Food Security
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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. Recent advances in fine-resolution satellite sensors offer promise for monitoring and characterizing the production of smallholder farms. In this study, we investigated the utility of different sensors, including the commercial Skysat and RapidEye satellites and the publicly accessible Sentinel-2, for tracking smallholder maize yield variation throughout a ~40,000 km2western Kenya region. We tested the potential of two types of multiple regression models for predicting yield: (i) a “calibrated model”, which required ground-measured yield and weather data for calibration, and (ii) an “uncalibrated model”, which used a process-based crop model to generate daily vegetation index and end-of-season biomass and/or yield as pseudo training samples. Model performance was evaluated at the field, division, and district scales using a combination of farmer surveys and crop cuts across thousands of smallholder plots in western Kenya. Results show that the “calibrated” approach captured a significant fraction (R2 between 0.3 and 0.6) of yield variations at aggregated administrative units (e.g., districts and divisions), while the “uncalibrated” approach performed only slightly worse. For both approaches, we found that predictions using the MERIS Terrestrial Chlorophyll Index (MTCI), which included the red edge band available in RapidEye and Sentinel-2, were superior to those made using other commonly used vegetation indices. We also found that multiple refinements to the crop simulation procedures led to improvements in the “uncalibrated” approach. We identified the prevalence of small field sizes, intercropping management, and cloudy satellite images as major challenges to improve the model performance. Overall, this study suggested that high-resolution satellite imagery can be used to map yields of smallholder farming systems, and the methodology presented in this study could serve as a good foundation for other smallholder farming systems in the world.

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Remote Sensing
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George Azzari
Marshall Burke
David Lobell
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Stanford welcomes Cousin, a global hunger expert, to the Center on Food Security and the Environment.

The Freeman Spogli Institute for International Studies (FSI) at Stanford University is pleased to announce that former U.S. Ambassador and World Food Programme (WFP) Director Ertharin Cousin will serve as this year’s Frank E. and Arthur W. Payne Distinguished Lecturer and Visiting Fellow at the Center on Food Security and the Environment (FSE).  

Cousin brings over 25 years of experience addressing hunger and food security strategies on both a national and international scale. As U.S. Ambassador to the United Nations Agencies for Food and Agriculture, she focused on advocating for longer-term solutions to food insecurity and hunger, and at WFP she addressed the challenges of food insecurity in conflict situations.

“Dr. Cousin’s outstanding leadership at the WFP and extensive experience in public service exemplifies the attributes we seek for Payne Lecturers,” says FSI Director Michael McFaul. The Payne Distinguished Lectureship is awarded to scholars with international reputations as leaders, with an emphasis on visionary thinking, practical problem solving, and the capacity to clearly articulate an important perspective on the global political and social situation. Past Payne Lecturers include Bill Gates, Nobel Laureate Mohamed El Baradei, UNAIDS Executive Director Peter Piot, and novelist Ian McEwan.

As a visiting fellow with FSE, Cousin will be working to further her research focus on global food security and humanitarian efforts. In November 2015, FSE welcomed Cousin as the featured speaker in their Food and Nutrition Symposium series, where she presented her paper “Achieving food security and nutrition for the furthest behind in an era of conflict and climate change.” FSE Director, Roz Naylor, sees Cousin’s appointment as a pivotal opportunity for FSE and FSI to advance a global agenda on food security and human rights. “Ertharin Cousin is one of the most inspirational leaders we could ever hope to attract to Stanford as a year-long visitor,” Naylor says.

“This is a truly humbling, yet exciting prospect,” says Cousin. “This position provides an opportunity for scholarly work and dialogue with distinguished academics across Stanford's schools and policy institutes.  I also look forward to the opportunity to convene thought leaders from a broad variety of backgrounds, who can help us explore some of the intractable issues plaguing humanitarian and development practitioners today.”

Following the completion of her term with the WFP, Cousin accepted an appointment as a Distinguished Fellow with The Chicago Council on Global Affairs, which conducts research on food and agriculture, global cities, economics, energy, immigration, security, public opinion, and water. Cousin hopes her appointments can provide a unique collaborative opportunity to expand her work on food security and nutrition issues.

