# Towards a comprehensive decision support system for agroforestry systems

Ahmed M S Kheir, Marie Gosme, Navid Bakhtiary, Priscilla Kephe, Juvenal Assou, Maren Langhof, Jörn Strassemeyer, Til Feike

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Ahmed M S Kheir, Marie Gosme, Navid Bakhtiary, Priscilla Kephe, Juvenal Assou, et al. Towards a comprehensive decision support system for agroforestry systems. Environmental Research Letters, 2025, 20 (12), pp.124016. 10.1088/1748-9326/ae1cd5 hal-05525938

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# Towards a comprehensive decision support system for agroforestry systems

To cite this article: Ahmed M S Kheir et al 2025 *Environ. Res. Lett.* **20** 124016

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# Towards a comprehensive decision support system for agroforestry systems

Ahmed M S Kheir<fnref n="1" />,<fnref n="2" />,* , Marie Gosme<fnref n="3" /> ID, Navid Bakhtiary<fnref n="1" /> ID, Priscilla Kephe<fnref n="1" /> ID, Juvenal Assou<fnref n="4" /> ID, Maren Langhof<fnref n="1" /> ID, Jörn Strassemeyer<fnref n="1" /> and Til Feike<fnref n="1" /> ID

E-mail: ahmed.kheir@julius-kuehn.de, Priscilla.Kephe@julius-kuehn.de, juvenal.assou@julius-kuehn.de and Til.Feike@julius-kuehn.de

Keywords: agroecosystems models, Hi-sAFe, artificial neural network, long short-term memory, life cycle assessment, meta-modeling

Supplementary material for this article is available online

## Abstract

Agroforestry systems (AFSs) enhance biodiversity, productivity, and climate resilience, yet their inherent complexity challenges traditional modeling approaches. This study presents an integrated framework combining process-based modeling (PBM), machine learning, and life cycle assessment (LCA) into a user-friendly decision support system (DSS) for AFS analysis and optimization. Multi-scenario simulations using Hi-sAFe (16 configurations varying in latitude and orientation) for maize-poplar systems produced detailed outputs, including land equivalent ratios (LER up to 1.29), crop yield (ranging from 4.3 to 8.1 t ha⁻<fnref n="1" />), and N₂O emissions (1.6–9.4 kg N ha⁻<fnref n="1" />). We trained Random Forest models on PBM diagnostics, microclimate, and design covariates; post-hoc SHapley Additive exPlanations (SHAP) identified maximum air temperature (Tmax), distance to tree rows, and photosynthetically active radiation (PAR) as the dominant predictors of yield and LER. To address data gaps, long short-term memory (LSTM) and TrAdaBoost-LSTM models were used for microclimate forecasting, achieving high fidelity (R<fnref n="2" /> = 0.88) in capturing hourly temperature and humidity trends. Outputs have been integrated into an LCA workflow, producing CO₂-equivalent and nitrogen footprint metrics that feed into a functional web-based DSS. This platform enables robust, transparent, and scalable AFS evaluation, supporting stakeholders with real-time scenario exploration and scientifically grounded decision-making tools. Furthermore, the framework enables greenhouse gas (GHG) accounting in AFSs by integrating modeled soil organic carbon dynamics and N₂O emissions. A Germany-wide assessment illustrates the significant GHG reduction potential achievable through large-scale implementation of AFSs.

## 1. Introduction

Agroforestry systems (AFSs) as the intentional integration of trees into cropping systems encompasses diverse indigenous, traditional, and modern farming practices (Nair 1991, Terasaki Hart et al 2023). It provides multiple agronomic, environmental, and socio-economic benefits, including improved soil health, climate resilience, and biodiversity (Beillouin et al 2019, 2021). As a nature-based solution, agroforestry sequesters carbon and reduces greenhouse

gas (GHG) emissions, supporting both climate mitigation and adaptation (Griscom et al 2017). However, trees may also introduce competition for light, water, and nutrients, potentially reducing crop yields if not well managed (Gibson et al 2011). Therefore, optimized site-specific agroforestry design is essential to balance trade-offs and harness synergies (Blaser et al 2018).

In agroforestry, experimentation is expensive and slow because trees mature over years, constraining the number of designs that can be tested

<footnote n="1">¹ Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Strategies and Technology Assessment, Kleinmachnow, Germany</footnote>

<footnote n="2">² Soils, Water and Environment Research Institute, Agricultural Research Center, Giza 12112, Egypt</footnote>

<footnote n="3">³ ABSys, Univ Montpellier, CIHEAM-IAMM, CIRAD, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote n="4">⁴ Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 58, 38116 Brunswick, Germany</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
across sites (Gosme et al 2025, Quadro et al 2025). Given the complexity of tree–crop–soil interactions and the impracticality of exhaustive field trials, process-based models (PBMs), used alongside empirical studies, provide a mechanism-based way to integrate measured processes and evaluate ‘what-if’ scenarios (Attia et al 2024, Shawon et al 2024, Kheir et al 2023). PBMs help pre-screen species-orientation-spacing combinations by region, guiding where field effort is most impactful. Crucially, PBMs enable long-term, sophisticated simulations, including climate-change and management scenarios that are difficult to realize experimentally, thereby complementing observations with transparent, uncertainty-aware projections. Modular and reusable PBMs such as Hi-sAFe (Dupraz et al 2019), DSSAT (Hoogenboom et al 2019), and APSIM (Mccown et al 1996), allow simulations of interactions across diverse management and climate conditions, and their integration into multi-model ensembles can enhance reliability (Mansour and Schain 2001, Kheir et al 2022). However, parameterization and calibration of these models require large volumes of high-quality input data, which remain scarce in agroforestry due to the complexity of spatially distributed measurements and long-term system monitoring.

