## Todd S. Rosenstock · Andreea Nowak Evan Girvetz *Editors*

# The Climate-Smart Agriculture Papers

Investigating the Business of a Productive, Resilient and Low Emission Future

The Climate-Smart Agriculture Papers

Todd S. Rosenstock • Andreea Nowak Evan Girvetz Editors

# The Climate-Smart Agriculture Papers

Investigating the Business of a Productive, Resilient and Low Emission Future

*Editors* Todd S. Rosenstock World Agroforestry Centre ICRAF Kinshasa Congo, Demographic Republic (Zaire)

Evan Girvetz International Center for Tropical Agriculture Centre of Insect Physiology and Ecology Nairobi, Kenya

Andreea Nowak World Agroforestry Centre Nairobi, Kenya

#### ISBN 978-3-319-92797-8 ISBN 978-3-319-92798-5 (eBook) https://doi.org/10.1007/978-3-319-92798-5

Library of Congress Control Number:<PHONE_NUMBER>

© The Editor(s) (if applicable) and The Author(s) 2019. This book is an open access publication.

**Open Access** This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

## **Acknowledgments**

We thank D. Rohrbach for his insights and critical eye as well as the 40 reviewers of the CSA Papers for their time. In addition, we thank the Section Leads including S. Midgley (Climate Risks and Impacts), B. Das (Adaptive Germplasm Delivery Systems), C. Thierfelder (The Climate-Smartness of Technologies), E. Girvetz (Climate-Resilient Value Chains) and R. Ruben (Taking Climate-Smart Agriculture to Scale) for their leadership. We thank the Vuna programme for providing financial support for the CSA Papers. Vuna was a UK aid-funded climate-smart agriculture programme in East and Southern Africa and was implemented by Adam Smith International. This work was led by the World Agroforestry Centre (ICRAF) in collaboration with the International Center for Tropical Agriculture (CIAT). It was implemented as part of the Partnerships for Scaling Climate-Smart Agriculture (P4S) project under the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https:// ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.

## **Contents**

#### **Part I Climate Risks and Impacts**

Given Mudenda, John Key, and Dusan Ristanovic

Jennifer Twyman, Kelvin M. Shikuku, Leigh Ann Winowiecki, Peter Läderach, Chris M. Mwungu, and Laurence Jassogne

## **Chapter 1 An Introduction to the Climate-Smart Agriculture Papers**

**Todd S. Rosenstock, David Rohrbach, Andreea Nowak, and Evan Girvetz**

#### **1.1 Tracking Progress**

In 2001, the Third Assessment Report of the Intergovernmental Panel on Climate Change highlighted the potential impacts of a changing climate on global agriculture. The Report stated that rising temperatures and drought could lead to significant declines in yields for many of the world's poorest nations, including Africa. This stimulated a new set of global commitments to research and promote agricultural practices that are more climate-smart. Since then, almost USD 1 billion has been committed to climate-smart programming in Africa, with more likely to follow (Fig. 1.1). Most African governments have formed climate-smart agriculture task forces. New transnational partnerships, such as the East African Regional Climate-Smart Agriculture Alliance, have linked government efforts to support regional change. In 2015, these commitments were reinforced by the adoption of a Statement

T. S. Rosenstock (\*)

A. Nowak World Agroforestry Centre (ICRAF), c/o INERA, Kinshasa, Democratic Republic of the Congo

E. Girvetz CGIAR Research Program on Climate Change, Agriculture and Food Security, Kinshasa, Democratic Republic of the Congo

International Center for Tropical Agriculture (IITA), Nairobi, Kenya

World Agroforestry Centre (ICRAF), c/o INERA, Kinshasa, Democratic Republic of the Congo

CGIAR Research Program on Climate Change, Agriculture and Food Security, Kinshasa, Democratic Republic of the Congo e-mail<EMAIL_ADDRESS>
D. Rohrbach Consultant, Washington, D.C., United States of America

**Fig. 1.1** Investments and alliances promoting CSA in Africa. (Source: Authors)

of Shared Ambition for climate-smart agriculture and a subsequent Action Plan by the corporate members of the World Business Council for Sustainable Development (WBCSD 2015). What's more, non-governmental and some civil society organisations have formed complementary advocacy groups, such as the Alliance for Climate-Smart Agriculture in Africa (ACSAA) that includes international nongovernmental organizations, policy institutions, technical partners and farmers groups.

The responses to these large commitments of strategic and financial support have been substantial. Hundreds of technological solutions have already been identified as climate-smart because they mitigate the effects of rising temperatures and variable rainfall; or contribute to a reduction of greenhouse gas (GHG) emissions; or accumulate carbon in biomass or soils (Table 1.1). New varieties of crops with greater drought tolerance are already in production, and more are being released each year (Challinor et al. 2016). Many land management systems conserve soil moisture (Thierfelder et al. 2017). Agroforestry systems reduce the ambient temperature of nearby crops and livestock (Lin 2007; Barton et al. 2016). Feeding strategies that increase productivity and reduce GHG emissions from livestock are well known (Bryan et al. 2013; Thornton and Herrero 2010). And information delivery systems help farmers to plan the right period(s) to plant.

Indeed, 'climate-smart' has bordered on becoming a brand. Carried to the extreme, today there are now climate-smart extension systems, climate-smart finance, climate-smart landscapes, climate-smart livestock, climate-smart soils, and climate-smart varieties etc. (Gledhill et al. 2012; Graefe et al. 2016; Minang et al. 2014; Paustian et al. 2016; Sala et al. 2016).