“In my career I have never before been given the opportunity of pursuing intellectual inspiration. Just thinking about the ‘what’s possible’ gives me genuine pleasure,” Cousin said.

About FSE

The Center on Food Security and the Environment (FSE) is a research center at Stanford University, jointly funded by the Freeman Spogli Institute for International Studies and the Stanford Woods Institute for the Environment.

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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. The conventional approach of using visible and near-infrared based vegetation index (VI) observations has prevailed for decades since the onset of the global satellite era. However, other satellite data encompass diverse spectral ranges that may contain complementary information on crop growth and yield, but have been largely understudied and underused. Here we conducted one of the first attempts at synergizing multiple satellite data spanning a diverse spectral range, including visible, near-infrared, thermal and microwave, into one framework to estimate crop yield for the U.S. Corn Belt, one of the world's most important food baskets. Overall, using satellite data from various spectral bands significantly improves regional crop yield predictions. The additional use of ancillary climate data (e.g. precipitation and temperature) further improves model skill, in part because the crop reproductive stage related to harvest index is highly sensitive to environmental stresses but they are not fully captured by the satellite data used in our study. We conclude that using satellite data across various spectral ranges can improve monitoring of large-scale crop growth and yield beyond what can be achieved from individual sensors. These results also inform the synergistic use and development of current and next generation satellite missions, including NASA ECOSTRESS, SMAP, and OCO-2, for agricultural applications.

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Remote Sensing of Environment
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David Lobell
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Using agricultural and economic characteristics in African nations as test cases, new research by David Lobell and Marshall Burke demonstrates the use of satellite data to address the long-standing problem of accurate data collection in developing countries. An often cited challenge in achieving development goals aimed at poverty and hunger reduction is the lack of reliable on-the-ground data. Limited or insuffiient data makes it difficult to establish baseline conditions and to assess effectiveness of various aid programs. In the past, researchers and policymakers had to rely on ground surveys, which are expensive, time-consuming, and rarely conducted. This has led to large data gaps in mapping sustainable development goal progress, such as in agricultural and poverty statistics.
 
This brief is based on findings from the papers “Satellite-based assessment of yield variation and its determinants in smallholder African systems,” published in Proceedings of the National Academy of Sciences in 2017 and “Combining satellite imagery and machine learning to predict poverty,” published in Science in 2016.
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Satellite-derived land cover maps play an important role in many applications, including monitoring of smallholder-dominated agricultural landscapes. New cloud-based computing platforms and satellite sensors offer opportunities for generating land cover maps designed to meet the spatial and temporal requirements of specific applications. Such maps can be a significant improvement compared to existing products, which tend to be coarser than 300 m, are often not representative of areas with fast-paced land use change, and have a fixed set of cover classes. Here, we present two approaches for land cover classification using the Landsat archive within Google Earth Engine. Random forest classification was performed with (1) season-based composites, where median values of individual bands and vegetation indices were generated from four years for each of four seasons, and (2) metric-based composites, where different quantiles were computed for the entire four-year period. These approaches were tested for six land cover types spanning over 18,000 locations in Zambia, with ground “truth” determined by visual inspection of high-resolution imagery from Google Earth. The methods were trained on 30% of these points and tested on the remaining 70%, and results were also compared with existing land cover products. Overall accuracies of about 89% were achieved for the season- and metric-based approaches for individual classes, with 93%and 94% accuracy for distinguishing cropland from non-cropland. For the latter task, the existing Globeland30 dataset based on Landsat had much lower accuracies (around 77% on average), as did existing cover maps at coarser resolutions. Overall, the results support the use of either season or metric-based classification approaches. Both produce better results than those obtained from previous classifiers, which supports a general paradigm shift away from dependence on standard static products and towards custom generation of on-demand cover maps designed to fulfill the needs of each specific application.

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Remote Sensing of Environment
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George Azzari
David Lobell
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By using high-res images taken by the latest generation of compact satellites, Stanford scientists have developed a new capability for estimating crop yields from space. Measuring yields could improve productivity and eventually reduce hunger.

