Publications

Smallholder agriculture is critical for current and future food security, yet quantifying the sources of smallholder yield variance remains a major challenge. Attributing yield variance to farmer management, as opposed to soil and weather constraints, is an important step to understanding the impact of farmer decision-making, in a context where smallholder farmers use a wide range of management practices and may have limited access to fertilizer. This study used a process-based crop model to simulate smallholder maize (Zea mays) yield at the district-level in Zambia and quantify the percent of yield variance (effect size) attributed to soil, weather, and three management inputs (cultivar, fertilizer, planting date). Effect sizes were calculated via an ANOVA variance decomposition. Further, to better understand the treatment effects of management practices, effect sizes were calculated both for all years combined and for individual years. We found that farmer management decisions explained 27–82 % of total yield variance for different agro-ecological regions in Zambia, primarily due to fertilizer impact. Fertilizer explained 45 % of yield variance for the average district, although its effect was much larger in northern districts of Zambia that typically have higher precipitation, where it explained 72 % of yield variance on average. When fixing a specific fertilizer amount, the “low-cost” management options of varying planting dates and cultivars explained 20–28 % of yield variance, with some regional variation. To better understand why management practices impact yield more in particular years, we performed a correlation analysis comparing yearly management effect sizes with four meteorologically based variables: total growing season precipitation, rainy season onset, extreme heat degree days, and longest dry spell. Results showed that fertilizer’s impact generally increased under favorable weather conditions, and planting date’s impact increased under adverse weather conditions. This study demonstrates how a national yield variance decomposition can be used to understand where specific management interventions would have a greater impact and can provide policymakers with quantification of soil, weather, and management effects. In addition, the variance composition can easily be adapted to a different range of management inputs, such as other cultivars or fertilizer quantities, and can also be used to assess the effect size of management adaptations under climate change.

Air pollution is the fourth-highest risk factor for premature deaths globally and the second-highest risk factor for premature deaths in Ghana. Understanding the socioeconomic correlates of cooking stove/fuel choices and stove stacking patterns is a precursor to designing effective household air pollution interventions. The study applies the multinomial logit model (MNL) to identify socio-economic determinants of household cooking stoves in three unique analyses. In the first analysis, the study examines socio-economic determinants of primary stove choice for LPG, charcoal, and three-stone stoves. Secondly, the study examines determinants of stove choice for primary and secondary stoves when the order is not taken into account: LPG-charcoal, LPG-3-stone, and charcoal-3-stone. The third analysis examines determinants of stove use patterns among stove stackers when the order is taken into consideration: LPG-charcoal, LPG-3-stone, charcoal-3-stone, charcoal-LPG, 3-stone-LPG, and 3-stone-charcoal stove combinations. A cross-sectional design is used to collect data from sampled districts in all 16 regions of Ghana, covering about 7400 household-level respondents in 370 enumeration areas (EAs); across 177 urban EAs and 193 rural EAs.

The results show that primary cooks with higher education, smaller, wealthier, and urban households are more likely to use LPG; more likely to use a stacking mix that includes LPG, and more likely to use a stacking combination where LPG is the primary stove-fuel used. Ghana is currently undertaking a National LPG Promotion Programme (NLPGPP) that seeks to increase LPG access by providing subsidized “starter kits” (stoves, regulators and cylinders) to periurban and rural households. Our findings can aid in targeting households effectively. Our results suggest that households that are female-headed; have less educated household heads; have household heads who double as the primary cook; have larger family sizes, have lower socio-economic status, and are rural, will have an increased probability of adopting and using a cleaner stack of cooking technologies and have an increased probability of using LPG long term, conditional on receiving a starter kit. Such a targeted intervention with a multifaceted approach to shifting households to a cleaner stack of cooking technologies could result in widespread adoption and contribute to significant reductions of CO and PM2.5 emissions in Ghana. A cleaner stack of cooking technologies, given the socio-demographics that are least likely to adopt and sustain use, will have a significant impact on health, the environment, and climate change.

Compared to excise taxes and carbon taxes, setback restrictions on new oil wells have larger health benefits and worker compensation losses, but are more equitable by bringing greater benefits and lower losses to disadvantaged communities in California. For California to meet green gas emissions (GHG) targets, larger setbacks than currently proposed or additional supply-side policies are needed.

