About

Hale Ann Tufan is an associate professor in the School of Integrative Plant Science with an adjunct appointment in the Department of Global Development at Cornell University. Her work involves collaboration with plant breeders, social scientists, and research institutions to explore how agricultural research processes and outputs can positively contribute to gender equality and social inclusion. Tufan develops methods and approaches that enable gender+ analysis in agricultural innovation and advocates for inclusive agricultural research by challenging power structures and norms within the research ecosystem. She holds leadership roles in several grant-funded projects focused on food security, crop improvement, seed systems, and gender relations. Currently, she serves as the priority setting co-lead of the Feed the Future Innovation Lab for Crop Improvement, principal investigator of the Gender Responsive Researchers Equipped for Agricultural Transformation (GREAT) project, principal investigator of Muhogo Bora: Cassava for All, survey division lead of NextGen Cassava, and gender research lead of the Feed the Future Insect-Resistant Eggplant Partnership. Tufan brings a multidisciplinary background to her research, including Ph.D.-level research in molecular plant pathogen interactions, experience in plant breeding with CIMMYT, international agricultural research for development program management, and gender research and capacity development across sub-Saharan Africa. She earned her Ph.D. in molecular biology from the John Innes Centre in the UK and was the recipient of the 2019 Norman Borlaug Field Award.

Research topics

• Biology
• Economics
• Business
• Computer Science
• Agroforestry
• Agronomy
• Agricultural economics
• Biotechnology
• Natural resource economics
• Mathematics
• Engineering
• Horticulture
• Environmental science
• Geography
• Marketing
• Medicine
• Environmental resource management
• Economic growth
• Ecology

Selected publications

• According to whom? Spousal, household, and plot differences in improved cassava variety adoption in Nigeria

  Plants People Planet · 2026-04-19

  articleOpen accessSenior authorCorresponding

  Societal Impact Statement Agricultural innovations underpin most investments that aim to increase agricultural productivity globally. Improved crop varieties have historically constituted the bulwark of agricultural innovation outputs and are credited with the success of large‐scale interventions such as the Green Revolution. Much research has shown, however, that gender shapes agricultural technology adoption, increasingly understood in relation to the age, race, and ethnicity of the respondent. Our study shows how decisions made by researchers when designing crop varietal adoption studies, including who in a farming household is asked and how they are asked about adoption, significantly shape reported adoption rates, highlighting potential errors in reported crop varietal adoption rates. Summary Accurately measuring agricultural technology adoption rates underpins impact claims made for new technologies. While numerous studies have documented gender‐based differences in the adoption of agricultural technologies, there remains an urgent need to understand how study design and respondent selection within households shape these reported differences. We explore the case of improved cassava varieties (ICV) in Nigeria to examine differences in reporting on varietal adoption rates based on sampling method, level of analysis, and household position. We compare intrahousehold (spousal), household, and plot‐level data for self‐reported rates of ICV adoption and compare these to data from DNA fingerprinting. We identify significant disparities in reported rates of ICV adoption at the household, spousal, and plot levels, most of which were different from DNA fingerprinting data collected from respondents' plots. Our findings shed light on the importance of participant selection in varietal adoption studies and raise questions around self‐reported adoption rates in the literature. Varietal adoption rates are used to measure a breeding program's success and impact. However, this study shows that estimated rates can differ significantly depending on adoption study design–including unit of analysis, selection of data source, and how the questions are asked. We call for more data feminism around crop varietal adoption studies, study designs that minimize bias, expanded design standards to include multiple respondents within each household, and multiple data analysis methods that reflect the plurality of experiences with adoption among farmers.