Machine learning (ML) has emerged as a powerful tool for processing large, complex datasets (Reichstein et al 2019). In agroforestry, ML and deep learning (DL) such as long short-term memory (LSTM) can help impute missing values, forecast microclimate variables, and reduce PBM computational demands by acting as efficient surrogates (Kheir et al 2024a). Synthetic datasets generated by PBMs can train ML models to fill spatial and temporal data gaps and support robust predictions. Compared to simpler methods like ARIMA (Han et al 2010, Ren et al 2022), LSTM models better capture nonlinear dependencies in environmental time series (Tsai et al 2020). Furthermore, cloud computing (Suman et al 2025) offers scalable platforms for PBM execution, making large-scale simulations more feasible (Gill et al 2024). This study proposes a hybrid modeling framework that combines PBMs and ML to overcome agroforestry modeling barriers. Specifically, we aim to: (I) assess existing PBMs (Hi-sAFe, DSSAT, APSIM) for AFS applications; (II) develop a hybrid PBM–ML workflow for data imputation and computational efficiency; (III) simulate multi-scenario AFS under varying latitudes and tree orientations; (IV) integrate outputs into a life cycle assessment (LCA) to quantify GHG emissions and soil carbon changes; (V) develop a web-based decision support tool to evaluate the national-scale GHG mitigation potential of agroforestry in Germany.

## 2. Methods

### 2.1. Microclimate data gaps and imputation protocol

Phenology for Scientific, Technical and Interdisciplinary simulator of soil-Crop System functioning (STICS) calibration, Sentinel-2 time series provide green area index and phenology cues (maximum leaf area index (LAI), end of leaf growth, onset of grain filling, physiological maturity). To handle cloud-induced gaps and noise, we use LSTM (PyTorch Forecasting package) sequence models to impute/smooth the remote sensing (RS) trajectories and estimate stage dates (emergence, booting, flowering, grain-filling, maturity). These RS + ML phenology products calibrate STICS parameters (table 1).

Hourly microclimate (air temperature, wind speed, relative humidity, soil moisture, soil temperature) measured by IoT sensors at 1, 4, 7, and 24 m from tree rows was gap-filled using sequence models Temporal Fusion Transformer (TFT-LSTM) that learn temporal structure from each sensor's complete segments while borrowing strength across sensors at other distances (figure 1). These sensor data are essential for quantifying the interactions between tree strips and crop alleys. However, outdoor sensor networks are inherently gap-prone, exposed to weather, wildlife, vandalism, and power/connectivity failures, and they also suffer from spatial incompleteness, since fixed stations can only sample a limited set of locations. The models are exogenous, i.e. they ingest time features (hour-of-day, day-of-year), and available external drivers (e.g. nearby station) to stabilize predictions during short gaps. We trained on observed windows and perform sequence-to-sequence imputation over missing windows (typically up to days), returning both point estimates and uncertainty (e.g. dropout/quantile intervals). For longer spatial/temporal gaps where purely temporal learning is insufficient, we produce coherent alley-scale fields via kriging with external drift using digital elevation model, aspect, and distance-to-trees as covariates. We fill internal sensor gaps with an encoder-decoder TFT-LSTM that uses context before/after each gap to reconstruct a continuous 1–24 m microclimate field. The networks were trained on observed contiguous segments and validated against measured microclimate from the current Wendhausen site and historical SIGNAL/BonaRes data (www.bonares.de) collected at multiple distances from tree strips. The resulting harmonized hourly 1–24 m fields are then combined with Hi-sAFe outputs in the hybrid modeling stage to predict system outcomes.

