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ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Food, Agriculture and Water
Volume 7 - 2024 |
doi: 10.3389/frai.2024.1402098
This article is part of the Research Topic Defining the Role of Artificial Intelligence (AI) in the Food Sector and its Applications View all 6 articles
Bayesian model of tilling wheat confronting climatic and sustainability challenges
Provisionally accepted- 1 University of Reading, Reading, United Kingdom
- 2 Department of Forestry and Wood Technology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Prague, Czechia
Conventional farming poses threats to sustainable agriculture in growing food demands and increasing flooding risks. This research introduces a Bayesian Belief Network (BBN) to address these concerns. The model explores tillage adaptation for flood management in soils with varying organic carbon (OC) contents for winter wheat production. Three real soils, emphasizing texture and soil water properties, were sourced from the NETMAP soilscape of the Pang catchment area in Berkshire, UK. Modified with OC content at four levels (1%, 3%, 5%, 7%), they were modelled alongside relevant variables in a BBN. The Decision Support System for Agrotechnology Transfer (DSSAT) simulated datasets across 48 cropping seasons to parameterize the BBN. The study compared tillage effects on wheat yield, surface runoff, and GHG-CO2 emissions, categorizing model parameters (from lower to higher bands) based on statistical data distribution. Results revealed that NT outperformed CT in the highest parametric category, comparing probabilistic estimates with reduced GHG-CO2 emissions from "7.34% to 7.31%" and cumulative runoff from "8.52% to 8.50%", while yield increased from "7.46% to 7.56%". Conversely, CT exhibited increased emissions from "7.34% to 7.36%" and cumulative runoff from "8.52% to 8.55%", along with reduced yield from "7.46% to 7.35%". The BBN model effectively captured uncertainties, offering posterior probability distributions reflecting conditional relationships across variables and offered decision choice for NT favouring soil carbon stocks in winter wheat (highest among soils "NT.OC-7%PDPG8", e.g., 286,634 kg/ha) over CT (lowest in "CT.OC-3.9%PDPG8", e.g., 5,894 kg/ha). On average, NT released minimum GHG-CO2 emissions to "3,985 kgCO2eqv/ha", while CT emitted "7,415 kgCO2eqv/ha". Conversely, NT emitted "8,747 kg CO2eqv/ha" for maximum emissions, while CT emitted "15,356 kg CO2eqv/ha". NT resulted in lower surface runoff against CT in all soils and limits runoff generations naturally for flood alleviation with the potential for customized improvement. The study recommends the model for extensive assessments of various spatiotemporal conditions. The research findings align with sustainable development goals, e.g., SDG12 and SDG13 for responsible production and climate actions, respectively, as defined by the Agriculture and Food Organization of the United Nations.
Keywords: Bayesian model, Climate Change, sustainable challenges, tillage preferences, NFM strategies, Synthetic datasets, DSSAT simulations, GHG-CO2 emissions
Received: 16 Mar 2024; Accepted: 18 Jul 2024.
Copyright: © 2024 Ali, Todman and Lukac. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Qaisar Ali, University of Reading, Reading, United Kingdom
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