# Crop adaptation and improvement for drought-prone environments

Ndjido Ardo Kane, Daniel Foncéka, Timothy J. Dalton

► To cite this version:

Ndjido Ardo Kane, Daniel Foncéka, Timothy J. Dalton (Dir.). Crop adaptation and improvement for drought-prone environments. New Prairie Press, 547 p., 2022, 978-1-944548-47-6. hal-05180333

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<figure><img src="image_1.png" /></figure>
Crop Adaptation
and Improvement
for Drought-Prone
Environments

Editors: Ndjido A. Kane, Daniel Foncéka,
and Timothy J. Dalton
Crop Adaptation and Improvement for Drought-Prone
Environments
<figure><img src="image_2.png" /></figure>
Crop Adaptation and
Improvement for Drought-Prone
Environments

Editors: Ndjido A. Kane, Daniel Foncéka, And
Timothy J. Dalton

NEW PRAIRIE PRESS
MANHATTAN, KS
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

*Crop Adaptation and Improvement for Drought-Prone Environments* by Editors: Ndjido A. Kane, Daniel Foncéka, and Timothy J. Dalton is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-SHAREALIKE/4.0), except where otherwise noted.

Copyright © 2022

Editors: Ndjido A. Kane, Daniel Foncéka, and Timothy J. Dalton

This book's authors retain copyright in their respective contributions where
applicable.

CC-BY-SA unless otherwise noted.

Cover image courtesy of: Alain Audebert (CIRAD), August 2021.

Farmers fields with millet and groundnut grown in rotation in a *Faidherbia albida* park located in the Groundnut Basin (Niakhar, Senegal).

<figure><img src="image_6.png" /></figure>

**New Prairie Press,**

Kansas State University Libraries,
Manhattan, Kansas

Electronic edition available online at: http://newprairiepress.org/ebooks/49

Digital Version ISBN: 978-1-944548-46-9

Paperback Book ISBN: 978-1-944548-47-6

This book was produced with Pressbooks (https://pressbooks.com) and rendered
with Prince.
# Contents

Foreword vii

Preface x

Acknowledgements xiv

Editorial Team and Contributors xvi

## Part I. Socio-Economic Analysis of Dryland Crops
Production

1. Production and Consumption Trends of Dryland Staples in CORAF Nations 3

2. Assessment of Farmers' Groundnut Varietal Trait Preferences and Production Constraints in the Groundnut Basin of Senegal 28

3. Yield Response of Dryland Cereals to Fertilizer on Smallholder Farms in Mali 50

4. Counterfeit Herbicides, Productivity and Family Labor Use on Farms in Mali: A Multivalued Treatment Approach 86

5. Economic Risks and Uncertainties in a Context of Climate Change: Teachings on the Use of Information Systems in the Senegalese Groundnut Basin 113

6. Consumer Willingness to Pay for Millet-based Food Attributes in Niger 137

## Part II. Advanced Phenotyping and Crop Modelling for
Adaptation to Drylands

7. UAV Method Based on Multispectral Imaging for Field Phenotyping 173

8. Agro-physiological Responses of 10 West Africa Sorghum Varieties to Early Water Deficit Assessed by UAV and Ground Phenotyping 188
<table>
  <tbody>
    <tr>
      <td>9. Toward a Regional Field Phenotyping Network in West Africa</td>
      <td>238</td>
    </tr>
    <tr>
      <td>10. High-throughput Root Phenotyping: Opportunities and Challenges for the Adaptation of Arid and Semi-arid Crops to Future Climates</td>
      <td>258</td>
    </tr>
    <tr>
      <td>11. Using Root-Soil Interactions in the Rhizosphere as Valuable Traits for Selection Against Drought</td>
      <td>283</td>
    </tr>
    <tr>
      <td>12. Designing Dual-purpose Sorghum Ideotypes for High Grain and Biomass Yields Suitable for Various Target Environments in Senegal</td>
      <td>305</td>
    </tr>
    <tr>
      <td colspan="2">Part III. Genetic Diversity and Improvement of Dryland Crops</td>
    </tr>
    <tr>
      <td>13. Biodiversity as a Cornerstone of Agrosystems' Sustainability in West Africa</td>
      <td>331</td>
    </tr>
    <tr>
      <td>14. Management of Cowpea [Vigna unguiculata L. (Walp)] Germplasm Diversity in Senegal: A Crucial Asset for Breeding Programs</td>
      <td>361</td>
    </tr>
    <tr>
      <td>15. From Shade to Light: Fonio, an African Orphan Crop, Towards Renewed Challenges</td>
      <td>383</td>
    </tr>
    <tr>
      <td>16. Past, Present, and Future of West African Sorghum Improvement: Building a Roadmap for Climate-adaptive, Farmer-adopted Varieties</td>
      <td>404</td>
    </tr>
    <tr>
      <td>17. Modern Approaches for Sorghum Breeding in Mali</td>
      <td>435</td>
    </tr>
    <tr>
      <td>18. Genetic Improvement of Pearl Millet in Senegal: Past, Present and Future Prospects</td>
      <td>465</td>
    </tr>
    <tr>
      <td>19. Breeding for Drought Adaptation and Fresh Seed Dormancy of Groundnut in Senegal: Advances, Challenges, and Prospects</td>
      <td>496</td>
    </tr>
    <tr>
      <td>Chapters' corresponding authors</td>
      <td>523</td>
    </tr>
    <tr>
      <td>Book Abstract</td>
      <td>524</td>
    </tr>
  </tbody>
</table>
Foreword

In the 1970s, while the green revolution contributed to a boom in agricultural production across many regions of the world, sub-Saharan Africa faced recurrent periods of drought. Its agriculture has been negatively impacted by socio-environmental constraints (e.g., rural exodus, scarcity, and irregularity of the rainy seasons) and agronomic research has been taken unawares resulting in inadequate management of biodiversity and its potentials, technologies, and agricultural practices to meet the needs of the context.

In 1982, the idea of a specialized African regional center on research, associating skills in physiology and genetics, was born from a common will of the Senegalese Institute of Agricultural Research (ISRA), the Center for International Cooperation in Agronomic Research for Development (CIRAD) and the Universities Paris VII and Paris XII, to develop innovative approaches to improve and stabilize groundnut production in Senegal. This regional dynamic highlighted the need to build a center to accommodate teams from the sub-region and to provide an effective scientific tool that no research institution in the south had the financial and human means to build in isolation.

In 1987, the West and Central African Council for Agricultural Research and Development (CORAF) and ISRA proposed that the expertise of this multidisciplinary research team be extended to the countries of the sub-region, which shared the same issues. This directive materialized in 1989 with the creation of the regional study center for drought improvement (CERAAS), a national center with a regional vocation. In 1997, with the impetus of the Cheikh Anta Diop University of Dakar (UCAD) and the École Normale Supérieure d'Agronomie (ENSA), new research and training facilities were built on the campus of University of Thiès, next to the ENSA. This major development by CERAAS has made it possible to invest in research and a diploma training system in the disciplines related to improving adaptation to drought.

In the early 2000s, the World Bank and the Forum for Agricultural Research in Africa (FARA), to operationalize pillar IV of the Comprehensive Africa Agriculture Development Program, launched the regional initiative for
the Multi-country African Agricultural Productivity Program. The objective of this initiative was to improve African producers' access to the technologies they need to increase their productivity and incomes by improving the relevance and efficiency of agricultural technology systems of generation and diffusion. This initiative was operationalized by the West Africa Agricultural Productivity Program (WAAPP), funded under the World Bank's "Adaptable Program Loan," (a progressive program for countries with several phases of implementation), under the leadership of the Economic Community of West African States (ECOWAS) and with the technical coordination of CORAF. Four phases of WAAPP were successively launched in 13 countries in West Africa. Among these countries, nine have a National Center of Specialization (CNS) responsible for conducting cutting-edge research on one or more speculations chosen by the country and included in the regional priorities defined by CORAF. Within this conceptual framework, CERAAS was the National Center of Specialization for dry cereals, including millets [Pennisetum glaucum (pearl millet), Sorghum bicolor (sorghum), Digitaria exilis (fonio)] and associated crops, such as groundnut [Arachis hypogaea], cowpea [Vigna unguiculata] and sesame [Sesamum indicum]). CERAAS benefited from 10-year funding from 2007-2017, provided by the government of Senegal and the World Bank, which offered it new perspectives in terms of mobilizing skills to tackle complex problems requiring multidisciplinary approaches. At the regional level, this partnership takes the form of a greater flow of technologies and expertise between CERAAS and several National Agricultural Research Systems (NARS) in the sub-region. After this ten-year program, CERAAS built up strong regional and international research partnerships and experience in crop adaption to dryland conditions. CERAAS has now fulfilled all requirements for gaining the status of Accredited Center of Excellence of ECOWAS on dryland crops.

Almost 40 years after its creation, CERAAS is in good time to share its
contributions on achieving improved crops adaptation to drought. This
book, entitled *Crops Adaptation and Improvement to Drought* aims to
disseminate key results and technologies from research performed in West
Africa and coordinated by an African Center of excellence to reach an
international community (including academic and non-academic
organizations as well as donors, policymakers, and the general public),
interested in improving crop performance under drought conditions.
The book explores the Socioeconomic Analysis of Dryland Crop
Production (Part I), the Advanced Phenotyping and Crop Modelling for
Adaptation to Drylands (Part II), and the Genetic Diversity and Improvement
of Dryland Crops (Part III), providing new insights in biodiversity and agro-
systems management and improvement to cope with drought conditions.

The chapters herein, made by contributors who conducted their research at the CERAAS and studied or collaborated with the local staff, present climate-smart technologies, varieties that have been adapted to drought, tools developed, technologies implemented, and methods used to study physiological, genetic, and agronomical bases of crop adaptation to drought.

With such comprehensive topics and very contextualized examples, the book is intended to stand as a legacy in the field for a wide audience: students, research scholars, professionals, and experts. More importantly, this book is the first comprehensive document that addresses the complexity of adapting crops to climate change in the Sahel and West Africa.

<figure><img src="image_7.png" /><figcaption>Abdou Tenkouano, Executive Director,
CORAF – The West and Central African
Council for Agricultural Research and
Development</figcaption></figure>
# Preface

The Sahel region is a semiarid climate zone that stretches from the east to the west of Africa and separates the Sahara Desert to the north and the tropical savannas to the south. During the second half of the 20th century, the Sahel region experienced a major climate shift – from a relatively wetter period in the 1950s and 1960s to a dryer climate in the 1970s and 1980s. This dry period led to major episodes of drought, food shortages, and dramatic environmental and societal effects (Tomalka et al., 2021). Although, the levels of rainfall have increased, they are lower than those in the 1940s and 1950s, with high year-to-year variability and increased unpredictability.

In the Sahel, agriculture is the main pillar of the countries' economy. In this region, 80-90% of the population is engaged in agriculture, on which they depend for both food and income. Therefore, even minor perturbations could threaten people's livelihoods and food security. Unfortunately, in the Sahel, agricultural production is primarily subsistence-based and rainfed, which makes the region highly dependent on rainfall patterns and thus vulnerable to climate change. Climate change projections indicate that the Sahel will gradually become hotter, with some areas experiencing increased, but erratic rainfall (Tomalka et al., 2021). In this context, droughts and floods are expected to intensify (i.e., become stronger and more frequent), hence the emergency for appropriate adaptation measures to reduce the insecurity and vulnerability in the region.

Sahelian droughts and their effects have been studied extensively since the 1970s. CERAAS, the lead organization behind this book project, is a full-fledged Senegalese research center entirely dedicated to studying drought adaptation and its improvement. It does so through genetic crop improvement in association with other disciplines since its creation 40 years ago. Sound expertise on drought research has earned CERAAS a renowned reputation, which was strengthened by its recognition as a Regional Center of Excellence on dry cereals and associated crops with the mandate to lead research and development on pearl millet, sorghum, fonio, peanut, cowpea, and sesame in West and Central Africa.

Over the years, CERAAS has accumulated a wealth of knowledge and
experience on dryland crops improvement, with the collaboration of numerous regional and international partners. This book pays tribute to the excellent work done by dedicated research teams on crop improvement and adaptation to drought-prone environments in West Africa. Rather than compiling 40 years of research on this topic, we opted to share the latest developments relevant to concepts, methods, and technologies associated with dry cereals and legume crops' production in the semiarid West Africa. More specifically, this book highlights key results from both applied and basic research undertaken in West Africa towards the development and deployment of climate-smart technologies, especially drought-adapted varieties. These tools include advanced analysis tools and methods used to characterize drought-prone environments and understand crops adaptation to drought and decision support tools designed to guide a better targeting of necessary technologies to the right environments. This book project was also an opportunity to strengthen young scientists' writing skills and self-confidence as some of them embraced lead author roles. Although, most of the contributors are francophone, to broaden the spectrum of readership, we have undertaken the task of writing and publishing the book in English.

The book encompasses 19 chapters and is organized into three parts – socioeconomic analysis of dryland crop production, advanced phenotyping and crop modelling for adaptation to drylands, and genetic diversity and improvement of dryland crops.

Part 1: Socioeconomic Analysis of Dryland Crop Production illustrates the socioeconomic factors that affect the food systems for these crops. It contains six chapters that provide an overview of crop production and consumption in the region (Chapter 1). Then, important inputs that affect system productivity are presented: preferences for new seed varieties (Chapter 2), yield response to fertilizer (Chapter 3), counterfeit herbicides (Chapter 4), and the way farmers develop expectations about the weather events that shape cropping outcomes (Chapter 5). Chapter 6 is dedicated to understanding urban consumer preferences for processed food products derived from pearl millet.

Part 2: Advanced Phenotyping and Crop Modelling for Adaption to Drylands addresses the state of the art of plant phenotyping and the modeling of crop adaptation to dryland cropping systems. The first three chapters focus on drone-based high throughput phenotyping in field
situations. Chapter 1 describes an automated data pipeline to fast-track
image data processing and analysis. Using sorghum as a test case, Chapter
2 provides evidence of the effectiveness and accuracy of drones for drought
phenotyping. Chapter 3 outlines the prospect of a regional network for
phenotyping to mutualize investment. Chapters 4 and 5 focus on root traits
and their use in crop improvement programs, while Chapters 6 presents
crop ideotypes to guide genetic improvement and variety recommendations.

Lastly, Part 3 investigates the genetic diversity and improvement of
dryland crops. The seven chapters in this section focus on biodiversity and
agricultural systems sustainability, orphan crops, such as fonio, and the
utilization of a regional germplasm collection in the improvement of cowpea,
groundnut, pearl millet, and sorghum. Opportunities offered by advances
in genetics and genomics for more effective use of genetic resources for
varietal development are also highlighted.

The richness of this book lies in the multidisciplinary expertise mobilized
by CERAAS through its partnerships, but unfortunately, not all chapters
received could be included in this book. To maintain the standards of
scientific quality and thematic relevance, some chapters were excluded.
However, throughout the review process, which involved both internal and
external steps, authors were guided with constructive feedback. In addition,
research related to dryland crop management, crop protection, nutrition, as
well as technology diffusion pathways, and gender and youth inclusion were
deliberately not addressed in this book to keep this first edition concise and
focused on CERAAS' core discipline – genetic improvement.

Overall, it has been a long journey since the time the call for chapters
was launched in 2020, to the identification of an appropriate publisher in
2021, and the publication of the book in 2022. A lot of time and energy has
been spent to compile the information contained in this book. It was a very
demanding experience but also very rewarding and satisfying, and a lot has
been learned through the process.

We hope that all the parties involved in this project are proud of the
outcome of this journey.

We hope that readers will find informative and useful answers herein
and better understand the kind of research that is being undertaken to
improve crops' adaptation to drought and how the findings can contribute
to increasing crop productivity and building more climate resilient cropping
and food systems.

We wish you all a happy reading!

The Editors

Reference

Tomalka, J., Birner, J., Dieye, A. M., Gleixner, S., Harper, A., Hauf, Y., Hippe, F., Jansen, L., Lange, S., Laudien, R., Rheinbay, J., Vinke, K., von Loeben, S. C., Wesch, S., Zvolsky, A., Gornott, C. (2021). *Climate risk profile: Sahel*. A joint publication by the Potsdam Institute for Climate Impact Research (PIK) and the United Nations High Commissioner for Refugees (UNHCR) under the Predictive Analytics project in support of the United Nations Integrated Strategy for the Sahel (UNISS), Potsdam: Potsdam Institute for Climate Impact Research (PIK) and the United Nations High Commissioner for Refugees (UNHCR). https://publications.pik-potsdam.de/pubman/item/item_26168
Acknowledgements

The work reported in this book is the fruit of various research collaborations
between NARS and advanced research institutions across West Africa and
abroad. We thank the authors for contributing chapters and for their
patience and perseverance.

We are grateful to the subject matter experts who reviewed the chapters
and improved their scientific quality:

Jacques Martin Faye, ISRA/CERAAS, Senegal

Christine Granier, INRAE, France

Laura Grégoire, IRD, France

Steven Haggblade, Michigan State University, USA

Manzamasso Hodjo, Kansas State University, USA

Tebila Nakelse, Tony Blair Institute for Global Change, UK

Jean-François Rami, CIRAD, France

Hannah Schneider, Wageningen University & Research, The Netherlands

Melinda Smale, Michigan State University, USA

Veronique Theriault, Michigan State University, USA

Edgar Twine, AfricaRice, Uganda

Vincent Vadez, IRD, Senegal & ISRA/CERAAS, Senegal

We thank Crystal Lenz and her team for their editorial contribution.

We are grateful to Emily Finch, former Assistant Professor in Scholarly
Communication and Copyright Librarian at Kansas State University, for her
strong commitment in guiding us through the production process. We also
thank Ambria Shawger, Gwendolyn Sibley, and Carolyn Jackson, from Kansas
State University Libraries, for their assistance.

The publication of this book was made possible with the financial support
of the American People provided to the Feed the Future Innovation Lab for
Collaborative Research on Sorghum and Millet (SMIL) through the United
States Agency for International Development (USAID) [Cooperative
Agreement No. AIDOAA-A-13-00047]. We thank the SMIL management
entity for technical support during the editing and production of this book.
The contents of the book are the sole responsibility of the authors and do not necessarily reflect the views of any donor or country.

**The Editors**
# Editorial Team and Contributors

## About the Content Editors

**Dr. Ndjido Ardo Kane, former Director of ISRA/CERAAS, Senegal**

<figure><img src="image_8.png" /></figure>

Geneticist and plant molecular biologist, Dr. Kane's research in Canada led to key contributions in deciphering the genetic basis of vernalization in wheat, as recognized by the Faculty of 1000 Biology in 2003. He worked for the agroindustry and holds a US patent on reducing the negative effects of tobacco in human health. In his home country of Senegal, he coordinated the Agrobiodiversity Management and Biotechnology program. Lately, he uses genomics tools to identify traits governing crop performance in response to climate change. Codirector of an international joint lab (LAPSE), Director of ISRA/CERAAS, the lead center of the ECOWAS Regional Center of Excellence for Dry Cereals and Associated Crops, he is also the author of numerous publications.

**Dr. Daniel Foncéka, Geneticist, CIRAD & ISRA/CERAAS, Senegal**

<figure><img src="image_9.png" /></figure>

Dr. Foncéka is a researcher working for CIRAD, France. He is outpost at ISRA/CERAAS, where he focuses on the genetic improvement of cultivated peanut varieties through the exploitation of wild species' genomes. Dr. Foncéka has developed several permanent interspecific populations that are used to map QTLs for important agronomic traits. He is also deploying GWAS to mine alleles for drought tolerance in the cultivated germplasm. Dr. Foncéka served as Scientific Coordinator at ISRA/CERAAS and has contributed to its acknowledgement as a center of excellence on dryland crops for West and Central Africa. Additionally, he
leads a network of crop scientists called Innovation and Plant Improvement in West Africa (IAVAO, French acronym for *Innovation et Amélioration Variétale en Afrique de l'Ouest*).

**Dr. Timothy J. Dalton, Agricultural Economist and Director of the Feed the Future Innovation Lab for Collaborative Research on Sorghum and Millet (SMIL), Kansas State University, USA**

<figure><img src="image_10.png" /></figure>

Dr. Dalton is a Professor of International Agricultural Development in the Department of Agricultural Economics at Kansas State University in Manhattan, Kansas, USA. With over 25 years of experience in ex-post and ex-ante assessment of new agricultural and food technologies in Africa, Asia, and the United States, he studies how new varieties of sorghum, rice, and maize affect food system productivity, production risk management, and consumer nutrition. He received a B.A. from Columbia University, an M.S. from the University of Illinois, and a PhD. from Purdue University. He is the author or coauthor on over 50 peer-reviewed publications and has received more than US$37 million in competitive research funding from state, federal, industry, and foundation sources.