### 2.2. Initial parameterization

To simulate tree–crop interactions under varying conditions, we utilized the 3D PBM Hi-sAFe (Dupraz
<figure><img src="image_6.png" /><figcaption>**Table 1.** Calibrated genetic coefficients for the maize cultivar Pioneer P9903 used in STICS model initialization and Hi-sAFe crop species file (.plt).</figcaption></figure>

<table><thead><tr><th>codevar</th><th>Definition</th><th>Value</th><th>Unit</th></tr></thead><tbody><tr><td>Stlevamf</td><td>Cumulated development units between the LEV and AMF stages</td><td>270</td><td>Degree.days</td></tr><tr><td>Stamflax</td><td>Cumulated development units between AMF and LAX stages</td><td>545</td><td>Degree.days</td></tr><tr><td>Stlevdrp</td><td>Cumulated development units between the LEV and DRP stages</td><td>1200</td><td>Degree.days</td></tr><tr><td>Stflodrp</td><td>Development range between FLO and DRP (indicative only)</td><td>210</td><td>Degree.days</td></tr><tr><td>Stdrpdes</td><td>Development range between DRP and start of water dynamics</td><td>650</td><td>Degree.days</td></tr><tr><td>Pgrainmaxi</td><td>Maximum weight of a grain (% water)</td><td>0.33</td><td>g</td></tr><tr><td>Adens</td><td>Parameter to compensate between the number of stems and the density of plants</td><td>-0.12</td><td></td></tr><tr><td>Croirac</td><td>Growth rate of root front</td><td>0.15</td><td>cm degree.day-1</td></tr><tr><td>durvieF</td><td>Lifespan of a cm of adult leaf</td><td>200</td><td>Q10</td></tr><tr><td>Jvc</td><td>Number of days of vernalization</td><td>0</td><td>DOY</td></tr><tr><td>sensiphot</td><td>Sensitivity to photoperiod</td><td>0</td><td></td></tr><tr><td>Stlaxsen</td><td>Cumulated development units between the LAX and SEN stages</td><td>763</td><td>Degree.days</td></tr><tr><td>Stsenlan</td><td>Cumulated development units between the SEN and LAN stages</td><td>197</td><td>Degree.days</td></tr><tr><td>Nbgrmax</td><td>Maximum number of grains</td><td>4560</td><td>Grains m-2</td></tr><tr><td>Stdrpmat</td><td>Cumulated development units between DRP and MAT stages</td><td>650</td><td>Degree.days</td></tr><tr><td>Afruitpot</td><td>Maximum number of set fruits per fluorescence and by degree.day</td><td>0</td><td>Nofruits °CJ-1</td></tr><tr><td>Dureefruit</td><td>Duration of growth of a fruit from setting to physiological maturity</td><td>0</td><td>Degree days</td></tr></tbody></table>

et al 2019), configured for a poplar–maize alley cropping system. The simulated AFS comprised summer maize grown in alleys flanked by 0.5 m-wide poplar tree lines. The baseline layout uses a 48m alley (distance between tree lines), 5 m within-row tree spacing, and a 0° (north–south) row orientation. A factorial combination of four latitudes (30°, 40°, 50°, and 60°) and four tree row orientations (0°, 45°, 90°, and 135°) yielded 16 unique simulation scenarios. Each simulation spanned 10 consecutive growing seasons and accounted for dynamic soil, crop, and microclimate interactions. Hi-sAFe was parameterized with site-specific inputs in Wendhausen (52.13° N and 10.63° E), including weather data (supplementary figure 1), soil properties (texture, depth, water holding capacity), crop management (sowing date, irrigation, fertilization), and tree configurations. Hi-sAFe is a 3D agroforestry PBM whose crop component derives from STICS. In our workflow, STICS was used first to calibrate phenology

and yield in monocrop; the resulting parameters were then used in the Hi-sAFe simulations to simulate the agroforestry plot. Thus, the STICS model was calibrated for grain maize (cv. Pioneer P9903) using the CROPTIMIZR package (https://github.com/SticsR Packs/CroPlotR.git), optimizing cultivar parameters against field-observed phenology and yield. Calibration performance and model fit were evaluated and visualized using the CroPlotR package to ensure reliable parameterization for subsequent Hi-sAFe integration. The calibration process resulted in a set of optimized genetic coefficients for the maize cultivar Pioneer P9903, which were subsequently used to initialize the STICS component within Hi-sAFe (table 1).

The Hi-sAFe model represents agroforestry as a 3D, spatially explicit system that simulates interactions between trees, crops, and soil through light interception, root water and nutrient competition, and microclimate modification (Talbot
and Dupraz 2012, Dupraz et al 2019). In this study, poplar (Populus sp.) was used as the tree component, parameterized from the official Hi-sAFe species library. Four latitudes (30°, 40°, 50°, and 60° N) were combined with four tree-row orientations (0°, 45°, 90°, and 135°) to generate 16 agroforestry scenarios. Key parameters, including tree spacing, alley width, initial age, height, diameter at breast height, crown radius, LAI, rooting depth, and pruning management—are summarized in supplementary table 5. All configuration files (agroforestry.pld, agroforestry.sim, and poplar.ttec) are publicly available in the Zenodo repository (https://doi.org/10.5281/zenodo.17436942) for transparency and reproducibility.