Whereas many technologies are available to help farmers better cope with climate risks, improving farmers' access to these technologies, while strengthening incentives around their adoption, remains the more significant challenge. Despite millions of dollars of investment, adoption rates of new agricultural technologies in much of eastern and southern Africa remain low (Giller et al. 2009; Asfaw et al. 2016). The majority of farmers continue to struggle with the costs and risks of new technologies. Increasing climate risks simply make these efforts more difficult.

This volume highlights current efforts being made by scientists in eastern and southern Africa in developing and disseminating climate-smart agriculture (CSA) technologies. Emphasis was placed on getting previously unpublished data written up and presented. Unlike many edited volumes, the book started with an open call for chapters on five key topics. More than 70 applications were submitted and evaluated against the criteria, which included: relevance of the topic, whether new data were being presented, and the quality of the science. Twenty-three applications were selected to move on to full chapter development. Twelve specific contributions were then commissioned by the book's editors to fill gaps in the discussion. After at least two technical reviews and multiple rounds of revision, 25 of these papers were accepted for publication within this volume. Unpublished chapters, which still contain important content for development, can be found on the webpage that accompanies this book.

**Table 1.1** Different climate hazards and associated field- and farm-level adaptation interventions, by broad agricultural land use

(continued)

**Table 1.1** (continued)

*C* cropland, *P* pastureland, *M* cropland and pastureland Source: CCAFS and GCF (2018)

#### **1.2 Overview of the Chapters**

The 25 chapters in this book have been divided among 5 themes. Four chapters explore issues around climate change, including impacts and risks. Six investigate mechanisms in seed and crop germplasm delivery systems. Six examine various perspectives and lessons learned on technologies and practices through a CSA lens. Five more examine the resilience to climate change of value chains; and four look at financing, extension and other mechanisms to reach scale. Each chapter reflects on a fundamental question: how to make complex crop and livestock systems more climate-smart? Each chapter ends with messages on the implications for development practitioners to inform future decision-making.

The chapters that explore climate change, along with its impacts and risks, include one on future projections, two on impacts and one on systems. Girvetz et al. investigate the certainty and uncertainty of future climate change in sub-Saharan Africa. This involves longer term and near-term predictions of traditional indices, such as temperature and precipitation, as well as new bioclimatic indicators that help make forecasts relevant to the risks faced by agricultural systems. The authors use a freely accessible online tool known as Climate Wizard (www.climatewizard. org), which is available to practitioners to help them incorporate climate information in programme and policy design. Bett et al. describe two cases of how predicted climate change will affect the occurrence of livestock pests and diseases that already cause significant damage to livelihoods and economies. The authors' concrete recommendations around mitigating future impacts support the notion of taking action today to prepare for the challenges of tomorrow. Hunter and Crespo analyse the climate risks and impacts for both staple (maize and cassava) and cash (coffee) crops at the subnational level in Angola. The authors' findings demonstrate a clear need for future investments—for example, in long-lived coffee—despite the inherent uncertainty in climate models. Lastly, Masikati et al. look at the likely responses of maize and groundnut under climate change using common crop models. Their findings suggest that improved soil management can help mitigate future negative risks to productivity. Taken together, these chapters illustrate why this type of research is critical in moving beyond projections of the future to concrete action that can be taken today. Nonetheless, commissioning this section of the book was not unproblematic, pointing to an urgent need for more information around climate change impacts and risks—detail that is instrumental for initiating meaningful conversations on CSA.

The next set of chapters describe the challenges and opportunities around improving the delivery of quality crop germplasm to farmers. Improved planting materials are typically among the first suggested responses to climate variability whether today's or tomorrow's—and this section explores some of the limits of this conventional wisdom. Das et al. open with a private-sector perspective on seed systems. The authors describe bottlenecks in the delivery of cereal seeds along with the necessary changes—such as public–private partnerships—they feel are needed to make investment opportunities more conducive to the private sector. Ertiro et al. bring fresh evidence in support of the development of drought-tolerant maize in Ethiopia. Droughts are already an every-year occurrence in the country under climate change, and this case highlights a suite of actions needed to move from breeding to widespread use of new varieties. Cramer focuses on one specific link in the seed system chain—early generation seeds. By comparing a successful case with an unsuccessful one, the author identifies a few key stumbling blocks that extend the time taken in breeding, delivery and adoption. Parker et al. illustrate that many of the issues presented for cereal crops are also applicable to roots, tubers and banana staples for 300 million people in the humid tropics of sub-Saharan Africa (SSA). However, the solutions recommended by the authors differ markedly to those of previous chapters due to the structure and development of the system that delivers vegetable planting-materials. Many of the challenges discussed—e.g. the lack of development and long generation time—are also presented in Dawson et al. In this chapter, authors discuss the contributions of trees and orphan crops to resilient food systems and make recommendations for investments that develop this system in future. Faddha and van Etten close this section by presenting a cost-effective participatory approach to evaluate varieties under farm conditions using novel material from national gene banks or plant breeding. Using a case study, the authors argue that this triadic comparisons of technologies (tricot) approach has the potential to contribute to making seed systems more dynamic when demand and supply are linked and more diversified, as more varieties per crop will be delivered in a locationspecific way. Together, the chapters in this section of the book present a sobering picture of the current germplasm delivery systems; with a low penetration of improved varieties within agricultural systems (20%), even for most well-developed breeding programmes, and a long development time (13–30 years). This may signal a massive development opportunity for the seed sector within CSA.

