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.

A better understanding of recent crop yield trends is necessary for improving the yield and maintaining food security. Several possible mechanisms have been investigated recently in order to explain the steady growth in maize yield over the US Corn‐Belt, but a substantial fraction of the increasing trend remains elusive. In this study, trends in grain filling period (GFP) were identified and their relations with maize yield increase were further analyzed.

The practice of planting winter cover crops has seen renewed interest as a solution to environmental issues with the modern maize- and soybean-dominated row crop production system of the US Midwest. We examine whether cover cropping patterns can be assessed at scale using publicly available satellite data, creating a classifier with 91.5% accuracy (.68 kappa).

Climate-induced shocks in grain production are a major contributor to global market volatility, which creates uncertainty for cereal farmers and agribusiness and reduces food access for poor consumers when production falls and prices spike.

International climate change agreements typically specify global warming thresholds as policy targets, but the relative economic benefits of achieving these temperature targets remain poorly underst

Aquaculture in many countries around the world has become the biggest source of seafood for human consumption. While it alleviates the pressure on wild capture fisheries, the long-term impacts of large-scale, intensive aquaculture on natural coastal systems need to be better understood. In particular, aquaculture may alter habitat and exceed the carrying capacity of coastal marine ecosystems.

Integrated assessment models generate climate change mitigation scenarios consistent with global temperature targets.

Large and regular seasonal price fluctuations in local grain markets appear to offer African farmers substantial inter-temporal arbitrage opportunities, but these opportunities remain largely unexploited: small-scale farmers are commonly observed to "sell low and buy high" rather than the reverse. In a field experiment in Kenya, we show that credit market imperfections limit farmers' abilities to move grain inter-temporally.

The availability of climate model experiments under three alternative scenarios stabilizing at warming targets inspired by the COP21 agreements (a 1.5 ºC not exceed, a 1.5 ºC with overshoot and a 2.0ºC) makes it possible to assess future expected changes in global yields for two staple crops, wheat and maize.

Crop yields in smallholder systems are traditionally assessed using farmer-reported information in surveys, occasionally by crop cuts for a sub-section of a farmer's plot, and rarely using full-plot harvests. Accuracy and cost vary dramatically across methods. In parallel, satellite data is improving in terms of spatial, temporal, and spectral resolution needed to discern performance on smallholder plots.

Worldwide, humans are facing high risks from natural hazards, especially in coastal regions with high population densities. Rising sea levels due to global warming are making coastal communities’ infrastructure vulnerable to natural disasters.

Ending world hunger is a universal goal, yet progress and social awareness of the issue waxes and wanes in the course of broader political and economic developments. The massive famine in China under Chairman Mao’s 1958–62 Great Leap Forward, a succession of severe droughts and associated famines in India in 1965–66, and the political violence that accompanied regime change in Indonesia in 1964–67 left tens of millions of people starving and drew global attention to the threat of food insecurity.

Globally, demand for food animal products is rising. At the same time, we face mounting, related pressures including limited natural resources, negative environmental externalities, climate disruption, and population growth.

Food retailers and manufacturers are increasingly committing to address agricultural sustainability issues in their supply chains. In place of using established eco-certifications, many companies define their own supply chain sustainability standards.

Fast, accurate and inexpensive estimates of crop yields at the field scale are useful for many applications.

Policies that promote biofuels in major agricultural economies raise important questions for food prices and food security at local to global scales. Global biofuel output rose from 38 billion liters to 131 billion liters between 2005 and 2015, boosting the demand for annual- and perennial-crop feedstocks such as maize, sugar, soy, rapeseed, and palm oil. Although ethanol volume was three times that of biodiesel in 2015, the share of biodiesel in total biofuel output rose from 10% to almost 25% over the course of the decade (EIA, n.d.; REN21, 2016).

A critical question for agricultural production and food security is how water demand for staple crops will respond to climate and carbon dioxide (CO

Oil palm production expanded 1.2 million hectares in sub-Saharan Africa since 1990, with expansion accelerating in several heavily forested countries since 2000.

Elevated atmospheric CO2

Food security will be increasingly challenged by climate change, natural resource degradation, and population growth. Wheat yields, in particular, have already stagnated in many regions and will be further affected by warming temperatures. Despite these challenges, wheat yields can be increased by improving management practices in regions with existing yield gaps.

Although development organizations agree that reliable access to energy and energy services—one of the 17 Sustainable Development Goals—is likely to have profound and perhaps disproportionate impacts on women, few studies have directly empirically estimated the impact of energy access on women's empowerment.

Accurate measurements of crop production in smallholder farming systems are critical to the understanding of yield constraints and, thus, setting the appropriate agronomic investments and policies for improving food security and reducing poverty. Nevertheless, mapping the yields of smallholder farms is challenging because of factors such as small field sizes and heterogeneous landscapes.

Wheat is the most important Ethiopian crop, and rust one of its greatest antagonists. There is a need for cheap and scalable rust monitoring in the developing world, but existing methods employ costly data collection techniques. We introduce a scalable, accurate, and inexpensive method for tracking outbreaks with publicly available remote sensing data. Our approach improves existing techniques in two ways. First, we forgo the spectral features employed by the remote sensing community in favor of automatically learned features generated by Convolutional and Long Short-Term Memory Networks.

Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and reporting.