To improve the performance of an intra-hour global solar irradiance forecasting algorithm, it is important to detect multiple layers of clouds. Horizontal atmospheric wind shear causes wind velocity fields to have different directions and speeds. In images of clouds acquired using ground-based sky imagers, clouds may be moving in different wind layers. The information provided by a solar forecasting algorithm is necessary to optimize and schedule the solar generation resources and storage devices in a smart grid. This investigation studies the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images. The images are acquired using an innovative infrared sky imager mounted on a solar tracker. Different mixture models are used to infer the distribution of the cloud features. Multiple Bayesian metrics and a sequential hidden Markov model are implemented to find the optimal number of clusters in the mixture models, and their performances are compared. The motion vectors are computed using a probabilistic implementation of the Lucas-Kanade algorithm. The correlations between the cloud motion vectors and temperatures are analyzed to discover the method that leads to the most accurate results. The findings point that a sequential hidden Markov model outperforms the detection accuracy of standard Bayesian model selection metrics.

Pollution concentrations (PM_2.5) in the United States have fallen in recent decades. Despite these improvements, disparities in concentrations between racial/ethnic groups persist. We combine administrative data on the universe of emergency room (ER) admissions across California with satellite information on PM_2.5 concentrations and compare recent trends in racial/ethnic disparities for PM_2.5 and asthma rates. We find that PM_2.5 concentrations fell for the average Black, Hispanic, and White individual. Similarly, disparities in PM_2.5

Like other climate-related disasters, wildfires are intensifying. Property owners can reduce their vulnerability to wildfire losses but are not well informed about the costs and benefits of available self-protection investments. Technological advances mean that insurers are increasingly able to monitor household mitigation behavior. We revisit the problem of self-protection from risk in a setting where households have incomplete insurance and limited information about self-protection investments. Insurer discounts for self-protection generate additional value by informing households about self-protection investments that also reduce uninsured losses. This information provision increases the responsiveness of self-protection to ex post disaster assistance, with implications for optimal government transfers.

Oil supply-side policies—setbacks, excise taxes and carbon taxes—are increasingly considered for decarbonizing the transportation sector. Understanding not only how such policies reduce oil extraction and greenhouse gas (GHG) emissions but also which communities receive the resulting health benefits and labour-market impacts is crucial for designing effective and equitable decarbonization pathways. Here we combine an empirical field-level oil-production model, an air pollution model and an employment model to characterize spatially explicit 2020–2045 decarbonization scenarios from various policies applied to California, a major oil producer with ambitious decarbonization goals. We find setbacks generate the largest avoided mortality benefits from reduced air pollution and the largest lost worker compensation, followed by excise and carbon taxes. Setbacks also yield the highest share of health benefits and the lowest share of lost worker compensation borne by disadvantaged communities. However, currently proposed setbacks may fail to meet California’s GHG targets, requiring either longer setbacks or additional supply-side policies.

Government agencies may be an additional source of unequal representation beyond that stemming from the differential responsiveness of elected officials. We use plausibly exogenous focusing events, which raise public demands for government provision of local public goods, to examine evidence of inequality in agency decision-making. Using the empirical case of wildfire risk management in the western United States, we find that experiencing nearby wildfires raises the salience of wildfire risk and leads agencies to place a greater number of risk reduction projects nearby, even when wildfire risk has already been reduced. This effect predominates among high socioeconomic status communities, especially higher income, more educated, and whiter communities. Empirical evidence is consistent with a formal model in which public agencies perpetuate inequality due to differences in the costs of lobbying across demographically varying communities and differences across communities in the benefits to agencies of responding to their demands.

Understanding the economics of food loss-reduction technologies is crucial to promote their adoption. We offer experimental evidence on returns to technology that studies a wide range of food security outcomes and accounts for the quality, quantity, and economic gains from adoption. Using two randomized experiments on improved storage with farmers in India, we find that food loss-reduction technology delivers large gains on multiple aspects of food security. The largest benefits of loss-reduction technology come from reduced quality and economic losses. Using a novel within-farmer experiment, we offer evidence of significant economic incentives for investing in loss-reduction technology in local rural markets. Our cost-benefit analysis shows that farmers recover the full unsubsidized cost of the reusable improved storage technology in one agricultural season.

Widespread degradation of marine ecosystems and ecosystem services, coupled with national and global commitments to improve protection of the oceans, has led to a proliferation of efforts to designate new marine protected areas (MPAs) and MPA networks. A range of design features must be considered when designating MPAs, including MPA size and shape, level of protection, and the species and habitats protected, and evidence suggests these design elements can be crucial in determining MPA effectiveness. Over the past several decades, expansive literature has emerged providing recommendations for MPA design, and yet collectively these recommendations can be overwhelming and even contradictory for MPA planners. To address this barrier, we reviewed and synthesized 307 unique MPA design recommendations across 56 peerreviewed and gray literature publications. We created a new set of 24 condensed design guidelines grouped by conservation objectives: ecological spatial connectivity (e.g., genetic, larval, community); habitat representation; species or population persistence; mitigation of and complementarity to human activities; and permanence and adaptability. We then discuss examples of datasets, models, and tools that can be utilized to implement specific guidelines. Our review and novel synthesis can help decision-makers understand and apply MPA design recommendations to achieve desired conservation objectives.