Subsequent chapters present perspectives on the climate-smartness of various technologies and management practices. In particular, they unpack the evidence and lessons learned on what makes a technology climate-smart. Rosenstock et al. conduct a systematic map —a rigorous and structured analysis of the available data—to examine the impact of 73 technologies on indicators of productivity, resilience and mitigation. They identify a significant skew in the available peer-reviewed literature towards maize-based systems, productivity outcomes and on-farm trials. This suggest that anyone interested in creating evidence-based programmes and plans will find many gaps in the scientific knowledge. A complementary quantitative approach towards assessing the multidimensionality of agricultural technologies can be found in Kimaro et al. Here, the authors collect agronomic data on the performance of technologies across the three pillars of CSA (productivity, resilience and mitigation) in three agroforestry systems of Tanzania (shelterbelt, intercropping and border plantings of fuelwood and food crops). Their findings highlight the perspective and flexibility needed to understand whether a technology is climate-smart or not. Performance assessments, however, only provide part of the evidence. Manda et al. design and pilot a participatory framework to evaluate practices against farmerselected criteria of productivity and resilience. This qualitative approach can help fill gaps in knowledge-which other chapters of the book have pointed towards-while being farmer-centric. Mwungu et al. present an analysis of barriers to the adoption of a technology. Specifically, the authors investigate drivers behind the adoption of improved varieties in rural, post-conflict Uganda. They find that household size and information networks influence adoption, with results pointing towards both general and context-specific rules on the adoption of CSA technologies. For example, while household size is typically positively correlated with adoption, trust in information networks may be increasingly important in some contexts, such as postconflict zones. Davies et al. analyse how culture and spirituality can affect the adoption of technologies. The introduction of culture as a determinant of adoption is unique in most discussions of technologies in general and of CSA in particular. This concern may be acutely pertinent for technologies aimed at addressing climate risks, given that weather—good or bad—is often viewed as a manifestation of divine intervention. Together, the chapters presented in this section of the book provide insights into the social considerations and scientific approaches that inform the adoption of CSA.

Because technologies are only part of the food system, the fourth set of chapters explores how value chains contribute to the climate-resilience of smallholder farmers and how climate risks to these value chains can be reduced. Barzola et al. focus on farmers and test the hypothesis that farmer entrepreneurship—the innovative use of agricultural resources to create opportunities for value creation—as well as engagement in the value chain facilitates the adoption of CSA technologies. The study found that farm size influences entrepreneurial innovativeness in a surprising way—with smaller farms more likely than larger ones to engage in all forms of innovation. Actors seeking to promote innovation, including the adoption of technology, might therefore consider investing in programmes that help farmers to develop a more entrepreneurial outlook. Hammond et al. further explore farmer participation and climate resilience. The authors use an innovative survey tool, the Rural Household Multi-Indicator Survey, to investigate how participation in *Shea* value chain activities benefit poor farmers. Shea trees serve as a buffer against desertification, accumulate carbon in the landscape and protect soil and water resources, while processing activities (more specifically, shea butter production)

can increase farmers' adaptive capacity by boosting incomes. In contrast, Sloan et al. examine how private-sector firms, in different parts of the supply chain, view, understand and engage with climate change and the promotion of CSA technologies. The key factors influencing the readiness of companies to incorporate CSA into their strategies were found to be specialised staff and a track record of actively promoting sustainability within the company. The scientific community therefore needs to provide actionable information to incentivise companies' investments in CSA; particularly emphasising returns on investment and the cost of inaction. Mwongera et al. discuss the need to link climate change analyses with value chain approaches in designing CSA interventions. Using a case study from Nyandarua County in Kenya, the authors illustrate how the climate risk profile (CRP) approach supports identification of major climate risks and their impacts on the value chain, identifies adaptation interventions, and promotes the mainstreaming of climatechange considerations into development planning at the subnational level. They conclude that the magnitude of a climate risk varies across value chains. Allen and de Brauw take an even broader perspective to explore mechanisms that promote nutrition-sensitive value chains, as part of efforts to manage climate risks and increase resilience through diversification. Access to improved, biofortified seeds, reducing post-harvest loss (for example, through adequate storage and the transportation of perishable crops), and diet diversification are key value-chain interventions for improved nutrition. Vermulen considers the very big picture, describing recent private-sector progress towards realising CSA targets. The author looks at the Climate-Smart Agriculture Initiative of the WBCSD and shows that the global agrifood sector is exceeding WBCSD targets for global food production, but falling short on emissions reductions, and failing to track outcomes for farmers' livelihoods. There are major gaps in information, monitoring, reporting and verification which need to be tackled if the ambitious CSA targets are to be met. Overall, this section of the book highlights the instrumental role of systemic, collective action for promoting climate-smart value chains.

In order to meet global food security ambitions, CSA technologies need to be accessible and accessed by farmers. The final section of the book discusses mechanisms for bringing CSA to scale. Franzel et al. explore farmer-to-farmer extension systems and find that these approaches can significantly increase the pool of farmers adopting CSA practices, but that this varies across practices and contexts. Their chapter suggests that this innovative advisory approach should not replace traditional, low-performing extension services, but rather complement existing approaches (such as extension campaigns, farmer field schools or information and communication technology). Acosta et al. study the role of multi-stakeholder platforms in promoting an enabling policy environment for climate action. These platforms can create ownership, knowledge and science-policy dialogue at various scales. In a similar vein, Kadzamira et al. discuss the role of different partnership arrangements in scaling CSA in Zambia, Zimbabwe and Malawi. Accordingly, successful partnerships for scaling build on existing structures and mechanisms, bring mutual benefits for all stakeholders, and ensure transparency in decision-making processes. Finally, Ruben et al. investigate the different rural financial instruments that are available for promoting CSA. While the adoption of practices and technologies may be stimulated through interventions that address very specific resource constraints (through credit, insurances, and input provisions, for example), scaling CSA requires more systematic investments (for example, blended mechanisms) that allow for increases in farm income while minimising risks.

#### **1.3 Implications for Development**

This book highlights a wide cross-section of effort to design and disseminate agricultural technologies and approaches that help farmers better cope with climate risks. During a review of the chapters, however, several common gaps were identified that may merit attention in future research.

