Abstracts
During multiple periods of economic crisis, the U.S. economy has depended on Mexican labor. The Bracero Program began during World War II during a massive labor shortage largely due to the military draft and the internment of Japanese Americans, a high percentage of whom worked in agriculture. Over 4.5 million contracts were awarded to over 2 million young male Mexican immigrants from 1942 to 1964 to work primarily in agriculture. The work of braceros, or “individuals who work with their arms,” to harvest fruits and vegetables across the United States was deemed essential. It was the largest guest worker program agreement in U.S. history. President Franklin Roosevelt noted, “Mexican farmworkers, brought to the United States in accordance with an agreement between our two governments,… are contributing their skill and their toil to production of vitally needed food.” Moreover, during the current COVID-19 pandemic, agricultural workers have been categorized as “essential workers” by the federal government. Yet, many of these workers lack legal status to work in the United States.
On June 2, 2020, Dr. Ignacio Ornelas Rodriguez, Archivist, Stanford University Special Collections and University Archives, spoke about the history of the Bracero Program and shared reflections on the current status of agricultural workers in a webinar to over 40 people, including many educators. He began by noting that because of writers like John Steinbeck, Americans have come to learn about the agricultural regions of the larger Monterey Bay Area, where Ornelas has focused his research. “Yet,” he stated, “little is known about the majority of the laborers who worked in these regions.”
Ornelas set the historical context for his talk by providing a broad sweep of the history of farm workers in California. He touched upon the work of indigenous people in the 18th century to grow the vast agricultural economy that surrounded the missions; Chinese immigrants who had previously worked on the Transcontinental Railroad from 1863; Mexican, Japanese, and Filipino agricultural workers in the late 19th and early 20th centuries; African Americans who were initially recruited to develop cotton growing techniques in the Central Valley during the late 19th century; and White migrants arriving from Oklahoma, Texas, Arkansas, and other states during the Great Depression.
Following this overview of California’s agricultural landscape, which Ornelas described as an “ethnic mosaic of the world,” he began his comments on the Bracero Program. He noted, “My interest… was ignited by my grandfather’s personal bracero journey. Who were these men? What were their contributions and why is so little known about how they view their work?" During his extensive research and conducting of oral histories with former braceros, he noted that he began to uncover previously underdiscussed perspectives that were often at odds with the most popular narratives regarding braceros. Ornelas noted that most of the braceros remembered their work “with dignity as opposed to viewing themselves as victims… Their stories were about hope and the opportunity to improve their lives and to make a lasting contribution to their family through difficult working conditions.” Ornelas’s grandfather, José Guadalupe Rodriguez Fonseca, for example, shared stories of betterment and progress and spoke about working with honor in the fields of Salinas Valley. Ornelas continued, “Yes, the work was very difficult but my family members learned to navigate the arduous labor and took great pride in their skill, work, and production of vegetables.” Some former braceros shared stories of using the experience in the program as a “launching pad” to greater opportunities in the agricultural industry.
The Bracero Program ended in 1964 but today the H-2A program is recruiting thousands of Mexican farmworkers. Section 218 of the Immigration and Nationality Act authorizes the lawful admission into the United States of temporary, nonimmigrant workers (H-2A workers) to perform agricultural labor or services of a temporary or seasonal nature. Ornelas posed the question, “So how far have we ultimately come since the labor crisis in 1942?” During the current pandemic, farm workers are deemed essential while many don’t have permanent legal status.
Ornelas, who concurrently teaches history at Willow Glen High School while working at Stanford, has the objective of helping young students critically consider issues surrounding H-2A guest worker status in the context of lessons learned from the Bracero Program. Ultimately, he has the goal of providing instruction that is more culturally inclusive. To help realize this goal, he recommends the following resources for use in schools: the 12-minute film Searching for the Bracero’s Legacy: A New American Encounter for a Place in History, the Bracero Legacy Project on Facebook, and the primary sources of the Ernesto Galarza Papers, 1973–1988 at Stanford.
During the Q&A, a teacher in Colorado mentioned that she is teaching about agricultural workers through a virtual agricultural field and interviews. Ornelas reacted with enthusiasm, saying “I am fascinated by your work.” In a post-webinar conversation, Ornelas stated that it was immensely gratifying for him to hear about the work already being done by teachers to heighten students’ awareness of the contribution of agricultural workers past and present. I also learned that Ornelas’s grandfather José Guadalupe Rodriguez Fonseca had died unexpectedly just a few days prior to the webinar. My hope is that the recording of this webinar will help to keep his memory alive and to help preserve the legacy of braceros.