## 2.3. ML framework

Hi-sAFe simulation outputs, comprising hundreds of variables, were subject to dimensionality reduction via principal component analysis (PCA) to identify dominant features. These selected features formed the predictor matrix for training supervised ML models, including Random Forest, XGBoost, and artificial neural networks (ANNs). Model performance was evaluated using $R^2$, RMSE, and cross-validation metrics. To address missing data in microclimate measurements (e.g. due to sensor gaps at 1–24 m), we implemented LSTM networks and Transfer Learning via TrAdaBoost-LSTM for temporal and spatial imputation. Additionally, the TFT-LSTM architecture was deployed to enhance multivariate forecasting by integrating static covariates such as slope, aspect, latitude, and tree orientation. For interpretability, we applied SHAP (SHapley Additive exPlanations), a game-theoretic method that attributes a model’s prediction to each input feature; this reveals which variables most strongly drive the forecast. In our case, SHAP identified maximum air temperature (Tmax), photosynthetically active radiation (PAR), and distance from tree rows as leading contributors to yield and $N_2O$ predictions.

## 2.4. LCA and decision support system (DSS) integration

Outputs from Hi-sAFe and ML models, including yield, soil organic carbon (SOC) changes, water use efficiency (WUE), and $N_2O$ emissions—were translated into environmental impact metrics using a standardized LCA workflow. These included $CO_2$-equivalent emissions, nitrogen footprints, and sustainability indices. Calculations followed ISO 14 040 guidelines, with functional units tailored to land productivity (kg grain/ha/year), GHG efficiency (kg $CO_2$-eq./kg yield), and sustainability (e.g. N surplus, WUE, kg grain m$^{-3}$). These results were compiled into a modular, web-based DSS interface. The system

automates preprocessing, model selection, imputation, scenario generation, and LCA calculations, supporting real-time user interaction. Both expert and non-expert users can explore alternative AFS configurations, assess trade-offs, and optimize designs for climate-smart agriculture.

## 2.5. Data sources and availability

All simulation input data, intermediate model outputs, and evaluation datasets are publicly available at the Zenodo repository: https://doi.org/10.5281/zenodo.17436942. In addition to the raw input data, the Zenodo deposit includes all Hi-sAFe configuration files (.pld, .sim, poplar.ttec), and the R script for simulation (hisafaer 1.6). Supplementary figures detail the dynamics of carbon, nitrogen, water, light, and yield responses under all simulated conditions.

# 3. Results

## 3.1. Process-based AFS models—potentials and limitations

PBMs are critical tools for understanding and simulating complex tree–crop interactions in AFS (Barbault et al 2024), encompassing biophysical processes such as photosynthesis, phenology, water and nutrient cycling, and biomass growth (Luedeling et al 2016). Effective PBMs must be scientifically robust, adaptable across diverse AFS contexts, scalable from plot to landscape levels, and grounded in accessible input data while accounting for uncertainty and data complexity (Palma et al 2007). Models also benefit from user-friendly interfaces, GIS compatibility, and integration capacity with other models to enhance usability by both researchers and decision-makers (Castle et al 2022). To evaluate model strengths and limitations, we conducted a structured review of existing AFS models (supplementary tables 1–3), classifying them by spatial scale, AFS type, process complexity, and spatial explicitness. We identified seven model groups, ranging from allometric and soil carbon models to plot-scale and 3D architectural models. While allometric and soil C models (e.g. $CO_2$FIX, SCUAF) often neglect tree–crop interactions (Segura and Kanninen 2005, Jia et al 2016, Huber et al 2017, Mahmood et al 2019, Aabeyir et al 2020, Magalhães et al 2021), plot-scale models (e.g. APSIM, DSSAT, Yield-SAFE) tend to simplify canopy structure and species diversity (Bergez et al 1999, Keating et al 2003, Kheir et al). Among them, Hi-sAFe stands out as the most advanced AFS model, offering voxel-based 3D simulations of light, water, nitrogen, and microclimate dynamics (Kheir et al 2024a), rooted in STICS and capable of simulating root architecture and tree-crop facilitation (Dupraz et al 2019). However, even Hi-sAFe lacks explicit modules for tree-induced microclimate modifications, N-fixation, hydrology, and pest dynamics.
Based on this review, we selected DSSAT, APSIM Next Generation, and Hi-sAFe as the core models for our hybrid PBM-ML ensemble, beginning with Hi-sAFe for its spatial resolution and process detail.

## 3.2. Integrated approach framework: leveraging DL and ML for AFS modeling

The hybrid framework links the 3D PBM Hi-sAFe with ML/DL to deliver high-resolution, scalable predictions of agroforestry performance (figure 2). Site-specific inputs (soil, weather, tree/crop traits, management; S. Figure 2) drive Hi-sAFe, which simulates tree-crop-soil-atmosphere interactions and produces mechanistic diagnostics (radiation partitioning, water and C-N dynamics, microclimate). Figure 2(A) summarizes the whole pipeline. Step 1: sequence models (TFT-LSTM) preprocess and impute gaps arising from sensor failure or irregular sampling, handling temporal and spatial interpolation along the 1–24 m transects. Step 2: supervised learners (e.g. XGBoost, ANN, Random Forest) are trained on the cleaned feature set to predict agroforestry outcomes, thus reducing reliance on repeated PBM runs. For learning and decision support we use predictors from three sources (figure 2, supplementary table 4): (i) Hi-sAFe diagnostics (e.g. direct/diffuse/total PAR intercepted, canopy/soil temperature, crop water demand/uptake, C-N pools/fluxes); (ii) microclimate at 1, 4, 7, 24 m (hourly Tair, RH, wind, soil temperature/moisture; imputed where needed); and (iii) design & site covariates (row orientation, distance/spacing to tree strips, latitude, soil texture/OM, management). We predict yield and land equivalent ratio (LER), SOC change and N₂O (climate/LCA inputs). PAR/LAI/temperature act as diagnostic features to tune orientation, spacing, and microclimate management (supplementary table 4).

