How Livelihood Change Affects Food Choice Behaviors in Low- and Middle-Income Countries: A Scoping Review

Livelihoods have changed dramatically over the past decade in low- and middle-income countries (LMIC). These shifts are happening in tandem with shifts in individual and household food choice behaviors. This scoping review aimed to identify and characterize mechanisms through which livelihood changes could affect food choice behaviors in LMIC, including behaviors relating to food production, acquisition, preparation, distribution, and consumption. A literature search was conducted using 4 databases: PubMed, PsycInfo, AGRICOLA, and Embase. The search was further enhanced by expert solicitations. Studies were included if they measured or focused on a livelihood change, described or assessed a change in ≥1 food choice behavior, and focused on LMIC. Studies were excluded if they focused on migration from LMIC to a high-income country. Of the 433 articles that were identified, 53 met the inclusion criteria. Five mechanisms of how livelihood change can affect food choice were identified: occupation, locality, time, income, and social relations. Changes in occupation altered the balance of the availability and affordability of foods in local food environments compared with individual food production. Changes in location, time use, and income influenced where food was purchased, what types of foods were acquired, and how or where foods were prepared. Additionally, changes in social relationships and norms led to expanded food preferences, particularly among urban populations. Time limitations and higher discretionary income were associated with consumption of ultraprocessed foods. Understanding the relationships between the changes in livelihood occuring in LMIC and food choices of households in these countries can inform the development of policies, programs, and other actions to promote sustainable healthy diets and planetary health.

Grassland and managed grazing policy review

Perennial grasslands, including prairie and pasture, have declined with tremendous environmental and social costs. This decline reflects unequal policy support for grasslands and managed grazing compared to row crops. To create a resource for community partners and decision-makers, we reviewed and analyzed the policy tools and implementation capacity that supports and constrains grasslands and managed grazing in the U.S. Upper Midwest. Risk reduction subsidies for corn and soybeans far outpace the support for pasture. Some states lost their statewide grazing specialist when the federal Grazing Lands Conservation Initiative lapsed. The United States Department of Agriculture, Natural Resources Conservation Service support for lands with prescribed grazing practices declined after 2005 but remained relatively steady 2010–2020. These results reveal the policy disadvantage for grasslands and managed grazing in comparison with row crop agriculture for milk and meat production. Grassland and grazing policies have an important nexus with water quality, biodiversity, carbon and outdoor recreation policy. Socially just transitions to well-managed, grazed grasslands require equity-oriented interventions that support community needs. We synthesized recommendations for national and state policy that farmers and other grazing professionals assert would support perennial grasslands and grazing, including changes in insurance, conservation programs, supply chains, land access, and fair labor. These policies would provide critical support for grass-based agriculture and prairies that we hope will help build soil, retain nutrients, reduce flooding and enhance biodiversity while providing healthy food, jobs, and communities.

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.

Implications of US agricultural data practices for sustainable food systems research

Using the tenets of data feminism, we analyse the National Agricultural Statistics Service Quick Stats database - the primary repository of United States agricultural data. We identify unstated assumptions built into the database's scaffolding through data collection, aggregation and dissemination practices, revealing how they facilitate granular analyses of agricultural topics historically judged as national priorities while leaving unilluminated many others of vital importance for contemporary sustainability needs. We argue that this entrenches an inequitable and unsustainable food systems status quo, and we offer recommendations for data providers and users based on principles of reflexivity, context and pluralism.

Explainable Machine Learning Approach Quantified the Long-Term (1981–2015) Impact of Climate and Soil Properties on Yields of Major Agricultural Crops Across CONUS

A comprehensive understanding of the long-term data on the crop, soils, environment, climate, and production management would facilitate efficient data-driven decision-making in agriculture production under changing climate. We have employed an explainable machine learning algorithm (random forest model coupled with LIME; Local Interpretable Model-Agnostic Explanations framework) using multi-decadal (1981–2015) data on climate variables, soil properties, and yield of major crops across the Coterminous United States (CONUS). This data-driven approach explained the multi-faceted factors of crop production for corn, soybean, cotton, and wheat under field conditions by leveraging agricultural informatics. We attempted to show how crop yields can better be correlated and explained when production input varies along with changing climatic/environmental and edaphic conditions. Our findings suggest Growing Degree Days (GDDs) as important climatic factors, while water holding capacity is one of the dominant soil properties in interpreting crop yield variability. Our findings will facilitate growers, crop production scientists, land management specialists, stakeholders, and policy makers in their future decision-making processes related to sustainable and long-term soil, water, and crop management practices.