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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.

Ahmed Akhter

researcher spotlight

Akhter Ahmed

Akhter Ahmed is a Senior Research Fellow in the IFPRI’s Poverty, Gender, and Inclusion Unit and Country Representative for IFPRI Bangladesh. He has worked on strategies for agricultural and rural development, social protection, and women’s empowerment to reduce poverty, food insecurity, and undernutrition in developing countries including Bangladesh, China, Egypt, India, Malawi, the Philippines, and Turkey.

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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

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

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Brief

How do policy environments influence technology adoption? Insights from Nigeria’s pod borer resistant (PBR) cowpea experience

2024Mockshell, Jonathan; Nwagboso, Chibuzo; Asante-Addo, Collins; Ritter, Thea; Zambrano, Patricia; Amare, Mulubrhan; Andam, Kwaw S.
Details

How do policy environments influence technology adoption? Insights from Nigeria’s pod borer resistant (PBR) cowpea experience

Policymakers are increasingly considering the promise of modern biotechnology, including genetically modified organisms (GMOs) to help solve development problems in health, agriculture, and other fields (Zambrano et al., 2022). However, debates persist around health and environmental implications (National Academies of Sciences, 2016; Raman, 2017; Smyth et al., 2021). The regulation of GMOs varies globally, with some countries implementing outright bans or imposing stringent controls (Sarkar et al., 2021; Yali, 2022). A recent study examines the Nigerian policy environment for Pod Borer Resistant (PBR) cowpea, which has been genetically engineered to resist the legume pod borer (Maruca vitrata) [Mockshell et al., (unpublished)]. Legume pod borers significantly reduce cowpea yield and quality, with losses of up to 80% reported (Andam et al., 2024; Mockshell et al., 2024). This policy note summarizes the findings of the paper, providing insights to guide policy development around the adoption of biotech food crops in Nigeria and other countries in Africa South of the Sahara (SSA). The primary research question is: Is there an enabling policy environment for PBR cowpea and what factors contribute to it?

Year published

2024

Authors

Mockshell, Jonathan; Nwagboso, Chibuzo; Asante-Addo, Collins; Ritter, Thea; Zambrano, Patricia; Amare, Mulubrhan; Andam, Kwaw S.

Citation

Mockshell, Jonathan; Nwagboso, Chibuzo; Asante-Addo, Collins; Ritter, Thea; Zambrano, Patricia; Amare, Mulubrhan; and Andam, Kwaw S. 2024. How do policy environments influence technology adoption? Insights from Nigeria’s pod borer resistant (PBR) cowpea experience. Nigeria SSP Policy Note 57. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/163386

Country/Region

Nigeria

Keywords

Africa; Western Africa; Sub-saharan Africa; Policies; Biotechnology; Health; Agriculture; Genetically Modified Organisms; Cowpeas; Boring Organisms

Language

English

Access/Licence

Open AccessCC-BY-4.0

Source

Source record

Project

National Policies and Strategies

Record type

Brief

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Conference Paper

Assessing the development impacts of bio-innovations: The case of genetically modified maize and cassava in Tanzania

2024Benfica, Rui; Zambrano, Patricia; Chambers, Judith A.; Falck-Zepeda, José B.
Details

Assessing the development impacts of bio-innovations: The case of genetically modified maize and cassava in Tanzania

Tanzania’s agriculture is characterized by low productivity due to unpredictable rainfall and the prevalence of pests and diseases. Genetically modified (GM) maize offering protection against drought and insects are being developed. Likewise, GM varieties resistant to cassava brown streak disease were developed. Building on prior crop-based analyses, we use the Rural Investment and Policy Analysis (RIAPA) CGE model to assess the impacts of the adoption of those GM crops. GM maize and cassava have positive effects on the economy, the Agri-Food System (AFS), and poverty. Given its stronger linkages in the AFS, the effects of the GM maize are stronger, especially in higher adoption and high yield scenarios. Likewise, the effects on the poorest and rural households are greater. The high variation across scenarios, and the significant effect of the high adoption/high yield scenarios, suggests a high return to investments and policies that realize these adoption rates and yield potential. JEL Codes: O10, O30, O55

Year published

2024

Authors

Benfica, Rui; Zambrano, Patricia; Chambers, Judith A.; Falck-Zepeda, José B.

Citation

Benfica, Rui; Zambrano, Patricia; Chambers, Judith; and Falck-Zepeda, Jose. 2024. Assessing the development impacts of bio-innovations: The case of genetically modified maize and cassava in Tanzania. 32nd International Conference of Agricultural Economists Conference, August 2-7, 2024, New Delhi, India. https://doi.org/10.22004/ag.econ.344284

Keywords

Tanzania; Africa; Eastern Africa; Agrifood Systems; Cassava; Genetically Modified Foods; Maize; Poverty

Language

English

Access/Licence

Open Access

Source

Source record

Record type

Conference 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 […]