Figure 2(B) shows how the framework handles data limitations: (A) lack of data—PBM-generated synthetic datasets train ML/DL surrogates to ease computation and enable scenario screening; (B) missing data where TFT-LSTM fill gaps before simulation so that downstream learning remains robust. This PBM-ML integration couples biophysical realism with computational efficiency and provides a practical decision-support tool for agroforestry design and management.

## 3.3. Multi-scenario simulations by Hi-sAFe and hybridization with ML

PCA of the Hi-sAFe outputs (figure 3(a)) identified intercepted radiation variables (total, diffuse, and direct PAR), soil and crop temperatures, and LAI as dominant contributors to Dim1 (57.5%), while yield and nitrogen uptake loaded heavily on Dim2 (14.3%), reflecting distinct agronomic drivers and informing feature selection for ML modeling. To

provide detailed insights into the system's biophysical responses, selected Hi-sAFe outputs are provided in supplementary figures 4–14 and archived in a public data repository (https://doi.org/10.5281/zenodo.17436942). Tree biomass dynamics (supplementary figures 4, 8 and 12) showed dominant carbon allocation to aboveground tissues, while seasonal variation in light interception (supplementary figures 7 and 11) illustrated vertical niche partitioning, with crops utilizing early-season PAR and trees capturing more radiation later without compromising crop light access. Nitrogen and water fluxes varied with fertilization and rainfall rather than spatial configuration (supplementary figures 5, 6, 9 and 10), while complementary uptake patterns between crops and trees enhanced system efficiency. Carbon allocation patterns confirmed belowground investment during transitional periods (supplementary figure 13). Simulated N₂O emissions, mainly following fertilization events, showed limited spatial sensitivity (supplementary figure 14), underscoring the primacy of soil and climate factors over layout effects. Together, these results validate Hi-sAFe's potential to simulate the spatiotemporal complexities of tree-crop-soil interactions and highlight its value as a foundation for developing robust, data-driven DSSs in agroforestry.

LER under the different simulation scenarios as a key integrative indicator of agroforestry performance is presented in figure 4.

Figure 4 presents the results of multi-year Hi-sAFe simulations examining relative yield and LER across 16 scenarios combining latitudes (30°–60°) and tree row orientations (0°–135°) for a maize-poplar silvoarable system. The dashed blue lines indicate that LER remained consistently above 1 in most cases, demonstrating a land-use advantage of intercropping over monoculture due to complementary light and water use between shallow-rooted maize and deep-rooted poplar. Cumulative yields (black lines) vary substantially with spatial configuration, with higher latitudes and North–South tree orientations maintaining more stable or increasing LER trends due to better radiation partitioning. In contrast, low-latitude scenarios (e.g. Sim_1, Sim_5) showed yield declines or tree-crop imbalances, likely from heightened shading and water competition. Orange and green lines in figure 4 reveal that maize yield dominates early years before tree canopy closure leads to increased competition, underscoring the need for scenario-based design optimization to ensure productivity trade-offs are managed over time. In conclusion, the main findings show that East–West (90°) is optimal at low latitudes (30°–40°) and North–South (0°) is optimal at high latitudes (50°–60°). These findings affirm the utility of PBM in capturing long-term, spatially dependent dynamics critical for agroforestry planning.
<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 3.** Implementation of multi-scenario Hi-sAFe simulations with machine-learning integration. (A) PCA of key simulation outputs: intercepted radiation (direct/diffuse/total), LAI, and canopy/soil temperature load strongly on Dim1 (57.5%), while yield and nitrogen uptake structure Dim2 (14.3%); arrow length indicates contribution and direction of association. (B) Wendhausen site topography—elevation (m), slope (°), aspect (°)—used as static covariates to enrich the ML feature set. (C) Scenario design grid of 16 simulations formed by four latitudes (30°, 40°, 50°, 60° N) crossed with four tree-row orientations (0°, 45°, 90°, 135°).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Figure 4.** Ten-year trajectories of relative yield and LER across 16 Hi-sAFe scenarios. Panels are arranged by latitude across columns (30°, 40°, 50°, 60° N) and tree-row orientation down rows (0°, 45°, 90°, 135°); panel headers indicate the scenario number. Each panel shows annual crop yield (orange), annual tree yield (green), annual combined agroforestry yield (black solid), cumulative agroforestry yield (black dashed), and LER (blue dashed). Yields are expressed as relative values normalized to their respective monoculture references. LER > 1 indicates a land-use efficiency advantage of agroforestry over separate monocultures, LER ≈ 1 parity, and LER < 1 a disadvantage. Relative values are in respect to monoculture tree and crop control simulations. Differences among panels illustrate how performance varies with latitude and row orientation.</figcaption></figure>