**Managing editors**

**Dr. Khady Nani Dramé**, ISRA/CERAAS, Senegal

**Dr. Benjamin E. Kohl**, Kansas State University, USA
## Chapter authors

**Michael D. Abrouk**, King Abdullah University of Science and Technology, Saudi Arabia

**Enoch G. Achigan-Dako**, University of Abomey-Calavi, Benin

**Wafa Achouak**, Aix-Marseille Université BIAM/LEMIRE, France

**Myriam Adam**, CIRAD, France

**Charlotte O.A. Adje**, University of Abomey-Calavi, Benin

**Joseph Adjebeng-Danquah**, CSIR-SARI, Ghana

**Hanin Ibrahim Ahmed**, King Abdullah University of Science and Technology, Saudi Arabia

**Louise Akanvou**, CNRA, Côte d'Ivoire

**Amidou Assima**, Michigan State University, USA

**Jonathan A. Atkinson**, University of Nottingham, UK

**Alain Audebert**, CIRAD, France & ISRA/CERAAS, Senegal

**Yacoubou Bakasso**, University Abdou Moumouni, Niger

**Adeline Barnaud**, IRD, France

**Mamadou Billo Barry**, IRAG, Guinea

**Malcolm J. Bennett**, University of Nottingham, UK

**Ezenwoko Benson**, University of Nottingham, UK

**Cécile Berthouly-Salazar**, IRD, France

**Gregory Beurrier**, CIRAD, France

**Rahul Bhosale**, University of Nottingham, UK

**Claire Billot**, CIRAD, France

**Amy Bodian**, ISRA/CERAAS, Senegal

**James Burridge**, IRD, France

**François Joseph Cabral**, Cheikh Anta DIOP University of Dakar, Senegal

**Ndiaga Cissé**, ISRA/CERAAS, Senegal

**Frederic Cossic**, Syngenta, France

**Flakoro Coulibaly**, IER-CRRA-Sotuba, Mali

**Laurent Cournac**, IRD, France

**Philippe Cubry**, IRD, France

**Timothy J. Dalton**, Kansas State University, USA

**Fabrice Davrieux**, CIRAD-Réunion, France

**Joseph Dembele**, Cheikh Anta DIOP University of Dakar, Senegal & ISRA/CERAAS, Senegal

**Yacouba Dembélé**, IER-CRRA-Sotuba, Mali

**Djiby Dia**, ISRA/BAME, Senegal
**Oumar Diack**, ISRA/CERAAS, Senegal

**Pape Bilal Diakhate**, Cheikh Anta DIOP University of Dakar, Senegal & ISRA/
BAME, Senegal

**Mahamadou Diakité**, IER-CRRA-Sotuba, Mali

**Mouhamadou Moussa Diangar**, ISRA/CNRA, Senegal

**Yagouba Diao**, ISRA/CNRA, Senegal

**Cyril Diatta**, ISRA/CNRA, Senegal

**Aiché Traoré dite Diop**, IER-CRRA-Sotuba, Mali

**Diaga Diouf**, Cheikh Anta DIOP University of Dakar, Senegal

**Gualbert Seraphin Dorego**, ISRA/CNRA, Senegal

**Mohamed Doumbia**, IER-CRRA-Sotuba, Mali

**Demba Dramé**, Cheikh Anta Diop University of Dakar & ISRA/CERAAS,
Senegal

**Cheikh Sadibou Fall**, ISRA/BAME, Senegal

**Safiétou Tooli Fall**, Colorado State University, USA & ISRA/CNRA, Senegal &
ISRA/CERAAS, Senegal

**Awa Faye**, ISRA/CERAAS, Senegal

**Issa Faye**, ISRA/CNRA, Senegal

**Jacques Martin Faye**, ISRA/CERAAS, Senegal

**Geneviève Fliedel**, CIRAD, France

**Amadou Fofana**, ISRA/CNRA, Senegal

**Daniel Fonceka**, CIRAD, France & ISRA/CERAAS, Senegal

**Boubacar Gano**, Cheikh Anta Diop University of Dakar, Senegal & ISRA/
CERAAS, Senegal

**Alexandre Grondin**, IRD, Senegal & ISRA/CERAAS, Senegal

**Mame Codou Gueye**, ISRA/CERAAS, Senegal

**Badara Guèye**, IITA, Nigeria

**Diarah Guindo**, IER-CRRA-Sotuba, Mali & CIRAD, France

**Aliou Guissé**, Cheikh Anta Diop University of Dakar, Senegal

**Baptiste Guitton**, CIRAD, France & International Plant Selection, France

**Steve Haggblade**, Michigan State University, USA

**Hamza Haider**, Michigan State University, USA

**Thierry Heulin**, Aix-Marseille Université BIAM/LEMIRE, France

**Manzamasso Hodjo**, Kansas State University, USA

**Abdou R. Ibrahim Bio Yerima**, University of Abomey-Calavi, Benin

**Dylan H. Jones**, University of Nottingham, UK

**Amadou Ka**, ISRA/CNRA, Senegal

**Ndjido Ardo Kane**, ISRA/CERAAS, Senegal
**Mahamady Kané**, Nouvelle France Genetics, USA

**Ghislain Kanfany**, ISRA/CNRA, Senegal

**Mamoutou Kouressy**, IER-CRRA-Sotuba, Mali

**Simon G. Krattinger**, King Abdullah University of Science and Technology, Saudi Arabia

**Laurent Laplaze**, IRD, France

**Denis Lespinasse**, Syngenta, France

**Delphine Luquet**, CIRAD, France

**Fanna Maina**, INRAN, Niger

**Modou Mbaye**, ISRA/CERAAS, Senegal

**Christian Mestres**, CIRAD, France

**Geoffrey P. Morris**, Colorado State University, USA

**Bertrand Muller**, CIRAD, Madagascar

**Tebila Nakelse**, Tony Blair Institute for Global Change, UK

**Malick Ndiaye**, ISRA/CNRA, Senegal

**Adama Ndour**, ICRISAT, Mali

**Papa Mamadou Sitor Ndour**, IRD, Senegal

**Ousmane Ndoye**, CORAF, Senegal

**Mor Ngom**, Cheikh Anta Diop University of Dakar, Senegal & ISRA/BAME, Senegal

**Happiness Oselebe**, Ebonyi State University, Nigeria

**Tony Pridmore**, University of Nottingham, UK

**Michel Ragot**, Syngenta, France & Nouvelle France Genetics, USA

**Jean-François Rami**, CIRAD, France

**Delphine Renard**, CEFE/CNRS, France

**Sani Saidou**, University of Diffa, Niger

**Dramane Sako**, IER-CRRA-Sotuba, Mali

**Moussa Sall**, ISRA/BAME, Senegal

**Diariétou Sambakhé**, ISRA/CERAAS, Senegal

**Aissatou Sambou**, ISRA/CERAAS, Senegal

**Sekouba Sanogo**, IER-CRRA-Sotuba, Mali

**Awa Sarr**, Cheikh Anta Diop University of Dakar, Senegal & ISRA/CERAAS, Senegal

**Thierno Sarr**, Gaston Berger University, Senegal

**Emmanuel Sekloka**, University of Parakou, Benin

**Desalegn D. Serba**, USDA, USA

**Maguette Seye**, ISRA/CERAAS, Senegal

**Bassirou Sine**, ISRA/CERAAS, Senegal
**Aliou Sissoko**, IER-CRRA-Sotuba, Mali

**Salifou Sissoko**, IER-CRRA-Sotuba, Mali

**Melinda Smale**, Michigan State University, USA

**Ousmane Sy**, ISRA/CNRA, Senegal

**Mohamed Tekete**, IER-CRRA-Sotuba, Mali

**Niaba Teme**, IER-CRRA-Sotuba, Mali

**Mamoutou Korotimi Thera**, IER-CRRA-Sotuba, Mali

**Veronique Theriault**, Michigan State University, USA

**Anne-Céline Thuillet**, IRD, France

**Hodo-Abalo Tossim**, ISRA/CERAAS, Senegal

**Edak Aniedi Uyoh**, University of Calabar, Nigeria

**Vincent Vadez**, IRD, Senegal & ISRA/CERAAS, Senegal

**Michel Vaksmann**, CIRAD, France & UMR-AGAP, France

**Yves Vigouroux**, IRD, France

**Tatiana Krasova Wade**, IRD, Senegal

**Darren M. Wells**, University of Nottingham, UK

**Ankounidjou Yebedié**, IER-CRRA-Sotuba, Mali
<figure><img src="image_11.png" /></figure>
# PART I

## SOCIO-ECONOMIC ANALYSIS
OF DRYLAND CROPS
PRODUCTION
<figure><img src="image_12.png" /></figure>
# 1. Production and Consumption Trends of Dryland Staples in CORAF Nations

**Manzamasso Hodjo**, Kansas State University, USA

**Timothy J. Dalton**, Kansas State University, USA

## Abstract

Sorghum, pearl millet, fonio, cowpeas, groundnuts, and livestock form the backbone of the agricultural economy in semiarid regions of West and Central Africa. This chapter presents descriptive statistics on the production, consumption, and trade of five commodities that are the mandate crops of the CORAF regional research center of excellence, Centre d'étude régional pour l'amélioration de l'adaptation à la sécheresse (CERAAS). Over the past decade, changes in the total production of these crops have been driven by land extensification at rates that are two to three times yield growth rates. While the consumption of dryland cereal crops has decreased in most nations, consistent with economic theory, evidence on groundnuts indicates increasing per capita consumption. Very limited quantities of all crops are formally traded across borders, with the exception of groundnuts, which is an important export crop in Senegal. Sorghum, pearl millet, fonio, cowpeas, and groundnuts play critical roles in the food economy of the CORAF region through the supply of calories and proteins to local populations. Strategies to increase the productivity of these crops will be beneficial in different ways to consumers and producers.

**Keywords:** production, consumption, trade, drylands staples, CORAF
# Introduction

This chapter presents recent trends in the production, consumption, and trade of sorghum, pearl millet, fonio, cowpea, and groundnuts in 23 West and Central Africa nations covered by the West and Central African Council for Agricultural Research and Development (CORAF)¹. These five crops form the basis of the food systems in many Sahelian nations in the CORAF region and are the focus of the research center of excellence, *Centre d'étude régional pour l'amélioration de l'adaptation à la sécheresse* (CERAAS), which has led regional efforts to increase the productivity and resiliency of these crops since 1989.

The purpose of this chapter is to provide strategic country-level information on the dynamics of these staples and consumer needs in the future. This study is restricted to sorghum, pearl millet, fonio, cowpea, and groundnuts to draw attention to the critical role these crops play in the Sahelian food system. It does not focus on the entire basket of staples available in CORAF nations. Approximately 65 million hectares of cereals and 14 million hectares of legumes were planted in CORAF countries in 2019, accounting for 63 and 51 percent of cereal and legume areas in Sub-Saharan Africa. On a global scale, CORAF nations produce approximately 27% (sorghum), 37% (pearl millet), 100% (fonio), 84% (cowpeas), and 23% (groundnuts) of the global annual production.

Overall, production of these crops has increased over the past decade, except in a few countries, and consumption has also increased. Very limited quantities of the crops are traded within the region; a limited amount of sorghum is imported from outside the region, but groundnuts are exported throughout the world. These crops, apart from groundnuts, remain relatively isolated from international markets, unlike maize or soybeans. As such, national and regional production and consumption patterns will influence local prices as international price transmission is limited (Nakelse et al., 2018). We examine each of the five crops independently through discussion and statistics on production, consumption, and trade and then provide a discussion of aggregated trends².
# Sorghum

Sorghum is harvested from over 15.8 million hectares in the CORAF region, accounting for 56% of the total area in Sub-Saharan Africa. Still, production is dominated by five countries located in the “sorghum belt”: Nigeria, Niger, Burkina Faso, Mali, and Chad. These five countries account for 89% of the regional area (Figure 1), with approximately 51% of the regional total planted in Nigeria. Niger and Burkina Faso combine to occupy only one-half the acreage in Nigeria or approximately 26% of the regional total. From 1999 to 2019, cereal area increased by 52 percent in the CORAF region, sorghum area increased by 22%, and maize by 118%. Areal expansion occurred throughout the region except for in Nigeria, Ghana, Mauritania, and the Central African Republic (Figure 2). The decline in the relative importance of sorghum in the regional cereal economy is largely panterritorial with only Niger, Guinea-Bissau, and Togo showing an increase in their share of total cereal acreage (Figure 3).

Total sorghum production increased by 1.5% per year between 1999 and 2019. Land extensification drives the increase in grain production, with the area harvested increasing by about 1% per year. The average per-hectare yield in the region is less than 1.0 mt/ha, with national differences that flow along with the differences in agroecology. However, yields have not remained stagnant, increasing at 0.9% per year on average, with several nations such as Senegal, Niger, and Cameroon exceeding a 2% annualized rate of growth in average sorghum yields. The combination of higher yields and land expansion underlines the interaction between agroecological potential and sorghum: expansion occurs in less-productive areas where the baseline potential is lower, and the gain in total production is constrained by climatic edaphic conditions interacting with other stress factors.

Sorghum consumption mirrors production patterns. Nations located in the sorghum belt consume more than 50 kg/per capita/per year, while those outside the belt consume far less (Figure 4). Overall, production estimates exceed consumption, indicating surplus grain for loss and seed. The average ratio of production to consumption is 1.75 in the region, but the ratio is lower in the sorghum belt, where the ratio is 1.43. The Chad, Niger, and Nigeria ratio is even lower at 1.13, 1.37, and 1.18, respectively.
Approximately 21,057 mt of sorghum were imported into the CORAF region in 2019, with 99% of the total volume destined for Cameroon. The United States of America was the source of this grain. The remaining tonnage consisted of cross-border trade between neighboring nations (e.g., exports from Burkina Faso into Côte d'Ivoire and Ghana or from the Central African Republic to Cameroon). Overall, the volume of cross-border trade does not exceed 7,000 mt or, in other terms, one-twentieth of 1 percent of total regional production, reinforcing the observation that sorghum is consumed in the country where it is produced. Opportunities for increased trade of sorghum from Mali will depend on a decrease in transportation costs and regional harmonization of trade policies (Kaminski et al., 2013).

<figure><img src="image_13.png" /><figcaption>Figure 1 - Sorghum Area in 2019 ('000 hectares)</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Figure 2</figcaption></figure>

Figure 2 – Relative Change in Sorghum Area Between 1999 and 2019 (%)
<figure><img src="image_15.png" /><figcaption>Figure 3</figcaption></figure>

Figure 3 – Change in the Share of Cereal Area Planted to Sorghum Between 1999–2019 (%)
<figure><img src="image_16.png" /><figcaption>Figure 4</figcaption></figure>

Figure 4 – Annual Per Capita Consumption of Sorghum in 2019 (kg per capita year)

## Pearl Millet

Pearl millet production spans nations from west to east in the CORAF region, but it is most intensively cultivated in the Sahelian agroecology (Figure 5). Five nations plant more than a million hectares of pearl millet annually, and Senegal is not far behind with nearly 890,000 hectares (Figure 5). Niger is an outlier among all nations and planted 6.8 million hectares of pearl millet in 2019, Nigeria planted 2.8 million, Mali 2.0 million hectares, and Burkina Faso and Chad about 1.2 million hectares each. The average regional yield is 0.851 mt/ha, and yields in Niger, Burkina Faso, Chad, and Niger are below the regional average (Figure 6).

Millet plays an important role in the cereal economy and occupies a large percentage of cereal area, especially in the dryland areas. In Niger, pearl millet is planted on 64% of all cereal area. In Senegal, it accounts for 51% of
the area and about 1/3 of the area in Mali, Burkina Faso, and Chad (Figure 7). Over the past two decades, this share has decreased across the region, despite cereal area increasing, underscoring the faster increase in acreage allocated to other cereals.

Millet remains a very important staple for many nations, but few rely upon millet like Niger. Nigeriens consume an average of 124 kg/per capita/ per year, which has declined from 152 kg/per capita/per year in 1999. Mali and Burkina Faso are the next highest consumers of millet but at less than half the amount consumed in Niger (61.3 and 55.6 kg/per capita/per year, respectively; Figure 8). Millet consumption is highly nation-specific, and just five nations (Niger, Nigeria, Mali, Burkina Faso, and Chad) consume 90% of all millet produced in the CORAF region (Figure 9).

International trade in pearl millet is thinner than that of sorghum. Regionally, Niger exported 5,200 mt towards Burkina Faso, accounting for 88% of the total traded volume of 6,362 mt. Trade amounts to approximately one-twentieth of one percent of regional production. Approximately 61 mt of millet were shipped off the continent towards Europe, the United States, and Japan in 2019. Cross-border flows of millet are limited by high transaction costs associated with weak farm and market logistics combined with high transportation costs (Kaminski et al., 2013). In Senegal, high social capital, represented by business networks, increased the opportunities for millet trade despite high freight costs (Jacques et al., 2018).
<figure><img src="image_17.png" /><figcaption>Figure 5</figcaption></figure>

Figure 5 - Pearl Millet Area in 2019 ('000 hectares)
<figure><img src="image_18.png" /><figcaption>Figure 6 – Pearl Millet Yield in 2019 (mt ha)</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>Figure 7</figcaption></figure>

Figure 7 – Share of Cereal Area Planted to Pearl Millet in 2019 (%)
<figure><img src="image_20.png" /><figcaption>Figure 8</figcaption></figure>

Figure 8 - Per Capita Consumption of Pearl Millet in 2019
<figure><img src="image_21.png" /><figcaption>Figure 9</figcaption></figure>

Figure 9 – Top Five Millet Consumers in Terms of the Share of Total CORAF Region Millet Consumed in 2019 (%)

## Fonio

Three countries in West Africa account for 95% of all fonio areas: Guinea, Nigeria, and Mali. Nearly 70% of all fonio area is in Guinea, and the nation produces 76% of the total regional output estimated at 701,000 mt (Figure 10). This compares to 10.4 million mt of millet and 15.6 million tons of sorghum.

There is no information on the consumption of fonio. Under the assumption that there is very little trade (as described below) and that nearly all fonio is consumed in the country in which it was produced, per capita consumption is approximately 48.7 kg per year in Guinea. In comparison, it is less than 2.5 kg/person in all other countries. Fonio in West Africa, outside Guinea, is a specialty crop and is rarely consumed.
Nearly all international trade of fonio (355 mt of 359 mt) was exported to high-income countries in North America and Europe in 2019. Most grains were sourced in Senegal and Mali (124 and 11 mt, respectively), followed by Burkina Faso and Guinea. Like pearl millet, the trade volume of fonio is less than one-twentieth of one percent of production.

<figure><img src="image_22.png" /><figcaption>Figure 10 - Production of Fonio in 2019 (mt)</figcaption></figure>

## Cowpea

Cowpea, like fonio, is a highly specialized crop, and while grown in ten CORAF nations, it is concentrated in Niger, Nigeria, and Burkina Faso. These three nations account for 93% of all area planted for cowpea in the CORAF region and 82% of the planting across Sub-Saharan Africa (Figure 11). Across the region, cowpea grain production has increased 138% between 1999 and 2019, and the largest increases occurred in Niger, Burkina Faso, Senegal, and Cameroon. Across Sub-Saharan Africa, cowpea production has increased 1.5
times during the same period. There is no recorded systematic information
on cowpea hay and fodder production or value despite its economic
importance in the livestock sector.

The consumption of cowpea is concentrated in local production areas
due to limited trade. Statistical agencies do not record the official trade of
cowpeas, and these sources do not capture informal trade across border
regions. Based on the production statistics and controlling for seed use and
estimated losses, it is approximated that Niger, Burkina Faso, and Nigeria
consume, on average, 85, 26, and 14 kg/per capita/per year in each country,
respectively (Figure 12). Consumption is estimated to be below 10 kg/per
capita/per year in the seven remaining countries with measured production.
There are no national trade statistics on cowpea, but a few studies have
examined the ex-ante impact of new cowpea technology on trade
(Langyintuo & Lowenberg-DeBoer, 2006).

<figure><img src="image_23.png" /><figcaption>Figure 11 – Area Planted to Cowpea in 2019</figcaption></figure>
<figure><img src="image_24.png" /><figcaption>Figure 12</figcaption></figure>

Figure 12 – Cowpea Consumption in 2019

Groundnuts

Groundnut production is widespread across the CORAF region and Sub-Saharan Africa (Figure 13). Approximately 10.7 million hectares of groundnuts were planted in the CORAF region in 2019, accounting for 77% of the total area in Sub-Saharan Africa. In contrast to other crops, where very few nations accounted for 90% of planted areas, ten nations in the region account for 90% of all area and total production. These regions stretch from the western side of the region eastward into central Africa. The region produced 85% of the total output of groundnuts in Sub-Saharan Africa (Figure 14). Nearly all nations in the CORAF region have experienced an increase in the area allocated to groundnuts, resulting in an increase in production over the past twenty years. While the area harvested has increased 89% between 1999 and 2019, total production has increased by
a 78%, underscoring the extensification into areas with lower production potential.

The average regional groundnut yield is 1.07 mt/ha, and this level is the highest among all Sub-Saharan African regions, but country-specific averages range between 0.53 to 1.49 mt/ha. Over time, per hectare yield has increased at approximately 0.75% per year in the region, with only four nations exceeding 1% per year (Figure 15). However, our data does not determine whether total factor productivity growth or input use is the driving factor of higher yields.

On average, 6.8 kg/per capita/per year of groundnuts are consumed in the CORAF region, but this ranges from 0.1 kg/per capita/per year in Mauritania to 17.1 kg/per year/per capita/per year in The Gambia. The nations with the greatest consumption of groundnuts include Nigeria, Niger, Burkina Faso, Cameroon, and Ghana. Combined, these nations account for 75% of the total regional consumption (Figure 16).

