DOI: 10.5061/dryad.1zcrjdfxw
title: Joint environmental and social benefits from diversified agriculture

description: Agricultural simplification continues to expand at the expense of more
 diverse forms of agriculture. This simplification in the form of, for
 example, intensively-managed monocultures, poses a risk to keeping the
 world within safe and just Earth system boundaries. Here, we estimate how
 agricultural diversification simultaneously affects social and
 environmental outcomes. Drawing from 24 studies in 11 countries across
 2,655 farms, we show how five diversification strategies focusing on
 livestock, crops, soils, non-crop plantings, and water conservation
 benefit social (human well-being, yields, food security) and environmental
 (biodiversity, ecosystem services, reduced environmental externalities)
 outcomes. We find that applying multiple diversification strategies
 creates more positive outcomes than individual management strategies
 alone. To realize these benefits, well-designed policies are needed to
 incentivize the adoption of multiple diversification strategies in unison.
description: As a first step to identify relevant datasets,
 participants of the SESYNC working group ‘Can Enhancing Diversity Help
 Scale Up Agriculture's Benefits to People and the Environment?’ were
 identified who had studied farm sites with varying levels of
 diversification in different parts of the world. Our goal was to encompass
 a wide range of farming systems across geographic latitudes, from
 subsistence to commercial farming, while also considering the geophysical
 landscape context of agricultural diversification outcomes. As a second
 step to obtain a sufficient representation of various geographic regions
 as well as types of agricultural diversification strategies, the group
 identified additional data contributors with relevant datasets (done
 through a broader solicitation request for data) as potential
 collaborators. We had three inclusion criteria for datasets.
 That is, datasets should have recorded at least:  1) one agricultural
 diversification practice, while also including control study sites without
 or with less diversification, 2) one environmental outcome variable, and
 3) one social outcome variable. We compiled 24 datasets that cover 11
 countries from 5 world regions: Brazil (5); Malawi (3); Costa Rica (2);
 Ethiopia (2); Germany (2); Bolivia (1); Canada (1); Colombia (1); USA (5);
 Ghana (1); and Indonesia (1).
description: The raw data file is .xlsx and the compound variables and other
 constructed variables are in a CSV file.  All data processing and
 statistical analyses were performed using R software.
description: # Joint environmental and social benefits from diversified agriculture ##
 Description of the data and file structure As a first step to identify
 relevant datasets, participants of the SESYNC working group Can Enhancing
 Diversity Help Scale Up Agriculture's Benefits to People and the
 Environment? were identified that had studied farm sites with varying
 levels of diversification in different parts of the world. Our goal was to
 encompass a wide range of farming systems across geographic latitudes,
 from subsistence to commercial farming, while also considering the
 geophysical landscape context of agricultural diversification outcomes. As
 a second step to obtain a sufficient representation of various geographic
 regions as well as types of agricultural diversification strategies, the
 group identified additional data contributors with relevant datasets (done
 through a broader solicitation request for data) as potential
 collaborators. We had three inclusion criteria for datasets. That is,
 datasets should have recorded at least: 1) one agricultural
 diversification practice, while also including control study sites without
 or with less diversification, 2) one environmental outcome variable, and
 3) one social outcome variable. We compiled 24 datasets that cover 11
 countries from 5 world regions: Brazil (5); Malawi (3); Costa Rica (2);
 Ethiopia (2); Germany (2); Bolivia (1); Canada (1); Colombia (1); USA (5);
 Ghana (1); and Indonesia (1). The .xlsx file contains the raw data. The
 first sheet of the .xlsx file includes two of the social variables: Food
 security and human well-being. The second sheet includes the third social
 variable: Yield. The third sheet includes one of the environmental
 variables: Biodiversity The fourth sheet includes the second environmental
 variable: Ecosystem Services The fifth sheet includes the third
 environmental variable: Reduced externalities. Also, this sheet includes
 all data on agricultural diversification practices. The csv-file includes
 the constructed variables: six compound outcome variables (including
 different configurations of human well-being used for the robustness
 checks), diversification variables as well as landscape variables. We
 combined the 24 datasets by standardizing within each dataset by computing
 z-scores for each data point (field and/or farm) across the subcategories
 of social and environmental outcome variables. Subsequently, we averaged
 across computed scores for each of the six aggregated outcome variables.
 For example, if food security had been measured through two variables,
 such as the number of hungry months and a dietary diversity score, we
 first standardized data for each of these two variables within the dataset
 using z-scores, and then averaged the z-scores to obtain the final value
 for the aggregate food security outcome variable for that data point
 within that study. We used the same standardization procedure whether data
 were entered by data contributors as a binary variable or continuous
 variable, by transforming binary data entries to 0/1 values before
 computing z-scores. For example, one indicator used to assess human
 well-being was men's'/women's' participation in
 networks. Participation was recorded by some data contributors as a binary
 variable of participation/no participation whereas other data contributors
 recorded the actual number of networks that men/women engaged in. For all
 binary data entries, we transformed these. For some variables such as the
 number of hungry months as an indicator for food security, the direction
 was reversed with higher values indicating lower food security. We
 transformed these variables by multiplying by -1 before computing
 z-scores. We note that the directionality of variables was defined
 individually by each data contributor thereby accounting for the context
 in which it was measured. The variables with reversed directionality were:
 * Food Security: Number of hungry months * HFIAS (The Household food
 insecurity access scale) * Food security: Other food insecurity metrics *
 Human well-being: Mental health * Ecosystem services: Fruit damage *
 Ecosystem services: Pest and disease damage * Ecosystem services: Partial
 nitrogen mass balance ## Code/Software All data processing and statistical
 analyses were performed using R software. The R script is available.