Managing Risks to Improve the Resilience of Arable Farming in the East of England

SummaryWith the withdrawal of the UK from the European Union and increasing pressures from climate change, English arable farming resilience is in a fragile position. Most Brexit impact assessments have focused on quantitative analysis, however here we take a qualitative approach to assess how future trade agreements could impact the resilience of the UK arable farming system. We discuss the main strategies that are currently taken by English arable farmers to improve resilience using evidence from a large‐scale survey in the East of England. Using information from a multi‐stakeholder workshop, we look at arable farming resilience in three forms characteristic of the farming system; namely, robustness, adaptability and transformability and how these relate to and are potentially influenced by three different Brexit trade scenarios. Stakeholders’ recommendations suggest that a ‘hard’ no‐deal scenario will require policies for social protection of farmers in more vulnerable rural areas, while in a ‘softer’ scenario a ‘public money for public goods’ policy could be implemented effectively by learning from previous environmental schemes. Nevertheless, resilience can be enhanced only by addressing structural and policy issues, such as generational renewal, advice and extension, tenancy duration limits and smarter PPP regulations, regardless of what post‐Brexit deal with the EU finally emerges.

Abstract. One direct measurement and two indirect estimates suggest that 35–40 kg nitrogen per hectare are deposited on arable land from the atmosphere each year in the south and east of England. This could contribute markedly to nitrate leaching and soil acidification. It may also change the flora and fauna of ‘natural’ ecosystems, as such amounts are likely to exceed the critical load.

The development of forward scenarios is a useful method of envisaging the environmental implications of potential changes in land use, as a tool for policy development. In this paper, a spatially explicit case study is used to provide insight into the environmental impacts of Common Agricultural Policy reform on Skylark Alauda arvensis, a species which is widespread on arable farmland, breeds in crops and has declined in recent decades. A generalized linear mixed model was used to estimate Skylark breeding population densities in different crops, using survey data collected from farms in the east of England, supplemented by the literature. Model outputs were then used to predict Skylark densities in an East Anglian Joint Character Area dominated by arable cropping. Predicted densities were mapped at field level using GIS, based on actual cropping derived from Integrated Administration and Control System data collected for the administration of subsidy payments. Three future scenarios were then created, based on expert opinion of potential changes in cropping over the next 5 years, and potential changes in Skylark density mapped on the basis of the predicted changes in cropping patterns. Overall, Skylark densities were predicted to decrease on average by 11–14% under ‘market‐led’ (increasing wheat and oilseed rape, reduced set‐aside) and ‘energy crop’ (5% area under short rotation coppice) scenarios, but remained virtually unchanged under an ‘environment‐led’ (diverse cropping) scenario. The ‘market‐led’ scenario is closest to short‐term agricultural trajectories, but wider cultivation of biomass energy crops as modelled under the ‘energy crop’ scenario could occur in the medium term if energy policies are favourable. Appropriate mitigation strategies therefore need to be implemented if a continued decline in the Skylark population on lowland arable farmland is to be averted. The results provide a readily accessible visualization of the potential impacts of land‐use change for policy‐makers; similar techniques could be applied to visualize effects of changes arising through other drivers, including climate change.

Nitrate and water supplies in the United Kingdom

Based on recent spatially aggregated June Agriculture Survey data and site-specific environmental data, information from common farm types in the East of England was sourced and collated. These data were subsequently used as key inputs to a mechanistic environmental modelling tool, the Catchment Systems Model, which predicts environmental damage arising from various farm types and their management strategies. The Catchment Systems Model, which utilises real-world agricultural productivity data (samples and appropriate consent provided within the Mendeley Data repository) is designed to assess not only losses to nature such as nitrate, phosphate, sediment and ammonia, but also to predict how on-farm intervention strategies may affect environmental performance. The data reported within this article provides readers with a detailed inventory of inputs such as fertiliser, outputs including nutrient losses, and impacts to nature for 1782 different scenarios which cover both arable and livestock farming systems. These 1782 scenarios include baseline (i.e., no interventions), business-as-usual (i.e., interventions already implemented in the study area) and optimised (i.e., best-case scenarios) data. Further, using the life cycle assessment (LCA) methodology, the dataset reports acidification and eutrophication potentials for each scenario under two (eutrophication) and three (acidification) impact assessments to offer an insight into the importance of impact assessment choice. Finally, the dataset also provides its readers with percentage changes from baseline to best-case scenario for each farm type.

Agroforestry integrates woody perennials with arable crops, livestock, or fodder in the same piece of land, promoting the more efficient utilization of resources as compared to monocropping via the structural and functional diversification of components. This integration of trees provides various soil-related ecological services such as fertility enhancements and improvements in soil physical, biological, and chemical properties, along with food, wood, and fodder. By providing a particular habitat, refugia for epigenic organisms, microclimate heterogeneity, buffering action, soil moisture, and humidity, agroforestry can enhance biodiversity more than monocropping. Various studies confirmed the internal restoration potential of agroforestry. Agroforestry reduces runoff, intercepts rainfall, and binds soil particles together, helping in erosion control. This trade-off between various non-cash ecological services and crop production is not a serious constraint in the integration of trees on the farmland and also provides other important co-benefits for practitioners. Tree-based systems increase livelihoods, yields, and resilience in agriculture, thereby ensuring nutrition and food security. Agroforestry can be a cost-effective and climate-smart farming practice, which will help to cope with the climate-related extremities of dryland areas cultivated by smallholders through diversifying food, improving and protecting soil, and reducing wind erosion. This review highlighted the role of agroforestry in soil improvements, microclimate amelioration, and improvements in productivity through agroforestry, particularly in semi-arid and degraded areas under careful consideration of management practices.

