Selecting suitable sites for livestock manure composting via the integration of machine learning, median center and geographic information system

IF 6.1 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Agricultural Systems Pub Date : 2025-03-14 DOI:10.1016/j.agsy.2025.104300
Jingjie Yan , Bojie Yan , Wenjiao Shi , Yulin Feng
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Abstract

CONTEXT

Composting livestock manure in suitable areas near farmlands is important for the application of livestock manure to farmlands and the realization of resource utilization of livestock manure and crop–livestock integration. However, few studies have focused on selecting suitable sites for livestock manure composting.

OBJECTIVE

To process and evaluate the spatial clustering of farmland via six machine learning methods, analyze the priority and limiting factors of suitable site selection for livestock manure composting, and determine suitable sites for livestock manure composting and the annual amount of livestock manure composting in three scenarios.

METHODS

This research investigated the spatial clustering of farmlands by using six machine learning methods. Then, a priority and limitation analysis of suitable sites for livestock manure composting was conducted, and suitable sites for livestock manure composting and the annual amount of livestock manure composting in three scenarios were documented.

RESULTS AND CONCLUSIONS

Results indicated that the algorithm called balanced iterative reducing and clustering using hierarchies could effectively identify uneven spatial clusters of farmlands in hilly areas. A total of 114 suitable sites for livestock manure composting were identified based on the priority and limitation analysis. Then, the spatial association relation between suitable sites for livestock manure composting and farmlands were established. Finally, the annual amount of livestock manure composting at 114 suitable sites for livestock manure composting was estimated as pig manure equivalent in the three scenarios.

SIGNIFICANCE

These findings have significant implications for promoting the resource utilization of livestock manure and crop–livestock integration. The results also help to improve the utilization rate of livestock manure, reduce the economic cost of applying livestock manure to farmland, and alleviate the environmental pollution risk of livestock manure. In addition, the results have good application for the utilization of livestock manure and the layout planning of livestock and poultry breeding.

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内容提要在农田附近的适宜区域堆肥畜禽粪便对于将畜禽粪便应用于农田、实现畜禽粪便资源化利用和农作物与畜禽一体化具有重要意义。目的通过六种机器学习方法对农田空间聚类进行处理和评估,分析畜禽粪便堆肥适宜地点选择的优先性和限制性因素,确定三种情景下的畜禽粪便堆肥适宜地点和年畜禽粪便堆肥量。结果与结论结果表明,平衡迭代还原和分层聚类算法能有效识别丘陵地区不均衡的农田空间聚类。根据优先级和限制条件分析,共确定了 114 个适合进行禽畜粪便堆肥的地点。然后,建立了畜禽粪便堆肥适宜地点与农田之间的空间关联关系。这些研究结果对促进畜禽粪便资源化利用和农牧结合具有重要意义。这些结果还有助于提高畜禽粪便的利用率,降低农田施用畜禽粪便的经济成本,减轻畜禽粪便的环境污染风险。此外,该结果对畜禽粪便利用和畜禽养殖布局规划也有很好的应用价值。
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来源期刊
Agricultural Systems
Agricultural Systems 农林科学-农业综合
CiteScore
13.30
自引率
7.60%
发文量
174
审稿时长
30 days
期刊介绍: Agricultural Systems is an international journal that deals with interactions - among the components of agricultural systems, among hierarchical levels of agricultural systems, between agricultural and other land use systems, and between agricultural systems and their natural, social and economic environments. The scope includes the development and application of systems analysis methodologies in the following areas: Systems approaches in the sustainable intensification of agriculture; pathways for sustainable intensification; crop-livestock integration; farm-level resource allocation; quantification of benefits and trade-offs at farm to landscape levels; integrative, participatory and dynamic modelling approaches for qualitative and quantitative assessments of agricultural systems and decision making; The interactions between agricultural and non-agricultural landscapes; the multiple services of agricultural systems; food security and the environment; Global change and adaptation science; transformational adaptations as driven by changes in climate, policy, values and attitudes influencing the design of farming systems; Development and application of farming systems design tools and methods for impact, scenario and case study analysis; managing the complexities of dynamic agricultural systems; innovation systems and multi stakeholder arrangements that support or promote change and (or) inform policy decisions.
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