## 1 Introduction  
Machine learning (ML) has emerged as a crucial domain in science and technology, exerting a substantial socioeconomic-environmental influence on various aspects of human and natural systems [1; 2]. ML allows us to learn from vast amounts of data and improve the predictive performance of models. However, ML often employs complex algorithms, which results in black box models due to their intricate internal processes that are not readily interpretable [3; 4; 5]. Such opaqueness may lead stakeholders to overlook meaningful patterns or issues arising from hidden biases in the data. This hinders the handling of effective predictive resource management, mainly when based on large-scale ML [6]. For example, the accuracy problem of yield mapping is well-known due to errors inherent in high data volumes and algorithm opacity [7]. Considerable concern has been expressed about relying on opaque models that may result in decisions that are not fully comprehended or, even worse, violate ethical principles regarding business and the environment or legal norms [1; 8]. These risks are particularly relevant for decision-making in real-life scenarios and for access to public benefits [9], for example, digitalization in agriculture [10] and terrestrial conservation [11]. This partly explains the low adoption rate of current ML-based decision support systems in many areas. Land managers, government agencies, and companies that incorporate black box ML models into their practices, products, and applications potentially face efficiency, safety, and trust issues [12]. Therefore, the lack of interpretability must be addressed, and increasing the explainability of predictive modeling for data analysis is becoming increasingly important for mitigating these unintended risks and promoting the correct application of ML models in critical domains. In 2018, the European Parliament implemented the General Data Protection Regulation, which established provisions regarding automated decision-making [1]. These regulations aim to ensure that individuals have the right to receive \"comprehensible explanations of the underlying reasoning\" when automated decision-making processes are used. Additionally, in 2019, the European Union's High-Level Expert Group on Artificial Intelligence (AI) introduced ethical guidelines for trustworthy AI, and one of the requirements is explainability [1]. This requirement has been incorporated into the proposed EU regulation known as the AI Act [13], which establishes standardized rules for AI, thus affecting ML as a subfield of AI. Similar but nonregulatory proposals exist for AI risk management in the U.S., such as the \"Identifying Outputs of Generative Adversarial Networks Act\" and the \"National Artificial Intelligence Initiative Act of 2020\" [14]. Overall, the consensus on the importance of developing practical explanation tools is growing. Meaningful explanations are critical for describing data, testing models, identifying potential biases, addressing risks, and fostering trust and collaboration between humans and their AI assistants. However, this remains an ongoing scientific challenge [5]. An optimum model should be highly accurate and easy to interpret [2]; however, despite the rising interest in these models, achieving both interpretable and highly accurate model outputs has presented a considerable challenge [15], particularly in response to the abovementioned concerns at the management and policy scales. Consequently, the development of various explanation methods for black box models has increased in both academia and industry [16; 17; 18; 19; 20]. Explainable ML emerged in the late 2010s for prediction in different systems to better explain black box models and comprehensively address diverse aspects of the food and agriculture sector [3; 21]. Explainable ML seeks to enhance the interpretability of complex algorithms while still maintaining their accuracy.