Managing agricultural landscapes to support biodiversity conservation requires profound structural changes worldwide. Often, discussions are centered on management at the field level. However, a wide and growing body of evidence calls for zooming out and targeting agricultural policies, research, and interventions at the landscape level to halt and reverse the decline in biodiversity, increase biodiversity-mediated ecosystem services in agricultural landscapes, and improve the resilience and adaptability of these ecosystems. We conducted the most comprehensive assessment to date on landscape complexity effects on nondomesticated terrestrial biodiversity through a meta-analysis of 1,134 effect sizes from 157 peer-reviewed articles. Increasing landscape complexity through changes in composition, configuration, or heterogeneity significatively and positively affects biodiversity. More complex landscapes host more biodiversity (richness, abundance, and evenness) with potential benefits to sustainable agricultural production and conservation, and effects are likely underestimated. The few articles that assessed the combined contribution of linear (e.g., hedgerows) and areal (e.g., woodlots) elements resulted in a near-doubling of the effect sizes (i.e., biodiversity level) compared to the dominant number of studies measuring these elements separately. Similarly, positive effects on biodiversity are stronger in articles monitoring biodiversity for at least 2 y compared to the dominant 1-y monitoring efforts. Besides, positive and stronger effects exist when monitoring occurs in nonoverlapping landscapes, highlighting the need for long-term and robustly designed monitoring efforts. Living in harmony with nature will require shifting paradigms toward valuing and promoting multifunctional agriculture at the farm and landscape levels with a research agenda that untangles complex agricultural landscapes’ contributions to people and nature under current and future conditions.

Trade-off analysis (TOA) is central to policy and decision-making aimed at promoting sustainable agricultural landscapes. Yet, a generic methodological framework to assess trade-offs in agriculture is absent, largely due to the wide range of research disciplines and objectives for which TOA is used. In this study, we systematically reviewed 119 studies that have implemented TOAs in landscapes and regions dominated by agricultural systems around the world. Our results highlight that TOAs tend to be unbalanced, with a strong emphasis on productivity rather than environmental and socio-cultural services. TOAs have mostly been performed at farm or regional scales, rarely considering multiple spatial scales simultaneously. Mostly, TOAs fail to include stakeholders at study development stage, disregard recommendation uncertainty due to outcome variability and overlook risks associated with the TOA outcomes. Increased attention to these aspects is critical for TOAs to guide agricultural landscapes towards sustainability.

Ecosystem service-support tools are commonly used to guide natural resource management. Often, empirically based models are preferred due to low data requirements, simplicity and clarity. Yet, uncertainty produced by local context or parameter estimation remains poorly quantified and documented. We assessed model uncertainty of the Revised Universal Soil Loss Equation – RUSLE developed mainly from US data. RUSLE is the most commonly applied model to assess watershed-level soil loss. We performed a global sensitivity analysis (GSA) on RUSLE with four dissimilar datasets to understand uncertainty and to provide recommendations for data collection and model parameterization. The datasets cover varying spatial levels (plot, watershed and continental) and environmental conditions (temperate and tropical). We found cover management and topography create the most uncertainty regardless of environmental conditions or data parameterization techniques. The importance of other RUSLE factors varies across contexts. We argue that model uncertainty could be reduced through better parameterization of cover management and topography factors while avoiding severe soil losses by targeting soil conservation practices in areas where both factors interact and enhance soil loss. We recommend incorporating GSA to assess empirical models’ uncertainty, to guide model parameterization and to target soil conservation efforts.

With the Convention on Biological Diversity conference (COP15), United Nations Climate Change Conference (COP26), and United Nations Food Systems Summit, 2021 is a pivotal year for transitioning towards sustainable food systems. Diversified farming systems are key to more sustainable food production. Here we present a global dataset documenting outcomes of diversified farming practices for biodiversity and yields compiled following best standards for systematic review of primary studies and specifically designed for use in meta-analysis. The dataset includes 4076 comparisons of biodiversity outcomes and 1214 of yield in diversified farming systems compared to one of two reference systems. It contains evidence from 48 countries of effects on species from 33 taxonomic orders (spanning insects, plants, birds, mammals, eukaryotes, annelids, fungi, and bacteria) of diversified farming systems producing annual or perennial crops across 12 commodity groups. The dataset presented provides a resource for researchers and practitioners to easily access information on where diversified farming systems effectively contribute to biodiversity and food production outcomes.