Figure 5 compares the predictive accuracy of RF, XGBoost, and ANNs for estimating maize yield from Hi-sAFe simulation outputs. RF performed best ($R^2$ = 0.82, RMSE = 0.99), followed by XGBoost and ANN, indicating strong alignment with process-based predictions. SHAP analysis highlighted maximum temperature (TMAX) and distance from tree rows as the most influential yield predictors, reflecting both inter-annual thermal stress and local microclimate buffering by trees, as well as the balance between thermal benefits and reduced competition. Orientation and latitude also had moderate effects, while LAI and total PAR intercepted contributed weakly, possibly due to redundancy or data quality

issues. Figure 6 presents a similar comparison for N₂O emission predictions, where RF again showed superior performance ($R^2$ = 0.73, RMSE = 3.57 kg N ha⁻¹). SHAP interpretation revealed TMAX and distance from tree rows as top predictors, suggesting that warmer conditions and reduced plant nitrogen uptake in outer zones drive emissions. Other influential variables included incident PAR, TMIN, and crop temperature, whereas LAI and carbon pools had inconsistent or weak effects. Together, these results underscore the utility of explainable ML tools for identifying key biophysical drivers in agroforestry and guiding data refinement for robust hybrid modeling.
<figure><img src="image_10.png" /><figcaption>**Figure 5.** ML yield predictions and feature importance. Left: Predicted vs. simulated maize yield using Random Forest ($R^2$ = 0.82), XGBoost, and ANN. Right: SHAP analysis shows top drivers of yield, e.g. TMAX, distance, and orientation—based on mean contributions (top) and their effects across observations (bottom).</figcaption></figure>

### 3.4. Integrating PMB, ML, and LCA into a user-friendly DSS

The web-based DSS operationalizes our hybrid pipeline so that non-specialists can explore agroforestry designs without running models manually. Figure 7 summarizes the data flow from modeling to decision: Hi-sAFe simulations for monocrop, monotree, and agroforestry generate mechanistic outputs (e.g. yield, biomass, ΔSOC, N₂O, WUE) that are used to train ML surrogates; the ML outputs then feed the LCA module to compute CO₂-equivalent and nitrogen footprints and to derive composite indicators—such as GHG efficiency and climate-smart productivity—reported in decision units (kg grain ha⁻¹ yr⁻¹, t C ha⁻¹, kg CO₂-eq ha⁻¹ yr⁻¹). This linkage couples the biophysical realism of PBMs with the scalability of ML and converts model outputs into policy-ready metrics.

Figure 8 shows the DSS interface (https://agroforestry.nbdev.space/simulation). A left-hand control panel collects scenario inputs that mirror the model covariates, latitude, north orientation (0/45/90/135°), grid cell (cell number for each zone), distance to the tree row (1, 4, 7, 24 m), crop, and zone (main crop or intercrop), while the main panel renders interactive plots for yield and biomass, crop and soil-surface temperature, and total and diffuse PAR. Users can switch distance and cell, scroll through hourly-to-annual time series, zoom, and export figures or tables. Variable names, units, and roles used in learning are catalogued in supplementary table 4. The DSS is designed for accessibility and transparency. Non-expert users can screen orientation × distance × latitude combinations by yield and LER, examine climate trade-offs via ΔSOC and N₂O (propagated to LCA), and assess water productivity through WUE with PAR/temperature diagnostics;
<figure><img src="image_11.png" /><figcaption>**Figure 6.** ML-based prediction and SHAP analysis of N₂O emissions. Left: predicted vs. Hi-sAFe-simulated N₂O emissions using Random Forest (R² = 0.73), XGBoost, and ANN. Right: SHAP plots showing key predictors—TMAX, distance, and totalParIncident—with their average contributions (top) and detailed impacts (bottom).</figcaption></figure>

advanced users can inspect data-quality flags (share of imputed data, out-of-distribution alerts), model notes, and prediction intervals. Performance is strongest within the training envelope of climates and managements; long contiguous data gaps or regime shifts are flagged and widen uncertainty. Within these boundaries, the DSS enables rapid ‘what-if’ exploration, translating PBM-ML outputs into actionable guidance for agroforestry design and management.

## 4. Discussion

This study introduces a novel, hybrid framework integrating PBM, ML, and LCA into a unified DSS for AFSs. By bridging complex Hi-sAFe simulation outputs with predictive ML algorithms and converting biophysical data into sustainability indicators via LCA, our approach addresses major technical and operational challenges in AFS design, evaluation, and optimization. To our knowledge, this the first study

that integrates PBM-ML-LCA in developing an agro-forestry DSS.

