Organic farming provides reliable environmental benefits but increases variability in crop yields: a global meta-analysis

To promote food security and sustainability, ecologically intensive farming systems should reliably produce adequate yields of high-quality food, enhance the environment, be profitable, and promote social wellbeing. Yet, while many studies address the mean effects of ecologically intensive farming systems on sustainability metrics, few have considered variability. This represents a knowledge gap because producers depend on reliable provisioning of yields, profits, and environmental services to enhance the sustainability of their production systems over time. Further, stable crop yields are necessary to ensure reliable access to nutritious foods. Here we address this by conducting a global meta-analysis to assess the average magnitude and variability of seven sustainability metrics in organic compared to conventional systems. Specifically, we explored the effects of these systems on (i) biotic abundance, (ii) biotic richness, (iii) soil organic carbon, (iv) soil carbon stocks, (v) crop yield, (vi) total production costs, and (vii) profitability. Organic farms promoted biotic abundance, biotic richness, soil carbon, and profitability, but conventional farms produced higher yields. Compared to conventional farms, organic farms had lower variability in abundance and richness but greater yield variability. Organic farms thus provided a “win-win” (high means and low variability) for environmental sustainability, while conventional farms provided a “win-win” for production by promoting high crop yields with low variability. Despite lower yields, and greater yield variability, organic systems had similar costs to conventional systems and were more profitable due to organic premiums. Our results suggest certification guidelines for organic farms successfully promote reliable environmental benefits, but greater reliance on ecological processes may reduce predictability of crop production

The DisHuman child

In this paper we consider the relationship between the human and disability; with specific focus on the lives of disabled children and young people. We begin with an analysis of the close relationship between ‘the disabled’ and ‘the freak’. We demonstrate that the historical markings of disability as object of curiosity and register of fear serve to render disabled children as non-human and monstrous. We then consider how the human has been constituted, particularly in the periods of modernity and the rise of capitalism, reliant upon the naming of disability as antithetical to all that counts as human. In order to find a place for disabled children in a social and cultural context that has historically denied their humanity and cast them as monstrous others, we develop the theoretical notion of the DisHuman: a bifurcated complex that allows us recognise their humanity whilst also celebrating the ways in which disabled children reframe what it means to be human. We suggest that the lives of disabled children and young people demand us to think in ways that affirm the inherent humanness in their lives but also allow us to consider their disruptive potential: this is our DisHuman child. We draw on our research projects to explore three sites where the DisHuman child emerges in moments where sameness and difference, monstrosity/disability and humanity are invoked simultaneously. We explore three locations – (i) DisDevelopment; (ii) DisFamily and (iii) DisSexuality – illuminating the ways in which the DisHuman child seeks nuanced, politicized and complicating forms of humanity

The Underestimation Of Egocentric Distance: Evidence From Frontal Matching Tasks

There is controversy over the existence, nature, and cause of error in egocentric distance judgments. One proposal is that the systematic biases often found in explicit judgments of egocentric distance along the ground may be related to recently observed biases in the perceived declination of gaze (Durgin & Li, Attention, Perception, & Psychophysics, in press), To measure perceived egocentric distance nonverbally, observers in a field were asked to position themselves so that their distance from one of two experimenters was equal to the frontal distance between the experimenters. Observers placed themselves too far away, consistent with egocentric distance underestimation. A similar experiment was conducted with vertical frontal extents. Both experiments were replicated in panoramic virtual reality. Perceived egocentric distance was quantitatively consistent with angular bias in perceived gaze declination (1.5 gain). Finally, an exocentric distance-matching task was contrasted with a variant of the egocentric matching task. The egocentric matching data approximate a constant compression of perceived egocentric distance with a power function exponent of nearly 1; exocentric matches had an exponent of about 0.67. The divergent pattern between egocentric and exocentric matches suggests that they depend on different visual cues

Identifying Farming Strategies Associated With Achieving Global Agricultural Sustainability