SPICE is grateful to the Center for Latin American Studies at Stanford University for co-sponsoring this webinar. Special appreciation is extended to Sabrina Ishimatsu, Event Coordinator, SPICE, for planning this webinar, and to Jonas Edman, Instructional Designer, SPICE, for moderating.
Related articles:
To Be a Bracero: Seeing Beyond Abuses
Reflecting on a Childhood Shaped by Immigration Policy
During multiple periods of economic crisis, the U.S. economy has depended on Mexican labor.
COVID-19, combined with the effects of ongoing civil conflicts, hotter and drier weather in many areas, and an unfolding locust invasion in Africa and the Middle East, could cut off access to food for tens of millions of people. The world is “on the brink of a hunger pandemic,” according to World Food Program (WFP) Executive Director David Beasley, who warned the United Nations Security Council recently of the urgent need for action to avert “multiple famines of biblical proportions.”
Understanding how these conditions – alone or in combination – might affect crop harvests and food supply chains is essential to finding solutions, according to David Lobell, the Gloria and Richard Kushel Director of the Center on Food Security and the Environment. Below, Lobell discusses the connection between immigration and U.S. food security, a counter-intuitive effect of COVID-19 and more.
How could COVID-19 affect global food security?
I think the biggest effects will probably be related to lost incomes for many low-income people. Even if food prices don’t change, potentially hundreds of millions could be pushed into a much more precarious food situation. I’d be especially worried about remittances – the money immigrants in wealthy nations send home to developing nations – falling, since these are a surprisingly large source of stability for many poor people. Beyond the income effects, there are definitely prospects for reduced supply of foods, but I think these are secondary, especially because global stocks right now are quite large.
Another counter-intuitive effect is that the drop in gasoline demand due to social distancing may be a big driver of changes in food prices. A lot of corn demand is for use in ethanol fuel, and corn prices can affect the prices of many other crops. The price of corn has dropped by about 20 percent since February.
What are the biggest risks in terms of food supply?
Three things come to mind. First, for crops that require a lot of labor, there are some indications that planting and harvest activities are being affected. Even though these are usually included as essential activities, they often rely on migrant populations that can no longer cross state or national borders. California is going to be a prime case study in this.
Second, some countries, like Russia, have started to restrict food exports in an effort to calm domestic consumers worried about food shortages. Even if there is enough global supply, there is a risk that supply for importing countries could be curtailed. This was a big part of the food price spikes a decade ago. Now, we have the added potential that exports will be limited by a lack of mobility to get products to the port – for instance, there are reports from South America that towns won’t let trucks through for fear of the virus.
Third, COVID-19 could really limit the ability of governments and international groups to address other crises that emerge. Nearly every year there are at least a few surprises around the world affecting food that are usually handled before they make big news. Things like livestock diseases and crop pest outbreaks, for example. But without the ability to deploy people to assess and fix problems, there is more scope for issues to go unchecked. Right now, the biggest example of this is the desert locust outbreak in Eastern Africa.
What current and/or likely future weather conditions might have significant impacts on food production?
As the globe warms, we continue to see more “surprises” in most years in terms of record hot or dry growing seasons. It’s a bit too soon to say if and where those will emerge this year. Since global food stocks are high, we have some ability to cope with a shock, but if governments are already nervous it may take less to induce export bans and all of the negative effects those entail.
Ahead of the summer harvest, what is the prospect for controlling locust swarms in threatened countries, and how might the swarms further complicate the global food security picture?
If not for COVID-19, this would likely be the biggest development related to food this year. My understanding is that they are spreading fast in Africa and the Middle East, and while they haven’t yet had big effects in the main production regions, the next couple of months will be critical. The hope is that the winds change and drive them back toward the desert areas they came from. If not, there are at least 20 million people at risk of major food security impacts in the region.
Could we see locust swarms in the U.S.? What can we do to prevent them?
Locusts can occur anywhere. A few years back there was a major outbreak in Israel. They haven’t been a big issue in the U.S. because control methods are available, such as widespread spraying. But again, in a time of COVID-19, these types of responses are harder.
What does history teach us about the situation we are in with multiple threats to food security, and how to deal with it?
