Abstract

Abstract Sustainable agriculture aims to produce sufficient food while minimizing environmental damage. To achieve this, we need to understand the role of agricultural landscapes in providing diverse ecosystem services and how these affect crop production and resilience, that is, maintaining yields despite environmental perturbation. We used 10 years of English wheat yield data to derive three metrics of resilience (relative yield across the time series, yield stability around a moving average and resistance to an extreme weather event) at 10 km × 10 km resolution. We used remotely sensed maps to calculate measures of landscape structure, including composition (proportions of different land cover types) and configuration (metrics of connectivity and proximity), known to affect ecosystem service delivery (e.g. control of pests by beneficial invertebrates). We then used an information‐theoretic approach to identify the best‐fitting combination of landscape structure predictors for each resilience metric, using a potential yield model to account for the effects of climate and soils. Relative yield showed a strongly positive relationship with the area of arable land. For yield stability, this relationship was evident but alongside other landscape structure variables in the best‐fitting model. No relationship with arable land was evident for resistance. Yield stability showed a strongly positive effect of proximity to semi‐natural habitats. For resistance, the best‐fitting model included positive relationships with the cover of semi‐natural habitats and proximity to semi‐natural grasslands. Synthesis and applications. Landscapes with the highest relative wheat yields did not show the highest yield stability or resistance to extreme events. As resilience metrics were derived from shorter portions of the time series, the importance of semi‐natural habitats compared to arable land increased. This is probably driven by the complex interplay between landscape structure, agricultural management and ecosystem services. These results demonstrate that measuring relative yield over time may be insufficient to capture the full effect that non‐arable components of the landscape, and the ecosystem services they deliver, have on other aspects of resilience, and that there are clear trade‐offs in managing agricultural landscapes to maximize different aspects of crop yield resilience.

Highlights

  • Global food systems are under pressure to produce sufficient food for a growing human population (Godfray et al, 2010)

  • As resilience metrics were derived from shorter portions of the time series, the importance of semi-natural habitats compared to arable land increased

  • Our results show that wheat yield resilience, as measured by three different metrics, was influenced by landscape structure

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Summary

| INTRODUCTION

Global food systems are under pressure to produce sufficient food for a growing human population (Godfray et al, 2010). Sustainable intensification aims to increase agricultural productivity, while maintaining or bolstering biodiversity (Bommarco, Kleijn, & Potts, 2013; Garnett et al, 2013; Kleijn et al, 2019) This approach has been driven in part by increasing awareness that biodiversity provides vital ecosystem services which maintain the viability of agricultural systems (Bommarco et al, 2013), including crop pollination and natural pest control (Kremen & Chaplin-Kramer, 2007; Naylor & Ehrlich, 1997). The need to identify and develop resilient cropping systems has been embraced in research (Altieri, Nicholls, Henao, & Lana, 2015; Bullock et al, 2017) and policy (Defra, 2018a), but the question of how landscapes and the ecosystem services they deliver affect the resilience of agricultural systems remains a key knowledge gap preventing the widespread uptake of sustainable intensification (Kleijn et al, 2019). Metrics relating to different aspects of resilience would differ in the strength of these relationships and the relative importance of landscape composition and configuration

| MATERIALS AND METHODS
| DISCUSSION
| CONCLUSIONS
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