (Malus × domestica Borkh.). Unless a considerable portion of flowers in apple orchards is removed every year, naturally occurring high crop load (ON-year) strongly suppresses flowering in the following year, leading to low yields (OFF-year). This ON-OFF bearing cycle significantly diminishes the profitability of apple orchards. This phenomenon generally occurs in all apple varieties, but is much more pronounced in some genotypes (biennial-bearing) than in others (regular-bearing). Although apple fruits of the current season and flower buds for the next season develop simultaneously, it remains unclear whether biennial bearing is triggered by signaling compounds from the fruits or results from carbohydrate competition between growing fruits and buds. To test the carbohydrate competition hypothesis, we analyzed nine carbohydrates in bourse buds of the biennial-bearing cultivar 'Fuji' and the regular-bearing cultivar 'Gala'. Bud samples were collected from high-cropping (ON) and non-cropping (OFF) trees during the period of flower bud formation. Our results showed no evidence of carbohydrate deficiency in buds from ON-trees compared to those from OFF-trees. Contrary to the hypothesis, the concentrations of glucose and fructose in 'Gala' were higher in buds from ON-trees. Furthermore, we analyzed 15 carbohydrates in the leaves of nine regular-bearing and eight strongly biennial-bearing apple cultivars and found no clear connections between carbohydrates in leaves and bearing behavior of these cultivars. Our data therefore do not support the hypothesis that carbohydrate competition between fruits and buds is the primary trigger of biennial bearing in apple.

CONTEXT: Late spring frosts are a major problem for apple production in Germany. Frost events frequently lead to yield losses and quality reduction. This has motivated the development of several frost protection measures, which differ in terms of effectiveness, costs and workload. In many cases, it is an open question for fruit growers if investing in frost protection is worthwhile and which strategy would most positively affect their bottom line. OBJECTIVE: To support decision-making, we applied a participatory process with frost protection experts to build a probabilistic model. METHODS: The model was designed to investigate the impact of choices between eight active protection measures on an orchard's economic performance (Net Present Value, NPV) and apple yield, compared to apple production without frost protection. We applied this model to two important German apple production regions, the Rhineland and the Lake Constance region. RESULTS AND CONCLUSIONS: The highest chance for increasing the NPV was determined for the use of stationary wind machines in the Lake Constance region (46 %), while overhead irrigation had the strongest effect on apple yield in both regions. Results indicate that frost protection measures do not necessarily increase farmers' revenues in the current economic situation. However, as these measures improve yield stability, supporting the investment in frost protection could help to maintain and stabilize regional apple production. SIGNIFICANCE: The results indicate the importance of effectively managing uncertainties inherent in horticul- tural decision-making processes. They help growers make informed choices on frost protection measures to ensure economically feasible apple production under changing climatic and economic conditions.

Spring frost poses a significant risk to apple production, prompting growers to invest in mitigation measures. Climate change is expected to affect both the occurrence of frost and the timing of phenological stages sensitive to frost. While warmer winters may reduce frost occurrence, earlier phenology could increase frost exposure. The combined effect on potential frost damage remains uncertain. We updated the PhenoFlex model to project multiple phenological stages-budbreak, first and full bloom-and assessed frost risk during the period from budbreak to full bloom for three apple cultivars in Ravensburg, southern Germany. We chose a frost threshold of-1 degrees C, as temperatures below trigger automated frost irrigation. Using the RMAWGEN weather generator, we simulated temperature scenarios under historical (2008-2022) and future (2035-2065, 2070-2100) conditions across four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Phenology advanced by 2050, more strongly for full bloom (3.9 f 2.8 days under SSP1-2.6, 6.3 f 2.6 days under SSP5-8.5) than budbreak (0.4 f 3.8 days under SSP1-2.6, 2.7 f 3.3 days under SSP5-8.5). Frost risk dropped notably in early February (75 % in 2015 to 49-40 % by 2050), but less so in mid-March (32 % in 2015 to 23-31 % by 2050). Despite more frost-free seasons, the chance of 1-10 frost hours during the budbreak-to-bloom phase remains stable (2015: 31 %; 2050: 33 %; 2085: 21-27 %). The share of frost-free seasons remains similar (67 % in 2015; 54-57 % in 2050), though up to three frost nights per season remains possible. These findings inform future frost protection planning under climate change.

