An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems

Kutalmış Turhal; Ümit Çiğdem Turhal

doi:10.31015/2025.4.35

An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems

Abstract

This research introduces a framework for assessing sustainability in agricultural production, enhanced by AI, that merges environmental, agronomic, and management aspects into a cohesive decision-support system. Drawing on data from Karnataka, India, the study examined six critical parameters including crop type, soil pH, rainfall, temperature, soil classification, and nitrogen-phosphorus-potassium (NPK) fertilizer inputs. A Composite Sustainability Indicators (CSI) was created to assess the multifaceted performance of various crops through five normalized sub-index computation: fertilizer efficiency, pH suitability, rainfall adequacy, temperature compatibility, and soil quality. Specific optimal thresholds were established for key crops like wheat, barley, and maize to align with their unique ecological needs. An efficient Life Cycle Assessment (LCA) component calculated energy equivalents (N = 60.6 MJ kg⁻¹, P₂O₅ = 11.1 MJ kg⁻¹, K₂O = 6.7 MJ kg⁻¹) and greenhouse gas emissions (N = 6.6, P₂O₅ = 1.0, K₂O = 0.6 kg CO₂e kg⁻¹) associated with fertilizer usage. An AI-driven regression model (R² = 0.93; MAE = 0.024) was employed to predict CSI under diverse environmental conditions, to facilitate scenario analyses for variations in soil pH (+0.5), rainfall (±100 mm), temperature (±2 °C), and fertilizer rates (±20%). The findings indicated that moderate reductions in fertilizer use improved sustainability scores, whereas climatic variability elicited varied responses based on the crop. Overall, this AI-LCA-enhanced framework offered a data-driven and flexible solution for precision agriculture, melding resource efficiency with environmental responsibility. It fosters sustainable production planning, adaptive management amidst climate variability, and informed policymaking aimed at building resilient agricultural systems.

Keywords

Precision agriculture, Artificial intelligence, Sustainability assessment, Life Cycle Analysis (LCA), Composite Sustainability Index (CSI), What-if simulation, Multi-criteria decision analys

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Details

Primary Language

English

Subjects

Environmental Assessment and Monitoring, Sustainable Agricultural Development

Journal Section

Research Article

Authors

Kutalmış Turhal ^*
0000-0002-5347-8513
Türkiye

Ümit Çiğdem Turhal
0000-0003-2387-1637
Türkiye

Publication Date

December 26, 2025

Submission Date

November 4, 2025

Acceptance Date

December 9, 2025

Published in Issue

Year 2025 Volume: 9 Number: 4

DOI

https://doi.org/10.31015/2025.4.35

IZ

https://izlik.org/JA65BL42XZ

APA

Turhal, K., & Turhal, Ü. Ç. (2025). An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems. International Journal of Agriculture Environment and Food Sciences, 9(4), 1352-1360. https://doi.org/10.31015/2025.4.35

AMA

1.Turhal K, Turhal ÜÇ. An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems. int. j. agric. environ. food sci. 2025;9(4):1352-1360. doi:10.31015/2025.4.35

Chicago

Turhal, Kutalmış, and Ümit Çiğdem Turhal. 2025. “An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems”. International Journal of Agriculture Environment and Food Sciences 9 (4): 1352-60. https://doi.org/10.31015/2025.4.35.

EndNote

Turhal K, Turhal ÜÇ (December 1, 2025) An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems. International Journal of Agriculture Environment and Food Sciences 9 4 1352–1360.

IEEE

[1]K. Turhal and Ü. Ç. Turhal, “An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems”, int. j. agric. environ. food sci., vol. 9, no. 4, pp. 1352–1360, Dec. 2025, doi: 10.31015/2025.4.35.

ISNAD

Turhal, Kutalmış - Turhal, Ümit Çiğdem. “An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems”. International Journal of Agriculture Environment and Food Sciences 9/4 (December 1, 2025): 1352-1360. https://doi.org/10.31015/2025.4.35.

JAMA

1.Turhal K, Turhal ÜÇ. An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems. int. j. agric. environ. food sci. 2025;9:1352–1360.

MLA

Turhal, Kutalmış, and Ümit Çiğdem Turhal. “An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems”. International Journal of Agriculture Environment and Food Sciences, vol. 9, no. 4, Dec. 2025, pp. 1352-60, doi:10.31015/2025.4.35.

Vancouver

1.Kutalmış Turhal, Ümit Çiğdem Turhal. An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems. int. j. agric. environ. food sci. 2025 Dec. 1;9(4):1352-60. doi:10.31015/2025.4.35

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An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems

An AI-Driven Cradle-to-Farm-Gate Life Cycle Assessment Framework for Energy and Carbon Sustainability in Crop–Soil–Climate Systems

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