I think it comes down to a combination of investing in science-based solutions to avoid problems to begin with, and then having good social safety nets for when problems arise. At that level, it’s not really any different than dealing with infectious disease. The absence of any problems is our goal. At the same time, that absence always seems to breed complacency and neglect. Hopefully, the experiences of 2020 will help strengthen support for a society based on facts, science and compassion.
David Lobell, Center on Food Security and the Environment: (650) 721-6207; dlobell@stanford.edu
Rob Jordan, Stanford Woods Institute for the Environment: (650) 721-1881; rjordan@stanford.edu
COVID-19 and other looming threats could make it much harder for people to access food. David Lobell, director of Stanford’s Center on Food Security and the Environment, outlines likely scenarios and possible solutions.
Join us for a talk with agricultural and development economist Christopher B. Barrett, this quarter’s visiting scholar with the Center on Food Security and the Environment. Barrett is the Stephen B. and Janice G. Ashley Professor of Applied Economics and Management and an International Professor of Agriculture with Cornell’s Dyson School of Applied Economics and Management.
Professor Barrett will discuss food systems advances over the past 50 years that have promoted unprecedented reduction globally in poverty and hunger, averted considerable deforestation, and broadly improved lives, livelihoods and environments in much of the world. He’ll share perspectives on the reasons why, despite those advances, those systems increasingly fail large communities in environmental, health, and increasingly in economic terms and appear ill-suited to cope with inevitable further changes in climate, incomes, and population over the coming 50 years. Barrett will explore the new generation of innovations underway that must overcome a host of scientific and socioeconomic obstacles.
Also a Professor of Economics in the Department of Economics, Barrett is co-editor in chief of the journal Food Policy, is a faculty fellow with David R. Atkinson Center for a Sustainable Future and serves as the director of the Stimulating Agriculture and Rural Transformation (StART) Initiative housed at the Cornell International Institute for Food, Agriculture and Development.
Accurate automated segmentation of remote sensing data could benefit applications from land cover mapping and agricultural monitoring to urban development surveyal and disaster damage assessment. While convolutional neural networks (CNNs) achieve state-of-the-art accuracy when segmenting natural images with huge labeled datasets, their successful translation to remote sensing tasks has been limited by low quantities of ground truth labels, especially fully segmented ones, in the remote sensing domain. In this work, we perform cropland segmentation using two types of labels commonly found in remote sensing datasets that can be considered sources of “weak supervision”: (1) labels comprised of single geotagged points and (2) image-level labels. We demonstrate that (1) a U-Net trained on a single labeled pixel per image and (2) a U-Net image classifier transferred to segmentation can outperform pixel-level algorithms such as logistic regression, support vector machine, and random forest. While the high performance of neural networks is well-established for large datasets, our experiments indicate that U-Nets trained on weak labels outperform baseline methods with as few as 100 labels. Neural networks, therefore, can combine superior classification performance with efficient label usage, and allow pixel-level labels to be obtained from image labels.
The economic costs of Indonesia’s 2015 forest fires are estimated to exceed US $16 billion, with more than 100,000 premature deaths. On several days the fires emitted more carbon dioxide than the entire United States economy. Here, we combine detailed geospatial data on fire and local climatic conditions with rich administrative data to assess the underlying causes of Indonesia’s forest fires at district and village scales. We find that El Niño events explain most of the year-on-year variation in fire. The creation of new districts increases fire and exacerbates the El Niño impacts on fire. We also find that regional economic growth has gone hand-in-hand with the use of fire in rural districts. We proceed with a 30,000-village case study of the 2015 fire season on Sumatra and Kalimantan and ask which villages, for a given level of spatial fire risk, are more likely to have fire. Villages more likely to burn tend to be more remote, to be considerably less developed, and to have a history of using fire for agriculture. Although central and district level policies and regional economic development have generally contributed to voracious environmental degradation, the close link between poverty and fire at the village level suggests that the current policy push for village development might offer opportunities to reverse this trend.
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. This prediction mechanism is then integrated with a weather forecasting model and three different approaches for decision making under uncertainty to select seed varieties for planting so as to balance yield maximization and risk. The model was applied to the problem of soybean variety selection given in the 2016 Syngenta Crop Challenge. The prediction model achieved a median absolute error of 235 kg/ha and thus provides good estimates for input into the decision models. The decision models identified the selection of soybean varieties that appropriately balance yield and risk as a function of the farmer’s risk aversion level. More generally, the models can support farmers in decision making about which seed varieties to plant.