Stanford researchers have developed a new way to estimate crop yields from space, using high-resolution photos snapped by a new wave of compact satellites.

The approach, detailed in the Feb. 13 issue of Proceedings of the National Academy of Sciences, could help estimate agricultural productivity and test intervention strategies in poor regions of the world where data are currently extremely scarce.

“Improving agricultural productivity is going to be one of the main ways to reduce hunger and improve livelihoods in poor parts of the world,” said study-coauthor Marshall Burke, an assistant professor of Earth system science at Stanford’s School of Earth, Energy & Environmental Sciences. “But to improve agricultural productivity, we first have to measure it, and unfortunately this isn’t done on most farms around the world.”

Improved satellites

Earth-observing satellites have been around for over three decades, but most of the imagery they capture has not been of high enough resolution to visualize the very small agricultural fields typical in developing countries. Recently, however, satellites have shrunk in both size and cost while simultaneously improving in resolution, and today there are several companies competing to launch into space refrigerator- and shoebox-sized satellites that take high-resolution images of Earth.

“You can get lots of them up there, all capturing very small parts of the land surface at very high resolution,” said study-coauthor David Lobell, an associate professor of Earth system science. “Any one satellite doesn’t give you very much information, but the constellation of them actually means that you’re covering most of the world at very high resolution and at very low cost. That’s something we never really had even a few years ago.”

Accurate predictions

In the new study, Burke and Lobell set out to test whether the images from this new wave of satellites are good enough to reliably estimate crop yields. The pair focused on an area in western Kenya where there are a lot of smallholder farmers that grow maize, or corn, on small, half-acre or one-acre lots. “This was an area where there was already a lot of existing field work,” Lobell said. “It was an ideal site to test our approach.”

The scientists compared two different methods for estimating agricultural productivity yields using satellite imagery. The first approach involved “ground truthing,” or conducting ground surveys to check the accuracy of yield estimates calculated using the satellite data, which was donated by the company Terra Bella. For this part of the study, Burke and his field team spent weeks conducting house-to-house surveys with his staff, talking to farmers and gathering information about individual farms.

“We get a lot of great data, but it’s incredibly time consuming and fairly expensive, meaning we can only survey at most a thousand or so farmers during one campaign,” said Burke, who is also a Center Fellow at the Stanford Woods Institute for the Environment. “If you want to scale up our operation, you don’t want to have to recollect ground survey data everywhere in the world.”

For this reason, the team also tested an alternative “uncalibrated” approach that did not depend on ground survey data to make predictions. Instead, it uses a computer model of how crops grow, along with information on local weather conditions, to help interpret the satellite imagery and predict yields.

“Just combining the imagery with computer-based crop models allows us to make surprisingly accurate predictions, based on the imagery alone, of actual productivity on the field,” Burke said.

The researchers have plans to scale up their project and test their approach across more of Africa. “Our aspiration is to make accurate seasonal predictions of agricultural productivity for every corner of sub-Saharan Africa,” Burke said. “Our hope is that this approach we’ve developed using satellites could allow a huge leap in in our ability to understand and improve agricultural productivity in poor parts of the world.”

Lobell is also the deputy director of Stanford’s Center on Food Security and the Environment and a senior fellow at the Stanford Woods Institute for the Environment.

Funding for the study, titled “Satellite-based assessment of yield variation and its determinants in smallholder African systems,” was provided by AidData at the College of William and Mary, the USAID Global Development Lab and the Center for Effective Global Action.

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Temperature data are commonly used to estimate the sensitivity of many societally relevant outcomes, including crop yields, mortality, and economic output, to ongoing climate changes. In many tropical regions, however, temperature measures are often very sparse and unreliable, limiting our ability to understand climate change impacts. Here we evaluate satellite measures of near-surface temperature (Ts) as an alternative to traditional air temperatures (Ta) from weather stations, and in particular their ability to replace Ta in econometric estimation of climate response functions. We show that for maize yields in Africa and the United States, and for economic output in the United States, regressions that use Ts produce very similar results to those using Ta, despite the fact that daily correlation between the two temperature measures is often low. Moreover, for regions such as Africa with poor station coverage, we find that models with Ts outperform models with Ta, as measured by both R 2 values and out-of-sample prediction error. The results indicate that Ts can be used to study climate impacts in areas with limited station data, and should enable faster progress in assessing risks and adaptation needs in these regions.

