# Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning

Lily-Belle Sweet, Ioannis N Athanasiadis, Ron van Bree, Andres Castellano, Pierre Martre, Dilli Paudel, Alex C Ruane, Jakob Zscheischler

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Lily-Belle Sweet, Ioannis N Athanasiadis, Ron van Bree, Andres Castellano, Pierre Martre, et al. Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning. One Earth, 2025, 8 (4), pp.101233. 10.1016/j.oneear.2025.101233. hal-05061229

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## Perspective

# Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning

Lily-belle Sweet,<fnref n="1" />,<fnref n="2" />* Ioannis N. Athanasiadis,<fnref n="3" /> Ron van Bree,<fnref n="3" /> Andres Castellano,<fnref n="4" /> Pierre Martre,<fnref n="5" /> Dilli Paudel,<fnref n="3" /> Alex C. Ruane,<fnref n="4" /> and Jakob Zscheischler<fnref n="1" />,<fnref n="2" />,<fnref n="6" />

## SUMMARY

Crop models play a key role in the study of climate change impacts on food production as well as improving food systems resilience and analyzing the effect of potential adaptation interventions. Here, we illustrate opportunities that machine learning offers for tackling key challenges of agricultural modeling. However, to unlock the full potential of machine learning, and thereby accelerate progress toward a more secure and sustainable global food system, serious pitfalls must first be addressed. We argue that transdisciplinary coordination is needed to identify impactful research gaps, curate and maintain benchmark datasets, and establish domain-specific best practices.

## INTRODUCTION

Despite large increases in the worldwide harvested area of stable crops, the United Nation's Sustainable Development Goal of eradicating hunger by 2030¹ is in jeopardy. Yields in some breadbasket regions are stagnating, and the share of crops being used directly for food is decreasing.<fnref n="2" />⁻<fnref n="4" /> At the same time, the effects of climate change are already being felt by the agricultural sector, and these impacts are expected to increase over the coming decades.<fnref n="5" />⁻¹¹ Higher global temperatures are associated with reduced crop yields,¹²,¹³ largely offsetting the recent gains associated with rising CO₂.¹⁴ Food insecurity risk is expected to increase due to more widespread and pronounced extreme climate events such as droughts, heatwaves, and floods.¹⁵⁻¹⁸ The challenge of improving the resilience of our global food system overlaps with an urgent need to mitigate its climate impact.⁹

The complexity of the food system necessitates a transdisciplinary systems thinking approach¹⁹ that takes into account interactions and feedback mechanisms between economic, environmental, and biophysical processes at field, regional, national, or global scales.²⁰⁻²² This requires the use of empirical and process-based modeling approaches, with couplings between climatic, biophysical, and socioeconomic models, in order to accurately capture the behavior of food system decision processes²³ and disentangle underlying causal mechanisms.²⁴

The biophysical impacts of climate on agricultural yields are usually simulated using process-based crop models, which track day-by-day plant development according to the interactions of genetic, environmental, and management factors (GxExM).²⁵ This process-level representation allows crop

models to be used for applications outside of observed conditions, such as historical counterfactuals, future climate scenarios, novel crop traits, or adapted management systems. Process-based crop models can also simulate a large suite of information beyond yields, including water and nutrient use and loss, soil carbon sequestration, and greenhouse gas emissions—all of which are necessary for supply-side approaches to agricultural development and food security analysis. However, crop models differ in the mechanistic and functional components used, and some important plant processes are not captured.²⁶,²⁷ For example, models often use “stress factors” as a practical way of representing the impact of heat or drought, but their performance in cases of combined stresses has rarely been evaluated, and most do not consider the effects of compounding biotic and abiotic stresses.²⁸ Furthermore, most models do not consider the impact of pests and diseases, or the spatial variability of soils or management, despite being increasingly applied at spatial and temporal scales at which these effects become relevant. Any resulting lack of explanatory skill may not be identified given that evaluation strategies are often insufficient in spatial contexts²⁹ and high-quality reference data are scarce. Consequently, although broad signals are robust, current crop models exhibit substantial uncertainties in future projections of global yields under climate change.¹¹,³⁰

Over the last few years, the use of data-driven machine-learning (ML) methods has risen rapidly in agricultural science³¹ as well as many other scientific fields.³² While statistical models have a long history in agricultural modeling, the flexibility of ML models and their ability to learn complex, potentially unknown interactions makes them promising tools for capturing crop

<footnote n="1">¹Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany</footnote>

<footnote n="2">²Department of Hydro Sciences, TUD Dresden University of Technology, Dresden, Germany</footnote>

<footnote n="3">³Wageningen University and Research, Wageningen, the Netherlands</footnote>

<footnote n="4">⁴NASA Goddard Institute for Space Studies (GISS), New York, NY, USA</footnote>

