德国农业部门农场水平氮平衡和氮利用效率的评估

IF 6.1 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Agricultural Systems Pub Date : 2023-10-29 DOI:10.1016/j.agsy.2023.103796
Philipp Löw , Bernhard Osterburg
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引用次数: 0

摘要

为了确保粮食安全和粮食主权,减轻粮食生产的负面外部性,实现雄心勃勃的(国际)国家环境、气候和可持续性目标,必须优化氮(N)的利用。氮是一种必需的常量营养素。氮素利用效率(NUE)是评价农田氮素利用的适宜指标。本研究的目的是评估德国农业部门的氮素绩效,在基于农场核算数据的方法设计和估算氮素指标方面获得知识,估计外部性的作用和程度,并增加对农场特征与氮素绩效之间相互关系的理解,以支持政策制定者找到有针对性的氮素缓解措施。方法利用德国农场会计数据网络(FADN) 2016/17年至2018/19年5923个农场的数据,考虑农场层面的投入和产出参数以及外部性,计算了六种农场类型的平均农场水平氮素利用效率和氮素平衡值。我们还开发了两个解释模型,以确定所调查的N绩效指标与区域、社会经济和农场结构特征之间的相互关系。结果与结论奶牛场、养猪场和养殖场氮肥利用效率呈增加趋势,但各养殖场间差异较大,显示出氮肥利用的效率储备。牲畜养殖场降低了氮肥利用效率,并在很大程度上超过了氮肥盈余,以至于德国可持续发展战略中确定的2030年全国氮肥平衡的可持续发展目标可能会受到威胁。不同程度的外部化,即饲料进口和粪肥出口,可能会加深这一差距。多元回归分析显示,土壤肥力、作物选择和多样性、生产类型、经营利润和咨询服务等自变量与氮素绩效呈显著相关。因此,确定了减少氮浪费和提高氮肥利用效率的结构模式和策略。然而,不确定性的主要来源是由于缺乏FADN数据中的进口粪肥数量。意义氮素绩效的测定可以提高对农业-环境相互关系复杂性的认识,并支持决策者设计适当的政策来改善氮素管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Evaluation of nitrogen balances and nitrogen use efficiencies on farm level of the German agricultural sector

CONTEXT

Use of Nitrogen (N), an essential macronutrient, must be optimised in order to ensure food security and food sovereignty, mitigate negative externalities of food production and achieve ambitious (inter-)national environmental, climate and sustainability goals. Nitrogen use efficiency (NUE) is an appropriate indicator for assessing N utilisation on farms.

OBJECTIVE

The aim of the study was to evaluate N performance of the German agricultural sector, to generate knowledge gain regarding methodological design and estimating N indicators based on farm accounting data, to estimate the role and extent of externalities, and to increase understanding of interrelations between farm characteristics and N performance in order to support policymakers in finding targeted N mitigation measures.

METHODS

Using data from the German Farm Accountancy Data Network (FADN) covering 5923 farms between the years 2016/17 and 2018/19, we calculated mean farm-level NUE and N balance values for six farm types, considering input and output parameters on farm level as well as externalities. We also developed two explanatory models to identify interrelations between N performance indicators investigated, and regional, socio-economic and farm structural characteristics.

RESULTS AND CONCLUSIONS

The results indicated an increasing trend in NUE from dairy, to pig and poultry, and arable farms, but large variance within each farm type, indicating efficiency reserves in N use. Livestock farms undercut NUE and exceed N surpluses to such an extent that the sustainability goal for 2030 for the national N balance as defined in German Sustainable Development Strategy could be jeopardised. Different levels of externalisation, namely feed imports and manure exports, may deepen this gap. Multiple regression analysis revealed statistically significant interrelations between N performance and independent variables such as soil fertility, crop selection and diversity, production type, operating profit and consulting services received. Thereby, structural patterns and strategies in order to reduce N waste and increase NUE were identified. However, main source of uncertainty was due to the lack on imported manure amounts from FADN data.

SIGNIFICANCE

Determination of N performance can improve understanding the complexity of agri-environmental interrelations and support policymakers in designing appropriate policies to improve N management.

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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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