Ali Qaisar
Department of Sustainable Land Management, SAPD, The School of Agriculture, Policy, and Development, University of Reading, Reading, United Kingdom.
Front Artif Intell. 2024 Aug 27;7:1402098. doi: 10.3389/frai.2024.1402098. eCollection 2024.
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, United Kingdom. Modified with OC content at four levels (1, 3, 5, 7%), they were modeled 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-CO 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-CO 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 favoring 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- CO emissions to "3,985 kgCOeqv/ha," while CT emitted "7,415 kgCOeqv/ha." Conversely, NT emitted "8,747 kgCOeqv/ha" for maximum emissions, while CT emitted "15,356 kgCOeqv/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.
传统农业在满足不断增长的粮食需求和增加洪水风险方面对可持续农业构成威胁。本研究引入贝叶斯信念网络（BBN）来解决这些问题。该模型探讨了在不同有机碳（OC）含量的土壤中进行耕作适应性调整，以管理冬小麦生产中的洪水。从英国伯克郡庞集水区的NETMAP土壤景观中获取了三种真实土壤，重点关注质地和土壤水分特性。通过在四个水平（1%、3%、5%、7%）上改变OC含量对其进行改良，并在BBN中与相关变量一起建模。农业技术转移决策支持系统（DSSAT）模拟了48个种植季节的数据集，以对BBN进行参数化。该研究比较了耕作对小麦产量、地表径流和温室气体CO排放的影响，并根据统计数据分布对模型参数进行分类（从低到高）。结果表明，在最高参数类别中，免耕（NT）的表现优于传统耕作（CT），概率估计显示温室气体CO排放从“7.34%降至7.31%”，累积径流从“8.52%降至8.50%”，而产量从“7.46%增加到7.56%”。相反，CT的排放从“7.34%增加到7.36%”，累积径流从“8.52%增加到8.55%”，产量从“7.46%降至7.35%”。BBN模型有效地捕捉了不确定性，提供了反映变量间条件关系的后验概率分布，并为免耕提供了决策选择，免耕有利于冬小麦土壤碳储量（例如，在土壤“NT.OC - 7%PDPG8”中最高，为286,634 kg/ha），而传统耕作（在“CT.OC - 3.9%PDPG8”中最低，例如5,894 kg/ha）。平均而言，免耕释放的温室气体CO排放量最低，为“3,985 kgCOeqv/ha”，而传统耕作排放“7,415 kgCOeqv/ha”。相反，免耕在最大排放量时排放“8,747 kgCOeqv/ha”，而传统耕作排放“15,356 kgCOeqv/ha”。在所有土壤中，免耕导致的地表径流低于传统耕作，并自然限制径流产生以减轻洪水，具有定制改进的潜力。该研究建议将该模型用于对各种时空条件的广泛评估。研究结果符合可持续发展目标，例如联合国粮食及农业组织分别定义的关于负责任生产和气候行动的可持续发展目标12和可持续发展目标13。