<footnote n="5">⁵LEPSE, Université Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France</footnote>

<footnote n="6">⁶Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden-Leipzig, Germany</footnote>

<footnote>*Correspondence: lily-belle.sweet@ufz.de</footnote>

<footnote>https://doi.org/10.1016/j.oneear.2025.101233</footnote>
Box 1. A brief history of agricultural crop models

Agricultural modeling has been driven by a transdisciplinary network of scientists since the 1960s, and the field has repeatedly embraced emerging technologies such as dynamic programming, high-performance computing, and the open-source software movement.²⁵,⁴¹,⁴² Crop models have been used to improve our understanding of crop growth processes and their response to the environment and to support a variety of stakeholder decisions. These include the evaluation of GxExM interventions for climate adaptation and mitigation,⁴³,⁴⁴ the identification of optimal growing periods,⁴⁵ and estimation of future warming attributable to worldwide food consumption.⁴⁶ Furthermore, simulations can provide valuable food-security information in regions where the availability, frequency, and/or quality of data are lacking.⁴⁷ Today, there exists a large diversity of crop models,⁴⁸ with over 40 developed for wheat alone.⁴⁹

The release of global datasets of cropping areas, sowing dates, and yields in the early 2000s led to the establishment of data standards and harmonized inputs.⁵⁰ This then enabled the use of agricultural models to create projections of crop productivity, trade, food prices, and other impacts under future climate-change scenarios.¹¹,⁵¹,⁵² However, crop simulation experiments were primarily run with diverse protocols and data and without consistent accuracy assessment or uncertainty quantification,⁵³ meaning that results from different studies could not be synthesized or compared. In response to this, the Agricultural Model Intercomparison and Improvement Project (AgMIP) launched in 2010, fostering a global community of climate, crop, livestock, economics, and nutrition modelers to conduct multi-model ensemble intercomparison experiments.⁵⁴,⁵⁵ AgMIP intercomparison activities and large-scale ensemble simulations have been used to evaluate and improve crop models⁵⁶,⁵⁷ and to robustly assess current and future challenges to food systems as well as historic variability⁵⁸ and agro-climatic sensitivity.⁵⁹

growth and development. Furthermore, ML models are skilled in dealing with missing and noisy data, which are common problems in agricultural settings. ML models have been used to forecast yields at multiple scales, to emulate existing process-based crop models,³³,³⁴ to generate datasets of planting dates³⁵ and soil characteristics,³⁶ and to downscale simulations to the fine spatial resolutions needed to inform regional decision making³⁷ (which can be anywhere from the field to district level depending on the application). Their ability to extract information from image and text data has allowed researchers to exploit data sources such as satellite imagery³⁸,³⁹ and news articles.⁴⁰

However, some pitfalls in the use of ML for typical agricultural modeling applications must be recognized. Data-driven predictive models may not necessarily capture true biophysical processes, even if they exhibit excellent predictive performance. This means that ML model predictions outside of the training data distribution could be poor or even physically implausible, which impedes their use for model emulation or yield projections in future climate scenarios, alternative systems, or data-scarce regions. Additionally, crop models are used to encapsulate and improve scientific understanding of plant growth processes, but most ML models are “black boxes.” While there has been considerable research effort into explainable or interpretable AI,⁴¹ current methods may not be robust to dependencies among features or sampling variability.

While the existence of these theoretical limitations is well known, their relevance is dataset and task specific. Agricultural models are used for a wide range of applications, including farmer decision support, national yield forecasting, and the exploration of climate-change adaptation, each of which may have specific usability requirements (such as computational and data efficiency, auditability, ease of use without technical expertise, precision, or accuracy). To drive research progress at the intersection of ML and agricultural modeling, and to capitalize on the strengths of both ML and process-based modeling paradigms, transdisciplinary coordination and sustained engagement with stakeholders is vital. In this article, we

aim to foster such collaboration by providing an introduction to the history, importance and pitfalls of both agricultural crop modeling (Box 1) and ML research (Box 2). We outline several ways researchers are leveraging ML to enhance existing crop models and identify critical priorities to advance research progress such as the creation and maintenance of comprehensive benchmark datasets, documentation of the diverse range of stakeholder requirements, establishment of model intercomparison protocols, and support for the development of innovative hybrid modeling approaches.

