Grand Challenges in Pedometrics-AI Research

Grunwald, Sabine

doi:10.3389/fsoil.2021.714323

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…A comprehensive review of AI-ML algorithms applied in digital soil mapping, including soil carbon modeling, was provided by Khaledian and Miller [29] (2020), a review of DL for digital soil mapping was provided by Padarian et al [30] (2019), and a review of DL in agriculture was provided by Kamilaris and Prenafeta-Boldú [31] (2018). Recently, a comprehensive review of ML and remote sensing methods to estimate various soil indicators was presented by Diaz-Gonzalez et al [32] (2022). In this section I present a brief overview of some of the most prominent AI methods that have been employed in soil carbon modeling which informs a critical discussion of the power, potentials, and perils of these methods.…”

Section: Ai Modeling Of Soil Carbon Storage and Dynamicsmentioning

confidence: 99%

Artificial intelligence and soil carbon modeling demystified: power, potentials, and perils

Grunwald¹

2022

Carbon Footprints

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The global soil carbon pool has been estimated to exceed the amount of carbon stored in the atmosphere and vegetation, though uncertainties to quantify below-ground carbon and soil carbon fluxes accurately still exist. Modeling soil carbon using artificial intelligence (AI) - machine learning (ML) and deep learning (DL) algorithms - has emerged as a powerful force in the carbon science community. These AI soil carbon models have shown improved performance to predict soil organic carbon (SOC) storage, soil respiration (Rs), and other properties of the global carbon cycle when compared to other modeling approaches. AI systems have advanced abilities to optimize fits between inputs (geospatial environmental covariates) and outputs (e.g., SOC or Rs) through advanced pattern recognition, learning algorithms, latent variables, hyperparameters, hyperplanes, weighting factors, or multiple stacked processing (e.g., convolution and pooling). These machine-oriented applications have shifted focus from knowledge discovery and understanding of ecosystem processes, carbon pools and cycling toward data-driven applications that compute digital soil carbon outputs. The purpose of this review paper is to explore the emergence, applications, and progress of AI-ML and AI-DL algorithms to model soil carbon storage and Rs at regional and global scales. A critical discussion of the power, potentials, and perils of AI soil carbon modeling is provided. The paradigm shift toward AI modeling raises questions how we study soil carbon dynamics and what conclusions we draw which impacts carbon science research and education, carbon management, carbon policies, carbon markets and economies, and soil health.

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Section: Ai Modeling Of Soil Carbon Storage and Dynamicsmentioning

confidence: 99%

Artificial intelligence and soil carbon modeling demystified: power, potentials, and perils

Grunwald¹

2022

Carbon Footprints

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“…Furthermore, laboratory determination resulted in a negative environmental impact since the destructive analyses require hazardous chemical extractants. Modeling techniques based on the pedotransfer function provide an alternative pathway to estimate soil nutrient contents using soil and environmental-related factors and variables (or predictors/covariates; Van Looy et al, 2017;Grunwald et al, 2021). Linear-based regression model employed with an ordinary least square/OLS algorithm was a common example.…”

Section: Introductionmentioning

confidence: 99%

“…During the last decades, machine learning/ML, a part of artificial intelligence/AI, was rapidly employed as the main algorithm for soil (Padarian et al, 2020;Grunwald et al, 2021), agriculture (Meshram et al, 2021), and environmental modeling (Zhong et al, 2021). ML algorithms have an exceptional ability to automatically learn the complex pattern from data, store the learned information, and use it to improve their performance when learning a new task.…”

Section: Introductionmentioning

confidence: 99%

“…ML algorithms have an exceptional ability to automatically learn the complex pattern from data, store the learned information, and use it to improve their performance when learning a new task. This capability is considered more powerful and flexible than commonly widespread modeling algorithms, such as OLS employed by linear-based regression, particularly for the model that consists of non-linear interrelationships between its variables (Jordan & Mithel, 2015;Grunwald et al, 2021). Several researchers reported promising performance of the ML-based regression (or pedotransfer) model in predicting or mapping soil properties in tropical mineral soil, such as cation exchange capacity (Teixeira et al, 2020), soil organic carbon (Gomes et al, 2019;Poppiel et al, 2019), soil macro-and micronutrients (Pulunggono et al, 2022b), and soil acidity (Teixeira et al, 2020;Pulunggono et al, 2022b).…”

Section: Introductionmentioning

confidence: 99%

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Identifying the Underlying Factors and Variables Governing Macronutrients in Cultivated Tropical Peatland Using Regression Tree Approach

Pulunggono¹,

Madani²,

Zulfajrin³

2022

CELEBES Agricultural

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The capability of machine learning/ML algorithms to analyze the effect of human and environmental factors and variables in controlling soil nutrients has been profoundly studied over the last decades. Unfortunately, ML utilization to estimate macronutrients and their governing factors in cultivated tropical peat soil are extremely scarce. In this study, we trained regression tree/RT, ML-based pedotransfer models to predict total N, P, and K in peat soils based on oil palm/OP and OP+bush datasets. Our results indicated that the dataset might contain outliers, non-linear relationships, and heteroscedasticity, allowing RT-based models to perform better compared to multiple linear regression/MLR models (as a benchmark) in estimating total N and P in both datasets, contrastingly, not in K. The difference of important variables in each RT-based model partially showed the vital role of land use in nutrient modeling in peat. The depth of sample collection, organic C, and ash content became the prominent factor and variables in regulating the entire predicted nutrients. Meanwhile, the distance from the oil palm tree and pH were the salient features of total P prediction models in OP and OP+bush sites, respectively. This study proposed employing ML-based pedotransfer models in analyzing and interpreting complex tropical peat data as an alternative to linear-based regression. Our initial study also shed more light on the development possibility of the pedotransfer models that agricultural practician, researchers, companies, and farmers can use to predict macronutrients, both in tabular and spatial terms, in cultivated tropical peatlands

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Leveraging legacy data with targeted field sampling for low-cost mapping of soil organic carbon stocks on extensive rangeland properties

Xia,

Sanderman,

Watts

et al. 2024

Geoderma

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Grand Challenges in Pedometrics-AI Research

Cited by 7 publications

References 34 publications

Artificial intelligence and soil carbon modeling demystified: power, potentials, and perils

Artificial intelligence and soil carbon modeling demystified: power, potentials, and perils

Identifying the Underlying Factors and Variables Governing Macronutrients in Cultivated Tropical Peatland Using Regression Tree Approach

Leveraging legacy data with targeted field sampling for low-cost mapping of soil organic carbon stocks on extensive rangeland properties

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