CONTEXT: Fruit yield and quality are critical determinants of the economic performance of apple orchards. However, these economic metrics are highly uncertain due to various quality-reducing factors during the growing season, and fruit growers would greatly benefit from reliable predictions. OBJECTIVE: In this study, we aim at developing a new tool to support fruit growers in anticipating yield and potential quality losses under the specific conditions of their orchards. The tool should allow application at four key time points during the growing season (at full bloom, before fruit thinning, after June drop, and four weeks before harvest) and capture uncertainty in the quality-reducing factors and the resulting yield parameters. METHODS: Using expert knowledge, we designed and parameterized the probabilistic 'ProbApple' model and conducted Monte Carlo simulations to project probability distributions for total and high-quality apple yield for a 'Gala' orchard in the German Rhineland. We compared scenarios with and without anti-hail netting to demonstrate the use of the model for predicting apple yield. RESULTS AND CONCLUSIONS: Applying the model four weeks before harvest, the median forecasted apple yield was 50.4 t/ha (25 %-quantile: 44.0; 75 %-quantile: 57.8 t/ha) with anti-hail netting and 49.3 t/ha (25 %-quantile: 42.7; 75 %-quantile: 56.5 t/ha) without. The forecasted high-quality yield was 34.9 t/ha (25 %-quantile: 27.5; 75 %-quantile: 41.6 t/ha) with the protection measure and 30.0 t/ha (25 %-quantile: 15.8; 75 %-quantile: 38.7 t/ha) without. These results are in line with commonly achieved 'Gala' apple yields in the Rhineland region. SIGNIFICANCE: We show that ProbApple is a customizable tool for forecasting apple yield and quality, offering producers valuable insights for operational planning and informed management decisions.

Climate change is shifting the timing of leaf emergence and bloom in temperate-zone trees. While warming typically advances spring phenology, insufficient winter chill can delay or prevent bloom. Understanding speciesand cultivar-specific responses is vital for adaptation planning. We calibrated the PhenoFlex phenology model using long-term bloom data for 110 cultivars of seven temperate fruit and nut tree species (apple, pear, apricot, sweet cherry, plum, almond, pistachio) across Spain, Tunisia, Morocco and Germany. The models projected bloom dates and potential bloom failure - when agroclimatic requirements are not met - under current (2015) and future scenarios for two time periods (2035-2065, 2070-2100) and four warming scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5), using 14-18 General Circulation Models. Three key trends emerged: advancing bloom dates in Germany, delayed bloom for most species in southern Spain, Tunisia, and Morocco, and largely unchanged bloom dates in northern Spain and for almonds in Morocco. The contrasting shifts in bloom result from differences in the primary driver of bloom timing: heat where bloom advances, chill where bloom is delayed and chill and heat substitution where bloom is stationary. In the short term (2035-2065), agroclimatic requirements for most species are expected to be met, except for apricots in southern Spain and pistachios in central Tunisia. Predicted bloom failure rates spiked for most species in Tunisia, Morocco, and southern Spain under pessimistic warming scenarios in the long term (2070-2100) and, to a lesser extent, in northern Spain. Our results reveal cultivar-specific differences in bloom date shifts and failure rates, indicating variation among cultivars in their adaptability to winter warming. This information may guide the design of climate-resilient orchards based on cultivars' alignments with projected agroclimatic conditions.

In the Midwest region of the United States, the dormant or cold season has experienced significant changes over the past several decades due to human-caused global warming, and changes are projected to continue or intensify through the end of the century. Changes in chill accumulation and spring frost injury risk are particularly concerning for specialty crop growers in the Midwest. Despite their importance for the industry, relatively little work has been done to assess these changes and help guide crop management strategies accordingly. We use a combination of historical observations and CMIP6 multi-model ensemble projections to assess recent and potential future changes in chill accumulation and phenology for apple in the region. Observations show increased chill accumulation in much of the Midwest over the past 70 years, and CMIP6 projections indicate continued increases through the next 70+ years. The southern Midwest is expected to lose chill, but not at a rate that would require a substantial shift to fruit cultivars with very low chill requirements. Additionally, apple full bloom estimates using the PhenoFlex model combined with CMIP6 model projections show shifts earlier in the spring for both apple phenology and spring freeze dates. We do not find any appreciable change in spring freeze injury risk by mid-century under any scenario. This study provides an important assessment of climate change impacts on specialty crops in an understudied region of the United States for non-commodity agriculture. More collaborative work is needed between scientists, practitioners, and growers to (1) assess the current and future risks to specialty crop agriculture in the Midwest that result from climate change and (2) explore viable solutions to ensure a resilient specialty crop industry in the face of changing climatic, economic, and social systems.