Sustainable agroecosystems provide adequate food while supporting environmental and human wellbeing and are a key part of the United Nations Sustainable Development Goals (SDGs). Some strategies to promote sustainability include reducing inputs, substituting conventional crops with genetically modified (GM) alternatives, and using organic production. Here, we leveraged global databases covering 121 countries to determine which farming strategies—the amount of inputs per area (fertilizers, pesticides, and irrigation), GM crops, and percent agriculture in organic production—are most correlated with 12 sustainability metrics recognized by the United Nations Food and Agriculture Organization. Using quantile regression, we found that countries with higher Human Development Indices (HDI) (including education, income, and lifespan), higher-income equality, lower food insecurity, and higher cereal yields had the most organic production and inputs. However, input-intensive strategies were associated with greater agricultural greenhouse gas emissions. In contrast, countries with more GM crops were last on track to meeting the SDG of reduced inequalities. Using a longitudinal analysis spanning 2004–2018, we found that countries were generally decreasing inputs and increasing their share of agriculture in organic production. Also, in disentangling correlation vs. causation, we hypothesize that a country's development is more likely to drive changes in agricultural strategies than vice versa. Altogether, our correlative analyses suggest that countries with greater progress toward the SDGs of no poverty, zero hunger, good health and wellbeing, quality education, decent work, economic growth, and reduced inequalities had the highest production of organic agriculture and, to a lesser extent, intensive use of inputs

Organic Farming Provides Reliable Environmental Benefits but Increases Variability in Crop Yields: A Global Meta-Analysis

To promote food security and sustainability, ecologically intensive farming systems should reliably produce adequate yields of high-quality food, enhance the environment, be profitable, and promote social wellbeing. Yet, while many studies address the mean effects of ecologically intensive farming systems on sustainability metrics, few have considered variability. This represents a knowledge gap because producers depend on reliable provisioning of yields, profits, and environmental services to enhance the sustainability of their production systems over time. Further, stable crop yields are necessary to ensure reliable access to nutritious foods. Here we address this by conducting a global meta-analysis to assess the average magnitude and variability of seven sustainability metrics in organic compared to conventional systems. Specifically, we explored the effects of these systems on (i) biotic abundance, (ii) biotic richness, (iii) soil organic carbon, (iv) soil carbon stocks, (v) crop yield, (vi) total production costs, and (vii) profitability. Organic farms promoted biotic abundance, biotic richness, soil carbon, and profitability, but conventional farms produced higher yields. Compared to conventional farms, organic farms had lower variability in abundance and richness but greater yield variability. Organic farms thus provided a “win-win” (high means and low variability) for environmental sustainability, while conventional farms provided a “win-win” for production by promoting high crop yields with low variability. Despite lower yields, and greater yield variability, organic systems had similar costs to conventional systems and were more profitable due to organic premiums. Our results suggest certification guidelines for organic farms successfully promote reliable environmental benefits, but greater reliance on ecological processes may reduce predictability of crop production

No transfer of calibration between action and perception in learning a golf putting task

We assessed calibration of perception and action in the context of a golf putting task. Previous research has shown that right-handed novice golfers make rightward errors both in the perception of the perfect aiming line from the ball to the hole and in the putting action. Right-handed experts, however, produce accurate putting actions but tend to make leftward errors in perception. In two experiments, we examined whether these skill-related differences in directional error reflect transfer of calibration from action to perception. In the main experiment, three groups of right-handed novice participants followed a pretest, practice, posttest, retention test design. During the tests, directional error for the putting action and the perception of the perfect aiming line were determined. During practice, participants were provided only with verbal outcome feedback about directional error; one group trained perception and the second trained action, whereas the third group did not practice. Practice led to a relatively permanent annihilation of directional error, but these improvements in accuracy were specific to the trained task. Hence, no transfer of calibration occurred between perception and action. The findings are discussed within the two-visual-system model for perception and action, and implications for perceptual learning in action are raised

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License