Understanding the economics of food loss-reduction technologies is crucial to promote their adoption. We offer experimental evidence on returns to technology that studies a wide range of food security outcomes and accounts for the quality, quantity, and economic gains from adoption. Using two randomized experiments on improved storage with farmers in India, we find that food loss-reduction technology delivers large gains on multiple aspects of food security. The largest benefits of loss-reduction technology come from reduced quality and economic losses. Using a novel within-farmer experiment, we offer evidence of significant economic incentives for investing in loss-reduction technology in local rural markets. Our cost-benefit analysis shows that farmers recover the full unsubsidized cost of the reusable improved storage technology in one agricultural season.

When fisheries managers cannot see who is fishing or where fishing occurs, their scope for management interventions is limited. This lack of transparency in spatial fishing activity is considered a key enabler of illegal fishing and overfishing and hinders managers and consumers who aim to achieve sustainable fisheries. Increasing transparency in vessel location tracking is gaining momentum as a promising way to improve management of global fisheries. However, the mechanisms through which transparency in vessel activity can improve management have not been carefully studied. This paper provides a conceptual overview of the potential for greater transparency, both in quantity of vessels tracked and availability of data, from vessel tracking to help achieve sustainable fisheries goals. We identify four pathways through which these data can improve fisheries management and the conditions that enable transparency to be an effective governance tool. We qualitatively examine the costs and benefits of alternative models of transparency, including a hypothetical, fully transparent system. We highlight how potential costs and benefits of greater transparency depend on both governance context and management goals, and identify opportunities for future research to address key information gaps.

The winemaking technique of saignée is common for some varietals, and the ensuing flavor profiles have been carefully analyzed by oenologists. However, we argue that saignée is fundamentally about economic tradeoffs between the quantity of primary wine that is ultimately produced, the quality (and thus, price) of that wine, and the amount of rosé wine that is bled off in the process. We develop the first theoretically-grounded economic model of saignée and analyze the model to shed light on the winemaker's optimal choice of saignée, and on the properties of wine and wine markets that should empirically give rise to more, or less, saignée. The model helps to explain several real-world regularities such as the absence of saignée for most Bordeaux wines, the specialization in rosé for many wines in Provence, and the practice of moderate amounts of saignée for varietals such as grenache and pinot noir.

Sharks are susceptible to industrial longline fishing due to their slow life histories and association with targeted tuna stocks. Identifying fished areas with high shark interaction risk is vital to protect threatened species. We harmonize shark catch records from global tuna Regional Fisheries Management Organizations (tRFMOs) from 2012–2020 and use machine learning to identify where sharks are most threatened by longline fishing. We find shark catch risk hotspots in all ocean basins, with notable high-risk areas off Southwest Africa and in the Eastern Tropical Pacific. These patterns are mostly driven by more common species such as blue sharks, though risk areas for less common, Endangered and Critically Endangered species are also identified. Clear spatial patterns of shark fishing risk identified here can be leveraged to develop spatial management strategies for threatened populations. Our results also highlight the need for coordination in data collection and dissemination by tRFMOs for effective shark management.

Landscapes can affect parasite epidemiology in wild and agricultural animals. Honey bees are threatened by loss of floral resources and by parasites, principally the mite Varroa destructor and the viruses it vectors. Existing mite control relies heavily on chemical treatments that can adversely affect bees. Alternative, pesticide-free control methods are needed to mitigate infestation with these ectoparasites. Many flowering plants provide nectar and pollen that confer resistance to parasites. Enrichment of landscapes with antiparasitic floral resources could therefore provide a sustainable means of parasite control in pollinators. Floral rewards of Asteraceae plants can reduce parasitic infection in diverse bee species, including honey and bumble bees. Here, we tested the effects of sunflower (Helianthus annuus) cropland and pollen supplementation on honey bee resistance to macro- and microparasites. Although sunflower had nonsignificant effects on microparasites, We found that increased sunflower pollen availability correlated with reduced Varroa mite infestation in landscapes and pollen-supplemented colonies. At the landscape level, each doubling of sunflower crop area was associated with a 28% reduction in mite infestation. In field trials, late-summer supplementation of colonies with sunflower pollen reduced mite infestation by 2.75-fold relative to artificial pollen. United States sunflower crop acreage has declined by 2% per year since 1980, however, suggesting reduced availability of this floral resource. Although further research is needed to determine whether the observed effects represent direct inhibition of mite fecundity or mite-limiting reductions in honey bee brood-rearing, our findings suggest the potential for sunflower plantings or pollen supplements to counteract a major driver of honey bee losses worldwide.

