欧盟农场的肥料成本预测:通过人工神经网络的机器学习方法

IF 1.8 Q2 AGRICULTURE, MULTIDISCIPLINARY Open Agriculture Pub Date : 2023-01-01 DOI:10.1515/opag-2022-0191
V. Martinho
{"title":"欧盟农场的肥料成本预测:通过人工神经网络的机器学习方法","authors":"V. Martinho","doi":"10.1515/opag-2022-0191","DOIUrl":null,"url":null,"abstract":"Abstract Machine-learning methodologies are part of the artificial intelligence approaches with several applications in different fields of science and dimensions of human life. These techniques appear in the frameworks of the digital transition, where smart technologies bring relevant contributions, such as improving the efficiency of the economic sectors. This is particularly important for sectors such as agriculture to deal with the challenges created in the context of climate changes. On the other hand, machine-learning approaches are not easy to implement, considering the complexity of the algorithms associated. Taking this into account, the main objective of this research is to present a model to predict fertiliser costs in the European Union (EU) farms through artificial neural network analysis. This assessment may provide relevant information for farmers and policymakers in the current scenario where the concerns are to identify strategies to mitigate the environmental impacts, including those from the agricultural sector and the respective use of chemical resources. To achieve these objectives, statistical information for the EU agricultural regions from the Farm Accountancy Data Network was considered for the period 2018–2020. The findings obtained show relative errors between 0.040 and 0.074 (showing good accuracy) and the importance of the total utilised agricultural area and the total output to predict the fertiliser costs.","PeriodicalId":45740,"journal":{"name":"Open Agriculture","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks\",\"authors\":\"V. Martinho\",\"doi\":\"10.1515/opag-2022-0191\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Machine-learning methodologies are part of the artificial intelligence approaches with several applications in different fields of science and dimensions of human life. These techniques appear in the frameworks of the digital transition, where smart technologies bring relevant contributions, such as improving the efficiency of the economic sectors. This is particularly important for sectors such as agriculture to deal with the challenges created in the context of climate changes. On the other hand, machine-learning approaches are not easy to implement, considering the complexity of the algorithms associated. Taking this into account, the main objective of this research is to present a model to predict fertiliser costs in the European Union (EU) farms through artificial neural network analysis. This assessment may provide relevant information for farmers and policymakers in the current scenario where the concerns are to identify strategies to mitigate the environmental impacts, including those from the agricultural sector and the respective use of chemical resources. To achieve these objectives, statistical information for the EU agricultural regions from the Farm Accountancy Data Network was considered for the period 2018–2020. The findings obtained show relative errors between 0.040 and 0.074 (showing good accuracy) and the importance of the total utilised agricultural area and the total output to predict the fertiliser costs.\",\"PeriodicalId\":45740,\"journal\":{\"name\":\"Open Agriculture\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Open Agriculture\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1515/opag-2022-0191\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open Agriculture","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/opag-2022-0191","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

摘要机器学习方法是人工智能方法的一部分,在不同的科学领域和人类生活维度中有多种应用。这些技术出现在数字转型的框架中,智能技术带来了相关贡献,例如提高了经济部门的效率。这对农业等部门应对气候变化带来的挑战尤为重要。另一方面,考虑到相关算法的复杂性,机器学习方法不容易实现。考虑到这一点,本研究的主要目的是通过人工神经网络分析,提出一个预测欧盟农场化肥成本的模型。在当前情况下,这一评估可能会为农民和决策者提供相关信息,其中关注的是确定减轻环境影响的战略,包括农业部门和各自使用化学资源的战略。为了实现这些目标,考虑了2018-2020年期间来自农场会计数据网络的欧盟农业地区统计信息。所获得的结果显示,相对误差在0.040到0.074之间(显示出良好的准确性),以及农业总利用面积和总产量对预测化肥成本的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks
Abstract Machine-learning methodologies are part of the artificial intelligence approaches with several applications in different fields of science and dimensions of human life. These techniques appear in the frameworks of the digital transition, where smart technologies bring relevant contributions, such as improving the efficiency of the economic sectors. This is particularly important for sectors such as agriculture to deal with the challenges created in the context of climate changes. On the other hand, machine-learning approaches are not easy to implement, considering the complexity of the algorithms associated. Taking this into account, the main objective of this research is to present a model to predict fertiliser costs in the European Union (EU) farms through artificial neural network analysis. This assessment may provide relevant information for farmers and policymakers in the current scenario where the concerns are to identify strategies to mitigate the environmental impacts, including those from the agricultural sector and the respective use of chemical resources. To achieve these objectives, statistical information for the EU agricultural regions from the Farm Accountancy Data Network was considered for the period 2018–2020. The findings obtained show relative errors between 0.040 and 0.074 (showing good accuracy) and the importance of the total utilised agricultural area and the total output to predict the fertiliser costs.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Open Agriculture
Open Agriculture AGRICULTURE, MULTIDISCIPLINARY-
CiteScore
3.80
自引率
4.30%
发文量
61
审稿时长
9 weeks
期刊介绍: Open Agriculture is an open access journal that publishes original articles reflecting the latest achievements on agro-ecology, soil science, plant science, horticulture, forestry, wood technology, zootechnics and veterinary medicine, entomology, aquaculture, hydrology, food science, agricultural economics, agricultural engineering, climate-based agriculture, amelioration, social sciences in agriculuture, smart farming technologies, farm management.
期刊最新文献
Effects of co-inoculation of indole-3-acetic acid- and ammonia-producing bacteria on plant growth and nutrition, soil elements, and the relationships of soil microbiomes with soil physicochemical parameters Supplementation of P-solubilizing purple nonsulfur bacteria, Rhodopseudomonas palustris improved soil fertility, P nutrient, growth, and yield of Cucumis melo L. Impact of nematode infestation in livestock production and the role of natural feed additives – A review Yield gap variation in rice cultivation in Indonesia The fate of probiotic species applied in intensive grow-out ponds in rearing water and intestinal tracts of white shrimp, Litopenaeus vannamei
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1