Sustainable intensification of arable agriculture:The role of Earth Observation in quantifying the agricultural landscape

By 2050, global food production must increase by 70% to meet the demands of a growing population with shifting food consumption patterns. Sustainable intensification has been suggested as a possible mechanism to meet this demand without significant detrimental impact to the environment. Appropriate monitoring techniques are required to ensure that attempts to sustainably intensify arable agriculture are successful. Current assessments rely on datasets with limited spatial and temporal resolution and coverage such as field data and farm surveys. Earth Observation (EO) data overcome limitations of resolution and coverage, and have the potential to make a significant contribution to sustainable intensification assessments. Despite the variety of established EO-based methods to assess multiple indicators of agricultural intensity (e.g. yield) and environmental quality (e.g. vegetation and ecosystem health), to date no one has attempted to combine these methods to provide an assessment of sustainable intensification. The aim of this thesis, therefore, is to demonstrate the feasibility of using EO to assess the sustainability of agricultural intensification. This is achieved by constructing two novel EO-based indicators of agricultural intensity and environmental quality, namely wheat yield and farmland bird richness. By combining these indicators, a novel performance feature space is created that can be used to assess the relative performance of arable areas. This thesis demonstrates that integrating EO data with in situ data allows assessments of agricultural performance to be made across broad spatial scales unobtainable with field data alone. This feature space can provide an assessment of the relative performance of individual arable areas, providing valuable information to identify best management practices in different areas and inform future management and policy decisions. The demonstration of this agricultural performance assessment method represents an important first step in the creation of an operational EO-based monitoring system to assess sustainable intensification, ensuring we are able to meet future food demands in an environmentally sustainable way

Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data

High resolution wheat yield mapping using Sentinel-2

Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate within-field wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10 m resolution using Sentinel-2 data (RMSE 0.66 t/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61 t/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22 t/ha, with a total crop production of approx. 289,000 t

Near-infrared digital hemispherical photography enables correction of plant area index for woody material during leaf-on conditions

Indirect optical measurement techniques enable efficient and non-destructive estimation of plant area index (PAI). However, because they cannot distinguish between foliage and other canopy elements, corrections are needed to determine leaf area index (LAI), which is typically the property of interest. In this study, we investigate near-infrared digital hemispherical photography (DHP) as a means of estimating and correcting for woody material. Using data collected at a deciduous broadleaf forest site, we show that near-infrared DHP could successfully estimate effective wood area index (WAIe) and wood area index (WAI) during leaf-on conditions, providing similar mean values (WAIe = 0.88, WAI = 1.53) to those determined from visible DHP during leaf-off conditions (WAIe = 0.87, WAI = 1.38). This information was used to correct estimates of effective PAI (PAIe) and PAI, enabling effective LAI (LAIe) and LAI to be derived with low RMSD (0.33 for LAIe and 0.76 for LAI), NRMSD (12% for LAIe and 19% for LAI), and bias (−0.01 for LAIe and −0.16 for LAI). Not correcting for woody material led to overestimation of LAIe by 31% on average and 46% in the worst observed case, and the degree of overestimation was further enlarged for LAI (42% on average and 61% in the worst observed case). In agreement with previous studies, the effects of clumping and woody area were found to be partly compensatory. On average, PAIe provided a reasonable approximation of LAI without correction, though overestimation of 52% and underestimation of 20% occurred at the lowest and highest LAI values, respectively. Compared to WAIe and WAI measurement using leaf-off visible DHP, near-infrared DHP offers two crucial advantages: i) data collection can be conducted at the same time as leaf-on PAIe and PAI measurements, and ii) it is likely that the approach could provide an indirect WAIe and WAI measurement option for evergreen species