Trade-in groundnuts is the highest among the five crops discussed in this chapter. Approximately 2.1% of the total production volume in the CORAF region, or 236,069 mt, is traded across international borders, and 95% of the volume is sent to high- and middle-income countries outside of Sub-Saharan Africa. The leading exporter is Senegal, shipping approximately 225,304 mt, or 16% of its total national production, to fifteen countries. Exports from Senegal account for 95.4% of the total trade volume across all CORAF nations. China was the leading destination for Senegalese groundnuts and accounted for 220,877 tons or 93.6% of the total volume of regional exports. Senegal was the only nation in the CORAF region where China purchased groundnuts. The global price for groundnuts ranged from US$1,500/mt to US$2,000 during the first half of the 2020 marketing year, making it a lucrative crop for earning foreign exchange. Sector studies have highlighted strategies to increase the value of the groundnut value chain in Senegal (World Bank, 2015).
<figure><img src="image_25.png" /><figcaption>Figure 13 - Area Planted to Groundnuts in 2019 (ha)</figcaption></figure>
<figure><img src="image_26.png" /><figcaption>Figure 14 – Total Production of Groundnuts by Nation in 2019 (mt)</figcaption></figure>
<figure><img src="image_27.png" /><figcaption>Figure 15</figcaption></figure>

Figure 15 – Change in Average Yield (mt ha) by Nation Between 1999 and 2019 (%)
<figure><img src="image_28.png" /><figcaption>Figure 16</figcaption></figure>

Figure 16 – Change in Per Capita Consumption of Groundnuts Between 1999 and 2019 (%)

Synthesis and Projections

The previous sections have described the production, consumption, and trade of sorghum, millet, fonio, cowpeas, and groundnuts using national statistics. These statistics are pooled into crop-specific data sets to estimate trends across the CORAF region, and these trends are projected into the next decade. The data set covers the period from 1961 until 2019.
<table><caption>**Table 1**
Annualized Growth Rates Estimated by a Log-Linear Regression (%/year)</caption><thead><tr><th>Indicator</th><th>Sorghum</th><th>Millet</th><th>Fonio</th><th>Groundnuts</th><th>Cowpea</th></tr></thead><tbody><tr><td>Production</td><td>2.16</td><td>1.70</td><td>2.60</td><td>1.98</td><td>4.29</td></tr><tr><td>Acreage</td><td>1.42</td><td>1.18</td><td>2.20</td><td>1.26</td><td>2.60</td></tr><tr><td>Yield</td><td>0.59</td><td>0.62</td><td>0.72</td><td>0.52</td><td>1.85</td></tr><tr><td>Consumption</td><td>-0.40</td><td>-0.81</td><td>*</td><td>0.71</td><td>*</td></tr></tbody></table>

* Data does not allow an estimation of a log-linear growth rate. ** All trends are significant at 1%.

Total production of all five crops has increased between 1.7% to 4.3% per year (Table 1). Sixty to 85% of the production increase is driven by land extensification. At the same time, the yield component, a combined measure of crop varietal improvement, input intensification, and other elements of total factor productivity growth, accounts for 26% to 43% of the increase in production. The highest yield growth rate is with cowpeas, where yield growth is 2/3rds of the annual rate of areal expansion.

<table><caption>**Table 2**
Average Rural and Urban Annual Population Growth Rates in CORAF Nations (2009-2018)</caption><thead><tr><th rowspan="2"></th><th colspan="2">2010's</th><th colspan="2">2010's</th></tr><tr><th>Urban</th><th>Rural</th><th>Urban</th><th>Rural</th></tr></thead><tbody><tr><td>Benin</td><td>2.9%</td><td>2.7%</td><td>Guinea</td><td>3.1%</td><td>3.2%</td></tr><tr><td>Burkina Faso</td><td>2.8%</td><td>3.0%</td><td>Guinea-Bissau</td><td>3.1%</td><td>3.0%</td></tr><tr><td>Cameroon</td><td>2.9%</td><td>3.0%</td><td>Liberia</td><td>1.9%</td><td>3.6%</td></tr><tr><td>Cape Verde</td><td>-0.2%</td><td>2.0%</td><td>Mali</td><td>2.8%</td><td>2.4%</td></tr><tr><td>Central African Republic</td><td>3.2%</td><td>3.9%</td><td>Mauritania</td><td>2.2%</td><td>1.9%</td></tr><tr><td>Chad</td><td>4.1%</td><td>4.1%</td><td>Niger</td><td>2.1%</td><td>2.4%</td></tr><tr><td>Congo</td><td>1.4%</td><td>3.4%</td><td>Nigeria</td><td>2.5%</td><td>2.7%</td></tr><tr><td>Cote d'Ivoire</td><td>3.1%</td><td>3.0%</td><td>Senegal</td><td>2.4%</td><td>2.2%</td></tr><tr><td>DR Congo</td><td>3.7%</td><td>3.0%</td><td>Sierra Leone</td><td>2.2%</td><td>2.2%</td></tr><tr><td>Gabon</td><td>-0.9%</td><td>3.6%</td><td>Togo</td><td>2.1%</td><td>2.0%</td></tr><tr><td>Gambia</td><td>2.6%</td><td>2.6%</td><td>Sao Tome and Principe</td><td>-0.9%</td><td>3.8%</td></tr><tr><td>Ghana</td><td>2.8%</td><td>3.0%</td><td>Average</td><td>2.4%</td><td>3.1%</td></tr></tbody></table>
We can estimate trends in consumption, and these trends are consistent with economic theory. Sorghum and millet are staple goods that are “inferior” to other cereals, such as wheat, rice, and maize. Over time, as income grows, consumption of these crops (on a per capita basis) decreases as food consumption diversifies into alternative staples and towards proteins, fats, fruits, and vegetables. This does not imply that the aggregate demand for these staples is decreasing. Population growth in the region remains high in many nations (Table 2). Since population growth rates are 2- to 4-times higher than the absolute value of consumption reductions, and there are more mouths to feed, demand for these crops will continue to increase. But as income grows and population growth rates stabilize, consumption of these crops as food will decrease, and alternative usages as feed or industrial products should grow.

Conclusion

Sorghum, pearl millet, fonio, cowpeas, and groundnuts, with livestock, form the backbone of the agricultural economy in semi-arid regions of West and Central Africa. This chapter aimed to explore the graphical distribution of these crops in the region, their productivity and consumption through national averages, and changes over time. The national maps oversimplify the distribution of the crops in the region because the data do not provide resolution to be displayed on a finer scale where agroecological, edaphic, and other constraints can be correlated with outcomes.

Over the past twenty years, cropland has expanded in the CORAF region, but the area allocated to sorghum, millet, and fonio increased more slowly than other crops. Overall changes in the total production of these crops have been driven by land extensification at rates two to three times the growth rate in yields. The consumption of dryland cereal crops has decreased in most nations, consistent with economic theory, while evidence on groundnuts indicates increasing per capita consumption. We do not have data on fonio or cowpea consumption.

There is very limited information on the trade of the five commodities in the region, apart from groundnuts and cowpea (Jacques et al., 2018; Kaminski
et al., 2013; Langyintuo & Lowenberg-DeBoer, 2006). Groundnuts have served as an important income-generating crop for farmers in Senegal since the colonial period, and evidence indicates that this is correlated with an increase in consumption (World Bank, 2017). The lack of trade data implies that most production is locally consumed or saved for seed. Future research on regional trade may look to novel data sets that collect information from border crossings where informal exchange occurs.

Sorghum, pearl millet, fonio, cowpeas, and groundnuts play critical roles in the food economy of the CORAF region through the supply of calories and protein to local populations. Strategies to increase the productivity of these three crops will be beneficial in different ways. Increased productivity will accelerate a decline in the share of area allocated to these crops without increased trade or new market opportunities for sorghum and pearl millet. It may also spur innovative food products to meet consumer needs. This will free land to be allocated to other cereals, legumes, or high-valued crops with higher returns to land and labor. In the case of dryland crops in the CORAF region, this is likely to include cowpeas and groundnuts.

## References

* Jacques, D. C., Marinho, E., D'Andrimont, R., Waldner, F., Radoux, J., Gaspart, F., & Defourny, P. (2018). Social capital and transaction costs in millet markets. *Helion*, 4(1), 1–25.

* Kaminski, J., Elbehri, A., & Samake, M. (2013). An assessment of sorghum and millet in Mali and implications for competitive and inclusive value chains. In FAO/ IFAD (Ed.), *Rebuilding West Africa's food potential* (pp. 481–501).

* Langyintuo, A. S., & Lowenberg-DeBoer, J. (2006). Potential regional trade implications of adopting bt cowpea in West and Central Africa. *AgBioForum*, 9(2), 111–120.

* Nakelse, T., Dalton, T. J., Hendricks, N., & Hodjo, M. (2018). Are smallholder farmers better or worse off from an increase in the international price of cereals? *Food Policy*.78, 213–223.
World Bank. (2015). *Etude diagnostique de la chaîne de valeurs arachide au Sénégal*. World Bank.

World Bank. (2017). *Groundnut value chain competitiveness and prospects for development: Final report*. World Bank.

ENDNOTES

[1] CORAF is composed of the national agricultural research systems of Benin, Burkina Faso, Cameroon, Cape Verde, Central African Republic, Chad, Congo, Cote d'Ivoire, Democratic Republic of the Congo, Republic of Congo, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo and Sao Tome and Principe.

[2] This chapter draws upon statistics available from the United Nations Food and Agriculture Organization.
## 2. Assessment of Farmers' Groundnut Varietal Trait Preferences and Production Constraints in the Groundnut Basin of Senegal

**Aissatou Sambou**, ISRA/CERAAS, Senegal

**Maguette Seye**, ISRA/CERAAS, Senegal

**Daniel Foncéka**, CIRAD, France & ISRA/CERAAS, Senegal

### Abstract

Groundnut is a cash crop that generates income and improves the livelihoods of smallholder farmers in several developing countries. Although Senegal is one of the major producing countries, the national average grain yield remains low. A participatory rural appraisal was conducted in the Groundnut Basin of Senegal in 2015 and aimed to assess farmer perceptions of production constraints and varietal trait preferences. Ninety farmers were randomly selected in nine villages distributed across three regions of the Groundnut Basin. Data was collected through Focus Group Discussions (FGDs) and Semi-Structured Interviews (SSI). The results of the study indicated that seed availability was the most limiting factor in groundnut production across villages. Low soil fertility and storage insect pests were the second and third most important constraints respectively. Other constraints included limited access to land, drought, and commercialization. Among varietal traits, farmers unanimously considered pod and haulm yields as the most important. Other important traits mentioned were adaptation to drought, high pod weight, and earliness. These production constraints and varietal trait preferences should be taken into consideration when defining
the groundnut product profiles of the national breeding program in order to
improve productivity and increase the adoption of newly bred cultivars.

Keywords: groundnut, production constraints, farmers, trait preferences

**Introduction**

Groundnut is one of the most important oilseed legumes grown worldwide. Asia and Africa account for more than 90% of global groundnut area and it is cultivated mostly under rainfed conditions by small-scale farmers. The African continent accounts for 31.1% of total global production (FAOSTAT, 2018). In Senegal, groundnut occupies a central role in the agricultural economy since its introduction by explorers in 1836. Historically, the Senegalese economy has relied on groundnuts, which accounts for up to 60% of GDP, 80% of export earnings, and more than half of the planted area from 1961 to 2020 (FAOSTAT, 2020). The crop was grown throughout the country, and particularly in the central area called the Groundnut Basin.

Over recent decades, the contribution of groundnut to GDP has declined. Groundnut production has been reduced due to numerous factors. These include a long drought period starting in the 1970s and continuing to the present. Added to this is poor soil fertility, climate change, a disorganized value chain, inconsistency of the various governmental and donor policies, and the low adoption of newly developed agricultural technologies. A study conducted by Faye et al. (2019) on the adoption rate of improved varieties of main crops grown in Senegal showed evidence that improved groundnut varieties are not widely adopted by farmers; the reported rate is 55%. In the same report, results also showed that the varieties that are cultivated have an average age of more than 10 years. This shows a low penetration of new varieties in the farming environment. Some authors explain this situation by an inadequate awareness, omission of farmers during the process of cultivar development, and farmers' fear of taking risks in testing varieties that are not yet well known by the majority (Ceccarelli & Grando, 2007; Omanya et al., 2007).

Technology transfer to farmers is one of the major bottlenecks of many
agricultural projects in Africa. Efforts should concentrate on improving the
access to the developed innovations because technological change in
agriculture is essential for reducing poverty and stimulating economic
growth, particularly in low-income countries (Dhirifi, 2014). It is also
important that the technologies developed and proposed consider the needs
of the target users. This shortcoming has opened the way to alternative
approaches for increasing the rate of technological adoption by farmers
(Chambers, 1994; Duraiappah et al., 2005).

Participatory rural appraisal (PRA) is one of the most popular strategies. PRA enables local farmers to mobilize and participate in the early stages of technology development. In the particular case of plant breeding, the involvement of farmers as an integral part of the process ensures the participation of the most important stakeholders in the definition of breeding objectives and therefore influences the direction of the research. Plant breeders cannot respond to every quirk of farmers' circumstances but the relevance of breeding research in poor nations can be improved (Haugerud & Collinson, 1990; Sattar et al., 2017). The main objective of most PRA approaches is for a better incorporation of farmers' technical knowledge and identification of research priorities into the breeding programs to develop varieties that meet their needs (Morris & Bellon, 2004). PRA results guide the breeder to focus on farmers preferred traits while addressing the constraints prevailing in the production areas. Many studies, such as Ceccarelli and Grando (2009), reported that PRA approaches enhance the adoption of newly bred cultivars.

To date, there is scant formal documentation on the main groundnut
constraints and the traits preferred by farmers in Senegal. Existing
documents refer to commercialization aspects and to the diagnosis of the
problems related to the whole value chain (Dia et al., 2003; Georges et
al., 2016; Ndiaye, 2018; Noba et al., 2014). Agricultural extension agencies
have often reported low soil fertility and drought as the major constraints
to groundnut production in Senegal. However, the scientific method has
not been used to ascertain groundnut production constraints and farmers'
preferred traits.

Such studies have been conducted and published in most groundnut
growing countries of West Africa. In the study conducted by Coulibaly et al.
(2017) concerning two main groundnut production zones in Niger, drought
appeared as the major constraint affecting production, followed by low soil
fertility. In Burkina Faso, besides organizational difficulties (i.e., access to
credit and access to material, among others), pest attacks and drought were
identified as important constraints followed by diseases (Boubacar et al.,
2020). As a starting point for the breeding program in Togo, a PRA was
conducted in three regions, identifying leaf spot diseases, rosette,
groundnut bud necrosis, and insects, such as pod sucking bug and bruchid,
as the most important constraints limiting the production (Banla et al., 2018).
High pod yield is the most preferred trait in almost all reported studies.
However, pod size and oil content were the second and third preferred traits
in the Savanes region in Togo, and early maturing in Niger (Banla et al., 2018;
Coulibaly et al., 2017).

The PRA approach has been widely used across Africa for other crops
including potato, maize, cowpea, cassava, sweet potato, and pearl millet
(Abakemal et al., 2013; Baafi et al., 2015; Egbadzor et al., 2013; Kanfany et
al., 2020; Muhinyuza et al., 2012; Njoku et al., 2014). Those studies clearly
established that constraints and the trait preferences of farmers for a given
crop are location specific. Hence, plant breeders should consider both local
adaptation and, when possible, adaptation to a wide range of environments
when developing a new cultivar (Omanya et al., 2007). This study applies
PRA to gather information and farmers' knowledge to identify groundnut
production constraints and to assess traits preferred by farmers in the
Groundnut Basin of Senegal. This will serve as important background
information to the breeding program and will enable the development of
more popular varieties that meet farmers' needs for wide adoption.

Materials and Methods

1. Study Areas and Sampling

The study was conducted between March and April 2015 in the Groundnut
Basin located in central Senegal, where groundnut and pearl millet are the
predominant crops (FAOSTAT, 2015). Three representative groundnut-
growing regions of Senegal, Kaolack, Kaffrine, and Tambacounda, were
purposefully selected based on the importance of groundnut in terms of
production and area (Table 1). During the 2014 cropping season, these three
regions accounted for 39% of the national groundnut area and over 42%
of total groundnut production (FAOSTAT, 2015). With the help of the local extension officers, one department was selected in each region based on the number of farmers and farmers' organizations in the department. Three villages were then selected in each department according to their accessibility and the importance of groundnut production.

<table><caption>**Table 1**

**Characteristics of Selected Groundnut Production Regions in Senegal**</caption><thead><tr><td>Regions</td><td>Cultivated area (ha)</td><td>Production (mt)</td><td>Precipitation (mm)</td><td>Selected communities</td></tr></thead><tbody><tr><td>Kaffrine</td><td>209,100</td><td>272,570</td><td>655</td><td>Kathiot, Kahi</td></tr><tr><td>Kaolack</td><td>169,843</td><td>223,001</td><td>655</td><td>Guinth Kaye, Kaymor, Porokhane</td></tr><tr><td>Tambacounda</td><td>104,210</td><td>161,941</td><td>741</td><td>Koussanar, Makacolibatang</td></tr><tr><td>Senegal</td><td>1,134,330</td><td>1,432,086</td><td></td><td></td></tr></tbody></table>

Nine study sites were considered in the study (Figure 1). The villages were located in seven rural communes: three in Kaolack, two in Kaffrine, and two in Tambacounda. A focus group discussion was held in each of the selected villages to develop a general idea of groundnut cultivation in the corresponding area. In addition, within each village, 10 farmers were randomly selected for semi-structured interviews, creating a sample of 90 producers.
<figure><img src="image_29.png" /><figcaption>Figure 1</figcaption></figure>

Figure 1 – Map Showing Sampling Locations in the Groundnut Basin

## 2. Data Collection and Analysis

Local extension staff at the respective localities facilitated the PRA process by mobilizing farmers to participate in the focused group discussions. Both formal and informal approaches were employed to collect data for the study. The informal approach was used to generate information on farmer perceptions at the community level. The discussions involved nine focus group discussions (FGDs) with one at each village. Each focus group comprised of 10 to 17 farmers. The FGDs were standardized using a checklist of topics to be covered. The topics discussed included farm characteristics and farmers' agronomic practices, farmers' trait preferences for groundnut varieties, marketing of their products, and the perceived constraints affecting production.

The group discussions were followed by formal interviews using semi-structured questionnaires to explore issues that were more specific.
Individual farmers were surveyed using a semi-structured questionnaire to collect information on production constraints and farmer's preferences for varieties. This enabled each individual farmer to express their opinion independently.

Scoring and ranking techniques were used to assess farmers' varietal trait preferences and constraints to groundnut production. Data was coded and entered in Microsoft Excel. Analysis was conducted using Statistical Package for the Social Scientists (SPSS version 19) and summarized into percentages and means. Variables were subjected to descriptive statistics, cross-tabulation, one-sample t (two-sided), and Fisher tests. R was used to draw the map showing the PRA sites.

# Results

## 1. Demographic and Socio-economic Characters

In the studied sites, mostly men produce groundnut. The sex ratio is 88.9% males to 11.1% females (Table 2). In our sample, 72.22% of the respondents were farmers by occupation while 27.78% had additional income-generating activities. Most of the interviewed farmers (82.23%) were uneducated. A few (8.89%) possessed a primary school certificate and 5.56% a secondary certificate. The age of respondents ranged between 35 and 60 years. The proportion of the active population that were groundnut producers was 71.11%. Most of the farmers (63%) did not have any form of support or training and only 36% of the farmers belonged to a farmers' association.
<figure><img src="image_30.png" /><figcaption>Figure 2</figcaption></figure>

Figure 2 – Average Pod Yield Obtained as a Function of the Area and Application Rate of Mineral Fertilizer

## 2. Groundnut Importance and Production Level

The selected rural communes were predominantly groundnut producing areas, but other crops, like cereals, played a key role in the agricultural system. The average landholding varied from 1 to 30 ha and there was a significant difference in the importance of the crops grown in each location. A ranking of the importance of groundnut compared to other crops was completed based on the cultivated area allocated to groundnut and the production level. As expected, in almost all rural communes, groundnut occupied the largest share of land and was ranked first with an average mean rank of 1.3. In the central part of the peanut basin (Kaolack, Kaffrine) groundnut was the most important crop followed by pearl millet. Elsewhere in the eastern part of the country (Tambacounda), corn or sorghum were ranked as the second most important crop. Mean comparison of yields reveals significant differences between localities (p = 0.042). The average estimated grain yield in 2014 was 1,112 Kg ha⁻¹. The highest estimated grain yield (1,500 Kg ha⁻¹) was recorded in Guinth Kaye, while the lowest value 0.9 Kg ha⁻¹ was recorded in Porokhane. Both localities are in the same region (Kaolack). A similar trend was observed for the application of fertilizer
(Figure 2). On average, 116 Kg ha-1 of NPK fertilizer (6-20-10) was recorded at Guinth Kaye while 28 Kg ha-1 was recorded at Porokhane.

<table><caption>Table 2
Average Socio-demographic Profile of Interviewed Farmers by Region</caption><thead><tr><th>Variables</th><th>Kaolack</th><th>Kaffrine</th><th>Tambacounda</th><th>Total</th><th>%</th></tr></thead><tbody><tr><td><strong>Gender</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>3</td><td>3</td><td>4</td><td>10</td><td>11</td></tr><tr><td>Male</td><td>27</td><td>27</td><td>26</td><td>80</td><td>89</td></tr><tr><td><strong>Age</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;15</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td></tr><tr><td>15-35</td><td>3</td><td>5</td><td>5</td><td>13</td><td>14</td></tr><tr><td>35-60</td><td>18</td><td>15</td><td>18</td><td>51</td><td>57</td></tr><tr><td>60" &gt;= 60</td><td>9</td><td>10</td><td>6</td><td>25</td><td>28</td></tr><tr><td><strong>Education</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Koranic school</td><td>27</td><td>20</td><td>12</td><td>59</td><td>66</td></tr><tr><td>Local language</td><td>1</td><td>8</td><td>6</td><td>15</td><td>17</td></tr><tr><td>none</td><td>1</td><td>1</td><td>1</td><td>3</td><td>3</td></tr><tr><td>Primary</td><td>0</td><td>1</td><td>7</td><td>8</td><td>9</td></tr><tr><td>Secondary</td><td>1</td><td>0</td><td>4</td><td>5</td><td>6</td></tr><tr><td><strong>Member of a Farmer Organization</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>26</td><td>17</td><td>11</td><td>54</td><td>60</td></tr><tr><td>Yes</td><td>4</td><td>13</td><td>19</td><td>36</td><td>40</td></tr><tr><td><strong>Occupation Other than Farming</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>10</td><td>7</td><td>8</td><td>25</td><td>28</td></tr><tr><td>Yes</td><td>20</td><td>23</td><td>22</td><td>65</td><td>72</td></tr><tr><td><strong>Subsidy from the Government</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>25</td><td>19</td><td>19</td><td>63</td><td>70</td></tr><tr><td>Yes</td><td>5</td><td>11</td><td>11</td><td>27</td><td>30</td></tr><tr><td><strong>Received Training in Groundnut Cultivation</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>30</td><td>24</td><td>20</td><td>74</td><td>82</td></tr><tr><td>Yes</td><td>0</td><td>6</td><td>10</td><td>16</td><td>18</td></tr></tbody></table>
## 3. Varieties Grown and Their Provenances

Five known and named varieties were cultivated in the surveyed zones: GH119-20, 73-33, 28-206, Fleur 11, and 55-437 (Table 3). There were differences in the most commonly planted varieties by location (Figure 3). Out of the five varieties, 73-33 was the most cultivated and was ranked first in almost all rural communes except in Guinth Kaye, Porokhane, and to some extend Kaymor, where either 28-206 or GH119-20 were more commonly cultivated.