In 2021, 40% of India’s global greenhouse gas emissions came from electricity generation, mainly powered by coal plants (1). Air pollution from these power plants, composed of predominantly sulfur dioxide and particulate matter, is one of the leading causes of respiratory and heart diseases in India, which result in ∼80,000 premature deaths annually (2). Understanding the impacts of electricity sector policies aimed at mitigating carbon emissions and air pollution is critical for addressing these climate and public health crises. On page 618 of this issue, Sengupta et al. (3) examine how carbon taxes, balancing electricity generation and consumption across larger regions, and sulfur-control regulations affect near-term costs and emissions of India’s electricity sector. They find that these policies can cause inequalities in air pollution exposure across different regions.

This paper studies the response of agricultural production to rural labor loss during the process of urbanization. Using household microdata from India and exogenous variation in migration induced by urban income shocks interacted with distance to cities, we document sharp declines in crop production among migrant-sending households residing near cities. Households with migration opportunities do not substitute agricultural labour with capital, nor do they adopt new agricultural machinery. Instead, they divest from agriculture altogether and cultivate less land. We use a two-sector general equilibrium model with crop and land markets to trace the ensuing spatial reorganization of agriculture. Other non-migrant village residents expand farming (land market channel) and farmers in more remote villages with fewer migration opportunities adopt yield-enhancing technologies and produce more crops (crop market channel). Counterfactual simulations show that over half of the aggregate food production losses driven by urbanization is mitigated by these spillovers. This leads to a spatial reorganization in which food production moves away from urban areas and towards remote areas with low emigration.

Multiple price lists are a convenient tool to elicit willingness to pay (WTP) in surveys and experiments, but choice patterns such as “multiple switching” and “never switching” indicate high error rates. Existing measurement approaches often do not provide accurate standard errors and cannot correct for bias due to framing and order effects. We propose to combine a randomization approach with a random-effects latent utility model to detect bias and account for error. Data from a choice experiment in South Africa shows that significant order effects exist which, if uncorrected, would lead to distorted conclusions about subjects’ preferences. We provide templates to create a multiple price list survey instrument in SurveyCTO and analyze the resulting data using our proposed methods.

Tropical deforestation continues at alarming rates with profound impacts on ecosystems, climate, and livelihoods, prompting renewed commitments to halt its continuation. Although it is well established that agriculture is a dominant driver of deforestation, rates and mechanisms remain disputed and often lack a clear evidence base. We synthesize the best available pantropical evidence to provide clarity on how agriculture drives deforestation. Although most (90 to 99%) deforestation across the tropics 2011 to 2015 was driven by agriculture, only 45 to 65% of deforested land became productive agriculture within a few years. Therefore, ending deforestation likely requires combining measures to create deforestation-free supply chains with landscape governance interventions. We highlight key remaining evidence gaps including deforestation trends, commodity-specific land-use dynamics, and data from tropical dry forests and forests across Africa.

Ecological interactions are not uniform across time and can vary with environmental conditions. Yet, interactions among species are often measured with short-term controlled experiments whose outcomes can depend greatly on the particular environmental conditions under which they are performed. As an alternative, we use empirical dynamic modeling to estimate species interactions across a wide range of environmental conditions directly from existing long-term monitoring data. In our case study from a southern California kelp forest, we test whether interactions between multiple kelp and sea urchin species can be reliably reconstructed from time-series data and whether those interactions vary predictably in strength and direction across observed fluctuations in temperature, disturbance, and low-frequency oceanographic regimes. We show that environmental context greatly alters the strength and direction of species interactions. In particular, the state of the North Pacific Gyre Oscillation seems to drive the competitive balance between kelp species, asserting bottom-up control on kelp ecosystem dynamics. We show the importance of specifically studying variation in interaction strength, rather than mean interaction outcomes, when trying to understand the dynamics of complex ecosystems. The significant context dependency in species interactions found in this study argues for a greater utilization of long-term data and empirical dynamic modeling in studies of the dynamics of other ecosystems.