CURRENT LIMITATIONS OF CROP MODELS

Despite a relatively strong research focus leading to robust emerging information on the effects of climate change on agricultural yields.⁵⁵,⁷¹ crop models have been found to underestimate the impacts of heavy rainfall, extreme heat, and drought.⁷²⁻⁷⁵ A number of non-linear responses of fertility and phenology to extreme climate events are not addressed adequately or at all in most models.⁷⁶,⁷⁷ Additionally, the compounding effects of multiple non-extreme weather events can lead to severe yield impacts,⁷⁸ and scientific understanding of these interactions is lacking.²⁸ This was exemplified by the extreme 2016 wheat failure in France, which was not anticipated by forecasters until shortly before harvest.⁷⁹,⁸⁰ In general, crop models do not explicitly consider interactions between multiple stresses,²⁸ but such combinations are critical for food security risk assessments (for example, heat and drought stress lead to intensified impacts when they co-occur⁹¹). Beyond climate factors, the multiple interactions between ozone,⁸² CO₂,⁸³ salinity, nitrogen, phosphorus, soils, and genetic factors remain poorly understood.

Some important factors are not included in simulations because of the challenges that come with coupling external process models, such as diverging input data requirements. For example, the impacts of weeds, pests, and diseases are usually not considered in crop models, most of which were originally
Box 2. The rise of ML

While ML dates from the 1950s or earlier, more recent advances in computation speed, data availability, and the success of deep learning architectures have precipitated a new wave of interest in the field. The term “ML” encompasses a range of models, including decision trees, random forests, gradient boosted machines, and neural networks (deep learning). Algorithms can exploit labeled training examples (supervised learning), interact with a dynamic environment to maximize some reward (reinforcement learning), or find patterns in unlabeled data (unsupervised learning). Neural network architectures are able to exploit data properties such as temporal dependence, spatial patterns, or graph structures and have proved highly successful for complex tasks such as protein folding⁶⁰ and weather forecasting.⁶¹,⁶² These methods can achieve impressive performance on high-dimensional input such as image or text data, but tree-based models may be preferable on tabular (low dimensional) or small datasets and in some domains such as time series forecasting.⁶³,⁶⁴

The culture of ML research prioritizes predictive skill over understanding the underlying data-generating process. For example, unlike traditional statistical models, where input variable interactions are clearly designed and the number of model parameters kept small, neural network models involve several layers of non-linear interactions and can contain billions of parameters. In general, ML methods are able to learn complex interactions that can improve model performance, but it is difficult to explain their predictions,⁵⁵ and models can overfit to the training data.⁶⁶ Motivated by the need to build trust in “black box” ML models, research in “explainable” or “interpretable” methods has grown rapidly in recent years. Usually, *post hoc* methods such as feature importances, partial dependence plots, and Shapley values are used to give human-understandable explanations of the reasoning behind individual predictions or a holistic overview of the underlying mechanisms.⁶⁷⁻⁷⁰

designed to capture biophysical processes relating to water, energy, and carbon. Separate models exist for simulating these effects, but they require spatially explicit and hourly data for variables not normally utilized by crop models.⁸⁴

Crop models rely on knowledge of biophysical processes gained from field-scale experimental studies<sup>72</sup> and are usually run independently for each location or grid cell studied. At larger scales, the climate, topography, soil, and management data used to drive crop models are often spatially aggregated and may have substantial gap filling,<sup>85,86</sup> introducing an additional source of uncertainty.<sup>87</sup> Factors not considered in process-based models may have a larger effect at the regional, national, or global spatial scales relevant for some decision makers (e.g., farmer behavior, government policies, and the availability of workers). Some studies have found that scale-dependent parameterization can ameliorate these errors,<sup>87,88</sup> but validation of model performance requires high-quality data at similar scales. Furthermore, crop model evaluation frequently fails to adequately consider spatial context.<sup>29</sup>

Finally, in part to make up for incomplete representation of rele-
vant processes happening at very fine scales, crop models
contain a large number of parameters. These are derived from
experimental data or empirically estimated by minimizing errors
of simulated output against observations (often by manual trial
and error).⁸⁹ Outside of experimental settings, often only bulk
end-of-season yield or spatial production data are available for
calibration, and a precise tuning is not feasible given that the num-
ber of parameters is often larger than the number of observations.
Different calibration strategies can result in substantial variation in
model skill, even when the same validation data are used.⁸⁷,⁹⁰
Simulated yields and yield variability at the global scale have
been found to be highly sensitive to parameterization.⁹¹

RECENT USE OF ML IN AGRICULTURAL MODELING

The successful use of ML methods could help researchers
tackle several challenges in agricultural modeling. Their ability

to extract information from remote-sensing data products, social
media, or news articles,³⁹,⁴⁰,⁹²⁻⁹⁴ for example, could improve
yield predictions in regions where typical crop model input
data are inaccessible or low quality. ML models can be trained
on datasets comprising multiple crop types and cultivars and
then fine-tuned for relatively under-studied crops, like millet,
cassava, and groundnut. Although currently underrepresented
in research studies, there are many opportunities for ML ap-
proaches to complement existing biophysical crop models
(Figure 1). In this section, we highlight some of the ways in which
ML methods have already been used in agricultural modeling, as
well as some recent advances in related fields that suggest ave-
nues for further exploration.

