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关于被忽视和未充分利用的作物种的土地适用性方法的评估:一个范围审查

Hillary Mugiyo¹, Vimbayi G P Chimonyo¹, Mbulisi Sibanda^1,2

¹Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth & Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Pietermaritzburg 3209, South Africa.

Land

|July 22, 2024

概括

本范围审查评估了土地适用性分析 (LSA) 方法用于农业,发现机器学习和多标准决策等现代方法对于作物适用性优越,特别是对于被忽视和未充分利用的物种 (NUS).

科学领域:

农业科学农业科学
环境科学环境科学
地理空间分析是什么

背景情况:

土地适用性分析 (LSA) 对于理解农业生态中的作物分布至关重要.
现有的LSA方法缺乏共识,特别是在被忽视和未充分利用的作物物种 (NUS) 中.
需要对LSA方法进行全面评估,以指导农业规划.

研究的目的:

进行土地适用性分析 (LSA) 方法的范围审查.
评估已识别的LSA方法对被忽视和未充分利用的作物种 (NUS) 的适用性.
为LSA开发确定可靠的方法和标准参数.

主要方法:

关于土地适用性分析 (LSA) 发表的文章的范围审查.
将LSA方法分为传统和现代类别的分类.
分析现代方法,包括多标准决策 (MCDM),作物模拟模型和机器学习.

主要成果:

现代LSA方法 (63.4%) 比传统方法 (26.6%) 更普遍.
机器学习 (25.7%) 和MCDM方法,如AHP (14.9%) 和模糊逻辑 (12.9%) 越来越受欢迎.
气候,水文,土壤,社会经济和景观因素的67个参数对LSA至关重要;存在数据挑战.

结论:

现代LSA方法,特别是MCDM和机器学习,为作物适合性分析提供了强大的解决方案.
整合大数据和物联网可以提高LSA的准确性和可靠性.
建议将定性和定量方法相结合的混合土地评估系统用于改进LSA,特别是在NUS.

关键词:

在MCDM中,MCDM是MCDM.这就是NUS NUS.混合土地评估系统是混合土地评估系统.土地管理土地管理机器学习是机器学习.

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