<table><caption>**Table 3**
**Attributes of the Cultivated Varieties**</caption><thead><tr><th rowspan="2">Varieties</th><th rowspan="2">Released Year</th><th colspan="6">Attributes</th></tr><tr><th>Pod Yield (t/ha)</th><th>Haulm Yield (t/ha)</th><th>Cycle (Days)</th><th>100 Seed Weight (g)</th><th>Seed Size</th><th>Main Use</th></tr></thead><tbody><tr><td>GH119-20</td><td>1960</td><td>1.5-2</td><td>2-2.5</td><td>110</td><td>85-90</td><td>large</td><td>confectionery</td></tr><tr><td>73-33</td><td>1973</td><td>2-2.5</td><td>1.5-2</td><td>110</td><td>50-52</td><td>medium</td><td>oil</td></tr><tr><td>Fleur11</td><td>1995</td><td>2.5-3</td><td>2</td><td>90</td><td>50-55</td><td>medium</td><td>oil-confectionery</td></tr><tr><td>55-437</td><td>1994</td><td>1.5-2</td><td>1</td><td>90</td><td>35-38</td><td>small</td><td>oil</td></tr><tr><td>28-206</td><td>1928</td><td>1.5-2</td><td>1.5</td><td>120</td><td>45-49</td><td>small</td><td>oil</td></tr></tbody></table>
<figure><img src="image_31.png" /><figcaption>Figure 3</figcaption></figure>

Figure 3 - Popular Varieties Cultivated in the Studied Sites

The variety 73-33 was the most grown across all the studied sites. The 28-206 was specific in Guinth Kaye and Porokhane and the variety GH119-20 was present in Kaymor. Fleur 11 was not very popular in Kaffrine (Kahi and Kathiot). A small portion of respondents (2%) did not have any idea about the type of variety they grew. Most farmers (83%) recycled seed by keeping a portion of the previous harvest. Only 20% obtained government-subsidized seeds or procured them from the local market. It was only in Koussanar, a rural commune of Tambacounda, where farmers were organized in a cooperative and multiplied their seed for the next coming growing season.

## 4. Trait Preferences

The mean rankings of farmer-preferred traits for groundnut varieties are presented in Figure 4. Characteristics with the smallest mean rank (1) are perceived to be the most important and the highest value (5) is considered as less important. Except for tolerance to biotic stresses (P = 0.007), where a significant difference was observed between rural communes, traits were ranked similarly by farmers interviewed in the study zones. Most of the farmers identified haulm yield as the second most important characteristic after pod yield, except in Koussanar where good pod filling was ranked
highest. The same locality identified erect plant stature as an important groundnut characteristic for variety adoption. High seed weight is also a preferred feature for groundnut growers in Kahi, Kathio, Kaymor, and Makacolibatang. Other important traits mentioned were adaptation to abiotic stresses such as short cycle and tolerance to drought. Tolerance to biotic constraints like resistance to leaf diseases is desired in groundnut variety. In general, farmers from the different sites did not consider good taste as an important trait for groundnut variety.

<figure><img src="image_32.png" /><figcaption>Figure 4</figcaption></figure>

Figure 4 - Farmers Preferences for Groundnut Variety Traits Across Rural Communes

## 5. Production Constraints

Prioritization of the constraints was based on the mean ranking given by farmers. These constraints were identified and listed during the focus group discussions. Each interviewed farmer further ranked them (Table 4). Characteristics with the lowest mean rank are classified as the most important. The most important constraints mentioned by farmers across sites were seed availability, land degradation, insects at storage, and land availability. Drought was ranked fifth, with a significant difference between sites. Marketing and leaf diseases were ranked sixth and seventh respectively with substantial differences in farmers' responses in ranking marketing.
These constraints were common to groundnut growing areas but the order varied from site to site. Insects, mainly pod-sucking bugs (Pentatomidae and Lygaeidae) and groundnut bruchid (Bruchidae), were considered the most common constraint to groundnut storage and were ranked as the most important in Kathiot alongside leaf diseases. Lack of tools was ranked in Koussanar, Kaymor, and Kathiot as an important constraint. Regarding drought, there was a noteworthy difference between respondents in their respective studied areas. More than half of the respondents (58%) did not classify drought as a major constraint to groundnut cultivation, while the remaining participants (42%) said the opposite. The damages were not so important and drought at the vegetative stage of the life cycle was more frequent. However, terminal drought caused more damage when it happened.
<table><caption>Table 4 Relative Ranking of Major Groundnut Production Constraints by Location</caption><thead><tr><th>Constraints</th><th>Guinth Kaye</th><th>Kahi</th><th>Kathiot</th><th>Kaymor</th><th>Koussanar</th><th>Maka</th><th>Porokhane</th><th>Total Mean</th><th>F</th><th>Rank</th></tr></thead><tbody><tr><td>Land degradation</td><td>2.31</td><td>2.33</td><td>2.2</td><td>2.06</td><td>1</td><td>1</td><td>4.66</td><td>2</td><td>0.47</td><td>2</td></tr><tr><td>Storage insects</td><td>4.01</td><td>2</td><td>1</td><td>3</td><td>2.4</td><td>1.56</td><td>4</td><td>2.04</td><td>0.063</td><td>3</td></tr><tr><td>Leaf diseases</td><td>3.89</td><td>2.78</td><td>1</td><td>1</td><td>2</td><td>3.2</td><td>3.41</td><td>2.56</td><td>0.406</td><td>7</td></tr><tr><td>Drought</td><td>4</td><td>2.67</td><td>3</td><td>3.67</td><td>1.75</td><td>1.75</td><td>4</td><td>2.36</td><td>0.023*</td><td>5</td></tr><tr><td>Land availability</td><td>2.17</td><td>1</td><td>3</td><td>2</td><td>3.71</td><td>4.1</td><td>3</td><td>2.13</td><td>0.597</td><td>4</td></tr><tr><td>Seed availability</td><td>2.8</td><td>1.63</td><td>2</td><td>1.6</td><td>1.67</td><td>2.2</td><td>2</td><td>1.91</td><td>0.145</td><td>1</td></tr><tr><td>Commercialization</td><td>3.44</td><td>2</td><td>2.5</td><td>3</td><td>1.75</td><td>2</td><td>2.4</td><td>2.5</td><td>0.018*</td><td>6</td></tr><tr><td>Lack of tools</td><td>5</td><td>3</td><td>2</td><td>2.4</td><td>1.67</td><td>2.5</td><td>2</td><td>2.58</td><td>0.047*</td><td>8</td></tr><tr><td>Cattle damage</td><td>5.07</td><td>4.22</td><td>4.34</td><td>3</td><td>3.5</td><td>4</td><td>5</td><td>3.33</td><td>0.667</td><td>9</td></tr></tbody></table>
Discussion

The presence of women farmers (11.1%) in this study was very low and reflects their limited access to land. One major consideration of this situation is socio-cultural. In Senegal, land acquisition is through inheritance from father to son. Another aspect that contributes strongly to this imbalance is the status of the family chief, typically conferred to the man who theoretically is in charge of the family's daily expenses. The limited involvement of women in groundnut cultivation has also been reported in other countries of West Africa (Banla et al., 2018; Oppong-Sekyere et al., 2015). Women do not own land, but they are involved in the cultivation. Arable land access is a real problem for women in West Africa, particularly in Senegal. PRA conducted on sorghum and pearl millet reported limited access to land by women, justifying their low involvement in the production (Kanfany et al., 2020). Conversely, in Burkina Faso, Sinare et al. (2021) highlighted the large involvement of women (more than 50%) in groundnut cultivation despite their limited and unequitable access to production resources. A similar situation was reported in Tanzania where men and women were equally involved in groundnut production (Daudi et al., 2018). Women's engagement in groundnut cultivation is socio-culturally specific.

Because the constraints listed by the farmers in the studied sites involve different actors of the value chain, we classified them into two categories. The first group included seeds availability, land availability, and commercialization. The second group encompassed low soil fertility, insects at storage, drought, and leaf diseases. The consistency of ranking the first group of constraints over the communities suggests that these are major constraints affecting groundnut production in the Groundnut Basin. These difficulties are similar in that the government and the private sector plays a greater role than research in helping farmers solve these problems. However, it is important to underscore the efforts made by the government to reorganize and make the groundnut sector more efficient. The most recent is the Program for Accelerated Pace of Agriculture (PRACAS) with a seed capital reconstitution program of 3.5 billion CFA. This program aims to
provide producers with a quantity of quality seeds. These efforts are
beneficial, but it is more than important to put in place a system or tool to
estimate the needs and consequently establish a production plan of the first
generations in terms of seed multiplication (G1, G2, and G3). The traceability
of the subsequent levels (G4, R1, and R2) allows one to know in real-time
who has which variety, what level, where, and when the product will be
available. The answers to these different questions will help resolve in part
the availability of seeds, one of the major constraints.

In the second group, low soil fertility, insects at storage, and drought
were identified as major constraints limiting the production, followed by
leaf diseases. This finding confirms the deteriorating state of soil fertility in
Senegal reported earlier by previous studies conducted in the same regions
(Kanfany et al., 2020). Senegal falls in the Sahelian zone and is subjected
to different scenarios of drought characterized by unequal distribution of
rainfall in both time and space. Consequently, the production of many crops
is threatened yearly by the interannual and across location rainfall variability.
Almost all West African countries identified drought as a major constraint
to groundnut production (Banla et al., 2018; Coulibaly et al., 2017; Oppong-
Sekyere et al., 2015; Ntare et al., 2008). In the present study, drought is
perceived to be more important in the eastern part of the Groundnut Basin
in the Tambacounda region (annual average rainfall 800 mm) than in the
central zone (average annual rainfall between 500-600 mm). This
paradoxical situation could results from the rainfall distribution and the type
of cultivated varieties. In fact, 73-33, a long cycle variety (110 days), was the
most cultivated in the eastern zone; in the central areas a mix of medium
and short cycle varieties was grown (55-437 and Fleur 11, 90 days). The
most drastic years in groundnut cultivation in Senegal were 2002 and 2011,
due to low and erratic rainfall patterns. The impact of such events is not
only extremely damaging, but also lasting. Farmers revealed that the rainy
seasons become shorter from year to year. Therefore, short cycle varieties
are wanted. However, it is important to report that during FGD, farmers in
the studied areas did not want extra-early maturing varieties because such
varieties might be the first to mature in an area and therefore might suffer
from too much rainfall and from livestock damage. They need groundnut
varieties that can mature early and escape the drought before it sets in.
Drought tolerance was unevenly ranked for any given site, showing that
they depended greatly on specific localities. Therefore, the development
of varieties better adapted to drought becomes an important breeding objective for each specific location.

In Kathiot and Kaymor (Nioro zone), leaf diseases were the major constraint that affected groundnut production; in other localities, leaf diseases were not perceived by farmers as important. The traditional measure taken by farmers to deal with leaf diseases and soil borne diseases in the study areas was crop rotation. Primarily, groundnut is cultivated in rotation with pearl millet in most of the studied zones or with other cereal species such as sorghum, maize, rice, and fonio. Crop rotation plays an important role in groundnut management. It is known that crop rotation is beneficial to restore soil fertility, as well as to prevent crop pests and soil borne diseases. According to farmers, crop rotation is a good strategy that can restore soil fertility and avoid severe leaf diseases. These results indicated that breeding for improved drought tolerance and leaf disease resistance should constitute major breeding objectives to ensure high yield and varietal acceptance by farmers in specific zones.

High pod yield is the most preferred trait reported in most studies conducted on groundnut across Africa (Banla et al., 2018; Coulibaly et al., 2017; Ntare et al., 2008; Oppong-Sekyere et al., 2015). Contrary to the findings reported by Banla et al. (2018) in Togo, where pod size and oil content followed high pod yield as the most important traits, in the present study, high biomass yield was ranked as the second preferred trait in all study areas. This is attributed to the high feeding values of groundnut haulm to animals and the Sahelian context of Senegal. Hence, breeding for dual purposes, high pod and biomass become essential for variety adoption. Farmers gave much importance to seed and pod size in the study and correlated big pod size to high pod yield. However, scientific studies have shown that there is no correlation between pod yield and morphometric characteristics in the Sahel (Foncéka et al., 2012). Another important consideration for farmers is the attractiveness and the marketability side of large pods and seeds. The fact that groundnut is a cash crop could justify why farmers paid much attention to pod and seed size. These findings corroborate the importance of large pods and seeds reported in West African countries, especially in Togo, Burkina, and Ghana (Banla et al., 2018; Sinare et al., 2021. In the past, groundnut was initially cultivated for its oil correlated to maturity. Oil content and good taste received little attention at the farmers'
level in the present study. This result could differ if the survey had considered the perspectives of industrial processors and end-users. These suggest that in the selection process for new improved varieties, yield and yield-related traits must constitute the primary selection criteria for a new improved variety to be accepted by farmers. The agreement between farmers and breeders in some of the criteria used for selecting groundnut varieties suggests the need for collaborative work to improve the efficiency of selection.

## Conclusion

In view of the results, it appears that breeding efforts should focus on developing high yielding (pod and biomass) and early-maturing varieties that will minimize crop loss through drought escape. To increase adoption and meet farmers' needs in the Groundnut Basin of Senegal, groundnut breeders should take into consideration the varietal preferences identified. The soil degradation identified by farmers as a major constraint lays down a multidisciplinary research approach based on innovative practices to limit and prevent soil degradation. The findings also call out the government and the private sector on land issues, access to equipment, seed availability, and commercialization, all identified as major constraints to increasing groundnut productivity.

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3. Yield Response of Dryland
Cereals to Fertilizer on
Smallholder Farms in Mali

**Hamza Haider**, Michigan State University, USA

**Melinda Smale**, Michigan State University, USA

**Veronique Theriault**, Michigan State University, USA

Abstract

In Mali, over 60% of the population lives in rural areas, and about half of
them live under the poverty line. Since farming is the primary livelihood of
those in rural areas, raising productivity is crucial for decreasing poverty.
This chapter explores the effectiveness of nitrogen fertilizer in raising yields
of dryland cereals on smallholder farms in Mali, using regional and national
datasets. Simple econometric analysis suggests there is minimal effect of
nitrogen fertilizer use on crop yields. However, when accounting for the
endogeneity of fertilizer use, we find yield response rates within the range
reported in the literature. As expected, sorghum has lower response rate
to fertilizer than maize. Yield responses are stronger in the Sudan Savanna
region than nationwide, highlighting the importance of agroecological
factors and the farming system. Soil texture and practices (anti-erosion
structures) affect both yields and estimated marginal effects of fertilizer. We
also find phosphorus to be a binding constraint in increasing agricultural
productivity. While most emphasis in the literature is placed on
understanding nitrogen fertilizer use, as recommended by agronomists, it is
crucial to promote balanced use of fertilizers so that other complementary
nutrients are available in the soil.

Keywords: yield response, cereals, Mali, smallholder farmers
Introduction

In Mali, over 60% of the population is rural, and half that segment lives below
the national poverty line (World Bank, 2017). Most rural people depend on
agriculture as their main source of livelihood. Dryland cereal crops, such
as maize, millet, and sorghum, account for two-thirds and three-quarters
of all cultivated land, depending on the years. Over recent decades, the
production of dryland cereals has grown primarily through the expansion of
cultivated areas rather than intensification, which is unsustainable. Despite
the continued release of improved varieties, millet and sorghum yields have
stagnated, with national averages hovering below 1 t/ha. Meanwhile, national
average maize yields have risen from 1.4 t/ha in 2001/05 to 2.6 t/ha in 2016/
18 (http://www.fao.org/faostat/en/#data/QC, November 2, 2020). Mali is
West Africa's third-largest producer of maize even though it stands fifth
in the area harvested, with the highest average yields among all 15 maize-
producing countries in the region (Abate et al., 2015). Nonetheless, as is
common throughout Sub-Saharan Africa, maximum yields for improved
maize varieties in farmers' fields remain substantially below yield potential
based on experimental conditions (4-6 t/ha according to Coulibaly 2008;
Macauley & Ramadjita, 2015) given the challenges of growing conditions and
incomplete input markets.

Inadequate use of mineral fertilizer has often been pinpointed as a cause
of stagnating productivity in dryland cereals in Sub-Saharan Africa (NEPAD,
2003). In 2008, the Malian government decided to reinstate an input subsidy
program to boost cereal productivity through improved access to fertilizer
while contributing to food and nutrition security via higher income and
lower consumer prices (Kone et al., 2019; Smale et al., 2012). Fertilizer
subsidies now constitute the largest expense item, accounting for about
25% of government spending on rural development (Theriault et al., 2018).
Given that extremely small amounts of fertilizer are currently used on either
sorghum or millet, we do not expect the fertilizer subsidy program to have
a generalized effect on production decisions nationwide. However, Theriault
et al. (2018) found a significant impact on fertilizer use and yields for
sorghum and maize in the Sudan Savanna, a region suitable for agriculture.
A more resounding critique of policy regarding mineral fertilizer is that
the soils of Mali are generally deficient in specific nutrients and that soil
organic matter is necessary for effective integration of nutrients (Dicko et al., 2016; Mason et al., 2014). In addition, long-term losses due to soil erosion have been documented (Bishop and Allen 1989). However, these have been offset in some areas by successful resource management programs (Tappan & McGahuey, 2007).

Despite the policy emphasis on mineral fertilizer, few studies have systematically examined the cereal yield response to fertilizer in Mali—which is fundamental for evaluating program impacts. An important exception is the analysis of farm experimental data by Dicko et al. (2016), who estimated response functions for nitrogen (N), phosphorus (P), and potassium (K) on maize, rice, and millet across the four bioclimate-based agro-ecological zones of Mali. In general, the authors found that economic optima of nutrient application rates diverged from recommended application rates, which remain uniform throughout the country and vary only by crop.

We know of only two other studies that address the productivity of dryland cereals in Mali, both of which use data collected in farm household surveys rather than under experimental conditions. Using a 12-year farm household panel dataset (1994-2006) from Mali's Sikasso region, Foltz et al. (2015) found a strongly significant response of maize yields to fertilizer, concluding that increasing fertilizer use has driven most of the maize productivity growth. Sikasso is a region of high productivity potential for maize, where it is grown in rotation with cotton. Applying a stochastic production frontier to nationally representative data from the Living Standards Measurement Survey-Integrated Survey of Agriculture (LSMS-ISA), Ahmed et al. (2017) found no significant response of yields to fertilizer across crops and regions. We know of no published analyses of yield response to fertilizer in sorghum or millet that employ farm household data.

We thus contribute to sparse literature by estimating dryland cereals response functions (maize, millet, and sorghum) using two farm household survey datasets. The first dataset, the Living Standards Measurement Survey-Integrated Survey of Agriculture (LSMS-ISA), is nationally representative and includes information on all three crops (maize, millet, and sorghum). The second dataset, collected by a research team from the Institut d'Economie Rurale, was collected only in the Sudan Savanna and focused on sorghum and maize. Both datasets were collected during the 2014/2015 growing season. In estimating our yield response functions, we
employ a combination of econometric approaches to compare and check the
robustness of our findings.

# Methods

## 1. Data Sources

We utilize data from the Living Standards Measurement Survey-Integrated Survey of Agriculture (LSMS-ISA), conducted in Mali in two visits during the 2014-15 growing season. Summary information about the survey is provided in a document compiled by the Planification and Statistics Unit (2016). With the probability of selection proportional to population size as of the 2009 Census, the statistical sample is nationally representative of rural and urban areas, excluding the region of Kidal. The total sample size was limited by the inability to collect data in some regions because of political insecurity, with the largest sample losses in the Mopti, Tombouctou, and Gao regions. The final sample includes about 3,804 households compared to the planned sample of 4,218. The number of standard enumeration areas (SEs, or grappes) was 1070, with 80% in rural areas, including 2-3 households per *grappe*. Compared to LSMS surveys which focused on household consumption, expenditures and income, the LSMS-ISA survey also contains plot-level data on input use and crop production. One-third of all plots inventoried by households in each SE were randomly sampled after grouping them by crop and crop association.¹ This procedure was necessary given that in Mali, large numbers of plots may be simultaneously cultivated by extended family farms, augmenting respondent burden and survey costs. We conduct our analysis only on data from the main rural agricultural regions of Mali, excluding Tombouctou, Gao, and some observations around Bamako. Therefore, our analytical sample covers the regions of Kayes, Koulikoro, Sikasso, Segou, and Mopti.