One of the simplest ways to make use of ML is to generate and gap-fill data that are required as input for current crop models. For instance, ML models have been used to map soils in high spatio-temporal resolutions from sparse observations³⁶ and to identify annual field-scale planting dates from satellite imagery,³⁵ both of which are key sources of uncertainty in global crop yield simulations.⁹⁵,⁹⁶ ML could also help to provide estimates that can be used for calibrating process-based crop models, such as the days from sowing to anthesis and maturity, plant canopy characteristics such as chlorophyll content or leaf area index, or above-ground biomass at different stages of the growing season.

ML models have also been trained on simulated datasets to
create emulators of existing process-based crop models (some-
times referred to as “meta-models” or “surrogate models”).
These can be used to generate simulations for a wide range of
scenarios comparatively cheaply in terms of computation time
and/or using fewer input data.⁵³,³⁴,⁹⁷⁻⁹⁹ Emulators can also be
used for post-processing model output, for instance to down-
scale gridded simulations to the higher resolutions that are often
required for local impact assessments.³⁷

The increasing popularity and accessibility of explainable ML
tools (section “the rise of ML”) has led to a wave of research
that makes use of these techniques to analyze the relationships
between different factors, such as management practices, soil
**Coarse, gap-filled or inaccessible data**

ML could be used to obtain soil, management, yield and mid-season plant growth data needed to force and calibrate crop growth models.

<figure><img src="image_2.png" /></figure>

**Limited experimental resources**

Researchers could use interpretable or explainable ML to analyse observational datasets and generate hypotheses to guide experimental research design.

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

**Incompletely represented or missing processes**

Using ML to estimate parameters or replace certain modules (i.e. hybrid ML) could improve predictive skill of process-based models, while adhering to known constraints.

<figure><img src="image_5.png" /><figcaption>**Figure 1.** Examples of ways in which ML can be used to address current challenges in agricultural modeling</figcaption></figure>

characteristics and health, climate, plant phenotype, crop growth, production, and food prices.31,79,100–106 Given the infeasibility of conducting experimental trials that take into account all potential factors that influence crop growth, researchers have expressed hope that this avenue of research could improve scientific understanding of the compounding effects of multiple stressors.28 Similarly, these methods can be used on emulators to study the relationships embodied in process-based models and diagnose incorrectly captured mechanisms.37 In this type of research, ML models are usually trained to predict a single target variable (commonly, yield) from a number of predictive features. *Post hoc* interpretation methods are then used to identify important predictors and/or estimate the functional relationships learned.69

The hybridization of ML and process-based crop models is another promising avenue of research. Hybrid modeling aims to retain known biophysical relationships encoded in existing pro-

cess-based models while using ML to better fit to the data by including missing processes or to select optimal parameter combinations.99 This could make crop models relevant for new stakeholders and scientific communities by enabling the study of the biophysical and socioeconomic elements of transformative adaptation options (such as agroforestry, crop diversification, intercropping, pest control, or conservation agriculture).107 Hybrid (or knowledge-guided) ML models lie on a spectrum between data-driven and process-based paradigms.108 For example, a commonly used approach is to use simulated crop model output as input features for ML models109,110 or as synthetic data to augment the training set. Some studies have used ML to replace components of process-based models where the underlying mechanisms may not be accurately represented rather than using model parameters.111 This approach has been facilitated by the development of shared modeling frameworks and more modular and transparent crop models.112,113 Similarly, ML
A ML model overfitting

Spatiotemporal correlations in data can be exploited by ML models to improve predictive performance on a randomly-sampled test set.

<figure><img src="image_6.png" /></figure>

These correlations may not apply in unseen
times or regions, leading to unexpected failures.

B Disagreement in interpretations

Explainable ML methods provide a simplified version of the true model function. This means the results can be contradictory or ambiguous.

<figure><img src="image_7.png" /><figcaption>Figure 2. Some pitfalls in the use of ML for agricultural modeling applications</figcaption></figure>

(A–C) ML model overfitting to spatiotemporal (or other) correlations in the training data (A), disagreement between interpretations from different explanation methods (B), and a predictive model not correctly capturing underlying causal mechanisms (C).

C Predictive model does not capture causal mechanisms

<figure><img src="image_8.png" /></figure>

A model trained to predict yield based on
climate and management might give good
predictive performance, but may only identify
the effect of an intervention if there are no
confounding effects.

<figure><img src="image_9.png" /></figure>

If a certain treatment partly alleviates negative drought impacts, but is only applied when a drought is anticipated, a data-driven model might associate it with lower yields.

models can also be forced to adhere to known system dynamics by using knowledge-guided constraints,<sup>114-116</sup> which could reduce the data required for training and improve generalizability.