  PublisherDOI

• Genomic sampling and population structure of farmer-maintained varieties reveal previously uncharacterized diversity of <i>Theobroma cacao</i> L. in Costa Rica

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-01

  articleOpen access

  Abstract Understanding crop genetic diversity is essential for conservation and breeding, yet farmer-maintained germplasm remains largely underrepresented in genomic studies. Theobroma cacao L. has a complex domestication history and extensive global diversity, and cacao currently cultivated in Central America, particularly in Costa Rica, has been understudied compared to South American and Mexican cultivars despite cultural and historical importance. In this study, we investigate the genetic diversity of cacao from farmer-managed systems across Costa Rica to search for Criollo germplasm and identify and characterize any unique local genetic groups. Ninety-four trees were sampled from 17 farms across four regions of the country and sequenced using whole genome resequencing. Farmer materials were analyzed alongside 166 previously characterized reference accessions representing major cacao genetic groups. Population structure analyses, phylogenetic reconstruction, and network approaches revealed that Costa Rican cacao encompasses multiple known genetic groups, including Criollo-derived lineages, while also harboring locally distinct diversity not fully represented in current global reference collections. Analyses revealed close kinship between many accessions with no clear geographic patterns corresponding to the observed population differentiation, reflecting the effects of farmers in creating dominant patterns of gene flow through seed-saving, clonal propagation, and sharing genotypes among farms. Heterozygosity levels varied substantially among individuals, consistent with a mixture of highly inbred Criollo trees and more heterozygous, admixed genotypes. We find that farmer-managed cacao systems are reservoirs of genetic diversity, including possibly rare or historically important lineages, underscoring the value of these farming systems for effective conservation and management of genomic resources for cacao resilience and improvement.

  PublisherDOI

• Technology Exposure and Adoption Over Time: Lessons From Bt Eggplant in Bangladesh

  Agricultural Economics · 2025-09-21

  articleOpen accessSenior author

  ABSTRACT Adoption of improved crop varieties fuels agricultural productivity, but the impact of their adoption over time remains unexplored. Building on a randomized experiment fielded in 2017, we explore the long‐term adoption and impacts of a successful intervention with a genetically modified eggplant variety (Bt eggplant) in Bangladesh. We study the extent to which households exposed through this intervention to Bt eggplant continued to grow it after 5 years, and whether exposure to the improved variety had post‐intervention impacts on a battery of eggplant‐, agricultural‐, economic‐, and women's empowerment‐specific outcomes. We find wide‐scale dis‐adoption of the Bt eggplant technology, driven primarily by difficulties in accessing seed as well as the absence of post‐intervention support by extension specialists and implementors. Notably, we observe a change in seed‐sourcing behavior in households exposed to Bt eggplant, with a shift towards the formal seed sector. We see no other effects on a variety of eggplant‐ and agriculture‐related outcomes. We find no evidence that exposure to Bt eggplant at the household level had any positive or negative impact on dimensions of women's empowerment. This study is the first to demonstrate empirically that if delivery strategies and support structures are not sustained, short‐term benefits alone are not sufficient to guarantee long‐term adoption of improved varieties.

  PublisherOA PDFDOI

• Editorial: Gender intentional crop breeding: from integration to institutional innovation