Implementing environmental flows (e-flows) is crucial for protecting river ecosystems but often competes with water needs for small-scale agriculture. Understanding these trade-offs is essential for effective water management. We employ holistic modeling to explore the socioecological impacts of e-flows on agriculture along the lower Great Letaba River. Our approach uses conceptual impact pathways and quantitative models to account for smallholder water use, forecasting outcomes under varying river flow conditions. Our findings reveal that the irrigation water demand of small-scale agriculture amounts to only one-tenth of the required e-flow volume. However, farmers face increasing crop water gaps—shortfalls in available water for irrigation—especially during the dry season, which jeopardizes agricultural sustainability as e-flow needs persist. Addressing this challenge may require upstream water flow supplementation, though it would necessitate restricting current upstream water use. We also introduce a robust decision-making model, integrating expert knowledge and Monte Carlo simulations, to assess e-flow management options under uncertainty. By extending the e-flow concept to include the needs of vulnerable farming communities, our model provides decision-makers with an accessible tool for balancing ecological and agricultural demands, even when precise data are unavailable.

Peatlands in South Sumatra, Indonesia, covering 24% of the region, are vital for local livelihoods and ecosystem services. Unsustainable cultivation practices threaten their sustainability through irreversible drying of the peat, increased greenhouse gas emissions and fire risks. Agroforestry practices, when adapted to peatlands, may offer multiple socio-economic and environmental benefits. This study evaluated the economic viability of two rice-based and three rubber-based agroforestry systems, designed by World Agroforestry for cultivated peatlands in South Sumatra, comparing them to monoculture baselines. Using decision analysis and probabilistic modelling, including Monte Carlo simulations, we conducted probabilistic cost-benefits analyses, accounting for risks and uncertainties and incorporating expert knowledge. Our model simulated decision outcomes under two scenarios-with and without considering family labour in the costs-to assess the impact of family labour on the outcomes. We identified key uncertainties affecting model outcomes through sensitivity analysis and value of information calculations. Our results showed that rice-based agroforestry systems require substantial establishment costs, mainly for constructing dikes to enable dryland crop cultivation. Despite these upfront costs, the two designed rice-based agroforestry systems offer the potential for higher net returns compared to rice monoculture, especially when family labour costs are excluded from the calculation. All rubber-based agroforestry systems demonstrate higher net returns in the long term compared to rubber monoculture in both family labour scenarios. Narrowing knowledge gaps related to key variables, such as the discount rate, crop yields, crop prices, risk event probabilities and rice yield losses, is important for supporting the decision-making process for rice-based agroforestry systems.

Precision technology is often attributed great potential for more efficient and sustainable horticulture. Robots and variable-rate technology respond to the specific needs of individual plants and thus avoid over- or under-treatment. However, these potentials usually remain unquantified, which makes decisions on development and regulation challenging. In this work, we present the Value of Precision (VoP) as a novel conceptual and mathematical framework for assessing the benefits of increased application rate precision. Grounded in measurement science and decision theory, this approach allows the prospective quantification of expected financial return for varying levels of assumed future precision. First, we assess the VoP for a classic saturating yield response function analytically. Here, we obtain an Expected Value of Perfect Precision (EVPP) that quadratically depends on the mean absolute error (“imprecision”) of the applied quantity. Second, we apply the VoP framework in a comprehensive probabilistic case study to assess the potential of robotic flower thinning in apple production. We present a Bayesian hierarchical yield and quality response model for the probabilistic prediction of apple revenue as a function of thinning intensity. We find that thinning with perfect precision would increase the expected contribution margin by 1637 ± 285 €/ha/year or around 25 https://github.com/johanneskopton/value-of-precision/ . Model-based quantification of the economic benefits from more precise application. Introduction of the Value of Precision (VoP) as a novel conceptual and mathematical framework. Analytical calculation of the VoP for a simple fertilization model structure. Probabilistic quantification of the VoP for apple thinning using a hierarchical Bayesian yield model. Prospective assessment of the benefits of robotic systems to support decisions of users, technology developers and policy-makers. This work addresses a critical gap in the research on precision horticulture by providing a novel mathematical framework for quantifying the benefits of increased application rate precision. By developing the Value of Precision approach and demonstrating its application across multiple horticultural scenarios, our research directly supports assessment of resource use efficiency and profitability for emerging technologies while enabling data-driven decisions about within-field variability management.