The second data source is a case study undertaken in the sorghum belt of Mali, which we use as a comparison since it is more focused on a specific farming system. Survey details are provided in Smale et al. (2015). The sample was drawn from a baseline census of all sorghum-growing households in 58 villages in the Circles of Kati, Dioila (both Koulikoro Region), and Koutiala
(Sikasso Region) of Sudan Savanna, within the 800 mm isohyet. Villages surveyed included fewer than 1000 persons listed as sites where the national research program and farmer associations had implemented activities since 2009. The multi-visit survey was conducted in four rounds from August 2014 through June 2015 by a team of experienced enumerators employed by the Institut d'Economie Rurale. The sample is representative of areas in the Sudan Savanna with some exposure to agricultural research outreach activities. For cereals, many sorghum growers also grow maize and millet in this region. Millet is also grown, but due to budget constraints, detailed plot information was collected only for sorghum and maize.

The sample of households was drawn with simple random sampling and augmented by five percent to account for possible non-responses, leading to a total of 623 households and an overall sampling fraction of 25%. Enumerators listed all plots operated by each sampled household. One plot was randomly sampled per crop and management type per household. After removing yield and fertilizer use outliers, the total analytical sample employed here is 1,086 plots, including 421 sorghum plots and 665 maize plots.

In addition to the household survey data, this dataset includes soil nutrient indicators measured in laboratory tests conducted on soil samples by the Institut d'Economie Rurale, Sotuba, Mali. Due to budget constraints, soil samples could not be collected from all sorghum and maize plots. Plots were subsampled randomly within crop (maize and sorghum) and plot management (collective, individual) groups. After the harvest, soil samples were obtained following a standard protocol with 8 sub-samples per plot collected in a zig-zag pattern to assure overall plot representation. Laboratory analysis followed Sparks et al. (1996). The analytical sample for soil nutrients is 643 plots.

The two household datasets are not directly comparable with respect to sampling methodology or statistical representation of the farming population. We exploit both datasets because each provides information on nitrogen use and response on cereal crops grown in farmers' fields in Mali. Information of this type is scarce. The Sudan Savanna dataset is structured on a village list reflecting organizational structure and agroecology. The LSMS-ISA dataset is structured according to administrative units with census-based, standard enumeration areas. No georeferenced identifiers
enable us to link one dataset to the other and directly compare results. Finally, variables are not identical between the two datasets.

Rainfall data were downloaded and compiled from the Climatology Resource for Agroclimatology site of the National Air and Space Administration.²

## 2. Econometric Approach

Our objective is to quantify the effect of fertilizer use on the yields of dryland cereal crops using household survey data. First, we estimate the yield response model:

$$Y_i = \beta_1 F_i + \beta_2 I_i + \gamma X_i + \varepsilon_i. \quad (1)$$

The dependent variable $Y_i$ denotes the crop yield (kg/ha) on plot i. The key explanatory variable is $F_i$, the quantity of fertilizer applied on plot I, with coefficient $\beta_1$. Fertilizer quantity is measured in nitrogen kg/ha to standardize different fertilizer types. Quantities are summed across the nitrogen content of the urea (46%), NPK (14%), DAP (18%), and other fertilizer (16.5%) applied on plot i.

Other than fertilizer, agricultural inputs $I_i$ are applied to plots, and these are typically included in yield response functions estimated with either experimental or survey data. Accounting for other inputs is important because fertilizer is often used in conjunction with other inputs. For example, plots that apply more fertilizer may have more labor allocated. If we do not control for labor quantity, the coefficient on fertilizer will include the effect of labor and will overestimate the effect of fertilizer on crop yield. $X_i$ is a vector of factors other than inputs that affect crop yield, such as plot characteristics.

The estimation strategies for equation (1) are tailored to the two data sets. Despite controlling for plot characteristics, $X_i$, the estimate of $\beta_1$ may be inconsistent if other omitted variables explain crop yield and correlate with the fertilizer quantity applied. For example, wealthier households may have higher crop yields because they can acquire more efficient agricultural practices from radio, television, or agricultural extension agents. They may
also apply more fertilizer because they have money or credit available to purchase fertilizer. Farmers may also apply fertilizer on plots with soils that they observe to be more responsive.

To eliminate household level confounding factors, in the LSMS model, we use household fixed effects to estimate model (1). In addition, since the survey is cross-sectional and contains data from a single year, we compare yields on plots within the same household with varying fertilizer levels applied. This allows us to control for any unobserved household-level factors that explain crop yields and correlate with the fertilizer quantity applied.

While this estimation strategy can provide reliable estimates for $β_1$, we may still be concerned about omitted variables bias. Unobserved plot-level characteristics are likely to affect crop yields. We can measure such variables, as in the case of soil samples collected in the Sudan Savanna, and include them as explanatory variables. For example, the soil's organic matter partly determines the responsiveness of nitrogen fertilizer (Marenya & Barrett, 2009).

With the LSMS data, we use instrumental variables estimation, combined with household fixed effects, to provide more robust estimates of the effect of fertilizer on crop yields. The instrument captures the general diffusion of fertilizer in the region where the household is located. We use the average fertilizer rate used across all other plots of households growing that crop in the *grappe* (1 to 30 plots, with an average of 3.5 plots).

Since the identification strategy combines household fixed effects with instrumental variables estimation, the instrument must vary within households. The diffusion rate of fertilizer differs across crops, and hence the instrument varies within households when we consider more than one crop. If we estimate a yield-response function for each crop separately, the instrument will not vary within households, and we are unable to use household fixed effects combined with instrumental variables estimation. Thus, we estimate separate regressions for millet, sorghum, and maize with only household fixed effects.

We expect the instrument to be correlated with the potentially endogenous variable $F_i$, because it captures the general availability of fertilizer in the locality. We do not expect the fertilizer allocation of
households to directly influence the crop yields of other households – except through the increased use of fertilizer. Under these assumptions, we obtain consistent estimates of $β_1$. The instrument will not be valid if it affects yields through mechanisms other than more fertilizer being applied on that plot. For example, it would be problematic if the instrument was correlated with more intensive use of other inputs. Since we include the quantity of other inputs allocated to the plot as explanatory variables, we overcome this problem by controlling for the instrument's effect through other inputs.

In the regressions estimated with nutrients measured in laboratory tests on soil samples, we combine both dryland cereals in one regression, given the even smaller number of observations. Binary and interaction variables are included to control crop effects on grain yield and yield response to nitrogen nutrients per ha. We also test a quadratic term for nitrogen nutrients per ha, which expresses whether a turning point in yield response to nitrogen is observable in the data. In the set of regressions estimated with farmer-perceived soils classes, along with the combined regression, we have also estimated separate regressions for maize and sorghum because sample sizes are larger.

As a robustness check, we also test the final combined model with farmer-perceived soil classes (the largest sample) with the Control Function Approach. A Control Function Approach is applied instead of instrumental variable methods because of the potential endogeneity of multiple variables (nitrogen fertilizer applied, interaction, and squared terms). As instruments, we utilize whether the plot manager has benefited from the fertilizer subsidy and the village share of plot managers who are registered cooperatives. Theriault et al. (2018) found that Malian farmers who are members of cooperatives have better access to fertilizer than non-members. Participation in the subsidy program and cooperative is likely to affect fertilizer use but unlikely correlated with unobserved variables. All Sudan Savannah regressions are estimated with robust standard errors, clustered by household.

### 3. Variables

In the two analyses, explanatory variables differ somewhat to reflect
underlying differences in the data. Variables used in the LSMS analysis are listed in Table 1. The plot area, measured in hectares, allows us to examine whether productivity differs across plot sizes. Vector I includes manure, compost, other organic fertilizer (i.e., crop residues), pesticides, herbicides, fungicides, other protecting liquids, improved and local seed. The distance of the plot from the homestead is related to the time taken to reach the field for crop management. It is also, potentially, a measure of fertility since those located farther away may have been more recently cleared and brought into cultivation. Also, households may choose to invest more in nearby plots since they are more secure (Gebremedhin & Swinton 2003) and easier to reach. A dummy variable is included to control for whether there is an anti-erosion structure on the plot, such as stone contour bunds or dikes—which have been promoted in certain regions of the country to offset the heavy loss of soil nutrients during the rainy season and enable farmers to retain moisture (Tappan & McGahuey, 2007).

Location in the toposequence (lowland, plain, slope, plateau) and soil texture are important indicators of soil quality in this region and are highly correlated with the crops grown (Guirkinger et al., 2015; Udry, 1996). Bazile et al. (2008) explain that farmers define soil type according to the position of the field in the toposequence. Farmers distinguish the shallow soils of the plateaus or higher areas from medium-depth soils and alluvial, low-lying soils ('bas-fonds'). Soil differentiation observed within and among farms explains growing multiple varieties and crops per farm and across a landscape. To capture the location of the plot in the toposequence, we include binary indicators of location in the plain, lowland, on a slope, or plateau. Dummy variables for soil type are also included, representing farmer-perceived soil classes (sandy, silty, clayey).
<table><caption>**Table 1**

Descriptive Statistics, LSMS-ISA Data</caption><thead><tr><th>Variable</th><th>Mean</th><th>S.D.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Millet Yield (kg/ha)</td><td>695</td><td>629</td><td>0.660</td><td>3759</td></tr><tr><td>Sorghum Yield (kg/ha)</td><td>735</td><td>686</td><td>0.530</td><td>3930</td></tr><tr><td>Maize Yield (kg/ha)</td><td>1492</td><td>1221</td><td>1.26</td><td>6000</td></tr><tr><td><strong>Material Inputs</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Nitrogen Fertilizer (N nutrient kg/ha)</td><td>6.74</td><td>23.6</td><td>0.000</td><td>288</td></tr><tr><td>Manure (kg/ha)</td><td>1594</td><td>3800</td><td>0.000</td><td>29850</td></tr><tr><td>Compost (kg/ha)</td><td>25.8</td><td>247</td><td>0.000</td><td>4889</td></tr><tr><td>Other Organic Fertilizer (kg/ha)</td><td>5.64</td><td>60.6</td><td>0.000</td><td>1708</td></tr><tr><td>Pesticides (liter/ha)</td><td>0.053</td><td>0.51</td><td>0.000</td><td>10.5</td></tr><tr><td>Fungicide (liter/ha)</td><td>0.033</td><td>0.42</td><td>0.000</td><td>19.7</td></tr><tr><td>Herbicide (liter/ha)</td><td>0.270</td><td>1.17</td><td>0.000</td><td>19.1</td></tr><tr><td>Other Protecting Liquids (liter/ha)</td><td>0.007</td><td>0.11</td><td>0.000</td><td>3.14</td></tr><tr><td>Local Seed (kg/ha)</td><td>10.3</td><td>15.4</td><td>0.000</td><td>236</td></tr><tr><td>Improved Seed (kg/ha)</td><td>1.00</td><td>4.45</td><td>0.000</td><td>50.7</td></tr><tr><td><strong>Labor</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Total Labor (no. of days/ha)</td><td>45.1</td><td>84.8</td><td>0.000</td><td>1031</td></tr><tr><td><strong>Plot Characteristics</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Plot Area (ha)</td><td>3.07</td><td>6.12</td><td>0.020</td><td>52.7</td></tr><tr><td>Distance to plot from house (km)</td><td>2.81</td><td>4</td><td>0.000</td><td>60.0</td></tr><tr><td>Plain (0/1)</td><td>0.7</td><td>0.464</td><td>0.000</td><td>1.00</td></tr><tr><td>Plateau (0/1)</td><td>0.147</td><td>0.355</td><td>0.000</td><td>1.00</td></tr><tr><td>Lowlands (0/1)</td><td>0.034</td><td>0.18</td><td>0.000</td><td>1.00</td></tr><tr><td>Sloped (0/1)</td><td>0.13</td><td>0.34</td><td>0.000</td><td>1.00</td></tr><tr><td>Soil Sandy (0/1)</td><td>0.536</td><td>0.498</td><td>0.000</td><td>1.00</td></tr><tr><td>Soil Clay (0/1)</td><td>0.357</td><td>0.479</td><td>0.000</td><td>1.00</td></tr><tr><td>Soil Lateritic (0/1)</td><td>0.11</td><td>0.31</td><td>0.000</td><td>1.00</td></tr><tr><td>Anti-Erosion Structure (0/1)</td><td>0.043</td><td>0.2</td><td>0.000</td><td>1.00</td></tr></tbody></table>

Source: Authors, based on LSMS-ISA, Mali. Number of plot observations=3733
Explanatory variables used in the analysis of the Sudan Savanna data are listed in Table 2. We estimate maize-sorghum yield (grain harvested per ha) response to nitrogen but do not include millet plots, for which we do not have production data. Fertilizer application is computed in the same way as in the LSMS analysis. All regressions include conventional inputs, a common set of plot characteristics, and a rainfall indicator at the village level. Manure application is measured as a binary variable because of difficulties in measuring quantities reliably. Labor days, liters of herbicides, and hours of equipment use are computed per ha. Common plot characteristics are the distance (in minutes walking) from the homestead to the plot, presence of a soil erosion structure on the plot, association of the primary crop with a leguminous (groundnut, cowpea) intercrop. Average rainfall during the period of fertilization in the survey year is recorded at the geographical scale of the village.

We test two sets of soils characteristics. In the first, soils characteristics are by soil nutrient content as tested in the laboratory: percentage soil organic matter (C), sand, silt, and clay, the percentage total nitrogen (N), assimilable phosphorus (P), exchangeable potassium (K), and soil pH (KCl). Since total carbon content (C) changes over centuries and active carbon changes over 3-5 years (Weil et al., 2016), these are not affected by recent fertilizer applications. Similarly, recent fertilizer use does not affect total nitrogen content (N), which includes the nitrogen in the soil organic matter. Nor can farmers deduce the specific nutrient content of their soils (P, K). In the second, we use farmer-perceived soils characteristics. Binary variables are entered for the location of the plot in the toposequence and farmer-perceived soil classes.
<table><caption>Table 2
Descriptive Statistics, Sudan Savanna Data</caption><thead><tr><td>Variable</td><td>Mean</td><td>S.D.</td><td>Min</td><td>Max</td></tr></thead><tbody><tr><td>Maize yield (kg/ha)</td><td>1497</td><td>945</td><td>12.5</td><td>4730</td></tr><tr><td>Sorghum yield (kg/ha)</td><td>642</td><td>681</td><td>0.000</td><td>4286</td></tr><tr><td>Nitrogen fertilizer (nutrient kg/ha)</td><td>19.5</td><td>25.7</td><td>0.000</td><td>100</td></tr><tr><td>Sorghum plot (0/1)</td><td>0.609</td><td>0.488</td><td>0.000</td><td>1.00</td></tr><tr><td>Manure (0/1)</td><td>0.641</td><td>0.479</td><td>0.000</td><td>1.00</td></tr><tr><td>Labor (days/ha)</td><td>68.0</td><td>66.6</td><td>0.000</td><td>800</td></tr><tr><td>Herbicide (liters/ha)</td><td>1.68</td><td>2.24</td><td>0.000</td><td>25.0</td></tr><tr><td>Equipment (hours/ha)</td><td>475</td><td>474</td><td>0.000</td><td>5294</td></tr><tr><td>Distance from House (minutes)</td><td>17.4</td><td>17.5</td><td>1.00</td><td>160</td></tr><tr><td>Soil Erosion Structure</td><td>0.188</td><td>0.391</td><td>0.000</td><td>1.00</td></tr><tr><td>Legume Intercrop (0/1)</td><td>0.112</td><td>0.316</td><td>0.000</td><td>1.00</td></tr><tr><td>N (% total nitrogen)</td><td>0.028</td><td>0.023</td><td>0.010</td><td>0.200</td></tr><tr><td>C (% organic matter)</td><td>0.522</td><td>0.334</td><td>0.020</td><td>2.63</td></tr><tr><td>P (assimilable phosphorus)</td><td>1.29</td><td>1.31</td><td>0.210</td><td>15.9</td></tr><tr><td>K (exchangeable K)</td><td>0.246</td><td>0.210</td><td>0.020</td><td>1.87</td></tr><tr><td>Ph (KCl)</td><td>5.34</td><td>0.400</td><td>3.15</td><td>7.25</td></tr><tr><td>0.05)<sup>1</sup>}">Sand (% > 0.05)</td><td>59.6</td><td>12.8</td><td>7.00</td><td>90.0</td></tr><tr><td>Silt (%0.05-0.002 mm)</td><td>36.2</td><td>12.3</td><td>8.00</td><td>90.0</td></tr><tr><td>Clay (% < 0.002 mm)</td><td>4.26</td><td>2.88</td><td>0.000</td><td>23.0</td></tr><tr><td>Rainfall (mm, period of fertilization)</td><td>220</td><td>31.0</td><td>164</td><td>299</td></tr><tr><td>Plain</td><td>0.865</td><td>0.341</td><td>0.000</td><td>1.00</td></tr><tr><td>Lowlands</td><td>0.015</td><td>0.122</td><td>0.000</td><td>1.00</td></tr><tr><td>Slope</td><td>0.119</td><td>0.324</td><td>0.000</td><td>1.00</td></tr><tr><td>Sandy (0/1)</td><td>0.381</td><td>0.486</td><td>0.000</td><td>1.00</td></tr><tr><td>Silty (0/1)</td><td>0.203</td><td>0.403</td><td>0.000</td><td>1.00</td></tr><tr><td>Clayey (0/1)</td><td>0.269</td><td>0.444</td><td>0.000</td><td>1.00</td></tr><tr><td>Gravelly (0/1)</td><td>0.147</td><td>0.354</td><td>0.000</td><td>1.00</td></tr></tbody></table>

Source: Authors, based on Sudan Savanna case study data. n=1222 for all except manure (1096).
# Results

## 1. Descriptives

Millet, sorghum, and maize are grown on 39, 32, and 29 percent of the plots in the LSMS sample, respectively. Fertilizer application rates differ significantly across crops. Table 3 shows average use rates calculated from the LSMS and Sudan Savanna datasets by cereal crop and region, compared with recommended rates and economically optimal rates estimated with response functions based on experimental data (Dicko et al., 2016). They also differ in major ways by region.

Agronomic recommended rates of N per ha are 32 for sorghum and millet, and 84 for maize, throughout the country (Dicko et al., 2016). These correspond to 100 kg/ha on all three crops for cereal complex, 100 kg/ha of NPK (16-16-16) for millet and sorghum, 250 kg/ha of NPK (23-10-5) for maize, 100-150 of DAP on cereals, and 50-400 kg/ha for other crops and fertilizers (see also Thériault et al., 2016). The overall mean for N kg per ha on millet, sorghum, and maize in the LSMS data is 6.7, but use rates on maize are considerably higher except for the region of Kayes. For all three cereals and all five regions except millet in Sikasso, estimated mean rates of use are but a fraction of economically optimal rates. In Koulikoro, Segou, and Mopti, average N use rates on sorghum and millet are in the single digits or lower, while economic optima range from 21 to 26. Again, the mean rate of N applied per ha to maize in Sikasso (36.2) is closest but is still far from the economic optimum (56-65). Mean phosphorus application rates for the entire LSMS sample are only 1 kg/ha and 1.9 kg/ha for millet and sorghum, respectively, but 6.7 kg/ha for maize—which is close to recommended levels. Recommended use rates for P are 10 for sorghum and millet, and economically optimal rates are estimated to be higher (Dicko et al., 2016). Overall, applying their fertilizer optimization tool, Dicko et al. (2016) found that the economically optimal rates of N were well below recommendations for maize, sorghum, and millet, varying by bioclimate.

Mean applications rates of N per hectare on maize and sorghum are also shown in Table 3 for the Sudan Savanna. In this relatively high potential area, mean rates of use on sorghum (6.41) are closer to rates in Sikasso in the LSMS
(9.38) than in Koulikoro at (<1), and even roughly the same on maize (39.8) as in Sikasso (36.2). Again, both are but a fraction of the economically optimal rate estimated by Dicko et al. (2016), which is, in turn, but a fraction of the nationally recommended rate.

<table><caption>**Table 3**
Average Use Rates for N, Compared with Recommended and Economic Optima, by Region and Crop</caption><thead><tr><th></th><th>Average use rates (N kg/ha)</th><th>Economically optimal rate</th><th>Recommended rate</th></tr></thead><tbody><tr><td colspan="4"><strong>LSMS-ISA</strong></td></tr><tr><td></td><td></td><td><strong>Millet</strong></td><td></td></tr><tr><td>Kayes</td><td>0.000</td><td>no data</td><td>32.0</td></tr><tr><td>Koulikoro</td><td>0.316</td><td>no data</td><td>32.0</td></tr><tr><td>Sikasso</td><td>8.19</td><td>8.00</td><td>32.0</td></tr><tr><td>Segou</td><td>3.65</td><td>21.0</td><td>32.0</td></tr><tr><td>Mopti</td><td>1.50</td><td>21.0</td><td>32.0</td></tr><tr><td></td><td></td><td><strong>Sorghum</strong></td><td></td></tr><tr><td>Kayes</td><td>0.182</td><td>26.0</td><td>32.0</td></tr><tr><td>Koulikoro</td><td>0.722</td><td>26.0</td><td>32.0</td></tr><tr><td>Sikasso</td><td>9.38</td><td>26.0-28.0</td><td>32.0</td></tr><tr><td>Segou</td><td>7.15</td><td>20.0-26.0</td><td>32.0</td></tr><tr><td></td><td></td><td><strong>Maize</strong></td><td></td></tr><tr><td>Mopti</td><td>0.360</td><td>20.0</td><td>32.0</td></tr><tr><td>Kayes</td><td>0.453</td><td>54.0</td><td>84.0</td></tr><tr><td>Koulikoro</td><td>17.3</td><td>54.0</td><td>84.0</td></tr><tr><td>Sikasso</td><td>36.2</td><td>54.0-65.0</td><td>84.0</td></tr><tr><td>Segou</td><td>11.7</td><td>31.0-54.0</td><td>84.0</td></tr><tr><td colspan="4"><strong>Sudan Savanna</strong></td></tr><tr><td></td><td></td><td><strong>Sorghum</strong></td><td></td></tr><tr><td>Koulikoro, Sikasso</td><td>6.41</td><td>26.0</td><td>32.0</td></tr><tr><td></td><td></td><td><strong>Maize</strong></td><td></td></tr><tr><td>Koulikoro, Sikasso</td><td>39.8</td><td>54.0</td><td>84.0</td></tr></tbody></table>
Source: Authors, based on LSMS and Sudan Savanna survey data; recommended and economically optimal rates from Dicko et al. (2016).