  Frontiers in Sustainable Food Systems · 2025-05-19 · 1 citations

  editorialOpen access1st authorCorresponding

  Gender-intentional breeding designs and deploys new crop varieties and animal breeds responsive to the needs of poor rural women and men, with the dual aim of improving gender equality and accelerating adoption. This requires breeding programs that recognize users' divergent demands, taking gender differences into account. Meeting these diverse demands requires analysis of whether different user groups, men and women in particular, have different needs and preferences for new plant varieties or animal breeds, and whether addressing these preferences can increase adoption and enhance benefits. Gender-intentional breeding is a subset of client-oriented breeding that sets breeding objectives based on current and anticipated user demand. It includes but is not limited to purely commercial criteria for the acceptability of new varieties or breeds.The papers emphasize the need for plant breeding to transition from a traditionally supply-driven approach to one that is gender-intentional, demand-led, and participatory. By changing how breeders prioritize traits, varieties selected, and seed strategies developed to actively involve social scientists in decision-making, breeding programs can integrate social and gender considerations into their work. Programs should make sure that the design of new varieties, embodied in breeders' product profiles, takes into account the role of women in food systems and their constraints in accessing seeds and inputs. Several papers conclude that gender-intentional breeding requires integrating gender analysis into breeding objectives from early stages, ensuring that breeding programs consider trait preferences of both men and women in variety design.Gender-intentional breeding requires new impact assessment metrics that measure breeding success on a broader range of criteria: impact should be based not only on agronomic performance but also on genderdifferentiated adoption rates, effects on labor use and drudgery, especially processing ease, and food security r. Furthermore, breeding programs should engage in targeted outreach to redress structural barriers that limit women's access to improved varieties. Breeding programs should also seek to influence policy to gain institutional support for women's participation in variety selection, seed multiplication, and dissemination. One key recommendation is to move beyond simple sex-disaggregated approaches and apply intersectional analysis to understand how gender, social, economic, and ecological factors shape trait preferences. Several papers stress the importance of co-developing product profiles with men and women farmers, even within the same household, to ensure that breeding targets reflect real and diverse needs.The Collection highlights the value of novel participatory breeding approaches that involve a representative cross-section of value chain actors, and breeders in joint decision-making. It calls for innovative methods such as crowdsourcing information on varietal preferences to strengthen stakeholder engagement. Structural changes in breeding institutions are also needed, including hiring more women scientists and promoting interdisciplinary collaboration for gender research. The papers identify fostering transdisciplinary teams that combine breeding expertise with gender and social science expertise as one of the most essential transformations required.Overall, plant breeding must move from a gender-aware to a gender-intentional model, actively working to overcome inequalities in variety adoption and access. This transformation entails cultural change in breeding organizations, so that gender considerations are not peripheral or add-ons but are integral to impactful breeding. Change of this magnitude requires leadership commitment, institutional incentives, and long-term funding, not only to integrate gender concerns but to embed them into lasting and transformative institutional change.

  PublisherOA PDFDOI

• Editorial: Gender-intentional breeding case studies

  Frontiers in Sustainable Food Systems · 2025-07-31 · 1 citations

  editorialOpen accessSenior authorCorresponding
  PublisherDOI

• Applying large language models to extract information from crop trait prioritization studies

  Plants People Planet · 2025-08-06 · 2 citations

  articleOpen accessSenior authorCorresponding

  Societal Impact Statement Investigation of farmers', consumers', and other stakeholders' trait preferences is vital for the adoption and impact of improved crop varieties. While qualitative research methods are known to increase the depth and scope of information from respondents, only 5% of previous trait preference studies used qualitative data in their analyses. We show that AI‐based natural language processing, particularly GPTs, is both a time and cost‐effective mechanism for accurately analyzing open‐ended trait preference data. This will contribute to the selection and prioritization of breeding targets to better meet end‐user needs, with implications for food security and health outcomes globally. Summary Crop trait preference research is critical for the development of improved crop varieties, guiding breeding programs in setting trait priorities and targets that represent farmers' and consumers' needs. However, there is a dearth of methodological harmonization in trait preference studies, leading to high heterogeneity in collected data and analysis frameworks, which constrains comparability between studies. Qualitative research tools using open‐ended questions are among the most common methods used to elucidate crop trait preferences, but only a fraction of these data are used in analysis. The ascendance of AI tools in data analysis provides an opportunity to enhance capitalization of these data from open‐ended question types. We use natural language processing (NLP) techniques, including generative pretrained transformer (GPT) models, to elucidate labels from open‐ended question responses and perform multilabel text classification. We compare these labels to pre‐codes from close‐ended questions, as well as to existing crop trait ontology terms. We find that analyzing responses to open‐ended questions using NLP leads to information gain, including an increase in diversity of traits and insight into their social functions. We conclude that using NLP‐based approaches would allow breeding teams to extract trait terms from open‐ended question responses efficiently and to compare these to both existing ontology terms and close‐ended survey data. Our findings reveal the importance of using open‐ended questions to inform survey codes in mixed methods research design for trait preference studies.

