Efficient responses to climate change require accurate estimates of both aggregate damages and where and to whom they occur. While specific case studies and simulations have suggested that climate change disproportionately affects the poor, large-scale direct evidence of the magnitude and origins of this disparity is lacking. Similarly, evidence on aggregate damages, which is a central input into the evaluation of mitigation policy, often relies on country-level data whose accuracy has been questioned.
Wheat is the most important staple crop grown in Australia, and Australia is one of the top wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is important for regional and global food security. Prior studies use either climate data, or satellite data, or a combination of these two to build empirical models to predict crop yield. However, though the performance of yield prediction using empirical methods is improved by combining the use of climate and satellite data, the contributions from different data sources are still not clear.
Accurate measurements of maize yields at field or subfield scales are useful for guiding agronomic practices and investments and policies for improving food security. Data on smallholder maize systems are currently sparse, but satellite remote sensing offers promise for accelerating learning about these systems. Here we document the use of Google Earth Engine (GEE) to build “wall-to-wall” 10 m resolution maps of (i) cropland presence, (ii) maize presence, and (iii) maize yields for the main 2017 maize season in Kenya and Tanzania.
The ratio of plant carbon gain to water use, known as water use efficiency (WUE), has long been recognized as a key constraint on crop production and an important target for crop improvement. WUE is a physiologically and genetically complex trait that can be defined at a range of scales. Many component traits directly influence WUE, including photosynthesis, stomatal and mesophyll conductances, and canopy structure. Interactions of carbon and water relations with diverse aspects of the environment and crop development also modulate WUE.
Understanding the causes of economic inequality is critical for achieving equitable economic development. To investigate whether global warming has affected the recent evolution of inequality, we combine counterfactual historical temperature trajectories from a suite of global climate models with extensively replicated empirical evidence of the relationship between historical temperature fluctuations and economic growth. Together, these allow us to generate probabilistic country-level estimates of the influence of anthropogenic climate forcing on historical economic output.
We assess scientific evidence that has emerged since the U.S. Environmental Protection Agency’s 2009 Endangerment Finding for six well-mixed greenhouse gases and find that this new evidence lends increased support to the conclusion that these gases pose a danger to public health and welfare.
Millions of people worldwide are absent from their country’s census. Accurate, current, and granular population metrics are critical to improving government allocation of resources, to measuring disease control, to responding to natural disasters, and to studying any aspect of human life in these communities. Satellite imagery can provide sufficient information to build a population map without the cost and time of a government census.
Oil palm expansion resulted in 2 million hectares (Mha) of forest loss globally in 2000–2010. Despite accounting for 24% (4.5 Mha) of the world’s total oil palm cultivated area, expansion dynamics in sub-Saharan Africa have been overlooked. We show that in Southwest Cameroon, a top producing region of Africa, 67% of oil palm expansion from 2000–2015 occurred at the expense of forest. Contrary to the publicized narrative of industrial-scale expansion, most oil palm expansion and associated deforestation is occurring outside large agro-industrial concessions.
Indonesia’s oil palm expansion during the last two decades has resulted in widespread environmental and health damages through land clearing by fire and peat conversion, but it has also contributed to rural poverty alleviation. In this paper, we examine the role that decentralization has played in the process of Indonesia’s oil palm development, particularly among independent smallholder producers.
Our Report draws attention to a complex but understudied issue: How will climate warming alter losses of major food crops to insect pests? Because empirical evidence on plant-insect-climate interactions is scarce and geographically localized, we developed a physiologically based model that incorporates strong and well-established effects of temperature on metabolic rates and on population growth rates.
Increased intake of fruits and vegetables (F&V) is recommended for most populations across the globe. However, the current state of global and regional food systems is such that F&V availability, the production required to sustain them, and consumer food choices are all severely deficient to meet this need.
Companies' sustainable sourcing practices play an increasing role in addressing the social and environmental challenges in agricultural supply chains. Yet the approaches companies take to regulate their supply chains continue to evolve. I use the chocolate industry as a critical case to explore how and why companies have changed their approaches to sustainable cocoa sourcing over the last 20 years.
Crop responses to climate warming suggest that yields will decrease as growing-season temperatures increase. Deutsch et al. show that this effect may be exacerbated by insect pests (see the Perspective by Riegler). Insects already consume 5 to 20% of major grain crops. The authors' models show that for the three most important grain crops—wheat, rice, and maize—yield lost to insects will increase by 10 to 25% per degree Celsius of warming, hitting hardest in the temperate zone.
The extent to which armed conflicts—events such as civil wars, rebellions, and interstate conflicts—are an important driver of child mortality is unclear. While young children are rarely direct combatants in armed conflict, the violent and destructive nature of such events might harm vulnerable populations residing in conflict-affected areas. A 2017 review estimated that deaths of individuals not involved in combat outnumber deaths of those directly involved in the conflict, often more than five to one.
Linkages between climate and mental health are often theorized, but remain poorly quantified. In particular, it is unknown whether the rate of suicide, a leading cause of death globally, is systematically affected by climatic conditions. Using comprehensive data from multiple decades for both the United States and Mexico, we find that suicide rates rise 0.7% in US counties and 2.1% in Mexican municipalities for a 1 °C increase in monthly average temperature. This effect is similar in hotter versus cooler regions and has not diminished over time, indicating limited historical adaptation.
Rising atmospheric carbon dioxide concentrations are anticipated to decrease the zinc and iron concentrations of crops. The associated disease burden and optimal mitigation strategies remain unknown. We sought to understand where and to what extent increasing carbon dioxide concentrations may increase the global burden of nutritional deficiencies through changes in crop nutrient concentrations, and the effects of potential mitigation strategies.
Eradicating hunger and malnutrition is a key development goal of the twenty first century. This paper addresses the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision making framework. Specifically, a novel hierarchical machine learning mechanism for predicting crop yield (the yield of different seed varieties of the same crop) is introduced.
Accurate prediction of crop yields in developing countries in advance of harvest time is central to preventing famine, improving food security, and sustainable development of agriculture. Existing techniques are expensive and difficult to scale as they require locally collected survey data. Approaches utilizing remotely sensed data, such as satellite imagery, potentially provide a cheap, equally effective alternative. Our work shows promising results in predicting soybean crop yields in Argentina using deep learning techniques.