Spatial life cycle impact assessment data for catchment scale acidification and eutrophication potentials

The dataset presented here represents raw and analysed data for a catchment scale life cycle assessment of arable and livestock farming in the UK. The general hypothesis was that implementing on-farm interventions would reduce impacts to water quality in the study site. Input data were collected via a large-scale survey of commercial farmers in the East of England and subsequently collated into two separate farm typologies (arable and livestock). Once the input data were collated into a life cycle inventory, acidification and eutrophication potentials were calculated for each typology, catchment, and a range of scenarios which explore both individual and combined mitigation strategies at the farm-level. Each intervention (or combination of interventions) were compared with baseline farming activities (i.e., production without any consideration of mitigation) to determine how optimised management could reduce impacts to water quality. The arable interventions considered were: AA (All interventions); AB (Fertiliser); AC (Water management); AD (Machinery); AE (Zero tillage); AF (Cover crop). The livestock interventions considered were: LA (All interventions); LB (Fertiliser); LC (Water management); LD (Machinery); LE (Livestock management). Whilst Farm ID numbers need to be anonymised to protect farmers' identities, 1-22 represent arable farm typologies in the study site whilst IDs 23 and 24 represent median and mean arable typologies; IDs 25-37, on the other hand, represent livestock farm typologies in the study site whilst IDs 38 and 39 represent median and mean livestock typologies. The data underpinning the relevant study demonstrates that managing farm-based machinery optimally can make notable differences (~10% improvement) to water quality in the study site. In addition to the impact assessment dataset, we present five farmer's responses to standardised survey, as well as proof of consent, as examples which allowed the generation of such a geographically wide analysis. Please note that there were hundreds more responses covering the entirety of England, but we are unable to provide them all due to confidentially and participant protection clauses.本数据集收录了英国流域尺度下耕地与畜牧业生命周期评估的原始数据与经分析处理后的数据。本研究的核心假设为：实施农场端干预措施可降低研究区域内的水质影响。研究通过针对英格兰东部商业农户的大规模调研收集原始输入数据，并将其整理为两套独立的农场分类体系（耕地类与畜牧类）。将输入数据整理为生命周期清单后，研究针对每套分类体系、各流域以及一系列模拟农场端单一或组合型减排策略的场景，分别计算了酸化潜势与富营养化潜势。将每项（或组合）干预措施与基线农业生产活动（即未考虑任何减排措施的生产模式）进行对比，以此明确优化管理如何降低水质影响。本次研究涉及的耕地类干预措施包括：AA（全部干预措施）、AB（肥料管理）、AC（水资源管理）、AD（农机管理）、AE（免耕）、AF（覆盖作物）；畜牧类干预措施包括：LA（全部干预措施）、LB（肥料管理）、LC（水资源管理）、LD（农机管理）、LE（畜牧管理）。为保护农户身份信息，农场ID需进行匿名化处理：其中ID 1至22代表研究区域内的耕地类农场分类体系，ID 23与24分别代表耕地类分类体系的中位数与均值类型；ID 25至37代表研究区域内的畜牧类农场分类体系，ID 38与39分别代表畜牧类分类体系的中位数与均值类型。支撑本研究的数据显示，对农场农机进行优化管理可对研究区域的水质产生显著改善效果，提升幅度约为10%。除影响评估数据集外，本研究还附上了5份农户针对标准化调研的回复样本，以及知情同意书证明，以此作为支撑本次大范围地理区域分析的示例依据。请注意，本次调研覆盖全英格兰的有效回复多达数百份，但受保密条款与参与者保护条款限制，我们无法公开全部回复内容。