The Hi-sAFe simulations across 16 geo-spatial scenarios—spanning latitudinal gradients and tree row orientations—revealed important interactions in light partitioning, soil water dynamics, and crop-tree resource competition. These findings agree with those obtained by (Dupraz et al 2018). The LER (Mead and Willey 1980), consistently exceeds 1 in most scenarios, indicating that silvoarable systems, even with deciduous trees and summer crops such as maize-poplar, can enhance land-use efficiency over monocultures, although complementarity is often stronger with winter crops. Our LER values should be interpreted as the advantage of agroforestry over separate production at comparable line-planted tree density, not over a commercial forestry stand. Because the line-planted control is less dense than typical forestry, its yield is lower and LER can be inflated. Using realistic forestry densities would reduce LER (Graves
<figure><img src="image_12.png" /><figcaption>**Figure 7.** Framework linking Hi-sAFe simulations, ML, and LCA for agroforestry decision support. Hi-sAFe outputs (e.g. yield, N₂O, SOC, WUE) train ML models, whose predictions inform LCA calculations (CO₂ and N footprints). Composite indicators—grouped into productivity, climate impact, and sustainability units—support DSS outputs for optimal AFS design and policy recommendations.</figcaption></figure>

et al 2007), and quantifying this sensitivity to tree
density is a priority for future simulations and the
DSS. Notably, spatial configurations (e.g. orientation
× latitude) influenced yield dynamics, light inter-
ception, and microclimate buffering (Donat et al
2023). For example, high-latitude North-South

orientations optimized light use and productiv-
ity, while low-latitude configurations showed yield
declines likely due to shade-induced competition and
increased evapotranspiration (Pham et al 2024). Our
results align closely with the findings of (Dupraz
et al 2018), who demonstrated that the interaction
<figure><img src="image_13.png" /></figure>

between latitude and tree-row orientation strongly influences light availability and crop productivity in alley cropping systems. In our simulations, LER patterns confirmed that at lower latitudes (30° N–40° N), east–west (90°) oriented alleys achieved the highest LER values (>1.5), owing to reduced shading duration and more balanced daily irradiance. Conversely, at higher latitudes (50° N–60° N), north–south (0°) orientations consistently produced superior LER (>1.2) by maintaining more uniform light interception throughout the growing season under lower solar elevation angles. These outcomes reinforce the ‘latitude–orientation rule’ proposed by Dupraz *et al* (2018), while our study extends it by quantifying yield and radiation responses across 16 scenarios and incorporating microclimate dynamics and spatial gradients (1–24 m from tree rows). The addition of diagonal orientations (45°, 135°) revealed intermediate performance, smoothing spatial heterogeneity but rarely outperforming the optimal N–S or E–W alignments. Together, these results emphasize that optimal alley design must be latitude-specific, balancing light distribution, microclimate moderation, and system productivity.

The PCA of Hi-sAFe outputs identified light interception, crop temperature, and LAI as key drivers

of system behavior. These insights were foundational for training ML models such as Random Forest and XGBoost, which achieved high prediction accuracy ($R^2$ up to 0.82 for yield and 0.73 for N$_2$O emissions). The SHAP analysis corroborated the importance of microclimatic variables—particularly maximum air temperature, distance from tree rows, and intercepted radiation—as primary influencers of system performance.

Agroforestry microclimate dynamics, often marked by strong spatial gradients and temporal variation, pose a data collection challenge, especially in strip-based systems (Jacobs *et al* 2022). The LSTM and Temporal Fusion Transformer (TFT-LSTM) models demonstrated excellent potential for forecasting and imputing hourly temperature and humidity data, supporting the generation of spatially and temporally complete datasets. By interpolating across gaps in space (1 m–24 m) and time, these models enhanced PBM parameterization, improving simulation fidelity and enabling long-horizon forecasting.

One of the major limitations in process-based AFS modeling is the high computational demand and structural complexity (Rahman *et al* 2023), particularly for 3D simulations like Hi-sAFe. Our framework mitigates this by employing ML as a
surrogate model, reducing the need for repetitive sim-
ulations during scenario exploration. For instance,
trained ML models accurately predict key outputs—
such as crop yield, BBCH development stages, SOC,
and N₂O emissions—across the factorial matrix of
environmental and design scenarios. This streamlines
DSS usability in operational contexts without sacrifi-
cing scientific rigor.

The final step converts model outputs into LCA
indicators (e.g. CO₂-eq, nitrogen footprint), produ-
cing composite metrics like GHG efficiency, WUE,
and impact-based productivity. These are integrated
into a user-friendly web-based DSS, offering full
transparency for experts and intuitive tools for non-
experts—broadening the usability and impact of AFS
modeling.