  PublisherOA PDFDOI

• AI‐based data synthesis of crop trait prioritization studies

  Crop Science · 2025-11-01 · 1 citations

  articleOpen accessSenior author

  Abstract Synthesis of data from crop trait prioritization studies (CTPS) can provide insights to support decision‐making, such as institutional funding allocation, and trait prioritization in crop improvement programs. This type of data synthesis is constrained by the lack of standardized crop trait terminology and suitable methods to deal with data heterogeneity. Crop trait ontologies provide terminology standardization, but annotating documents to link terms to ontology terms is time‐consuming and may therefore miss trait terminology emerging from CTPS due to a data annotation bottleneck that constrains data synthesis. Natural language processing (NLP) techniques based on large language models (LLMs) can help in extracting information from unstructured text with no manual text annotation involved. This study applied NLP to synthesize unstructured text data extracted from CTPS by a recently published scoping review. Results show that (1) the trait vocabulary diversity used in CTPS varies per crop and by gender intentionality of CTPS, (2) crop trait preferences increasingly focus on food quality and climate adaptation traits, and (3) existing crop ontologies provide a good coverage of terms found in CTPS but might require the addition of terms, especially in crops such as cassava and sweet potato. This study demonstrates the utility of applying NLP and LLM to synthesize trait preference data across crops and timescales, potentially modeling an approach for broader utility to breeding programs and crop ontology curators alike.

  PublisherOA PDFDOI

• Correction to: Gender, Power and Politics in Agriculture

  2025-01-01

  book-chapterOpen access
  PublisherOA PDFDOI

• Mapping Definitions of Management and Leadership in Capacity Development in Africa’s Agrifood Systems: A Systematic Map Protocol

  2025-05-15

  preprintOpen access
  PublisherDOI

• Assessing genetic variation, genotype‐environment interactions, and genomic prediction for cassava garri quality

  Crop Science · 2025-05-01

  articleOpen access

  Abstract In this study, we assessed the genetic and genotype by environment interaction variation and genomic prediction of the garri quality traits of cassava ( Manihot esculenta Crantz). The study was conducted in the National Root Crop Research Institute experimental sites in Umudike and Otobi, Nigeria, in two planting seasons of 2021 and 2022. We estimated the broad and narrow‐sense heritability and correlation among measured physicochemical parameters on the garri and dried cassava roots (chips). A fivefold cross‐validation scheme was used to calculate the accuracy of the genomic prediction. A significant negative correlation was observed between dry matter content, swelling index, water absorption capacity, and amylose content in garri. The swelling index had a significant negative correlation with swelling power, bulk density, amylose content of cassava chips, crude fiber, and dry matter content. Bulk density had a significant negative correlation with the crude fiber of garri and a positive correlation with the amylose content of cassava chips, the crude fiber of cassava chips, and the dry matter content. Broad‐sense heritability ranged between 0 and 0.19 for garri traits and 0.02 and 0.49 for chips. Narrow‐sense heritability ranged between 0 and 0.18 for garri quality traits and 0 and 0.20 for chips. A significant genotype × environment effect was observed for most of the evaluated traits of chips and garri quality. The predictability of the sugar content, swelling index, swelling power, and water absorption capacity of garri was higher than that of the amylose content, bulk density, and starch content of garri. There was low predictive accuracy for the quality traits garri studied. The sugar content, swelling power, and water absorption capacity of cassava chips averaged approximately 20%. This study provides valuable insights into assessing the genetic variation, correlation, and genomic selection within breeding programs aimed at enhancing the quality traits of cassava for end users.

  PublisherOA PDFDOI

Frequent coauthors

• Chiedozie Egesi

  National Root Crops Research Institute

  40 shared

• Alexandre Bouniol

  Université d'Abomey-Calavi

  23 shared

• Elisabeth Garner

  Center for International Forestry Research

  22 shared

• Béla Teeken

  International Institute of Tropical Agriculture

  20 shared

• Dominique Dufour

  Université d'Avignon et des Pays de Vaucluse

  18 shared

• Olamide Olaosebikan

  International Institute of Tropical Agriculture

  16 shared

• Peter Kulakow

  International Institute of Tropical Agriculture

  15 shared

• Lesley A. Boyd

  National Institute of Agricultural Botany

  15 shared

Labs

• EQUAL LabPI

Awards & honors

• Norman Borlaug Field Award (2019)
• Kathy Druckman Berggren Diversity and Inclusion Award, 2024
• Einhorn Center Engaged Learning Fellowship, 2023
• PCCW Affinito Stewart Award, 2023
• Mario Einaudi Center Global Public Voices Fellow, 2021