We utilise a volunteer survey recording roadkills between 2001 and 2011 to examine the factors affecting hedgehog Erinaceus europaeus abundance and decline. Hedgehogs were most abundant in the North and East of England and in Scotland, regions characterised by low badger numbers. Hedgehogs selected arable land and urban areas relative to their availability. Badger Meles meles and fox Vulpes vulpes abundance were negatively associated with hedgehog abundance at the 10 km2 scale. At the county level, foxes were positively associated with hedgehog numbers and badgers negatively associated. The mechanism behind the relationships between hedgehogs and badgers and foxes merits further investigation.

The response to the increase in average farm size in England in recent decades, particularly by those with a concern for the rural landscape, has been to pose the question: how much bigger can farms become?This paper contributes to the discussion of this question by examining how different combinations of land, labour and capital and/or improvements in these resources can lead to the formation of large farms. Two models are constructed representing different cropping systems and Linear Programming is employed to test for the effect of variation in the quantity and characteristics of labour and machinery. The data used in the models were derived from random sampling among the large, arable farms of the South and East of England.‘Big scale farming in England still remains to be tried’. (Orwin, 1930).

Feedstocks from lignocellulosic biomass (LCB) include crop residues and dedicated perennial biomass crops. The latter are often considered superior in terms of climate change mitigation potential. Uncertainty remains over their availability as feedstocks for biomass provision and the net greenhouse gas emissions (GHG) during crop production. Our objective was to assess the optimal land allocation to wheat and Miscanthus in a specific case study located in England, to increase biomass availability, improve the carbon balance (and reduce the consequent GHG emissions), and minimally constrain grain production losses from wheat. Using soil and climate variables for a catchment in east England, biomass yields and direct nitrogen emissions were simulated with validated process‐based models. A ‘Field to up‐stream factory gate’ life‐cycle assessment was conducted to estimate indirect management‐related GHG emissions. Results show that feedstock supply from wheat straw can be supplemented beneficially with LCB from Miscanthus grown on selected low‐quality soils. In our study, 8% of the less productive arable land area was dedicated to Miscanthus, increasing total LCB provision by about 150%, with a 52% reduction in GHG emission per ton LCB delivered and only a minor effect on wheat grain production (−3%). In conclusion, even without considering the likely carbon sequestration in impoverished soils, agriculture should embrace the opportunities to provide the bioeconomy with LCB from dedicated, perennial crops. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd

The aim of the study is to indicate perceived resilience of Polish farms during the COVID-19 pandemic and post-pandemic crisis. Hence, one of our research question is: do farmers involved in short food supply chains perceive their resilience to external shocks higher than others? Our study is embedded in the resilience theory and framework with three resilience capacities: robustness, adaptability and transformability. We interviewed (using semi-structured questionnaire) a group of 199 small- and medium-scale farms in Poland. We used statistical methods to determine the differences between farms involved in SFSCs (SFSC-farms) and not involved (non-SFSC-farms). The analysis reveals a higher total perceived resilience of farms involved in short food supply chains, especially in the case of farms which sell 15–50% of their products using SFSC, hence farms combining extensively short and long chains in comparison with non-SFSC-farms. When considering the three specific resilience capacities, farms participating in SFSCs noted higher perceived adaptability and transformability. SFSC-farms represent (on average) smaller arable land and lower value of farm (assets), but plainly higher land productivity. Moreover, they have higher investment-to-output ratio and are less dependent on subsidies. This indicates that small and medium-sized family farms can develop well as SFSCs participants while achieving relatively high perceived resilience, even in turbulent periods.

The Nigerian agricultural production system is predominantly rain-fed. Over reliance of the agricultural production system on rainfall is an indication of vulnerability to climate change and variability of rainfall. On the other hand, climate resilience agriculture could ensure sustainable agricultural production and food security (including, availability, access, and stability) for Nigeria, being the most populous and largest economy in Africa. We therefore, investigated the impact of both rainfall variability and irrigation on agricultural production with a view to informing appropriate agricultural policy for adapting to climate change in Nigeria. Time series data spanning 43 years were used for the analyses on degree of variability and impact. The generalized methods of moment (GMM) econometric analytical technique was employed to quantify the impact of rainfall (in millilitres per annual) and irrigation (in proportion of arable land) on aggregate agricultural production index. We found evidence for the impact of irrigation as a tool for adapting to climate change, and for promoting climate-resilient agriculture in Nigeria. Irrigation had positive and significant impact on aggregate agricultural production. The findings suggest the need for the minimization of the impact of climate-induced agricultural production risks through climate- resilient agriculture which would involve expansion of arable land area under irrigation.