PITFALLS OF ML

The flexibility that makes ML methods so powerful can also, ironically, lead to serious impediments to their use for some crop-modeling applications (Figure 2). Researchers have called attention to the challenge of robustly evaluating ML models in spatial or spatiotemporal contexts, where data are not independent and identically distributed.<sup>117-119</sup> A recent review article identified data leakage issues leading to severely overoptimistic conclusions in studies from 17 scientific fields,<sup>120</sup> and there have been findings of previously undetected ML model failures in multiple scientific domains.<sup>66,121</sup> Replicating studies using ML for agricultural modeling is difficult, as research data are usually not openly accessible due to the financial interests and privacy concerns of stakeholders. This issue is, of course, not specific to ML, but it may have serious consequences for its use in agricultural modeling. Additionally, existing studies have made use of varying datasets, methods, and model evaluation strategies, which hinders comparison of results. This lack of reproducibility and comparability may contribute to a lack of trust and underutilization of ML methods in operational agricultural systems.<sup>122</sup>

Building trust requires transparency. Given the complex inter-
actions influencing crop growth, and the potential ability of ML
models to capture them, opening the black box is an attractive

concept. However, researchers using
explanation tools on ML models of crop
yields have found ambiguous or contra-
dictory results.<sup>98,123</sup> This may be partly
due to the sensitivity of commonly used
explanation methods to small data pertur-
bations or correlations between fea-
tures,<sup>124-126</sup> a lack of robustness to meth-
odological choices such as the sampling
method used for evaluation,<sup>118,127</sup> or the
small size and inherent biases of observa-
tional agricultural datasets. The unreliabil-
ity of these methods is particularly
worrying because supplying inaccurate
explanations for ML model predictions
could still increase their perceived trust-
worthiness to stakeholders.<sup>128</sup>

Further study and more careful use of
these methods could ameliorate these is-
sues, but a fundamental problem re-
mains: the difference between a predic-

tive and causal model. Supervised ML methods exploit
correlations to maximize predictive skill, and explanation
methods are designed to diagnose bias and expose model
mechanisms, not to identify the underlying causal structure of
the data-generation process—a fundamentally different, and
much larger, challenge.<sup>129</sup> Correlations learned by a predictive
model may not hold outside the training distribution, leading to
poor or physically implausible predictions.<sup>66,130</sup> This implies
that, while ML models may be excellent tools for prediction,
they should not be expected to give trustworthy answers to
counterfactual questions or to capture the effect of interventions,
particularly under new conditions (out of distribution). This is also
an issue with traditional statistical models, unless they are care-
fully developed, as well as process-based models that are often
calibrated under specific growth conditions, but the flexibility
and high-dimensionality of ML models make them more suscep-
tible while also making it more difficult to detect overfitting
behavior.

These caveats are particularly concerning for some agricultural
modeling applications. For example, a common crop model use
case is the assessment of yield impacts under potential future
climate scenarios, which entails extrapolation from the observed
data distribution. Simulations using process-based models, which
embody biophysical relationships identified through scientific ex-
periments and theoretical understanding, can be assumed to
adhere to physical constraints under these conditions; the same
is not true for ML models. Furthermore, ML models that cannot
be shown to reliably answer counterfactual “what-if” questions
may be inappropriate for applications such as climate-change adaptation planning or attribution analysis of the causal chains leading to yield failures. Suitable datasets and evaluation strategies to benchmark the performance of ML models, as well as crop models, under such scenarios are needed.

Hybrid modeling approaches aim to avoid these limitations, but this is not guaranteed. For example, the common approach of training ML models using simulated data does not necessarily mean that the relationships embodied by the process model are effectively learned. This has been shown in studies building crop model emulators, which then had difficulty generalizing to unseen regions or years.<sup>97,98</sup> Very rigorous evaluation is needed to identify model overfitting, and commonly used strategies might not be reliable. Random or temporal cross-validation, for example, can lead to severe overestimation of model skill.<sup>118</sup> Bayesian neural networks have been argued to help mitigate overfitting but come with an increase in computational complexity<sup>131</sup> and, in reality, are still vulnerable to overfitting due to the incorrect assumption of independent and identically distributed data.

Overall, these pitfalls suggest that, although ML methods can be a powerful tool, they must be used with caution. Ideally, predictions should be produced only where input data lie within the training-data distribution.<sup>117</sup> If not, the model used should be evaluated in a procedure corresponding to its intended application (for example, using a test set split by space or time that reflects operational usage). Where this is not possible, or where other pitfalls apply to the intended application, it may be that the use of ML is not appropriate.

