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With research staff from more than 60 countries, and offices across the globe, IFPRI provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition in developing countries.

researcher spotlight

Elodie Becquey

Elodie Becquey is a Senior Research Fellow in the Nutrition, Diets, and Health Unit, based in IFPRI’s West and Central Africa office in Senegal. She has over 15 years of research experience in diet, nutrition, and food security in Africa, including countries such as Burkina Faso, Chad, Ethiopia, Ghana, Kenya, Mali, and Tanzania.

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Innovation Policy and Scaling

Patricia Zambrano

Senior Program Manager

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Patricia Zambrano is a Senior Program Manager in the Innovation Policy and Scaling Unit. She works on themes related to the economic evaluation of biotechnologies and their regulation. Previously she supported work on gene bank evaluation, genetic resources, biodiversity, intellectual property rights, R&D expenditures, and other related themes.

Patricia holds an undergraduate degree in Economics from the Universidad de Los Andes in Bogotá, Colombia, and pursued postgraduate studies at the same institution. Additionally, she earned a master’s degree in economics from the University of California, Davis.

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Publications

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Working Paper

The impact of genetically modified cowpea on yields, postharvest losses, and profitability in Nigeria: Findings from a cluster randomized controlled trial

2025Amare, Mulubrhan; Andam, Kwaw S.; Spielman, David J.; Bamiwuye, Temilolu; Zambrano, Patricia; Chambers, Judith A.; Fasoranti, Adetunji; Popoola, Olufemi
Details

The impact of genetically modified cowpea on yields, postharvest losses, and profitability in Nigeria: Findings from a cluster randomized controlled trial

We assess the impacts of a genetically modified pod borer-resistant (PBR) cowpea variety in Nigeria through a cluster randomized controlled trial conducted in two major cowpea-cultivating states. Our design allows us to examine the impacts of PBR cowpea with and without a package of complementary inputs (fertilizer and insecticides) and in comparison to farmers who received only a conventional improved cowpea variety. Results indicate that farmers who received and planted PBR cowpea experienced significant increases in yield (21 percent) and net margins (49 percent) compared to those growing the conventional variety, with larger gains observed among those provided with the inputs package. Analysis of heterogeneous effects indicates substantial variation in outcomes based on baseline characteristics such as household size, landholding, pest control practices, and wealth. Estimation of group average treatment effects and classification analysis using a causal machine learning approach identify plot size, pesticide use, and assets as key drivers of impact heterogeneity. Findings highlight the need for targeted dissemination strategies to realize the sizable benefits of PBR cowpea for small-scale, resource-constrained farmers.

Year published

2025

Authors

Amare, Mulubrhan; Andam, Kwaw S.; Spielman, David J.; Bamiwuye, Temilolu; Zambrano, Patricia; Chambers, Judith A.; Fasoranti, Adetunji; Popoola, Olufemi

Citation

Amare, Mulubrhan; Andam, Kwaw S.; Spielman, David J.; Bamiwuye, Temilolu; Zambrano, Patricia; et al. 2025. The impact of genetically modified cowpea on yields, postharvest losses, and profitability in Nigeria: Findings from a cluster randomized controlled trial. IFPRI Discussion Paper 2383. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/178553

Country/Region

Nigeria

Keywords

Africa; Sub-saharan Africa; Western Africa; Impact; Genetically Modified Organisms; Seed Damaging Insects; Pest Resistance; Cowpeas; Randomized Controlled Trials; Machine Learning; Smallholders; Information Dissemination; Yields; Postharvest Losses; Profitability

Language

English

Access/Licence

Open Access

Source

Source record

Project

National Policies and Strategies

Record type

Working Paper

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Working Paper

A good global investment for the United States: How investing in CGIAR makes America stronger, safer and more prosperous

2025
Hill, Ruth Vargas; McNamara, Brian; Ringler, Claudia; Spielman, David J.; Thurlow, James; Berrospi, Maria Lucia; Chambers, Judith A.; Falck-Zepeda, José B.; Gilligan, Daniel O.; Glauber, Joseph W.
…more Hernandez, Manuel A.; Headey, Derek D.; Hoffmann, Vivian; Kosec, Katrina; Martin, Will; Nia, Reza; Nin-Pratt, Alejandro; Piñeiro, Valeria; Takeshima, Hiroyuki; Vos, Rob; Zambrano, Patricia; Zhang, Wei
Details

A good global investment for the United States: How investing in CGIAR makes America stronger, safer and more prosperous

This report documents how CGIAR investments have delivered substantial economic, security, and diplomatic benefits for the U.S. that far exceed financial contributions. By driving agricultural innovation and providing evidence-based insights, CGIAR has boosted U.S. farm productivity, expanded export markets, and made food more affordable for American consumers. It has also helped prevent billions of dollars in potential damage from pests and diseases that threaten U.S. food security and farm livelihoods. CGIAR promotes American leadership in agricultural innovation by supplying the majority of the genetic materials used in U.S. crop breeding and acting as a critical research partner for U.S. businesses and universities. CGIAR also makes America safer and stronger through its work on predicting crises, protecting food security, and on improving livelihoods as an alternative to migration.