Note: Tombouctou, Gao and Mopti (for maize) excluded because of very few observations.

## 2. Yield Response Functions

Tables 5-8 present yield response models estimated with the LSMS data. The yield and input variables, including the quantity of fertilizer, are included in logarithms to smoothen their distributions, which are concentrated in lower values and skewed in shape. $\beta_1$ and $\beta_2$ are interpreted in terms of percentage changes of yield. The coefficients have been converted into marginal products by computing the marginal change in yield (in kg/ha) from a one percent increase in the quantity of fertilizer (N kg/ha) at the mean and are indicated in the bottom rows of the tables.

In preliminary regressions, the coefficients on nitrogen fertilizer across the household fixed effects models that do not use instrumental variables suggest that a one percent increase in the quantity of nitrogen fertilizer applied to the plot results in only a 0.04-0.07 percent increase in yields of dryland cereals. These elasticities correspond to marginal physical products of only 5.2 to 8.8, on average, for dryland cereals (maize, millet, and sorghum).

FE-IV estimates for more robust inference of the effect of fertilizer on yields are shown in Table 4. In all specifications, the first-stage F-statistic is much greater than 10, which is often used as a rule of thumb for the inclusion restriction. The first-stage F-statistic is also greater than the Stock-Yogo 10 percent maximal IV size critical value of 16.38, suggesting that the inclusion restriction is satisfied. The FE-IV estimates for fertilizer are several times as large as those reported above, suggesting that not controlling for endogeneity may diminish estimates of the yield response to fertilizer. In all specifications, the effect of nitrogen fertilizer applied is statistically significant. A one percent increase in the quantity of fertilizer applied to the plot results in a 0.1-0.2 percent increase in yields of dryland cereal crops. This translates to a 17-27 kg/ha increase in dryland cereal crop yield for an additional nitrogen kg/ha fertilizer.³
Other control variables described above are included sequentially to see whether the coefficient on fertilizer is sensitive to these. We find that yields are decreasing in plot size - consistent with the inverse productivity relationship that has also been observed in this region (Guirkinger et al., 2015; Kazianga & Wahhaj, 2013; Udry 1996). Plots where manure has been applied, and those with anti-erosion structures, achieve greater yields. Yields are increasing in the quantity of local seed and increasing even more in the quantity of improved seeds. More labor allocated to a plot also raises yields. This effect is strong throughout and with a relatively high elasticity—suggesting that labor constrains productivity. We find no effects of pesticides, fungicides, or other protecting liquids, which are used in very limited amounts. The presence of anti-erosion structures has a meaningful effect on yields. Controlling for toposequence and soil type reduces the marginal product attributable to fertilizer. Models were also estimated with specifications that contained squared terms for fertilizer and interaction terms between fertilizer and crop dummy variables. The point estimates of these square and interaction terms were close to zero and not statistically significant, so we retained more parsimonious specifications.

<table><caption>**Table 4**
Dryland Cereals Yield Response to N Fertilizer Applied, Instrumental Variables-household Fixed Effects Model</caption><thead><tr><th>Variables</th><th>-1</th><th>-2</th><th>-3</th><th>-4</th></tr></thead><tbody><tr><td>Nitrogen Fertilizer</td><td>0.190***<br>(0.073)</td><td>0.218***<br>(0.081)</td><td>0.162**<br>(0.075)</td><td>0.149*<br>(0.080)</td></tr><tr><td>Manure</td><td></td><td>0.026***<br>(0.008)</td><td>0.024***<br>(0.009)</td><td>0.026***<br>(0.010)</td></tr><tr><td>Compost</td><td></td><td>-0.058<br>(0.038)</td><td>-0.044<br>(0.037)</td><td>-0.033<br>(0.041)</td></tr><tr><td>Other Organic Fertilizer</td><td></td><td>0.051<br>(0.066)</td><td>0.024<br>(0.066)</td><td>0.022<br>(0.067)</td></tr><tr><td>Pesticide</td><td></td><td>0.253<br>(0.211)</td><td>0.304<br>(0.201)</td><td>0.304<br>(0.280)</td></tr><tr><td>Fungicide</td><td></td><td>0.076<br>(0.272)</td><td>0.029<br>(0.248)</td><td>0.082<br>(0.259)</td></tr><tr><td>Herbicide</td><td></td><td>0.104</td><td>-0.063</td><td>-0.095</td></tr></tbody></table>
<table><thead><tr><th>Variables</th><th>-1</th><th>-2</th><th>-3</th><th>-4</th></tr></thead><tbody><tr><td></td><td></td><td>(0.137)</td><td>(0.134)</td><td>(0.144)</td></tr><tr><td>Other Protecting Liquids</td><td></td><td>0.673<br>(1.285)</td><td>0.769<br>(1.178)</td><td>0.967<br>(1.202)</td></tr><tr><td>Total Labor</td><td></td><td>0.447***<br>(0.030)</td><td>0.335***<br>(0.033)</td><td>0.283***<br>(0.039)</td></tr><tr><td>Local Seed</td><td></td><td></td><td>0.353***<br>(0.039)</td><td>0.294***<br>(0.044)</td></tr><tr><td>Improved Seed</td><td></td><td></td><td>0.488***<br>(0.073)</td><td>0.417***<br>(0.085)</td></tr><tr><td>Millet</td><td>-0.159*<br>(0.090)</td><td>0.071<br>(0.093)</td><td>0.249***<br>(0.096)</td><td>0.295**<br>(0.117)</td></tr><tr><td>Sorghum</td><td>-0.393***<br>(0.075)</td><td>-0.188**<br>(0.077)</td><td>-0.036<br>(0.079)</td><td>0.053<br>(0.101)</td></tr><tr><td>Plot Area</td><td></td><td></td><td></td><td>-0.025***<br>(0.005)</td></tr><tr><td>Distance (km) from House</td><td></td><td></td><td></td><td>0.017*<br>(0.009)</td></tr><tr><td>Plain</td><td></td><td></td><td></td><td>-0.008<br>(0.130)</td></tr><tr><td>Plateau</td><td></td><td></td><td></td><td>-0.019<br>(0.170)</td></tr><tr><td>Lowland</td><td></td><td></td><td></td><td>-0.005<br>(0.210)</td></tr><tr><td>Sandy</td><td></td><td></td><td></td><td>-0.032<br>(0.210)</td></tr><tr><td>Clay</td><td></td><td></td><td></td><td>-0.028<br>(0.212)</td></tr><tr><td>Anti-Erosion Structure</td><td></td><td></td><td></td><td>0.486**<br>(0.198)</td></tr><tr><td>Observations</td><td>2,453</td><td>2,043</td><td>1,707</td><td>1,307</td></tr><tr><td>Number of households</td><td>776</td><td>671</td><td>548</td><td>425</td></tr></tbody></table>
<table><thead><tr><th>Variables</th><th>-1</th><th>-2</th><th>-3</th><th>-4</th></tr></thead><tbody><tr><td>Kleibergen Paap F statistic</td><td>218.6</td><td>155.8</td><td>148.3</td><td>112.5</td></tr><tr><td>Marginal Effect of N</td><td>23.13</td><td>26.95</td><td>19.49</td><td>16.81</td></tr><tr><td>Nutrient Applied</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Source: Authors, based on LSMS data. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Sample sizes drop with missing observations in more complete models, particularly those including seed quantities.

Separate household fixed effects regressions are shown in Tables 5-7 by crop. The results suggest that millet and sorghum yields are unaffected by fertilizer use. For maize, in one of the simpler models, a one percent increase in the quantity of fertilizer applied to the plot results in about 0.15 percent higher yields, corresponding to a marginal product of 11. These models have a smaller sample size than the pooled household fixed effects model. Additionally, these coefficients may be downward biased, given that the estimates from the pooled household fixed effects models are smaller than the FE-IV estimates. However, we consider these estimates as lower bounds for the true effect of fertilizer on crop yields.

<table><caption>Table 5
Millet Yield Response to N Fertilizer Applied, Household Fixed Effects Model</caption><thead><tr><th>Variables</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Nitrogen Fertilizer</td><td>0.0392<br>(0.0724)</td><td>-0.00945<br>(0.0685)</td><td>0.00449<br>(0.0642)</td><td>-0.00372<br>(0.0714)</td></tr><tr><td>Manure</td><td></td><td>0.0271**<br>(0.0122)</td><td>0.0222*<br>(0.0122)</td><td>0.0156<br>(0.0137)</td></tr><tr><td>Compost</td><td></td><td>-0.0774<br>(0.0754)</td><td>-0.0518<br>(0.0744)</td><td>-0.0751<br>(0.0862)</td></tr><tr><td>Other Organic Fertilizer</td><td></td><td>0.0832<br>(0.0720)</td><td>0.0806<br>(0.0676)</td><td>0.0803<br>(0.0657)</td></tr><tr><td>Fungicide</td><td></td><td>-0.668<br>(1.004)</td><td>-0.630<br>(0.938)</td><td>-1.261<br>(0.923)</td></tr><tr><td>Herbicide</td><td></td><td>2.565<br>(10.59)</td><td>0.960<br>(9.893)</td><td>2.138<br>(9.408)</td></tr><tr><td>Total Labor</td><td></td><td>0.470***</td><td>0.314***</td><td>0.156***</td></tr></tbody></table>
<table><thead><tr><th>Variables</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td></td><td></td><td>(0.0481)</td><td>(0.0529)</td><td>(0.0602)</td></tr><tr><td>Local Seed</td><td></td><td></td><td>0.409***</td><td>0.360***</td></tr><tr><td></td><td></td><td></td><td>(0.0652)</td><td>(0.0698)</td></tr><tr><td>Improved Seed</td><td></td><td></td><td>0.550***</td><td>0.350</td></tr><tr><td></td><td></td><td></td><td>(0.208)</td><td>(0.259)</td></tr><tr><td>Plot Area</td><td></td><td></td><td></td><td>-0.0310***</td></tr><tr><td></td><td></td><td></td><td></td><td>(0.00828)</td></tr><tr><td>Distance to plot</td><td></td><td></td><td></td><td>0.0334**</td></tr><tr><td></td><td></td><td></td><td></td><td>(0.0150)</td></tr><tr><td>Plaine</td><td></td><td></td><td></td><td>0.0543</td></tr><tr><td></td><td></td><td></td><td></td><td>(0.186)</td></tr><tr><td>Plateau</td><td></td><td></td><td></td><td>0.0202</td></tr><tr><td></td><td></td><td></td><td></td><td>(0.314)</td></tr><tr><td>Lowland</td><td></td><td></td><td></td><td>-0.220</td></tr><tr><td></td><td></td><td></td><td></td><td>(0.383)</td></tr><tr><td>Sandy</td><td></td><td></td><td></td><td>-0.00658</td></tr><tr><td></td><td></td><td></td><td></td><td>(0.411)</td></tr><tr><td>Clay</td><td></td><td></td><td></td><td>0.411</td></tr><tr><td></td><td></td><td></td><td></td><td>(0.448)</td></tr><tr><td>Anti-Erosion Structure</td><td></td><td></td><td></td><td>1.789***</td></tr><tr><td></td><td></td><td></td><td></td><td>(0.369)</td></tr><tr><td>Constant</td><td>5.996***<br>(0.0282)</td><td>4.633***<br>(0.139)</td><td>4.267***<br>(0.150)</td><td>4.635***<br>(0.466)</td></tr><tr><td>Observations</td><td>1,376</td><td>1,182</td><td>1,018</td><td>813</td></tr><tr><td>Number of households</td><td>771</td><td>688</td><td>585</td><td>476</td></tr><tr><td>Marginal Effect of N</td><td>10.37</td><td>-2.34</td><td>0.99</td><td>-0.81</td></tr><tr><td>Nutrient Applied</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Source: Authors based on LSMS data. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Sample sizes drop with missing observations in more complete models, particularly those including seed quantities.
<table><caption>Table 6
Sorghum Yield Response to N Fertilizer Applied, Household Fixed Effects Model</caption><thead><tr><th>Variables</th><th>-1</th><th>-2</th><th>-3</th><th>-4</th></tr></thead><tbody><tr><td>Nitrogen Fertilizer</td><td>0.0503<br>(0.0590)</td><td>0.0663<br>(0.0545)</td><td>0.0265<br>(0.0582)</td><td>0.0399<br>(0.0579)</td></tr><tr><td>Manure</td><td></td><td>0.0356<br>(0.0220)</td><td>0.0411*<br>(0.0245)</td><td>0.0301<br>(0.0250)</td></tr><tr><td>Compost</td><td></td><td>-0.184<br>(0.441)</td><td>0.432<br>(1.146)</td><td>4.949<br>(5.166)</td></tr><tr><td>Other Organic Fertilizer</td><td></td><td>-1.010<br>(1.889)</td><td>-1.025<br>(1.878)</td><td>-0.910<br>(1.724)</td></tr><tr><td>Fungicide</td><td></td><td>2.347**<br>(1.168)</td><td>2.081*<br>(1.157)</td><td>4.539***<br>(1.726)</td></tr><tr><td>Herbicide</td><td></td><td>0.0986<br>(0.281)</td><td>0.0539<br>(0.310)</td><td>0.0319<br>(0.295)</td></tr><tr><td>Total Labor</td><td></td><td>0.496***<br>(0.0567)</td><td>0.316***<br>(0.0704)</td><td>0.309***<br>(0.0761)</td></tr><tr><td>Local Seed</td><td></td><td></td><td>0.269***<br>(0.0828)</td><td>0.264***<br>(0.0864)</td></tr><tr><td>Improved Seed</td><td></td><td></td><td>0.655***<br>(0.202)</td><td>0.444**<br>(0.213)</td></tr><tr><td>Pesticide</td><td></td><td>-0.727<br>(1.605)</td><td>-3.386<br>(5.036)</td><td>-23.47<br>(23.03)</td></tr><tr><td>Plot Area</td><td></td><td></td><td></td><td>-0.00449<br>(0.00972)</td></tr><tr><td>Distance to plot</td><td></td><td></td><td></td><td>-0.0140<br>(0.0151)</td></tr><tr><td>Plaine</td><td></td><td></td><td></td><td>-0.0652<br>(0.250)</td></tr><tr><td>Plateau</td><td></td><td></td><td></td><td>-0.291<br>(0.388)</td></tr><tr><td>Lowland</td><td></td><td></td><td></td><td>0.0337<br>(0.310)</td></tr></tbody></table>
<table><thead><tr><th>Variables</th><th>-1</th><th>-2</th><th>-3</th><th>-4</th></tr></thead><tbody><tr><td>Sandy</td><td></td><td></td><td></td><td>-0.325<br>(0.432)</td></tr><tr><td>Clay</td><td></td><td></td><td></td><td>-0.456<br>(0.401)</td></tr><tr><td>Anti-Erosion Structure</td><td></td><td></td><td></td><td>-0.171<br>(0.441)</td></tr><tr><td>Constant</td><td>5.961***<br>(0.0297)</td><td>4.389***<br>(0.175)</td><td>4.351***<br>(0.202)</td><td>4.985***<br>(0.462)</td></tr><tr><td>Observations</td><td>1,170</td><td>1,001</td><td>816</td><td>664</td></tr><tr><td>Number of households</td><td>767</td><td>666</td><td>534</td><td>445</td></tr><tr><td>Marginal Effect of N</td><td>9.56</td><td>11.70</td><td>4.73</td><td>6.44</td></tr><tr><td>Nutrient Applied</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Source: Authors based on LSMS data. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Sample sizes drop with missing observations in more complete models, particularly those including seed quantities.

<table><caption>Table 7
Maize Yield Response to N Fertilizer Applied, Household Fixed Effects Model</caption><thead><tr><th>Variables</th><th>-1</th><th>-2</th><th>-3</th><th>-4</th></tr></thead><tbody><tr><td>Nitrogen Fertilizer</td><td>0.0658<br>(0.0541)</td><td>0.146**<br>(0.0676)</td><td>0.00067<br>(0.0638)</td><td>-0.0620<br>(0.0747)</td></tr><tr><td>Manure</td><td></td><td>0.00286<br>(0.0268)</td><td>0.00169<br>(0.0237)</td><td>0.0125<br>(0.0275)</td></tr><tr><td>Compost</td><td></td><td>-0.0796<br>(0.0639)</td><td>-0.0520<br>(0.0585)</td><td>-0.0646<br>(0.0755)</td></tr><tr><td>Other Organic Fertilizer</td><td></td><td>0.218<br>(0.147)</td><td>0.435*<br>(0.231)</td><td>0.420<br>(0.290)</td></tr><tr><td>Fungicide</td><td></td><td>-0.0489<br>(0.509)</td><td>-0.367<br>(0.442)</td><td>0.0470<br>(1.038)</td></tr><tr><td>Herbicide</td><td></td><td>-0.0469<br>(0.226)</td><td>-0.236<br>(0.276)</td><td>0.129<br>(0.329)</td></tr><tr><td>Total Labor</td><td></td><td>0.540***</td><td>0.403***</td><td>0.443***</td></tr></tbody></table>
<table><thead><tr><th>Variables</th><th>-1</th><th>-2</th><th>-3</th><th>-4</th></tr></thead><tbody><tr><td></td><td></td><td>(0.0786)</td><td>(0.0849)</td><td>(0.111)</td></tr><tr><td>Local Seed</td><td></td><td></td><td>0.662***<br>(0.114)</td><td>0.706***<br>(0.186)</td></tr><tr><td>Improved Seed</td><td></td><td></td><td>0.719***<br>(0.133)</td><td>0.572***<br>(0.182)</td></tr><tr><td>Pesticide</td><td></td><td>0.366<br>(0.290)</td><td>0.459<br>(0.291)</td><td>-0.242<br>(0.669)</td></tr><tr><td>Other Protecting Liquids</td><td></td><td>0.887<br>(1.170)</td><td>0.891<br>(1.014)</td><td>-0.176<br>(1.168)</td></tr><tr><td>Plot Area</td><td></td><td></td><td></td><td>-0.0541***<br>(0.0147)</td></tr><tr><td>Distance to plot</td><td></td><td></td><td></td><td>0.0902<br>(0.0768)</td></tr><tr><td>Plain</td><td></td><td></td><td></td><td>0.0520<br>(0.590)</td></tr><tr><td>Plateau</td><td></td><td></td><td></td><td>-0.564*<br>(0.335)</td></tr><tr><td>Lowland</td><td></td><td></td><td></td><td>1.806*<br>(0.923)</td></tr><tr><td>Sandy</td><td></td><td></td><td></td><td>0.368<br>(0.589)</td></tr><tr><td>Clay</td><td></td><td></td><td></td><td>-0.207<br>(0.627)</td></tr><tr><td>Anti-Erosion Structure</td><td></td><td></td><td></td><td>1.016*<br>(0.514)</td></tr><tr><td>Constant</td><td>6.603***<br>(0.0787)</td><td>4.623***<br>(0.260)</td><td>3.614***<br>(0.319)</td><td>3.386***<br>(0.710)</td></tr><tr><td>Observations</td><td>781</td><td>657</td><td>512</td><td>357</td></tr><tr><td>Number of households</td><td>559</td><td>481</td><td>369</td><td>264</td></tr><tr><td>Marginal Effect of N</td><td>5.01</td><td>11.33</td><td>0.049</td><td>-3.83</td></tr><tr><td>Nutrient Applied</td><td></td><td></td><td></td><td></td></tr></tbody></table>
Source: Authors, based on LSMS data. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Sample sizes drop with missing observations in more complete models, particularly those including seed quantities.

Overall, analysis of the LSMS data demonstrates that nitrogen fertilizer has positive and statistically significant effects on yields of dryland cereals. When we account for the endogeneity of fertilizer use, predicted magnitudes raise the range of elasticities to 17-23 for the three crops combined. For maize, in particular, these fall within the expected range. Ahmed et al. (2017) found no statistically significant effects of mineral fertilizer on crop yield. Foltz et al. (2012) estimated significant maize yield elasticities of 0.2-0.3 for fertilizer—higher than we found here. However, they used total fertilizer kg and utilized data only from the highly productive region of Sikasso.

The Sudan Savanna has the greatest agricultural potential in Mali to produce both sorghum and maize (Dicko et al., 2016). Despite this, anecdotally, farmers surveyed reported that sorghum yields were lower than expected due to declining soil fertility, moisture problems, and pest damage—encouraging a switch from sorghum to maize. The data indicate a very modest yield response rate to fertilizer for either crop. One reason why, in sorghum, could be the extremely low rate of application—application rates per ha on maize plots in our survey averaged 158 kg total of fertilizer, or 39.8 N nutrients/ha, compared with only 27 total kg of fertilizer on sorghum (6.4 N nutrients/ha). There are many zeros in our sample for sorghum (66%), compared with only 14% on maize plots.