The main climate risk considered in these pages is drought—an obvious choice, given the long history of efforts to identify technologies suitable for droughtaffected regions of Africa. Drought is already endemic in large parts of eastern and southern Africa. A principal concern is that these areas will expand as the climate continues to change. However, there is relatively little discussion about the variation in drought across the region and how this is expected to alter over time. This is based on the assumption that current drought risks are indicative of weather patterns under a changing climate. Yet it is not obvious that current drought risks will simply expand spatially. Over the next generation or two, the types of drought may change (cf. Chavez et al. 2015). A larger proportion of farmers may find that the rains start late or end earlier, or that the seasons simply shorten. In some areas, mid-season dry spells affecting flowering may become more common. This points to a need to better characterise how drought risks are likely to change over time and, more explicitly, account for this in technology design.

While rising average temperatures are linked with the likely spread of drought, the chapters in this book suggest that comparatively little work has been completed on solutions to these temperature changes. This is surprising given the irrefutable evidence that temperatures are rising in line with the growth of GHGs, and may be rising faster in sub-Saharan Africa than in other parts of the world. Higher average temperatures are widely expected to shift the incidence of pests and diseases affecting crop and livestock production (Bett et al. 2017). However, models tracking the speed and incidence of this change remain rudimentary. Observers note that rising temperatures may also affect plant flowering and fruit production, as well as the timing and severity of drought. But the thresholds for these changes do not seem to be well defined in applied technology development programmes. If scientists remain uncertain about the levels, spatial distribution and timing of changing temperatures, designing technology suitable for the diverse farming systems of eastern and southern Africa will continue to be challenging.

Similarly, solutions to the endemic and possibly worsening climate risk of flooding are almost totally absent in this collection of studies. This includes the need to develop varieties that are more tolerant of water-logging and to strengthen water management and control systems.

Most of the chapters concentrate on the improvement of technologies and management strategies for coping with today's climate risks. This is understandable, given the pressing need to improve the productivity of farming systems. The emphasis is, therefore, on technologies that help farmers to better cope with today's risks, which are also likely to support larger numbers of farmers who may be affected by a changing climate in the future. As such, this body of work may be better characterised as 'climate-risk management' rather than 'climate change management'. But given that it can take several decades to develop a new crop or livestock variety, some investment needs to be allocated to coping with changes likely to occur over the next generation or two. And the possible differences between today's climate risks and the probable changes in these risks over time needs to be more consistently acknowledged.

Finally, while the focus of these papers has been on climate risks, greater attention needs to be directed to the trade-offs in household decision-making that may lead many farmers to identify climate risks as secondary. Indeed, market risks such as price and quality—may be more important than climate risks in regions benefiting from the expansion of commercial opportunities. Even in drought-prone regions, such as those growing cotton or sunflower or livestock, farmers may be willing to adopt technologies offering moderate risks and larger potential returns. Similarly, efforts to reduce market risks may allow farmers to experiment with a wider range of productivity-enhancing technologies. Ideally, every new technology will offer higher yields as well as lower risks, including climate risks. In practice, the distribution of technology traits will continue to vary for different environments.

Ultimately, these papers highlight the increasing attention being given by agricultural research and extension officers operating in eastern and southern Africa to problems of climate risk and the threats of climate change. Most of the chapters in this book emphasise concerns around technology targeting, dissemination and scaling up needed to speed the adoption of improved practices. The challenge remains to achieve faster gains on the ground. More evidence-based examples of scale up are therefore needed, along with greater attention on documenting and sharing lessons from successful and unsuccessful practices.

Next year, CSA will turn ten. The development community must face the existential question of whether it will be time to celebrate? Only 2 years remain before countries need to report progress towards implementing their Nationally Determined Contributions that are at the heart of the Paris Agreement—virtually all of which identify improving agricultural practices in Africa as a priority under climate change. And only 12 years remain before the 2030 deadline set by the United Nations Framework Convention on Climate Change to have 500 million climatesmart farmers. How can we best combine our future efforts to achieve this target?

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Part I Climate Risks and Impacts**

## **Chapter 2 Future Climate Projections in Africa: Where Are We Headed?**

**Evan Girvetz, Julian Ramirez-Villegas, Lieven Claessens, Christine Lamanna, Carlos Navarro-Racines, Andreea Nowak, Phil Thornton, and Todd S. Rosenstock**

#### **2.1 Introduction**

Farmers in Africa—like those across the globe—face rising temperatures and more extreme weather associated with climate change (Snyder 2016; IPCC 2012). Much of Africa's vulnerability to climate change lies in the fact that its agricultural systems remain largely rain-fed, with few technological inputs. The majority of Africa's farmers

E. Girvetz (\*)

CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Copenhagen, Denmark e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>
L. Claessens CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Copenhagen, Denmark

International Institute of Tropical Agriculture (IITA), Arusha, Tanzania e-mail<EMAIL_ADDRESS>
C. Lamanna · A. Nowak World Agroforestry Centre (ICRAF), Nairobi, Kenya e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>
T. S. Rosenstock World Agroforestry Centre (ICRAF), Kinshasa, Democratic Republic of the Congo e-mail<EMAIL_ADDRESS>
International Center for Tropical Agriculture (CIAT), Nairobi, Kenya e-mail<EMAIL_ADDRESS>
J. Ramirez-Villegas · C. Navarro-Racines International Center for Tropical Agriculture (CIAT), Cali-Palmira, Colombia

P. Thornton International Livestock Research Institute (ILRI), Nairobi, Kenya e-mail<EMAIL_ADDRESS>
work on a small-scale or subsistence level, with their opportunities limited by persistent poverty, lack of access to infrastructure and information, and challenges related to policy and governance. Climate change is expected to have major negative impacts on the livelihoods and food security of such farmers. Governments and development professionals must confront the challenge of helping them to adapt (Shackleton et al. 2015).