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Environmental Research Letters
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Sam Heft-Neal
David Lobell
Marshall Burke
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The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world. Applying machine learning to satellite images could identify impoverished regions in Africa.

One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.

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In the current issue of Science, Stanford researchers propose an accurate way to identify poverty in areas previously void of valuable survey information. The researchers used machine learning – the science of designing computer algorithms that learn from data – to extract information about poverty from high-resolution satellite imagery. In this case, the researchers built on earlier machine learning methods to find impoverished areas across five African countries.

“We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty,” said study coauthor Marshall Burke, an assistant professor of Earth system science at Stanford and a fellow at the Center on Food Security and the Environment. “At the same time, we collect all sorts of other data in these areas – like satellite imagery – constantly.”

The researchers sought to understand whether high-resolution satellite imagery – an unconventional but readily available data source – could inform estimates of where impoverished people live. The difficulty was that while standard machine learning approaches work best when they can access vast amounts of data, in this case there was little data on poverty to start with.

“There are few places in the world where we can tell the computer with certainty whether the people living there are rich or poor,” said study lead author Neal Jean, a doctoral student in computer science at Stanford’s School of Engineering. “This makes it hard to extract useful information from the huge amount of daytime satellite imagery that’s available.”

Because areas that are brighter at night are usually more developed, the solution involved combining high-resolution daytime imagery with images of the Earth at night. The researchers used the “nightlight” data to identify features in the higher-resolution daytime imagery that are correlated with economic development.

“Without being told what to look for, our machine learning algorithm learned to pick out of the imagery many things that are easily recognizable to humans – things like roads, urban areas and farmland,” said Jean. The researchers then used these features from the daytime imagery to predict village-level wealth, as measured in the available survey data.

They found that this method did a surprisingly good job predicting the distribution of poverty, outperforming existing approaches. These improved poverty maps could help aid organizations and policymakers distribute funds more efficiently and enact and evaluate policies more effectively.

“Our paper demonstrates the power of machine learning in this context,” said study co-author Stefano Ermon, assistant professor of computer science and a fellow by courtesy at the Stanford Woods Institute of the Environment. “And since it’s cheap and scalable – requiring only satellite images – it could be used to map poverty around the world in a very low-cost way.” 

Co-authors of the study, titled “Combining satellite imagery and machine learning to predict poverty,” include Michael Xie from Stanford's Department of Computer Science and David Lobell and W. Matthew Davis from Stanford's School of Earth, Energy and Environmental Sciences and the Center on Food Security and the Environment. For more information, visit the research group's website at: http://sustain.stanford.edu/

 

CONTACTS: 

Neal Jean, School of Engineering: nealjean@stanford.edu, (937) 286-6857

Marshall Burke, School of Earth, Energy and Environmental Sciences: mburke@stanford.edu, (650) 721-2203

Michelle Horton, Center on Food Security and the Environment: mjhorton@stanford.edu, (650) 498-4129

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Abstract: China's agricultural sector faces challenges because most farms are still small scale. China's policy is to encourage the consolidation of farms and promote farms that are larger in scale. A question that arises is: Are China's farms growing? The goal of the present paper is to determine whether large farms in China have emerged or if farms remain small. To meet this goal, we systematically document the trends in the operational sizes of China's farms and measure the determinants of changes in farm size. Using a nationally representative dataset, the study shows that in 2013 China's farming sector was still mostly characterized by small-scale farms. However, at the same time, there is an emerging class of middle-sized and larger-sized farms. Most large farms are being run by households but there is a set of large farms that are company/cooperative-run. Today, farmers on larger farms are younger and better educated than the average farmer.

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China and World Economy
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Scott Rozelle
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