## ACCELERATING PROGRESS WITH TRANSDISCIPLINARY COORDINATION

To conduct research at the intersection of process-based crop modeling and ML without falling victim to these pitfalls, a deep understanding of the challenges and assumptions underlying both agricultural science and ML methods is needed. Although the limitations we describe can have serious implications, their impact will depend on the specific task, dataset, and modeling approach used. Many of these pitfalls also apply to process-based modeling to a certain degree, and good modeling practice can be applied to address some of them.<sup>132,133</sup> However, we argue this is not always enough. More research is needed to understand the extent to which these limitations impact agricultural modeling tasks and how this is influenced by the use of different methodologies, model architectures, or other factors.

We argue that a transdisciplinary community is required, consisting of ML experts and agricultural modelers as well as stakeholders and experts from other fields who make use of crop model simulations. We identify three key areas in which collaborative activities conducted by such a community could advance research progress (Table 1).

## Exchanging knowledge, data, and domain-specific best practices

ML educational material is increasingly easy to access, but popular textbooks, courses, and software libraries often neglect issues that are relevant for agricultural modeling (such as spatial autocorrelation, sparse datasets, or susceptibility to covariates). Researchers applying ML have found that models often fail to

generalize outside of the dataset on which it was trained and have suggested that this may be due to underspecification of the intended task in the modeling pipeline.<sup>134</sup> In other words, the training dataset and evaluation strategy used are not rigorous enough and/or are not capturing stakeholder requirements in real-world use. Commonly used strategies to address pitfalls may not be helpful for all models or tasks. For example, a popular method of addressing the challenges of imbalanced binary classification is to balance the data using duplicate or synthetic samples. However, some recent research suggests that using this method does not lead to improved model performance for strong classifiers.<sup>135</sup> Better communication of the assumptions underlying both ML methods and agricultural datasets, together with collaborative research to identify domain-specific best practices, is needed.

The transfer of knowledge from crop modelers, agronomists, and scientists from related fields to ML researchers is also important. We have highlighted some of the ways in which ML methods can be used in complement to existing process-based crop models, such as the development of hybrid models. Here, targeting opportunities with the most potential impact is key. For example, modules or parameters of existing crop models that are known to less accurately reflect the intended processes may be more likely to lead to improved predictive skill (or other desired behavior) if replaced with ML. Effective model development that builds on the advantages of both paradigms requires a deep understanding of the strengths and weaknesses of both existing crop models and state-of-the-art ML methods.

Finally, accessing, handling, and processing agricultural data come with many domain-specific obstacles. Often, data used in individual studies cannot be shared due to privacy, ownership, and other governance issues. Publicly available data, where available, also tend to have issues related to size and quality. Coverage is often limited to major crops (such as wheat and maize). Other crops relevant for food security or nutritional diversity, such as millet and sorghum, may not have data available of the required size or quality. Most applications will require a range of data types (crop yields, weather, soil characteristics and moisture, planted and harvested areas, sowing and harvesting dates, irrigation and fertilization practices, to name a few) that vary in their spatial and temporal resolution, coverage, collection, and preprocessing needed. Combining these datasets for modeling is a daunting task that requires both deep and wide expertise. Data harmonization and exchange standards, protocols, and shared tools are also lacking.<sup>136</sup> AgMIP has made important contributions in this domain, but these have not been disseminated and used as widely as the challenge would require.

## Robustly quantifying performance of ML methods for agricultural modeling tasks

So far, studies have reported varying abilities of different ML methods for agricultural modeling tasks, and reconciling these results is difficult due to the range of performance metrics, modeling methodologies, and datasets used. Community efforts to define benchmark datasets, evaluation strategies, and protocols that well represent the intended task are vitally needed. These benchmarks should adhere to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, and have defined procedures for reproducing resulting model performance
<table><caption>Table 1. Transdisciplinary community activities to tackle ML pitfalls in agricultural modeling</caption><thead><tr><th>Knowledge exchange</th><th>Quantifying ML performance</th><th>ML method development</th></tr></thead><tbody><tr><td colspan="3"><strong>Pitfall: ML model overfitting and poor generalization</strong></td></tr><tr><td>For specific applications, identify where and how overfitting or lack of generalization ability might be problematic. Find thresholds of required model generalization performance in discussion with stakeholders and model users.</td><td>Create and maintain benchmark simulated and observational datasets (and corresponding robust evaluation strategies) designed to detect unacceptable overfitting for specific applications.</td><td>Use simulated or large observational datasets to test and develop improved model evaluation and uncertainty quantification methods.</td></tr><tr><td>-</td><td>Intercompare existing ML methods on a wide variety of datasets and tasks.</td><td>Develop and intercompare hybrid models that enforce certain physical processes, model architectures that adhere to physical constraints or explicitly handle spatiotemporal autocorrelation.</td></tr><tr><td colspan="3"><strong>Pitfall: Ambiguous model interpretations</strong></td></tr><tr><td>Assess and communicate the limitations of commonly used interpretable ML methods, identify state-of-the-art methods or inherently interpretable models that have promise for agricultural modeling applications.</td><td>Intercompare existing and state-of-the-art interpretable ML methods on simulated data where underlying processes are known.</td><td>Develop interpretable ML tools tailored to agricultural modeling tasks, research questions, and stakeholders.</td></tr><tr><td>Identify and make explicit the diverse needs of different users of model interpretations (e.g., model developers, scientists using interpretable ML for process understanding).</td><td>Investigate the relevance of providing model interpretations to different stakeholders and model users (e.g., increasing model trustworthiness).</td><td>Develop domain-specific interpretable ML evaluation and uncertainty quantification tools and platforms.</td></tr><tr><td colspan="3"><strong>Pitfall: Predictive models not capturing causal mechanisms</strong></td></tr><tr><td>Establish guidelines for the use of predictive ML models for scientific research in agricultural modeling.</td><td>Measure and intercompare abilities of current ML methods for counterfactual and intervention analysis in different tasks using simulated or experimental datasets.</td><td>Develop and intercompare causal ML methods for interventional and counterfactual queries on agricultural modeling applications.</td></tr><tr><td>Identify agricultural modeling tasks that require causal models (e.g., estimating the effect of a proposed climate adaptation strategy) and assess if assumptions of causal methods may be violated (e.g., the presence of non-stationarity or feedback loops).</td><td>-</td><td>Develop and intercompare hybrid or knowledge-guided models, in close collaboration with domain experts, to enforce consistency with known biophysical processes.</td></tr></tbody></table>

