Effects of landscape simplicity on crop yield: A reanalysis of a global database

Ecological theory on diversity suggests that agriculture requires sufficient biodiversity, ecological function, and critical ecosystem services to remain sustainable and resilient. As such, research related to the effect of ecosystem services and diversity on crop yields has increased significantly in the past decade. One such study by Dainese and colleagues that presented a global synthesis of a compiled database of 1,475 crop experiments related to pollination and pest control ecosystem services and crop yields quickly garnered attention in the literature with more than 540 citations since its publication in 2019. Given the strong influence of this study on the research on diversity and agricultural production, we conduct a reanalysis on the publicly available dataset from the global synthesis study to test the robustness of findings to modeling approach and assumptions. In our reanalysis we apply ordinary least squares regression methods rather than Bayesian path analysis to the same data to examine the robustness of observed field-scale landscape diversity-ecosystem services-crop yield relationships. The result of our reanalysis supports the findings of Dainese and colleagues, illustrating the robustness of findings that suggest that increasing landscape simplicity is associated with lower rates of pollination and pest control ecosystem service provisioning and lower crop yields. However, our analyses also suggest that provisioning of pollination and pest control services account for only a small fraction of the total effect of landscape simplicity on crop yields. Furthermore, we find that management and soil health may mediate the effects of landscape simplicity on ecosystem services and crop yields. While our results complement previous findings for landscape simplicity and ecosystem services, they also indicate that above and below ground ecosystem services are not mutually exclusive but concurrently contribute to support crop production in agriculture.

Defining features of diverse and productive agricultural systems: An archetype analysis of U.S. agricultural counties

Prior research suggests that greater spatial diversity in crops and land use is associated with higher crop yields and improved ecosystem function. However, what leads to the emergence of agricultural systems that meet both productivity and ecological health goals remains an open question. Understanding the factors that differentiate these places from other agricultural systems is key to understanding the mechanisms, pathways, consequences, and constraints to employing diversification as a tool for increasing agricultural sustainability. In this study, we employ archetype analysis to examine the factors uniquely associated with the conjoint existence of high crop diversity and high crop productivity. We identify five agricultural system classes that represent a range of diversity and productivity combinations using k-means cluster analysis then use random forests analysis to identify factors that strongly explain the differences between the classes—describing different agricultural production regimes. Our exploratory analysis of the difference in agricultural system factors across classes suggests (1) crop diversity and its preconditions are associated with the highest yields, (2) biophysical conditions bound diversity-productivity realities, (3) productivity comes at a petrochemical cost, and that (4) crop rotations are a key diversification strategy. Overall, our results suggest that despite clear biophysical constraints on transitions to high diversity—high productivity systems the role of actionable factors on crop production regimes is stronger, providing reason to be hopeful about transitions to agricultural production regimes fit for new climate realities.

Integrating Social-Ecological and Political-Ecological Models of Agrobiodiversity With Nutrient Management of Keystone Food Spaces to Support SDG 2

Agrobiodiversity—the biodiversity of food, agriculture, and land use—is essential to U.N. Sustainable Development Goal 2 by providing crucial food and nutritional quality of diets combined with strengthening agroecological sustainability. Focusing on the agrobiodiversity nexus to SDG 2, the current study utilized the interdisciplinary Agrobiodiversity Knowledge Framework (AKF), household-level surveys, and biodiversity sampling of crop fields and home gardens in a case study in Huánuco, Peru, in 2017. Statistical measures estimated agrobiodiversity of crop fields (n = 268 households) and home gardens (n=159 households) based on species richness (3.7 and 10.2 species/household, in fields and gardens, respectively) and evenness (Shannon diversity index; 0.70 and 1.83 in fields and gardens, respectively). Robust results of Poisson and OLS regression models identified several AKF-guided determinants of agrobiodiversity. Estimated species richness and evenness were significantly associated with 12 social-ecological and political-ecological factors from the four AKF thematic axes: farm characteristics and agroecology; diets and nutrition; markets, governance and sociocultural practices; and global change. This study's AKF approach, agrobiodiversity modeling, agroecological characterization, and field-based case study advanced a series of useful research insights, comparisons, and conceptual innovations to address SDG 2. Characterization of nutrient management through soil- and plant-focused cultural practices and livelihood roles distinguished the “keystone agrobiodiversity-and-food space” of multi-species maize fields (maizales) identified in AKF regression and characterization results. This key space furnished crucial food-nutrition and agroecological benefits that can be expanded by overcoming identified barriers. AKF-guided models incorporating key agrobiodiversity-and-food spaces and ecological nutrient management are needed to strengthen SDG 2 strategies.