This paper offers a general overview of historical climate change in Africa, and in particular how it has already led to rising temperatures and increased rainfall variability. It then examines the models that provide projections—with varying levels of certainty—of what climate change will mean for farmers across eastern and southern Africa in the coming decades (ESA).

The paper also highlights the strengths and limitations of the available information regarding the effects of climate change. Adapting to climate change requires better projections of the specific climate hazards that will be faced at the national, regional and local levels (Challinor et al. 2007; Muller et al. 2011). In particular, implementation of climatesmart agriculture (CSA) projects has been constrained by the lack of information on the best responses in specific regions. There are serious gaps in observed historical weather data at the local level across the continent, and the continuing collection of such data still lags far behind where it should be. Strengthening the database of observed weather is critical to understanding the changes that have occurred already, to project future changes and their impacts, and to plan appropriately to address them. Once collected and analyzed, climate data must be communicated in ways that help development practitioners and decision-makers understand climate impacts in specific places. Good tools are available, but practitioners at the local level must have the access and training to use them.

Much work remains to be done. However, given that the impacts of climate change are already being felt on the ground, it is imperative that adaptation begins immediately. Even in places where projections are uncertain, steps can be taken right now to implement CSA practices and make farmers more resilient in the face of climate change.

#### **2.2 Past and Present: Evidence Africa's Climate Has Already Changed**

There is clear evidence that average temperatures have become warmer across the globe. In Africa these changes became apparent starting in about 1975, and since then temperatures have increased at a rate of about 0.03 °C per year (NOAA 2018; Hartmann et al. 2013). In those regions of Africa for which data are available, most have also recorded an increase in the incidence of extreme temperatures as well as longer heat waves (Seneviratne et al. 2012).

Historic variability can provide useful context for understanding climate change. Climate variability can be thought of as a bell curve, with weather in any given year most likely to cluster around the average (the top of the bell) and extremes of temperature or precipitation occurring less often (the flatter parts of the curve). Climate change can shift both the mean and the overall shape of the bell curve, often flattening it out because of the rising frequency of extremes (Kirtman et al. 2013). Records are constantly being broken: in Africa 19 of the past 20 years have been hotter than any previous year on record. The new normal for temperature is hotter than ever experienced in the recorded past.

Historic precipitation patterns show that much of Africa is drying (Hartmann et al. 2013, Fig. 2.1). West Africa and parts of southern Africa, particularly Zambia

**Fig. 2.1** Historical changes in precipitation from 1951 to 2010 (From Niang et al. 2014). The map has been derived from a linear trend. Areas with insufficient data are marked as white, solid colors indicate statistically significant trends at 10% level, and diagonal lines indicate areas where trends are not statistically significant

and Zimbabwe, show rapid and statistically significant decreases in precipitation. By contrast, South Africa and limited parts of East and North Africa have experienced increased rainfall. At the same time, increased temperatures are leading to higher rates of evapotranspiration, which produces drier soil conditions (Girvetz and Zganjar 2014). Evaporative stress consistently increased in Zambia between 2001 and 2017 (Fig. 2.2). Even in the face of increasing precipitation, it is possible for the aridity of soils to increase. In southern Africa from 1961 to 2000, an increasing frequency of dry spells was accompanied by an increase in the intensity of daily rainfall, which has implications for runoff (New et al. 2006).

#### **2.3 Future: Climate Model Projections for Africa**

General circulation models (GCMs) provide the most straightforward and scientifically accepted way to project future climate conditions. However, climate-change simulations performed with GCMs are only possible at coarse resolutions (typically 50–100 km grid cells) that are not detailed enough to assess regional and national impacts. Agricultural livelihoods, soils and local climatic conditions vary vastly at much smaller spatial scales. Spatial downscaling techniques can and should be used to bring these coarse scale maps down to a finer resolution.

Despite their limitations, GCMs are the most commonly used tool to analyze changes in climates at a variety of spatial scales. The latest GCMs available—the Coupled Model Intercomparison Project Phase 5 (CMIP5)—suggest that temperature increases for Africa with the current emissions trajectory (i.e. RCP 8.5) is 1.7 °C by the 2030s, 2.7 °C by the 2050s, and 4.5 °C by the 2080s (Fig. 2.3). Even under the lowest greenhouse gas emissions scenario, by 2030 the climate average is

**Fig. 2.3** Projected changes in annual mean temperature (in °C) and total annual precipitation (in percentage) for the African continent as projected by 33 general circulation models (GCMs) of the CMIP5 model ensemble under RCP 8.5 and three different time periods. Thick black horizontal lines represent the median, boxes show the interquartile range, and whiskers represent the 5–95th percentiles of the data

projected to be completely different from what has ever been experienced historically (Girvetz et al. 2009, climatewizard.ciat.cgiar.org).

Future precipitation is much more difficult to model (Sillmann et al. 2013; Ramirez-Villegas et al. 2013). The median of the CMIP5 models indicates that by 2050, under the higher emission scenario (RCP 8.5, Fig. 2.4), annual precipitation will increase across much of eastern and central Africa, while decreasing across parts of southern, western and northern Africa (Fig. 2.4). Increases of over 200 mm and more than 25% annually are shown in some places, as well as decreases of over 100 mm and more than 20% in other places. Not all climate models agree on the magnitude or even direction of change. However, there are some places with high agreement among the climate models: over 80% of the climate models agree on decreased precipitation in the future for some parts of northern and southern Africa (Niang et al. 2014).