Excessive sediment loss degrades freshwater quality and is prone to further elevation and variable source contributions due to the combined effect of extreme rainfall and differing land uses. To quantify erosion and sediment source responses across scales, this study integrated work at both field and catchment scale for two hydrologically contrasting winters (2018–19 and 2019–20). Sediment load was estimated at the field scale (grassland-arable conversion system). Sediment source apportionment work was undertaken at the catchment scale (4.5 km2) and used alkanes, and both free and bound fatty acid carbon isotope signatures as diagnostic fingerprints to distinguish sediment sources: arable, pasture, woodland and stream banks. Sediment source apportionment based on bound fatty acids revealed a substantial shift in contributions, from stream banks dominating (70 ± 5%) in winter 2018–19, to arable land dominating (52 ± 7%) in the extreme wet winter 2019–20. Increases in sediment contributions from arable (∼3.9 times) and pasture (∼2.4 times) land at the catchment outlet during the winter 2019–20 were consistent with elevated sediment losses monitored at the field scale which indicated that low-magnitude high frequency rainfall alone increased sediment loss even from pasture by 350%. In contrast, carbon isotope signatures of alkanes and free fatty acids consistently estimated stream banks as a dominant source (i.e., ∼36% and ∼70% respectively) for both winters regardless of prolonged rainfall in winter 2019–20. Beyond quantifying the shifts in field scale sediment load and catchment scale sediment sources due to the changes in rainfall patterns, our results demonstrate valuable insight into how the fate of biotracers in soil and sediment manifests in the δ13C values of homologues and, in turn, their role in information gain for estimating sediment source contributions. Discrepancies in the estimated sediment source contributions using different biotracers indicate that without a careful appreciation of their biogeochemical limitations, erroneous interpretation of sediment source contributions can undermine management strategies for delivering more sustainable and resilient agriculture.

Modeling of natural and social capital on farms: Toward useable integration

Soil quality is an important determinant of agricultural productivity, farm resilience and environmental quality. Despite its importance, the incorporation of sustainable soil management in economic models is lacking. This study approaches farmers as decision makers on soil management. Sustainable soil management may be an investment that goes at the expense of short-term returns but increases future soil quality. Hence, the key problem is economic: establishing long-term sustainable soil management at a minimized loss of income. In this study, we define the Economic Value of Land (EVL) as the cumulative returns of a piece of land over a period in time. Maximum long-term EVL is obtained if a soil’s potential is maximally utilized in a sustainable way. From this follows that the Economic Value of Sustainable soil Management (EVSM) is defined as the difference between a sustainable and unsustainable EVL. To acquire a fundamental understanding of EVSM, agronomic and technical factors must be integrated with economics. Production management, the complete set of physical and non-physical inputs is the primary determinant of future soil quality and hence EVL. Maximizing EVL first requires a fundamental understanding of soil quality management: What are the properties of soil quality and how are these influenced by crop production? Subsequently, production management has to be organized in such a way EVL is maximized. This study provides an overview of soil quality management and crop production management linked to economics. The framework provides a qualitative blueprint for bio-economic modelling and a basis for policies to enhance sustainable soil management.

CONTEXTEnhancing farm resilience has become a key policy objective of the EU's Common Agricultural Policy (CAP) to help farmers deal with numerous interrelated economic, environmental, social, and institutional shocks and stresses. A central theme in resilience thinking is the role of the unknown, implying that knowledge is incomplete and that change, uncertainty, and surprise are inevitable. Important strategies to enhance resilience are exploiting social capital and learning as these contribute to improved knowledge to prepare farmers for change. OBJECTIVEThis paper explores how social capital and learning relate to farm resilience along the dimensions of robustness, adaptation, and transformation. METHODSWe study the resilience of Dutch arable farmers from the Veenkoloniën and Oldambt using a combination of four methods. Qualitative data from semi-structured farmer interviews, focus groups, and expert interviews are combined with quantitative data from farmer surveys. The qualitative data are analysed using thematic coding. Non-parametric tests are used to analyse the quantitative data. Based on methodological triangulation, we mostly find convergence in our qualitative and quantitative datasets increasing the validity of our findings. RESULTS AND CONCLUSIONSThe results reveal that social capital and learning help farmers to adapt and are, in certain cases, also related to robustness and transformations. Robust farmers often learned by exploiting farmers' informal social networks, primarily relying on bonding social capital to acquire knowledge about agriculture or develop financial skills. Farmers undertaking adaptation are characterised by bonding and bridging social capital obtained by formal and informal networks, are early adopters of innovation, and have high self-efficacy. Combinations of bridging and linking social capital from formal networks could foster farmers to learn new ideas and critically reflect on current farm business models. These learning outcomes relate to farm transformations. SIGNIFICANCEThis study provides some early results on the dynamic relationship between farmers' social capital and learning and how these concepts are associated with resilience. Our findings are relevant for agricultural policy makers, as we provide recommendations on how social capital and learning have some potential to facilitate farm adaptation and transformation and improve information exchange in Agricultural Knowledge and Innovation Systems (AKIS).