Current DSS for agroforestry design (e.g. FarmTree®, RegenWorks®, EcoAF) emphasize light competition (Santi et al 2022), but typically rely on growth tables for tree development and do not resolve fine tree–crop interactions or bidirectional feedbacks on crop and tree growth. Conversely, PBM capture these mechanisms but lack farmer-facing interfaces and fast scenario exploration. Our approach bridges this gap by: (i) starting from a 3D, alley-scale PBM (Hi-sAFe); (ii) integrating IoT-based microclimate with robust gap-filling; (iii) training surrogate ML models for rapid screening with uncertainty; and (iv) translating outputs into LCA-ready indicators—all within a single web interface. In doing so, we link Hi-sAFe -ML surrogates—LCA to expose orientation × spacing × latitude decisions with diagnostics (PAR/LAI/temperature), microclimate-aware drivers, and decision metrics (Yield, LER, ΔSOC, N₂O, WUE). To our knowledge, this is the first integrated workflow that turns 3D process-based simulations into farmer-relevant sustainability indices, enabling optimization of agroforestry layouts under specific scenarios while quantifying uncertainty and accounting for sustainability trade-offs.

The strength of the approach lies in its ability to
make the best use of incomplete data and to extract
meaningful insights where traditional methods might
fail, but the fundamental challenge of limited ground-
truth data in AFS still exists (Chen et al 2022).
Despite the framework's strengths, several constraints
remain. The current simulation scenario grid spans
only a few tree species and tree-line designs, a lim-
ited set of slopes/aspects, and a narrow range of soil
qualities (poor–good), which curtails transferability.
Simulations are relatively short, limiting inference on
long-term C/N dynamics and yield stability under
changing climate.

Immediate extensions of this work include longer
multi-decadal simulations to capture tree–crop feed-
backs and long-term soil–carbon dynamics, as well as
expanding the simulation domain to encompass con-
trasting soils, slopes, aspects, and climates, explicitly

linking climatic gradients to latitude-dependent radi-
ation regimes. We also plan to test more diversi-
fied agroforestry layouts (different tree densities) and
assess their effects on productivity and LER stability
over time.

In the near term, development will focus
on features that build directly on the current
framework—parameterizing additional tree species,
enabling diversified crop rotations include intensive
(i.e. winter wheat, winter rape and spring barley),
and sustainable systems (winter wheat, winter rape,
spring barley, pea, and grain maize), and expand-
ing the DSS backend to include ensemble models
for improved robustness. These tasks are technically
feasible within the existing architecture but require
additional data and manpower.

In the longer term, progress will depend on
advancing our process-level understanding of AFSs,
particularly the interactions involving pests, dis-
eases, and biotic stresses, which are not yet suffi-
ciently mechanistically represented. Incorporating
these dynamics, along with economic modules
and farmer-driven decision layers, will ultimately
enhance the DSS's ability to evaluate resilience and
sustainability trade-offs in realistic agroecosystem
contexts.

5. Conclusion

This study introduces a comprehensive hybrid mod-
eling framework that combines PBMs, ML, and
LCA into an integrated, web-based DSS for agro-
forestry. The framework uses Hi-sAFe simulations
to generate synthetic multi-scenario datasets that
train ML models for predicting yield, soil carbon,
and GHG emissions, thus addressing data gaps and
computational demands. Explainable ML techniques
(e.g. SHAP) identify key microclimatic and spa-
tial drivers, while DL supports accurate spatiotem-
poral interpolation in data-limited environments.
Coupling with LCA enables translation of model out-
puts into sustainability metrics for practical decision-
making. Results show that agroforestry can surpass
monocultures in land-use efficiency, but context-
specific optimization is essential. Future work should
expand to diverse environments, species, and longer
timeframes to fully capture long-term resilience.
This framework provides a scalable foundation for
operational, climate-smart agroforestry planning and
policy support.

Data availability statement

Data is available in article, supplementary file and
the data repository (https://doi.org/10.5281/zenodo.
17436942).

Supplementary information available at https://
doi.org/10.1088/1748-9326/ae1cd5/data1.
Acknowledgment

This research is funded by the German Federal
Ministry of Agriculture, Food and Regional Identity
(BMLEH).

Conflict of interest

The authors declare no conflicts of interest.

Author contributions

Ahmed M S Kheir ID 0000-0001-9569-5420

Conceptualization (equal), Data curation (lead),
Formal analysis (lead), Methodology (equal),
Visualization (equal), Writing – original
draft (equal), Writing – review & editing (equal)

Marie Gosme ID 0000-0003-1848-9230

Conceptualization (equal), Writing – original
draft (equal), Writing – review & editing (equal)

Navid Bakhtiary ID 0009-0005-6516-2435

Data curation (equal), Formal analysis (equal),
Methodology (equal)

Priscilla Kephe ID 0000-0002-8995-8148

Methodology (equal), Writing – original
draft (equal)

Juvenal Assou ID 0000-0001-9808-4043

Data curation (equal), Formal analysis (equal)

Maren Langhof ID 0000-0001-7691-7155

Data curation (equal), Methodology (equal)

Jörn Strassemeyer

Conceptualization (equal), Data curation (equal),
Writing – review & editing (equal)

Til Feike ID 0000-0002-1978-9473

Conceptualization (equal), Funding
acquisition (equal), Methodology (equal), Project
administration (equal), Resources (equal),
Supervision (equal), Writing – original draft (equal),
Writing – review & editing (equal)

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