scores. The curation and maintenance of these resources requires a transdisciplinary, coordinated effort with awareness of the specific needs of different model users and stakeholders.

For many crop-modeling applications, labeled ground-truth data are not available (for example, assessment of future climate-change impacts to crop yields). However, simulated data from process-based crop models could be used to create benchmark datasets with similar characteristics. Such set-ups enable in-depth study of the impact of evaluation strategy¹¹⁸ or training data availability⁹⁸ on model behavior. Similar approaches have been used to provide evidence for improved phenotypic prediction across different environments¹³⁷ and to estimate bias in the modeled temperature sensitivity of global crop yields arising from uneven spatial representation of observation data.¹³⁸

## Facilitating the development of new ML methods for agricultural modeling applications

The quest for improvement in model performance on benchmark datasets drives and quantifies progress in ML research.

Providing such datasets and evaluation criteria for specific agricultural modeling applications could enable ML experts with little domain knowledge to contribute, leading to the development of improved ML models.¹³⁹ For example, the recent release of a benchmark dataset for medium-range weather forecasting (WeatherBench¹⁴⁵) has led to rapid technological advances, with several ML models now reporting performance comparable to, or better than, physics-based models.⁶¹,⁶²

Experimental or simulated data from agricultural modeling studies could be repurposed for ML research in relevant subdomains, such as domain generalization, explainability, and robustness. For example, the use of computer vision for image-based plant phenotyping was accelerated by the compilation of benchmark datasets for a challenge hosted at a 2014 computer vision conference.¹⁴⁰ Since then, these datasets have been used in hundreds of published studies and are now a standard benchmark for multi-instance image segmentation.

Finally, ML researchers have called attention to the negative consequences of excessive focus on model scores on specific metrics and datasets. Benchmark dataset suites that represent
multiple locations, cultivars, and management strategies, including granular evaluation criteria aligned with the needs of specific end-users, could help to avoid ML models achieving the right answer for the wrong reasons and improve generalizability. However, we note that there is a tradeoff between the use of detailed evaluation criteria, which can help to democratize ML utilization by identifying performant approaches given the computation, data and expertise available, and the use of simple aggregate metrics that enable straightforward model intercomparison.

## RECOMMENDATIONS AND WAYS FORWARD

ML methods are already driving scientific progress in the agricultural modeling domain, and, with effective transdisciplinary collaboration, we expect these tools to lead to significant research advances in the coming years. For researchers working at the intersection of ML and agricultural modeling, we have several recommendations.

First, consider any potential impact of the fundamental limitations of ML methods on the intended use case of the model. There are many ways to integrate the scientific knowledge of生物physical processes embodied by existing crop models with ML (via parameterization, data generation, calibration, data assimilation, post-processing, or the use of hybrid modeling approaches) that may lead to better outcomes than a purely data-driven approach.