Year published

2025

Authors

Hill, Ruth Vargas; McNamara, Brian; Ringler, Claudia; Spielman, David J.; Thurlow, James; Berrospi, Maria Lucia; Chambers, Judith A.; Falck-Zepeda, José B.; Gilligan, Daniel O.; Glauber, Joseph W.; Hernandez, Manuel A.; Headey, Derek D.; Hoffmann, Vivian; Kosec, Katrina; Martin, Will; Nia, Reza; Nin-Pratt, Alejandro; Piñeiro, Valeria; Takeshima, Hiroyuki; Vos, Rob; Zambrano, Patricia; Zhang, Wei

Citation

Hill, Ruth; McNamara, Brian; Ringler, Claudia; Spielman, David; Thurlow, James; Berrospi, Maria Lucia; et al. 2025. A good global investment for the United States: How investing in CGIAR makes America stronger, safer and more prosperous. IFPRI Working Paper. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/176753

Country/Region

United States

Keywords

Americas; Northern America; Economic Aspects; Finance; Agricultural Innovation; Exports; Markets; Food Security; Research

Language

English

Access/Licence

Open AccessCC-BY-4.0

Source

Source record

Record type

Working Paper

Resource thumbnail

Working Paper

Parametric and machine learning approaches to examine yield differences between control and treatment considering outliers and statistical biases: The case of insect resistant/herbicide tolerant (IR/HT) maize in Honduras

2025Falck-Zepeda, José B.; Zambrano, Patricia; Sanders, Arie; Trabanino, Carlos Rogelio
Details

Parametric and machine learning approaches to examine yield differences between control and treatment considering outliers and statistical biases: The case of insect resistant/herbicide tolerant (IR/HT) maize in Honduras

Robust impact assessment methods need credible yield, costs, and other production performance parameter estimates. Sample data issues and the realities of producer heterogeneity and markets, including endogeneity, simultaneity, and outliers can affect such parameters. Methods have continued to evolve that may address data issues identified in the earlier literature examining genetically modified (GM) crops impacts especially those of conventional field level surveys. These methods may themselves have limitations, introduce trade-offs, and may not always be successful in addressing such issues. Experimental methods such as randomized control trials have been proposed to address several control treatment data issues, but these may not be suitable for every situation and issue and may be more expensive and complex than conventional field surveys. Furthermore, experimental methods may induce the unfortunate outcome of crowding-out impact assessors from low- and middle-income countries. The continued search for alternatives that help address conventional survey shortcomings remains critical. Previously, existing assessment methods were applied to the impact assessment of insect resistant and herbicide tolerant maize adoption in Honduras in 2008 and 2012. Results from assessments identified endogeneity issues such as self-selection and simultaneity concurrently with influential outliers. Procedures used to address these issues independently showed trade-offs between addressing endogeneity and outliers. Thus, the need to identify methods that address both issues simultaneously, minimizing as much as possible the impact of method trade-offs, continues. We structured this paper as follows. First, we review the literature to delineate data and assessment issues potentially affecting robust performance indicators such as yields and costs differentials. Second, we discuss and apply four types of approaches that can be used to obtain robust performance estimates for yield and cost differentials including: 1) Robust Instrumental Variables, 2) Instrumental Variable Regressions, and 3) Control/Treatment, and 4) Machine Learning methods that are amenable to robust strategies to deal with outliers including Random Forest and a Stacking regression approach that allows for a number of “base learners” in order to examine the pooled 2008 and 2012 Honduras field surveys. Third, we discuss implications for impact assessment results and implementation limitations especially in low- and middle-income countries. We further discuss and draw some conclusions regarding methodological issues for consideration by impact assessors and stakeholders.

Year published

2025

Authors

Falck-Zepeda, José B.; Zambrano, Patricia; Sanders, Arie; Trabanino, Carlos Rogelio

Citation

Falck-Zepeda, José B.; Zambrano, Patricia; Sanders, Arie; and Trabanino, Carlos Rogelio. 2025. Parametric and machine learning approaches to examine yield differences between control and treatment considering outliers and statistical biases: The case of insect resistant/herbicide tolerant (IR/HT) maize in Honduras. IFPRI Discussion Paper 2334. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/174327

Country/Region

Honduras

Keywords

Latin America and the Caribbean; Central America; Maize; Yields; Impact Assessment; Agriculture; Data; Capacity Building; Machine Learning; Parametric Programming; Herbicide Resistance

Language

English

Access/Licence

Open Access

Source

Source record

Record type

Working Paper

Events

ICTforAg 2024: Localizing impact
IFPRI at External Event

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May 28, 2024, 9:00 – 5:00 pm

ICTforAg 2024: Localizing impact

Event: May 28, 2024 – 9:00am to 5:00pm EDT. This event explores the localization of ICT, responsible AI in agriculture, the potential for AI to increase farm profitability, empowering citizen science, and harnessing digital solutions for sustainable and regenerative farming practices.

2018 International Consortium on Applied Bioeconomy Research Conference (ICABR)
IFPRI at External Event

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June 12 to 15, 2018

2018 International Consortium on Applied Bioeconomy Research Conference (ICABR)

IFPRI is participating extensively in the International Consortium on Applied Bioeconomy Research (ICABR) conference. Multiple changes in the structure of the food system and agriculture raise new opportunities and challenges for the agricultural sector.  In the developing world, these changes started in Asia and Latin America, and are rapidly emerging in Africa.  Urbanization and increased urban […]