Precipitation is also projected to change differently in different months, with alterations to the onset, length and cessation of the growing season. For example, in Tanzania precipitation is projected to increase during the middle of the wet season (November–May) and to decrease at the wet season's beginning (September– October) and end (May–June) (see http://climatewizard.ciat.cgiar.org/SBSTA/ Africa\_2050/). Overall precipitation is projected to increase, but within a shorter time frame, indicating both shortening of the rainy season and an increased frequency of extreme precipitation events.

Even in areas experiencing increased precipitation, crop production systems can be affected by worsening water stress. Depending on the timing of rainfall, the amount of the temperature increase, and the changes in cloud cover (and hence in

**Fig. 2.4** Projected changes in total annual precipitation by 2050 (top left), climate model agreement (top right), and average change in precipitation and temperature, by country (bottom)

**Fig. 2.5** Projected median changes in climatically suitable area and productivity by 2050s and RCP8.5, relative to a historical period (1970–2000). Median values given are based on ensemble simulations of niche and productivity models, and therefore should be interpreted in light of associated uncertainties. Livestock productivity refers to annual net primary productivity (ANPP) of rangelands (a proxy for livestock productivity), rather than to a direct measure of meat or milk productivity. (Source: Dinesh et al. 2015)

incoming shortwave radiation), many places are likely to have less available water both in streams and in the soil, because warmer temperatures will cause more water to evaporate directly from the soil or to be transpired through plants (Girvetz and Zganjar 2014).

Changes to temperature and precipitation have immediate implications for food production and security across the continent (Niang et al. 2014; Porter et al. 2014; Muller et al. 2011; Rosenzweig et al. 2014). Current growing areas of maize and beans are projected to experience yield reductions of 12–40% by the 2050s. The climate suitability of most major crops is also projected to shift as climate warms (Rippke et al. 2016; Zabel et al. 2014). Ramirez-Villegas and Thornton (2015) have shown that two of Africa's staple crops—maize and beans—are projected to have severe decreases in suitability across much of the continent (Fig. 2.5). Increasing atmospheric levels of carbon dioxide is likely to affect the nutrient content of plants, resulting in serious protein and micro-nutrient cold spots in parts of sub-Saharan Africa (Medek et al. 2017; Myers et al. 2014, 2015a, b). This poses a serious concern for food security and nutrition. While adaptation in the short- and mid-term may help some areas to continue growing these crops, by the end of the century it is estimated that over 30% of the area where maize is grown and over 60% of the area where beans are grown would need to grow entirely different crops (Rippke et al. 2016).

Some crops are much more resilient than maize and beans to changes in climate. In southern Africa, Ramirez-Villegas and Thornton (2015) showed that the suitable area for beans decreased greatly and maize decreased slightly, whereas other crops—banana, yam and pearl millet—increased in range. Similar results were found in East Africa, where maize and beans are projected to experience major decreases in suitable area, whereas cassava, groundnut, pearl millet and banana are projected to increase. These more resilient crops could be promoted as replacement options for areas that require adaptation.

Under climate change, pressures from pests, weeds and diseases are also expected to increase. In the highland regions of East Africa, warming trends could lead to the expansion of crop pests—such as the coffee berry borer—into previously coldlimited areas (Jaramillo et al. 2011). Threats to banana production could come from range expansion of burrowing nematodes (Nicholls et al. 2008) and black leaf streak disease. Striga weed, a major cause of cereal yield reduction in sub-Saharan Africa, could become a more widespread problem because of changes in temperature, rainfall and seasonality (Niang et al. 2014). By contrast, climate change may reduce the range of major cassava pests including whitefly, cassava mealybug, cassava brown streak virus and cassava mosaic geminivirus (Jarvis et al. 2012). However, certain areas of current cassava production—including Southeast Africa and Madagascar may see an increase in whiteflies, mites and mealybugs (Bellotti et al. 2012). In the case of livestock, changes in temperature and rainfall could increase the suitability of the main tick vector of East Coast fever across much of Southern Africa (Olwoch et al. 2008).

#### **2.4 Implications for Development**

#### *2.4.1 Adapting African Agriculture to Climate Change*

African agriculture must adapt in order to ensure food and nutritional security. Management adjustments and crop breeding will be critical in the short- and midterm, whereas at longer timescales planned transformations will likely be necessary (Rippke et al. 2016; Rickards and Howden 2012). Farmers and agricultural service providers—input suppliers, extension, financial services, safety net programs, etc.—will need to become resilient to new climate variability.

Although there is uncertainty in future climate projections, we have a great deal of solid information regarding how climate is already changing and the types of impacts farmers will need to address into the future. It is certain that temperatures

are rising and will continue to rise at a rapid pace. Although climate models often do not agree on precipitation changes, there is considerable agreement on the trends in some locations. Moreover, precipitation is becoming more extreme in many places, often resulting in too much rain, too little rain, or rain that falls at the wrong time. A location might experience more overall rain during the growing season, but if it falls intensively in the beginning or middle of the season, the end of the season might be too dry, especially as hotter temperatures dry out soils. A single location might experience increased flooding during the middle of the rainy season and increased aridity later. This situation creates a need for crop varieties that can withstand waterlogging, help prevent erosion from heavy rains, and reach maturity during a shorter growing season.

Recent studies show that the types of management practices beneficial for adaptation and increased productivity are highly varied (Challinor et al. 2014; Lamanna et al. 2016). For instance, a recent review and meta-analysis of field studies in Uganda and Tanzania found more than 20 practices in each country that could improve adaptation and productivity, each with varying effectiveness depending on the farming system and site in question. The use of fertilizers (both organic and inorganic) and water saving techniques generally have the largest positive effects on crop productivity (Lamanna et al. 2015). Similar findings have been reported elsewhere in Africa (Rosenstock et al. this volume).