Secondly, ML models should be evaluated more rigorously, with evaluation criteria that take into account the specific requirements and difficulties of stakeholders (such as the necessary lead time for forecasting, or time lag in data availability) as well as the presence of spatiotemporal autocorrelation or other dependencies between features. Ideally, multiple metrics should be used. For example, model performance under climate extremes, such as heatwaves or droughts, could be reported separately.¹⁴¹ Predictions used for model evaluation could be made available in public repositories to enable follow-up analyses.¹⁴²

It is important to note that defining and weighting diverse and context-specific model criteria is not strictly a scientific question but a matter of balancing the priorities of multiple stakeholders.¹⁴³ Optimizing agricultural yields, for example, can lead to biodiversity loss, degraded soils, and increased pollution.¹⁴⁴,¹⁴⁵ Therefore, deciding on criteria by which models are evaluated should take place in an open conversation between ML researchers, crop modelers, stakeholders from multiple communities, and experts from other scientific disciplines such as soil scientists, climatologists, agronomists, and ecologists. This transdisciplinary dialogue is likely to be as important for advancing scientific understanding as the resulting models, so these discussions and corresponding lessons learned should be documented.¹⁴⁶ Furthermore, quantifying model requirements can be challenging, and stakeholder priorities will change over time. The emphasis should be on establishing an efficient process for iterative improvement rather than the creation of a single definitive product.

For this type of undertaking, global and transdisciplinary coordination is needed. In 2023, we established the AgMIP ML community (AgML) as a first step toward addressing the above-mentioned challenges. While we were met with consider-

able enthusiasm and engagement from both ML researchers and crop modelers, channeling collective ideas and discussion into useful output is more difficult. The following recommendations are based on our experience launching and working with this large, open, and diverse community.

## Focus on application-driven challenges

It is important to decide on a specific problem or research question early on in the process, which should be tied to the needs of a real-world model user or researcher. For example, if we consider crop yield forecasting, the user could be an individual farmer trying to maximize profits or a governmental institution hoping to anticipate and avoid food insecurity. This decision will dictate the scale and type of data required, the forecasting lead time, suitable model architectures and corresponding learning algorithms, and the evaluation criteria. However, despite the narrow scope, receiving regular input from a wide range of participants is key. To facilitate this, we have found it useful to hold regular open meetings where the progress of multiple activities can be discussed, along with other relevant topics. This provides enough value to merit continual high attendance and has allowed small issues to be noticed by community members with specific expertise. We note that motivating engagement and collaboration between researchers is easier than with real-world users of crop models and other non-academic stakeholders.

## Develop a common language

Although transdisciplinary community discussion is important, in order to produce legible output (such as the publication of benchmark datasets), the problems tackled must be explained and motivated in field-specific language. To attract interest from the wider ML community, publications in ML conferences would be prioritized. These are highly competitive and have a fast-paced review system. Reviewers are unlikely to be familiar with crop modeling or adjacent topics, so this context and the research problem should be explained clearly using domain-specific vocabulary. A more general problem may be that inter-disciplinary research careers may not fulfill field-specific criteria needed to obtain professorship positions.

## Reward interdisciplinary work and dataset curation

Producing high-quality research and datasets in coordination with a large, interdisciplinary community has long been disencени-tized by academic structures. Research is driven by a need to publish quickly and preferentially focuses on novelty over itera-tive improvement. The actual costs involved in collecting agricul-tural data and the inherent time lag in generating them (i.e., a growing season may span several months) often do not receive proper attention. In ML, the hard work of benchmark dataset cu-ration has been undervalued, although there has been a recent push for "data-centric ML," which aims to improve this. Simi-larly, data journals in the environmental sciences offer prominent venues and credit for the often-laborious creation of relevant auxiliary data.

In conclusion, we advocate for open, accessible communities to facilitate important dialogues, help scientists to reap the po-tential benefits of ML for agricultural modeling while avoiding pit-falls, and conduct activities such as benchmark dataset creation
and model intercomparison experiments. These actions will help quantify the domain-specific utility of current ML methods and build trust in the use of these tools where warranted. We believe that such efforts will enable the development of more robust, usable, and trustworthy crop models that can be used to address the multiple challenges that face our global food system.

## ACKNOWLEDGMENTS

We acknowledge funding for a Short-Term Scientific Mission from the European COST Action DAMOCLES (CA17109) provided to L.-b.S., which helped initiate and develop this paper. L.-b.S. and J.Z. acknowledge the Helmholtz Initiative and Networking Fund (Young Investigator Group COMPOUNDX, grant agreement VH-NG-1537). P.M. acknowledges support from the meta-program Agriculture and Forestry in the Face of Climate Change: Adaptation and Mitigation (CLIMAE) of INRAE. A.C.R. and A.C. receive NASA GISS Climate Impacts Group support from the Earth System Directorate. I.N.A. acknowledges support from the European Union's Digital Europe Programme under grant agreement N° 101100622 (AgrifoodTEF). D.P. acknowledges support from Wageningen University and Research investment theme Data Driven Discoveries in a Changing Climate. This manuscript benefited from the input and feedback from the agricultural and ML modeling community at the ninth AgMIP Global Workshop, the first AgML workshop in January 2024 in Wageningen, and from regular AgML community meetings.

## DECLARATION OF INTERESTS

The authors declare no competing interests.

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