Three response function specifications are shown in Table 8, each including soils characteristics measured in laboratory tests on samples. Model 8(1)⁴ is a simple linear regression, with sorghum plot entered only as a binary variable affecting overall yields. The effect on yield is strong and negative, reducing average grain yields by about 600 kg/ha relative to maize, controlling for other factors. Model 8(2) includes an interaction effect between N nutrient kg/ha and sorghum plot. The effect is negative but not statistically significant. In Model 8(3), the squared term is added for N applied and is negative in sign but not statistically significant. The interaction effect becomes significant, indicating that growing sorghum reduced yield response by 9 N nutrient kg/ha relative to maize. On average, Model 8(3) suggests that an additional kg of N nutrients per ha contributes 10.4 kg
of maize grain per ha. This suggests a response rate of only about 1.3 for sorghum combined with the interaction effect.

Consistent across the three specifications, other coefficients of interest include a positive and significant effect of labor and equipment use and a negative and significant effect of distance to the plot and legume intercrop on yields. The magnitudes and significance of the input effects suggest that these may constrain productivity. This is supported by the negative effect on time walking to the plot from the homestead. The inverse relationship between yield and the legume intercrop is explained by the fact that we were unable to control for the area planted to primary and secondary crops—leading to a downward bias in the yield of the primary crop. On the other hand, any long-term, positive effects of intercropping would be difficult to discern in a single year's survey data of this type. Similarly, erosion structures were often constructed earlier and may not be repaired. Most are stone contour lines to control erosion on slopes, but most of the plots in the sample are on the plain. The insignificance of the manure variable may reflect the fact that while most farmers apply manure (64%), there is considerable variation in the quantity and quality applied.

Among measured soil nutrients, the effect of P is strongly significant (at 1%), suggesting that it poses a constraint to productivity. In many of Mali's sorghum-growing areas, we believe that phosphorus is a more binding constraint than N (Dicko et al., 2016; Kihara et al., 2016).

<table><caption>**Table 8**
Maize-sorghum Yield Response Including Measured Soil Nutrients, Sudan Savanna</caption><thead><tr><th></th><th>(1)<br>linear</th><th>(2)<br>interaction</th><th>(3)<br>quadratic and<br>interaction</th></tr></thead><tbody><tr><td>N nutrients/ha</td><td>2.864<br>(1.974)</td><td>4.356<br>(2.646)</td><td>10.42*<br>(5.543)</td></tr><tr><td>Sorghum plot</td><td>-596.8***<br>(131.5)</td><td>-502.7***<br>(156.6)</td><td>-351.7*<br>(197.8)</td></tr><tr><td>Sorghum plot x N nutrients/<br>ha</td><td></td><td>-5.593<br>(3.576)</td><td>-9.134*<br>(4.669)</td></tr><tr><td>(N nutrients/ha)2</td><td></td><td></td><td>-0.0381</td></tr></tbody></table>
<table><thead><tr><th></th><th>(1)<br>linear</th><th>(2)<br>interaction</th><th>(3)<br>quadratic and<br>interaction</th></tr></thead><tbody><tr><td></td><td></td><td></td><td>(0.0300)</td></tr><tr><td>Manure</td><td>80.4<br>(117.1)</td><td>73.3<br>(117.4)</td><td>83.28<br>(117.9)</td></tr><tr><td>Labor</td><td>3.099***<br>(1.020)</td><td>3.051***<br>(1.015)</td><td>3.155***<br>(1.013)</td></tr><tr><td>Herbicides</td><td>15.05<br>(27.43)</td><td>17.00<br>(27.46)</td><td>18.11<br>(27.03)</td></tr><tr><td>Equipment</td><td>0.721***<br>(0.153)</td><td>0.724***<br>(0.153)</td><td>0.724***<br>(0.152)</td></tr><tr><td>Distance to plot</td><td>-3.374*<br>(1.744)</td><td>-3.338*<br>(1.732)</td><td>-3.343*<br>(1.731)</td></tr><tr><td>Soil erosion structure</td><td>-78.13<br>(100.8)</td><td>-81.10<br>(101.7)</td><td>-82.52<br>(103.3)</td></tr><tr><td>Legume intercrop</td><td>-238.8***<br>(88.85)</td><td>-233.1***<br>(86.80)</td><td>-236.5***<br>(85.86)</td></tr><tr><td>lnN</td><td>-90.00<br>(71.55)</td><td>-94.77<br>(70.75)</td><td>-81.98<br>(69.99)</td></tr><tr><td>lnC</td><td>-92.00<br>(61.40)</td><td>-84.60<br>(62.12)</td><td>-89.99<br>(61.85)</td></tr><tr><td>lnP</td><td>137.2***<br>(47.61)</td><td>146.6***<br>(47.33)</td><td>149.7***<br>(47.14)</td></tr><tr><td>lnK</td><td>-58.34<br>(65.13)</td><td>-65.67<br>(66.11)</td><td>-61.44<br>(65.56)</td></tr><tr><td>lnPh(kcl)</td><td>191.6<br>(430.7)</td><td>150.2<br>(436.8)</td><td>286.6<br>(434.8)</td></tr><tr><td>Sand</td><td>14.18<br>(17.97)</td><td>15.36<br>(18.41)</td><td>14.66<br>(18.48)</td></tr><tr><td>Silt</td><td>17.07<br>(18.35)</td><td>17.93<br>(18.76)</td><td>17.23<br>(18.82)</td></tr><tr><td>Clay</td><td>60.80**</td><td>63.77**</td><td>62.86**</td></tr></tbody></table>
<table><thead><tr><th></th><th>(1)<br>linear</th><th>(2)<br>interaction</th><th>(3)<br>quadratic and<br>interaction</th></tr></thead><tbody><tr><td></td><td>(27.67)</td><td>(28.17)</td><td>(28.01)</td></tr><tr><td>Rainfall</td><td>-0.552<br>(1.339)</td><td>-0.306<br>(1.335)</td><td>-0.0562<br>(1.363)</td></tr><tr><td>Constant</td><td>-1,707<br>(2,235)</td><td>-1,891<br>(2,279)</td><td>-2,230<br>(2,305)</td></tr><tr><td>Observations (n plots)</td><td>643</td><td>643</td><td>643</td></tr><tr><td>R-squared</td><td>0.518</td><td>0.520</td><td>0.523</td></tr></tbody></table>

Source: Authors, based on Sudan Savanna data. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table 9 shows models for maize and sorghum in combined and separate regressions. The combined results in Model 9(3) are similar to those shown in Table 4 (4), both in terms of response magnitudes and significance and in terms of other main inputs (labor, equipment) influencing productivity. However, they cannot be directly compared because of differences in sampling and covariates. The yield response rate for maize taken separately is 14.4 and significant but is insignificant at 3.6 for sorghum. Some differences appear in key factors across the regressions reported in Table 9. Statistically, the maize regression is statistically weaker, with far fewer observations than the sorghum regression.

<table><caption>**Table 9**

**Maize-sorghum Yield Response Including Farmer-perceived Soil Types, Sudan Savanna**</caption><thead><tr><th></th><th>(1)<br>Combined</th><th>(2)<br>Maize</th><th>(3)<br>Sorghum</th></tr></thead><tbody><tr><td>N nutrients/ha</td><td>10.52**<br>(4.501)</td><td>14.43***<br>(5.427)</td><td>3.634<br>(4.065)</td></tr><tr><td>Sorghum plot</td><td>-273.1**<br>(112.1)</td><td></td><td></td></tr><tr><td>Sorghum plot x N nutrients/ha</td><td>-6.608**<br>(3.238)</td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th></th><th>(1)<br>Combined</th><th>(2)<br>Maize</th><th>(3)<br>Sorghum</th></tr></thead><tbody><tr><td>(N nutrients/ha)2</td><td>-0.0102<br>(0.0454)</td><td>-0.0471<br>(0.0569)</td><td>-0.00551<br>(0.0653)</td></tr><tr><td>Manure</td><td>89.49<br>(58.60)</td><td>229.4**<br>(90.18)</td><td>-43.94<br>(68.72)</td></tr><tr><td>Labor</td><td>2.184***<br>(0.693)</td><td>4.015***<br>(1.084)</td><td>1.802**<br>(0.800)</td></tr><tr><td>Herbicides</td><td>-0.835<br>(14.89)</td><td>-7.904<br>(26.35)</td><td>4.472<br>(16.08)</td></tr><tr><td>Equipment</td><td>0.391***<br>(0.103)</td><td>-0.0169<br>(0.182)</td><td>0.541***<br>(0.0723)</td></tr><tr><td>Distance to plot</td><td>-1.344<br>(1.656)</td><td>-1.193<br>(4.444)</td><td>-2.053*<br>(1.099)</td></tr><tr><td>Soil erosion structure</td><td>161.5**<br>(71.43)</td><td>195.8<br>(120.4)</td><td>149.0**<br>(75.23)</td></tr><tr><td>Legume intercrop</td><td>-386.8***<br>(54.41)</td><td></td><td>-320.8***<br>(56.26)</td></tr><tr><td>Plain</td><td>-65.03<br>(127.2)</td><td>-10.85<br>(158.1)</td><td>3.302<br>(81.89)</td></tr><tr><td>Lowland</td><td></td><td>-67.34<br>(504.9)</td><td>144.9<br>(150.9)</td></tr><tr><td>Slope</td><td>-63.54<br>(152.2)</td><td></td><td></td></tr><tr><td>Sandy</td><td>137.2*<br>(74.00)</td><td>-27.07<br>(119.0)</td><td>77.6<br>(52.43)</td></tr><tr><td>Silty</td><td>114.8<br>(78.54)</td><td></td><td></td></tr><tr><td>Clayey</td><td>149.8*<br>(81.99)</td><td>-159.2<br>(123.7)</td><td>168.2**<br>(78.65)</td></tr><tr><td>Gravelly</td><td></td><td>-176.5<br>(159.3)</td><td>-80.07<br>(68.94)</td></tr></tbody></table>
<table><thead><tr><th></th><th>(1)<br>Combined</th><th>(2)<br>Maize</th><th>(3)<br>Sorghum</th></tr></thead><tbody><tr><td>Rainfall</td><td>0.447<br>(0.936)</td><td>3.576**<br>(1.657)</td><td>-1.603*<br>(0.911)</td></tr><tr><td>Constant</td><td>402.2<br>(262.3)</td><td>-404.1<br>(456.5)</td><td>666.5***<br>(251.1)</td></tr><tr><td>Observations</td><td>1,086</td><td>421</td><td>665</td></tr><tr><td>R-squared</td><td>0.410</td><td>0.198</td><td>0.387</td></tr></tbody></table>

Source: Authors, based on Sudan Savanna data. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table 10 shows the coefficients from the second stage, yield response function estimated with the Control Function Approach. The yield regression is based on the combined maize-sorghum model with farmer-perceived soils classes to benefit from as many observations as possible. In the first-stage regression, which tests and controls for potential endogeneity of fertilizer use in yield response, both the coefficient on the binary variable indicating that the plot manager benefited from the fertilizer subsidy and the coefficient on the village proportion of plot managers who belong to a registered cooperative, are statistically significant at the 1% level. So too is the residual entered in the yield regression, failing to support exogeneity of fertilizer use in the yield response function. As was true in the LSMS estimates, the marginal product of nitrogen fertilizer rises meaningfully when we control for endogeneity.

The estimated response rates for maize reported for the Sudan Savanna fall within the range of other estimates for the same crops based on data collected from farmers' fields in Sub-Saharan Africa. A review conducted by Yanggen et al. (1998) shows that maize's response rates to nitrogen are generally lower in West Africa than in East and Southern Africa, with most in the 10-15 range. Based on nationally representative cross-sectional and panel datasets, Koussoubé and Nauges (2017) and Theriault et al. (2017) estimated a yield response rate of about 19 kg/ha to nitrogen on maize in Burkina Faso, respectively. By contrast, Marenya and Barrett (2009) estimated a marginal product of 40-44 kg/ha in Western Kenya, while Sheahan et al. (2013)
reported marginal products ranging from 14 to 25 kg/ha across the agro-ecologies of Kenya. Xu et al. (2009) reported response rates for Zambia varied from under 10 to 30 kg/ha, with a median of 16.

Estimates of sorghum yield response to fertilizer are statistically insignificant. Analysis of trial data by Institut de Recherches Agronomiques Tropicales (IRAT) from 1978-82 in Burkina Faso showed experimental responses of 10.3 kg grain of sorghum per N nutrient kg, with much lower figures measured in farmers' fields (Matlon, 1983). In an early review of literature on this topic, Yanggen et al. (1998) found that the marginal physical product of nitrogen nutrients in sorghum production was similar in Sub-Saharan Africa to other regions of the sorghum-producing world such as India, but were lower in West Africa, were most reported rates were in the 4-5 range. In a recent analysis conducted in Nigeria, Omonona et al. (2016) found response rates of only around 1 kg of sorghum in cereal-root crop and agro-pastoralist farming systems.

<table><caption>**Table 10**

Maize-sorghum Yield Response to Nitrogen Applied, Control Function Approach</caption><thead><tr><th></th><th>(1)</th></tr></thead><tbody><tr><td>Nitrogen fertilizer</td><td>23.98***<br>(5.035)</td></tr><tr><td>Residual, stage 1</td><td>-16.33***<br>(3.916)</td></tr><tr><td>Sorghum plot</td><td>622.1***<br>(236.6)</td></tr><tr><td>Sorghum plot x N nutrients/ha</td><td>-5.565*<br>(3.110)</td></tr><tr><td>(N nutrients/ha)2</td><td>0.00795<br>(0.0439)</td></tr><tr><td>Manure</td><td>180.7***<br>(60.61)</td></tr><tr><td>Labor</td><td>3.365***<br>(0.777)</td></tr><tr><td>Herbicides</td><td>-3.601</td></tr></tbody></table>
<table><tr><td></td><td>(15.01)</td></tr><tr><td>Equipment</td><td>0.351***<br>(0.101)</td></tr><tr><td>Distance to plot</td><td>-3.118*<br>(1.816)</td></tr><tr><td>Soil erosion structure</td><td>115.5<br>(70.55)</td></tr><tr><td>Legume intercrop</td><td>-524.4***<br>(65.38)</td></tr><tr><td>Plain</td><td>-100.5<br>(119.9)</td></tr><tr><td>Slope</td><td>-120.4<br>(144.2)</td></tr><tr><td>Sandy</td><td>-10.72<br>(76.59)</td></tr><tr><td>Silty</td><td>35.67<br>(80.92)</td></tr><tr><td>Clayey</td><td>173.7**<br>(77.86)</td></tr><tr><td>Rainfall</td><td>-0.54<br>(0.979)</td></tr><tr><td>Constant</td><td>-18.04<br>(262.7)</td></tr><tr><td>Observations</td><td>1,086</td></tr><tr><td>R-squared</td><td>0.424</td></tr></table>

Source: Authors, based on Sudan Savanna data. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

## Conclusion

In Mali, raising the production of dryland cereals (maize, millet, and sorghum) to improve food security must be achieved through higher yields
rather than a further extension of cultivated area. Although maize yields on farms have increased in recent years, they are far below their potential. Generally, yields of millet and sorghum on farms have remained low. Inadequate use of inorganic fertilizer has been pinpointed as a cause of low agricultural productivity in these crops. To encourage fertilizer use and spur productivity, the Malian government has implemented a fertilizer subsidy program since the global food price crisis in 2008. Beginning in rice, the program now also targets dryland cereals. Yet, little is documented about the responsiveness of those crops to fertilizer under farmer's conditions. This study aims to fill this gap by examining dryland cereal yield responses to fertilizer using two farm household datasets. The first, the LSMS-ISA, is nationally representative. The second, collected in the Sudan Savanna region, is representative of a relatively high-potential zone for sorghum and maize production.

We applied a combination of econometric techniques to control for potential endogeneity and check the robustness of the results. Four key findings emerge. First, it is important to control for endogeneity to avoid underestimating the effect of fertilizer use on yields. Second, soil texture and practices (anti-erosion structures) affect both yields and estimated effects of fertilizer. Third, sorghum yields have a lower response to fertilizer than maize yields. Fourth, dryland cereal yield responses to fertilizer are stronger in the Sudan Savanna region than nationally representative data, highlighting the importance of agroecological factors and farming systems. Together, these findings suggest that the use of mineral fertilizer can boost productivity, especially for maize, but in complementarity with other practices that reduce soil erosion and improve soil quality.

One key aspect that we have not addressed is the profitability of fertilizer use. Related work by Dicko et al. (2016), which supports the need for varied recommendations, also suggests that economic optima are generally lower than agronomic optima recommended by national programs. Given that maize yields do respond to fertilizer use, can inadequate application rates be explained by low economic incentives? How does the subsidy program affect economic incentives, and at what social cost? When response rates are so low, does it make sense to include millet and sorghum in the subsidy program? Further research is needed to tackle those important questions
and make sound policy recommendations on the subsidy program and other
mechanisms to promote agricultural intensification in Mali.

Acknowledgements

The authors thank anonymous reviewers for their comments and
suggestions and gratefully acknowledge the financial support of the Bill &
Melinda Gates Foundation under the project titled Guiding Investments in
Sustainable Agriculture in Africa (GISAIA), and USAID/Mali under the project
titled "Projet de recherche sur les politiques de sécurité alimentaire au Mali
(PRePoSAM) awarded under the Food Security Innovation Lab's Cooperative
Agreement Number AID-688-A-16-00001.

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ENDNOTES

[1] Pers. Comm. Assitan Traoré, Cellule de Planification Cellule de Planification et de Statistiques du Secteur #sdfootnote1ancDéveloppement Rural (CPS/SDR), pers. Comm, June 15, 2017 and November 15, 2017.

[2] Accessed from http://power.larc.nasa.gov/cgi-bin/cgiwrap/solar/agro.cgi?email=agroclim@larc.nasa.gov

[3] These figures are calculated by multiplying the estimated coefficients by the average yield and fertilizer quantities of the regression sample.

[4] The first number refers to table and second number refers to model.
4. Counterfeit Herbicides,
Productivity and Family Labor
Use on Farms in Mali: A
Multivalued Treatment Approach

**Amidou Assima**, Michigan State University, USA

**Melinda Smale**, Michigan State University, USA

**Steve Haggblade**, Michigan State University, USA

Abstract

Rapid growth in private sector herbicide imports has led to a dramatic
rise in use of these commercial products by Malian smallholder farmers.
Given weak regulatory capacity to monitor markets, the recent proliferation
in herbicide products and brands has been accompanied by widespread
sales of unregistered products. We test the effects of herbicides applied
to Mali's major dryland cereals, sorghum, and maize, on yield and labor
use per ha, differentiated by gender and age. We employ a multivalued
treatment model with data collected from 623 households and 1,273 plots.
We find that registered herbicides provide significant productivity benefits
to farmers. Registered herbicides reduce the intensity of weeding labor of
adult males nearly twice as much as unregistered herbicides, also reducing
the weeding labor of female and child household members. Unregistered
herbicides negatively affect maize and sorghum yields, while registered
herbicides control weed damage without reducing yield.

Keywords: herbicides, multivalued treatment, agricultural policy, Mali

Introduction

Raising the productivity of dryland cereal crops among smallholder farmers
in Mali depends critically on their use of modern inputs combined with practices that protect soil and water resources. Yet, despite long-term investments in seed improvement and more recently, government subsidy programs for fertilizer, adoption rates for these inputs remain low on dryland cereals (Kelly et al., 2015; Theriault et al., 2018). In contrast, spurred entirely by commercial supply and farmer demand, herbicide use by smallholder farmers has proliferated. The number of herbicides imported has more than doubled since 2000, even though farmers pay the full commercial price (Haggblade et al., 2017b; INSTAT, 2016).

Rising rural wages, urbanization, and market access are thought to underpin these patterns across Sub-Saharan Africa (Sheahan & Barrett, 2017; Tamru et al., 2017). Similar growth in herbicide use is apparent in other developing regions as well, driven by growing availability of generic products and falling prices (Haggblade et al., 2017a). In China, as in Africa, herbicide use has increased rapidly since the mid-2000s because of rising rural wage rates and falling herbicide prices (Huang et al., 2017).

In Mali, as is often the case elsewhere, not all of these herbicides are registered. While regulators effectively screen new herbicide products for efficacy and safety prior to registration, monitoring of the quality of herbicides actually sold in local markets following registration remains weak. As a result, a large volume of unregistered herbicides finds its way into Mali's agricultural input markets. Recent market surveys indicate that roughly a quarter (24%) of herbicides sold in Mali are unregistered (Haggblade et al., 2019b). Moreover, laboratory analysis of the most widely used herbicide active ingredient suggests that unregistered herbicides are underdosed by about 8-10% compared to duly registered products (Haggblade et al. 2019a). Broadly speaking, counterfeit and low-quality agricultural inputs are thought to be widespread in Sub-Saharan Africa (Ashour et al., 2018; Bayoumi, 2021; Bold et al., 2017; Gharib et al., 2021; MirPlus, 2012; Yao, 2020).

The survey data from the Sudanian Savanna, an area of high productivity potential for sorghum and maize, demonstrates that most Malian farmers who apply herbicides do not know the difference between registered and non-registered products and that many of the herbicides they use are not registered. Clearly, a situation like this has important ethical considerations in addition to economic implications. Misuse of herbicides has consequences for on-farm yields and potential negative externalities for human and
environmental health. Counterfeit inputs have caused considerable problems in fertilizer and seed use, but there has not been much analysis of this problem with respect to herbicides (Ashour et al., 2018).