Improving the available crop varieties is a key mid-term strategy to increase productivity, improve production stability and adapt to projected climate changes. For example, although climate change will hurt bean production across Africa (Rippke et al. 2016; Ramirez-Villegas and Thornton 2015), heat-tolerant bean varieties could greatly reduce the impact (CIAT 2015). Current work on inter-specific crosses between common and tepary bean show promise for creating breeding lines that maintain yield under heat stress (CIAT 2015). Similarly, drought-tolerant varieties of maize could be an option for adaptation to reduced or inconsistent rainfall (Cairns et al. 2013; Rippke et al. 2016).

In the long-term, planned transformations will be required for some areas. Rippke et al. (2016) report that some 3–5% of the arable land of sub-Saharan Africa may require a transformation out of crop-based systems to either livestock-based systems or to an entirely new land use.

#### *2.4.2 Collecting and Using Climate Data*

Historical data and climate projections clearly establish the need to act quickly to help African farmers adapt to a changing climate. Too often, however, CSA interventions are being promoted without a proper understanding of the climate risks for the specific areas involved. In some cases, reliable information on tightly focused geographical areas simply has not been collected. And even when good information is available from climate models and impact studies, often this information is not

presented in ways that are accessible and comprehensible for those doing the CSA planning and implementation.

Although historic climate trends can be identified across the African continent, there is a general lack of high-resolution data, including for key biomes and agricultural areas (white areas in Fig. 2.1 showing insufficient data). Some national meteorological agencies have made efforts to improve the information available by, for example, combining weather-station information with satellite imagery to create high-resolution gridded historical time-series climate datasets (Dinku et al. 2016). Overall, though, there remains a lack of precise information for decision-making. Weather-station record keeping has declined over the past decades due to lack of maintenance and a failure to install new stations. This trend must be reversed. Increasing the available data on observed weather across the continent is critical to understanding the changes that have occurred already, to predict future changes, and to plan appropriately to address them.

Even as new data are collected, development practitioners and decision-makers should make use of the information and tools now available to help them understand the climate context. CMIP5 projections are freely available through the Climate Wizard, a web application that allows anyone to easily query and map downscaled future climate change projections for specific places globally (ClimateWizard.org). Similarly, the Servir ClimateServ allows for easy online analysis and querying of historic observed precipitation, vegetation greenness and moisture stress, as well as seasonal forecasts looking forward in the short-term for most of the globe (climateserv.servirglobal.net/). More training is needed to help those implementing CSA learn how to access and use these tools. Such training should include profiling of CSA opportunities, prioritization of investment portfolios, design and implementation of CSA projects, and assessing the results of CSA projects (Girvetz et al. 2017).

In conclusion, we would like to emphasize these key points:

Farmers are already suffering from the effects of climate change. Average temperatures are rising, rainfall is becoming less predictable, and extreme weather events are growing more common. The situation poses a real and ever-increasing threat to rural livelihoods and food security. Government, civil society and the private sector must work together urgently to collect and analyze climate information, make it accessible to decision-makers on the ground, and to ensure that CSA planning and implementation are carried out based on the best information available.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Chapter 3 Climate Change and Infectious Livestock Diseases: The Case of Rift Valley Fever and Tick-Borne Diseases**

**Bernard Bett, Johanna Lindahl, and Grace Delia**

#### **3.1 Background**

The global-average surface temperature has risen steadily since the nineteenth century due to an increase in the concentration of heat-trapping gases such as carbon dioxide and methane in the atmosphere. These changes have had important consequences on rainfall patterns, the intensity of droughts, and the viability of ecosystems (Martin et al. 2008) among other changes. Taken together, these changes have substantial effects on the transmission patterns of infectious diseases.

A few studies have been done to identify processes through which climate change influences infectious disease occurrence. While more work needs to be done to fully characterise these processes, the existing knowledge suggests two broad categories of impact, often classified as 'direct' and 'indirect'. Direct impacts are realised when a rise in temperature, precipitation intensity, flooding, humidity, etc. increase pathogens' or vectors' metabolic processes, reproductive rates, and (or) population densities, resulting in enhanced vector–pathogen–host contact and, therefore, the risk of disease (Bett et al. 2017). These changes operate within defined biological limits. This is because an increase in temperature or flooding beyond a given threshold leads to the desiccation of these arthropods or the flushing of vector breeding sites, and hence a decline in disease transmission risk. Direct effects are often associated with diseases caused by pathogens that spend part of their life cycles outside

J. Lindahl International Livestock Research Institute, Nairobi, Kenya

Uppsala University, Uppsala, Sweden

B. Bett (\*) · G. Delia

International Livestock Research Institute, Nairobi, Kenya e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>
Swedish University of Agricultural Sciences, Uppsala, Sweden e-mail<EMAIL_ADDRESS>
a mammalian host. These include vector-borne diseases, helminthoses and fungal infections.

Indirect effects are less apparent and would include changes in disease transmission patterns associated with climate-induced ecological, socio-cultural or behavioural disruptions. In pastoral areas, for example, prolonged droughts trigger more frequent and long-distance movements which enhance contact between distinct populations of animals. This would also include movement into previously uninhabited areas potentiating exposure to new disease agents. Different indirect effects are reviewed in Lindahl and Grace (2015).

We use two well-studied vector-borne diseases—Rift Valley fever, which often occurs in epidemics in East Africa, and tick-borne diseases, which are endemic in many parts of the world—to demonstrate the impacts of climate change on livestock diseases. Our review focuses on the direct effects given that indirect effects are not well studied and are also difficult to quantify.