Patterns and predictors of place of cancer death for the oldest old

BACKGROUND: Cancer patients increasingly are among older age groups, but to date little work has examined the trends in cancer among older people, particularly in relation to end of life care and death. This study describes the older population who die of cancer and the factors which may affect their place of death. METHODS: A Cross-sectional analysis of national data was performed. The study included all people aged 75 and over dying of cancer in England and Wales between 1995 and 1999. The population was divided into exclusive 5 year age cohorts, up to 100 years and over. Descriptive analysis explored demographic characteristics, cancer type and place of death. RESULTS: Between 1995 and 1999, 315,462 people aged 75 and over were registered as dying from cancer. The number who died increased each year slightly over the 5 year period (1.2%). In the 75–79 age group, 55 % were men, in those aged 100 and over this fell to 16%. On reaching their hundreds, the most common cause of death for men was malignancies of the genital organs; and for women it was breast cancer. The most frequent place of death for women in their hundreds was the care home; for men it was hospitals. Those dying from lymphatic and haematopoietic malignancies were most likely to die in hospitals, those with head and neck malignancies in hospices and breast cancer patients in a care home. CONCLUSION: The finding of rising proportions of cancer deaths in institutions with increasing age suggests a need to ensure that appropriate high quality care is available to this growing section of the population

An adaptable integrated modelling platform to support rapidly evolving agricultural and environmental policy

The utility of integrated models for informing policy has been criticised due to limited stakeholder engagement, model opaqueness, inadequate transparency in assumptions, lack of model flexibility and lack of communication of uncertainty that, together, lead to a lack of trust in model outputs. We address these criticisms by presenting the ERAMMP Integrated Modelling Platform (IMP), developed to support the design of new “business-critical” policies focused on agriculture, land-use and natural resource management. We demonstrate how the long-term (>5 years), iterative, two-way and continuously evolving participatory process led to the co-creation of the IMP with government, building trust and understanding in a complex integrated model. This is supported by a customisable modelling framework that is sufficiently flexible to adapt to changing policy needs in near real-time. We discuss how these attributes have facilitated cultural change within the Welsh Government where the IMP is being actively used to explore, test and iterate policy ideas prior to final policy design and implementation

Qualitative Impact Assessment of Land Management Interventions on Ecosystem Services (“QEIA”). Report-1: Executive Summary: QEIA Evidence Review & Integrated Assessment

The focus of this project was to provide an expert-led, rapid qualitative assessment of land management interventions on Ecosystem Services (ES) proposed for inclusion in Environmental Land Management (ELM) schemes. This involved a review of the current evidence base for 741 land management actions on 33 Ecosystem Services and 53 Ecosystem Service indicators by ten teams involving 45 experts drawn from the independent research community in a consistent series of Evidence Reviews covering the broad topics of: • Air quality • Greenhouse gas emissions • Soils • Water management • Biodiversity: croplands • Biodiversity: improved grassland • Biodiversity: semi-natural habitats • Biodiversity: integrated systems-based actions • Carbon sequestration • Cultural services (including recreation, geodiversity and regulatory services). It should be noted that this piece of work is just one element of the wider underpinning work Defra has commissioned to support the development of the ELM schemes

Qualitative impact assessment of land management interventions on Ecosystem Services (‘QEIA’). Report-2: Integrated Assessment

The focus of this project was to provide an expert-led, rapid qualitative assessment of land management interventions on Ecosystem Services (ES) proposed for inclusion in Environmental Land Management (ELM) schemes. This involved a review of the current evidence base for 741 land management actions on 33 Ecosystem Services and 53 Ecosystem Service indicators by ten expert teams drawn from the independent research community in a consistent series of ten Evidence Reviews covering the broad topics of; • Air quality • Greenhouse gas emissions • Soils • Water management • Biodiversity: croplands • Biodiversity: improved grassland • Biodiversity: semi-natural habitats • Biodiversity: integrated systems-based actions • Carbon sequestration • Cultural services (including recreation, geodiversity and regulatory services) These reviews were undertaken rapidly at Defra’s request by ten teams involving 45 experts who together captured more than 2,400 individual sources of evidence. This was followed by the Integrated Assessment (IA) reported here to provide a more accessible summary of these evidence reviews with a focus on capturing the actions with the greatest potential magnitude of change for the intended ES, and their potential co-benefits and trade-offs for the other ES

Monitoring the sustainable intensification of arable agriculture: the potential role of Earth observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data