Published analysis of herbicide use on farms in Sub-Saharan Africa also remains limited. A recent analysis by Haggblade et al. (2017b) examined the origins and consequences of the rapid growth in herbicide use in Mali, finding adoption rates that range from 25% of farmers in remote areas to 75% of farmers in areas better served by market and road infrastructure. The authors found that on average, herbicides costs 50% less per ha than hiring labor to weed and documented a strong relationship between adoption and rising wage rates for weeding labor. In Ethiopia, Tamru et al. (2017) found a similar rapid rise in herbicide use that was driven by the private sector, strongly correlated with access to all-weather roads, and contributed to significantly higher labor productivity on farms. Sheahan and Barrett (2017) documented overall levels of input use in several countries of Sub-Saharan Africa, including agro-chemicals (i.e., insecticides, herbicides, fungicides). Among the six countries examined, Ethiopia and Nigeria showed substantial use of herbicides in particular. Sheahan et al. (2017) demonstrated the positive relationship of agro-chemical use with value of agricultural output, along with its negative consequences for indicators of human health. They analyzed nationally representative datasets from Ethiopia, Tanzania, Uganda, and Nigeria, finding the rate of use of agro-chemicals surpassed 10% of growers.

This chapter contributes to an emerging, still sparse knowledge about the use of herbicides by smallholder farmers in Sub-Saharan Africa, and in particular, to information about counterfeit herbicides. We contribute to the body of research cited above in several ways. First, we tested the effect of herbicide product quality on yield and labor productivity on Malian farms. In the absence of accredited testing laboratories in Mali, we use registered as compared to non-registered herbicides as a proxy for herbicide quality. Via registration of the product, we also tested the effects of regulations.

Second, we are able to disaggregate labor intensity, which we measured as weeding labor per ha, by gender and age (i.e., male, female, and child labor). The heavy demand for female labor generated by a combination of domestic and farm work has long been considered an impediment to their
productivity in Sub-Saharan Africa, although Palacios-Lopez et al. (2017) recently questioned women's share of farm labor.

Third, to test our hypotheses, we applied a multivalued treatment effects model to observational data collected in 58 villages of Mali's Sudanian Savanna zone from 2014 to 2015. Many treatment effects in agricultural development involve binary assignment (e.g., program participation or not). In the case of binary treatment when employing observations in cross-sectional data, matching has become a popular approach (Imbens & Wooldridge, 2009), especially given the challenges of identifying appropriate instruments for two-stage least squares analysis. To our knowledge, propensity score matching methods have not yet been fully extended to multivalued treatments. Multivalued models have been described by Wooldridge (2010) and Cattaneo (2010), but so far, we have found only one application in agriculture (Esposti, 2017). Here, we tested three multivalued effect estimators with regression methods and, for completeness, used propensity score matching to test the overall effects of herbicide use.

# Methods

## 1. Data

The sampling frame is a baseline census of all 2,430 sorghum-growing households in 58 villages located in the Cercles of Kati, Dioila, and Koutiala, regions of Koulikoro and Sikasso. Sikasso and Koulikoro regions lie largely within the Sudan Savanna zone, which has the greatest yield potential in Mali (Dicko et al., 2017). These two regions constitute the “sorghum belt” of the nation in terms of area cultivated and total production; as of the 2012–2013 season, the year before our survey, the two regions represented more than 51% of total sorghum area planted in the country (Cellule de Planification Statistique du Secteur du Développement Rural [CPS-SDR]). The zone is also favorable for maize production, an increasingly popular food and cash crop.

The enumeration unit in the baseline census, and generally in Mali, is the Exploitation Agricole Familiale (EAF), also known as the family farm enterprise. According to the national agricultural policy act (Loi
d'Orientation Agricole), the EAF is a production unit composed of several members who are related and who use production factors collectively to generate resources under the supervision of the head of household. The head can be a female or male household member, but female headship remains rare. The head's primary economic activity is to encourage the optimal use of production factors, as these are defined by the extended family. For the EAFs we studied, the first priority was universally food security. The head represents the EAF in all civil acts, including representation and participation in government programs. They may designate a team leader (chef de travaux) to supervise field work and manage the EAF on behalf or to assist the head when they have physical or other limitations.

The family farm enterprise is a complex organization that consists of numerous plots on which multiple crops are grown. Plots are managed collectively and individually by various family members. Members generally include the head, their spouse and children, married sons and their wives and children, unmarried daughters and brothers of the head, and other relatives. Collective plots belonging to the whole EAF are managed by the household head or the team leader on behalf of the EAF. Individual plots belong to the EAF but are planted and managed by individual members, including both men and women. The production from these plots is not managed collectively. At each cropping season, the head distributes individual plots based on the needs of the family.

Sorghum and maize are the major dryland cereals in this region of Mali, although some millet is also grown. For more detailed analysis of input use in sorghum and maize production, we drew a sample of EAFs with simple random sampling. The final sample size was 623 EAFs, with an overall sampling fraction of 25%. Enumerators inventoried all plots operated by each sampled EAF, grouping them by crop and plot management type. Considering sorghum and maize plots only (because of budget constraints), one plot was randomly sampled per management type per EAF. The total sample of sorghum and maize plots analyzed, including those collectively and individually managed, was 1,273. We controlled for plot manager in the analysis, and included all sorghum and maize plots, having obtained more detailed production data compared to other crops.

The multi-visit sample survey was conducted in four rounds from August
2014 through June 2015, with a combination of paper questionnaires and computer-assisted personal interviews, by a team of experienced enumerators employed by the Institut d'Economie Rurale. Modules included (a) inventories of plots, livestock, agricultural equipment, and household assets, utilizing the harvest from the previous season; (b) input use and labor use on sorghum and maize plots; (c) measurement of area and production on sorghum and maize plots; (d) consumption expenditures and migration remittances.

## 2. Econometric Strategy

Our aim was to test the impacts of counterfeit and registered herbicides, as compared to use of neither, on farm productivity (output per ha) and labor intensity (weeding labor per ha). We further differentiated labor intensity by gender and age.

Herbicide use has recently expanded in rural Mali, in the absence of any deliberate program or policy intervention. We expected the decision to use herbicides to be non-random in farming communities given that the input was novel and it was introduced entirely by local traders or other farmers. We imagined that at this early stage of adoption, users and non-users would be systematically different. Users may “self-select,” leading to potential bias in estimates of productivity impacts.

The Randomized Controlled Trial (RCT) is often seen as the “gold standard” of evaluation approaches because it eliminates selection bias (Imbens & Wooldridge, 2009). Bias due to non-random selection can occur because of program placement, participation criteria, or through processes of self-selection. Various approaches have been recommended to address the challenge of establishing a counterfactual with non-experimental data, including the class of treatment effect models, which we employed here. These models make treatment and outcome independent by conditioning on covariates or controls.

Various methods were used to address the question of establishing a counterfactual with non-experimental observations, including the class of treatment effect models, most of which involved a single treatment level represented by a binary (0,1) variable. Our interest in distinguishing the
effects of herbicide quality (no herbicides at all, counterfeit herbicides, registered herbicides, or 0,1,2 respectively) led us to apply a multivalued treatment effects model. The case of multivalued treatment effects was developed by Wooldridge (2010), Cattaneo (2010), and Cattaneo et al. (2013). Wooldridge (2010) presented the example of participation in a training program that occurred at different levels (part-time or full-time), or in different forms. Cattaneo (2010) developed a more general theory for semiparametric estimators and applied it to estimate quantile treatment effects. The Wooldridge (2010) conceptualization best represents our situation, where 0 represents the control group and higher integer values indicate different treatment options.

To our knowledge, propensity score matching methods have not been fully developed for more than a single value of treatment (Cattaneo et al., 2013). As in the binary case, multivalued treatment effects can be estimated by applying other regression approaches. Wooldridge (2010) recommended regression adjustment (RA) as a baseline approach and a simple extension of the binary case to the multivalued case. RA estimators model the potential outcome of treatment without any assumptions about the treatment model. Augmented, inverse-probability weighted (AIPW) and inverse-probability weighted, regression adjustment (IPWRA) estimators address the outcome model $Y_i = f(X_i, \beta) + \varepsilon_i$, as well as the treatment model for 1,...,G treatments, $\Pr(T_i=1,...,G) = h(Z_i; \lambda) + \omega_i$.

$X$ is a vector of covariates that influence the outcome $Y$ and $Z$ is a set of covariates explaining treatment assignment $T$, which may overlap. By exploiting inverse-probability weights derived from propensity scores estimated with multinomial, nested, or ordered logit, these estimators control for selection on observables. We utilized multinomial logit.

When either the outcome model or the treatment model or both are specified correctly, AIPW and IPWRA furnish consistent estimators of the treatment model parameters. Reflecting this property, the AIPW and IPWRA have been termed “doubly robust” (Wooldridge, 2010, p. 930). Cattaneo (2010) referred to the AIPW as the “efficient influence function”. AIPW and IPWRA estimators can be more efficient than RA (Cattaneo, 2010; Cattaneo et al., 2013; Glynn & Quinn, 2010). Use of these approaches in agricultural analyses appears to be infrequent. Esposti (2017) applied the multivalued Cattaneo
(2010) model to evaluate the impact of the 2005 reform of the Common Agricultural Policy on farm production choices using treatment intervals.

Identification of the treatment effect relies on satisfying the properties of conditional mean independence, which stipulates that the treatment does not affect the mean of the outcome variable. Apparently, the multivalued case relies on a weaker assumption than in the binary case (Esposti, 2017; Cattaneo, 2010). Among our estimators, weighting by the inverse of the estimated propensity score achieved covariate balance and created a sample in which the distribution of covariates was independent of treatment status (Karamba & Winters, 2015).

Potential bias generated by unobservable characteristics remained. We applied the bounds approach proposed by Rosenbaum (2002) to examine the robustness of our results to these unobserved characteristics. The Rosenbaum bounds approach determines how strongly an unobserved factor must affect the outcome variables to undermine the matching estimates. The technique calculates the confidence intervals for different level of Γ based on the following equation

$$ \frac{1}{\Gamma} < \frac{P_i}{P_j} \times \frac{1-P_j}{1-P_i} < \Gamma $$

where P_i and P_j are odds for two individuals i and j. γ is the effect of unobserved factor on the treatment selection

$$ \Gamma=\exp(\gamma) $$

The smallest value of Γ for which the confidence interval contains zero is the critical odds ratio for which we questioned the estimated impact. If the value of Γ is small (less than two), we considered that the effect of the estimated treatment was sensitive to unobservable factors (Duvendack & Palmer-Jones, 2012).

We also improved the covariate balance by introducing plot management type and education into the treatment model. Plot management type is highly correlated with observed and intrinsic characteristics of the manager (relationship to head, gender, age) that affect access to resources within the extended family household (EAF). We also examined the common support condition.
In addition to the multivalued models, we estimated a binary model with propensity score matching to test the effects of use vs. non-use of herbicides. All models were estimated in STATA 14 using plot-level data.

### 3. Model Specification

The objective of the econometric strategy was to quantify the potential outcomes that expressed changes in the output per ha and labor use in weeding per ha. For both outcomes, we used the same econometric specification

$$Y = \alpha + \beta' outcomecovar + \Theta' treatmentcovar + \mu,$$

where Y is grain yield in kg/ha or weeding labor/ha and outcomecovar is a vector of agricultural inputs applied on dryland cereals plots, which covary with the outcome. Corresponding to a notional yield response function, we included input quantities per ha (seed, fertilizer, adult male labor, adult female labor, children's labor, machinery use, manure), as well as plot characteristics (time in minutes to travel from homestead to the plot, whether any structure was built on the plot to offset soil and water erosion). These were the same covariates we expected to influence the intensity of labor use.

Treatmentcovar is a vector of plot manager, household, and market covariates that influence incentives for use (covarying with treatment), including the cost per ha of hiring weeding labor, the total household labor supply, and whether or not the EAF received a fertilizer subsidy. In Mali, access to formalized extension structures (encadrement) substitutes to some extent for commercial markets, influencing farmer access to inputs and services of various kinds, including subsidized fertilizer. The estimate of weeding costs per ha is derived from responses to a question asking how much weeding labor costs if herbicides were not applied. Since market infrastructure extends to weekly fairs conducted in villages, we included a dummy variable for the presence of a weekly fair in the village of the EAF. Finally, as described above, we recognized the social organization of production in this region of Mali, and added the characteristics of the plot manager (education of the manager, whether the plot is managed by the head
or another individual member of the household) among our explanatory variables.

Following the potential outcomes framework, we expressed the observed outcome Y as

$$Y=D_{0}Y_{0}+D_{1}Y_{1}+D_{2}Y_{2}$$

where D₀, D₁, and D₂ were dummy variables indicating the level of treatment. Dt={0,1,2} equals to one if the plot received treatment level t and zero otherwise. Y₀, Y₁, and Y₂ were the corresponding potential outcomes.

Letting t and s act as two treatment levels, the average treatment effects were given by the following equation:

$$ATE = E[Y_t - Y_s]$$

If we were in a randomized experiment setting, we could estimate the average treatment effect (ATE) of level t relative to level s by the sample means of observed outcomes. Given the observational nature of our data, to estimate ATE we needed to fulfil the weak unconfoundedness or conditional independence assumption, which required independence of each treatment level with the corresponding potential outcomes knowing X. Hence, formally

$$D_t \perp Y_t | X, \forall t \in \{0,1,2\}$$

where X= outcomecovar + treatmentcovar, ⊥ denotes independence and t treatment level.

In addition, the overlap or common support assumption, which implies positive probability of being in any treatment level, must be satisfied. The overlap assumption in multivalued treatment can be assessed using generalized propensity score (GPS) proposed by Imbens (2000) and computed with the following equation:

$$r(x,t) = P[T = t | X=x] = E[D_t | X=x] > 0$$

The GPS is used in the empirical implementation because it overcomes the problem of high dimensionality that might arise by conditioning directly on vector X.

Under these assumptions, we estimated the mean of the potential
outcome for the treatment level t knowing the vector of covariates X by taking the average of the conditional expectation of the potential outcome or by weighting the observed outcomes with the generalized propensity score through the below equation:

$$ \mu_t = EE[[Y/T = t, r(t, X) = r]] \text{ or } \mu_t = E[\frac{YD_t}{r(t, X)}] $$

The ATEs were then determined by pairwise comparison of these means of the potential outcome.

Model control variables are defined in Table 1. Outcome variable definitions are shown in Table 2, along with differences in the means of each outcome variable by treatment. Without controlling for other factors, lower mean yields were associated with unregistered herbicide use compared with no herbicide use, but the highest mean yields were associated with registered herbicide use. The lowest labor use per ha for adult males, adult females, or children was on plots receiving registered herbicides. In interpreting our labor outcome variables, it was important to recognize that hired labor use on farms surveyed was negligible. Absence of labor markets, and dependence on the extended family or labor sharing arrangements with neighbors, was a prominent feature of the dryland cereals farming system in our zone of study. These were the hypotheses we carried to the estimation.
<table><caption>**Table 1**
**Definition of Control Variables in Multivalued Treatment Effect Model**</caption><thead><tr><th>Control variable</th><th>Definition</th></tr></thead><tbody><tr><td><strong>Treatment covariates</strong></td><td></td></tr><tr><td>individually-managed</td><td>plot managed individually by male or female who is not the EAF head or designate=1, else 0</td></tr><tr><td>manager</td><td>head=1; individual male not head=2; individual female not head=3</td></tr><tr><td>education</td><td>plot manager attended primary school=1, 0 else</td></tr><tr><td>labor supply</td><td>number of adults in EAF between 12 and 55 years of age (inclusive)/total area operated by EAF</td></tr><tr><td>weeding cost</td><td>cost of hiring weeding labor per ha</td></tr><tr><td>subsidy</td><td>EAF benefited from fertilizer subsidy=1, 0 else</td></tr><tr><td>market</td><td>weekly market fair in village=1, 0 else</td></tr><tr><td>village</td><td>village fixed effect</td></tr><tr><td><strong>Outcome covariates</strong></td><td></td></tr><tr><td>seed</td><td>quantity of seed in kgs used per ha</td></tr><tr><td>fertilizer</td><td>kgs of fertilizer applied per ha</td></tr><tr><td>male labor</td><td>number of adult male person-days (14 years and above) per ha</td></tr><tr><td>female labor</td><td>number of adults female person-days (14 years and above) per ha</td></tr><tr><td>child labor</td><td>number of children person-days (under 14 years) per ha</td></tr><tr><td>machinery use</td><td>hours of equipment use per ha</td></tr><tr><td>manure</td><td>manure use on plot=1, 0 else</td></tr><tr><td>location</td><td>time in minutes to travel from home to the plot</td></tr><tr><td>erosion control</td><td>any anti-erosion structure built on plot=1, 0 else</td></tr></tbody></table>
<table><caption>Table 2
Outcome Results by Treatment</caption><thead><tr><th>Outcomes</th><th>Definition</th><th>No herbicide</th><th>Unregistered herbicide</th><th>Registered herbicide</th></tr></thead><tbody><tr><td>crop productivity</td><td>yield=grain kgs harvested/ ha</td><td>1183</td><td>890</td><td>1346</td></tr><tr><td>male labor intensity</td><td>days weeding labor per ha for adult males;</td><td>21.1</td><td>20.5</td><td>17.9</td></tr><tr><td>female labor intensity</td><td>days weeding labor per ha for adult females</td><td>6.13</td><td>8.01</td><td>3.77</td></tr><tr><td>child labor intensity</td><td>days weeding labor per ha for children (12 years and under)</td><td>3.38</td><td>4.18</td><td>2.83</td></tr></tbody><tfoot><tr><td colspan="5">N=1273 plots, 623 EAFs.</td></tr></tfoot></table>

# Results

## 1. Descriptives

Glyphosate, a broad-spectrum, non-selective herbicide which was developed by Monsanto under the trademark name of Roundup, is the world's most popular herbicide. Importers and input vendors also report that glyphosate is their top-selling herbicide in Mali (Diarra, 2016). Our survey data indicated that glyphosate accounted for about two-thirds of herbicide volumes applied by farmers to their sorghum and maize plots. Selective herbicides, intended for use on cotton, maize, and rice, represented the other third. Since many households surveyed belonged to the cotton growers' associations, use of selective herbicides may have reflected their experience and access to inputs via their membership. Based on Table 3, only slightly more than half of sorghum and maize plots (53%) or volume of total herbicides used by farmers surveyed (55%) were registered. The remainder were of unregistered and of uncertain quality, meaning either "knock-offs" or counterfeit.
<table><caption>**Table 3**
Farmer Use of Herbicides by Category (in %), Sudan Savanna of Mali, 2014/
15</caption><thead><tr><th rowspan="2">Herbicide type</th><th colspan="3">Herbicide registration</th></tr><tr><th>registered</th><th>unregistered</th><th>total</th></tr></thead><tbody><tr><td>% of plots</td><td>53</td><td>47</td><td>100</td></tr><tr><td>% of volume</td><td>55</td><td>45</td><td>100</td></tr></tbody></table>

Aside from the practice of full soil inversion (plowing) during land preparation, farmers in this region of Mali rely on hand and hoe to control weeds, and generally on the labor of family members or shared labor with friends. When asked what it would cost to hire labor to accomplish this task rather than apply herbicides, surveyed farmers indicated that they would have spent $52. In comparison, those who used herbicides instead would spend an average of only $23 per hectare.

Among households surveyed, female household members (primary wives of the household head, but also daughters-in-law) managed about one-quarter of sorghum plots, but none of the maize plots. Sorghum and maize plots managed by male family members who were not heads of household were relatively few in number, although these men often managed plots of cotton or other cash crops. Larger plots managed by the household head and worked collectively by members constituted over 80% of all sorghum plots and 95% of maize plots.

The data in Table 4 indicates that individual plot managers were more likely to apply herbicides on their plots than household heads were to use the input on family fields. According to social norms, the head reserves the right to demand labor on these plots, since the output benefits the family as a group; individuals must meet labor needs on their own fields after satisfying their duties—potentially, a powerful incentive for using herbicides. Female plot managers applied herbicides on their individual fields at nearly twice the rate applied on collective fields (2.6 liters per hectare compared to 1.1 liters). Male-managed individual plots received doses that were even higher per hectare.
<table><caption>Table 4
Herbicide Use by Plot Management Type</caption><thead><tr><th rowspan="2">Plot manager</th><th rowspan="2">Plot type</th><th colspan="3">Crop grown</th></tr><tr><th>Sorghum</th><th>Maize</th><th>All</th></tr></thead><tbody><tr><td colspan="5" style="text-align: left;">Percent of plots using herbicides</td></tr><tr><td>Household head</td><td>collective</td><td>47</td><td>69</td><td>58</td></tr><tr><td>Female</td><td>individual</td><td>79</td><td></td><td>79</td></tr><tr><td>Male</td><td>individual</td><td>90</td><td>60</td><td>80</td></tr><tr><td>All</td><td></td><td>56</td><td>69</td><td>61</td></tr><tr><td colspan="5" style="text-align: left;">Herbicide application rate (liters/ha)</td></tr><tr><td>Household head</td><td>collective</td><td>1.1</td><td>1.7</td><td>1.4</td></tr><tr><td>Female</td><td>individual</td><td>2.6</td><td></td><td>2.6</td></tr><tr><td>Male</td><td>individual</td><td>3.3</td><td>2.5</td><td>3.1</td></tr><tr><td>All</td><td></td><td>1.6</td><td>1.7</td><td>1.6</td></tr></tbody></table>

## 2. Econometric Findings

Before discussing the ATEs, it is important to examine the quality of the matching process. Conditional probabilities of herbicide use by treatment level are shown in Figure 1. Across all three treatment levels, the density distribution of the estimated probabilities confirmed that there was not much high-density mass near the values 0 or 1. Thus the common support condition was satisfied: there was substantial overlap in the distribution of the probability for non-users of herbicides, users of unregistered herbicides, and users of registered herbicides.