#### **3.2 Case Studies**

#### *3.2.1 Rift Valley Fever*

Rift Valley fever (RVF) is a mosquito-borne viral zoonosis mainly affecting sheep, goats, cattle, buffaloes and camels. People become infected following a bite from an infected mosquito, or after close contact with acutely infected animals or infected tissues. In people, the disease manifests as a mild influenza-like syndrome in a majority of cases (more than 80%) or a severe disease with haemorrhagic fever, encephalitis, or retinitis in a few cases (Njenga et al. 2009). In livestock, the disease manifests as increased abortion and perinatal mortality rates.

#### **<IP_ADDRESS> Drivers**

RVF outbreaks have been reported in some countries in East and southern Africa including Kenya, South Africa, Tanzania and Uganda following periods of abovenormal precipitation. The disease has also been reported in other countries including the Comoros archipelago, Madagascar, Mauritania, Saudi Arabia, Senegal and Sudan (Madani et al. 2003) and Yemen (Abdo-Salem et al. 2006). In South Africa, recent RVF outbreaks observed in 2008–2011 were associated with relentless and widespread strong seasonal rainfall and high soil saturation (Williams et al. 2016). Areas affected by these outbreaks are shown in Fig. 3.1. In East Africa, major outbreaks are often associated with the warm phase of the El Niño/Southern Oscillation (ENSO) phenomenon, although there have been a few incidences (e.g. in mid-1989) when an elevated RVF activity was not ENSO-driven. There have also been localised outbreaks in Uganda associated with seasonal rainfall and flooding. Figure 3.2

**Fig. 3.1** The spatial distribution of laboratory-confirmed human cases in South Africa in local administrative municipalities 2008–2011 (Archer et al. 2013). The spatiotemporal distribution of the RVF cases in humans paralleled those of livestock, which were triggered by heavy rainfall

gives an RVF risk map based on data that were collected during the 2006/2007 outbreak. Not all El Niño events lead to RVF outbreaks; El Niño events recorded in Kenya in 1964, 1969, 1972–1973, 1981 and 1991–1995, for example, did not lead to RVF outbreaks.

In West Africa, RVF outbreaks occurred in 1998, 2003, 2010 and 2012 following an interlude between a dry period, lasting for about a week, and a period of heavy precipitation (Caminade et al. 2014). The 2009–2010 outbreak, which affected small ruminants, camels and people was associated with a fourfold increase in rainfall in a desert region in northern Mauritania (Faye et al. 2014). Similar outbreaks occurred in Senegal in 2013–2014, exacerbated by extensive livestock movements that aided the dissemination of the virus (Sow et al. 2016).

#### **<IP_ADDRESS> Climate Change and RVF**

A few studies have been done to evaluate the expected impacts of climate change on RVF transmission. These suggest that climate change is likely to expand RVF's geographical range due to expansion of the vector niches (Mweya et al. 2017; Taylor et al. 2016). There are also indications that the average rainfall in eastern Africa, including the Horn of Africa, is expected to increase, while that for southern Africa is likely to decline with climate change (Conway 2009). ENSO-related precipitation

**Fig. 3.2** Risk map for RVF in East Africa based on reported cases in livestock during the 2006/2007 outbreak and rainfall distribution (Bett et al. 2017). Probability estimates given on the map indicate the chance that a given area could experience an RVF outbreak based on the environmental conditions observed in December 2006

variability is also predicted to intensify (Intergovernmental Panel on Climate Change 2014) and this might have stronger implications for RVF, given that the intense precipitation which drives an RVF epidemic follows El Niño and La Niña events in East and South Africa, respectively. Further evidence for an increased precipitation under intermediate warming scenarios in parts of equatorial East Africa is provided by Hulme et al. (2001). They suggest that the region will experience a 5–20% increase in rainfall between December and February and 5–10% reduction in rainfall between June and August by 2050.

Climate change may indirectly increase RVF risk through land use change associated with the development of irrigation schemes and dams. The increasing frequency of droughts and erratic rainfall in arid and semi-arid areas would necessitate the construction of dams and irrigation schemes to support water supply and food production. Previously, outbreaks of RVF have been reported following flood irrigation in the Orange River region and Western Cape province, South Africa (Williams et al. 2016), and the construction of dams, i.e. Aswan High Dam in Egypt in 1977

and the Senegal Dam on the Senegal–Mauritania border in 1987/1988 (Martin et al. 2008). A recent study conducted in Kenya confirmed that irrigated areas in arid and semi-arid areas support endemic transmission of RVF (Mbotha et al. 2017) but more work is needed to isolate the virus from such cases to confirm observations made from serological studies. Drought-resistant livestock species, including goats, which are thought to play a critical role in the epidemiology of RVF and other zoonotic diseases, are increasingly being raised in arid and semi-arid areas as one of the adaptation measures for climate change and variability. These changes are likely to increase the risk of infectious diseases that would compromise health and livelihoods of a large population of pastoralists.

#### *3.2.2 Ticks and Tick-Borne Diseases (TBDs)*

Ticks are important vectors of a wide range of pathogens that cause many diseases in livestock such as anaplasmosis, babesiosis, cowdriosis, coxiellosis (Q fever), Crimean–Congo haemorrhagic fever, ehrlichiosis and theileriosis. East Coast fever (ECF)—the disease with the greatest economic importance in dairy animals—is caused by *Theileria parva* and transmitted by *Rhipicephalus appendiculatus.* The disease causes high mortality, especially in highly productive, susceptible breeds where mortality can reach 100%. Other losses associated with the disease include poor weight gain, fertility losses, reduced growth and productivity, paralysis and increased susceptibility to other diseases. Its geographical range stretches from South Sudan to South Africa and up to Democratic Republic of the Congo (DRC) (Olwoch et al. 2008).

There are few studies in East and southern Africa that have looked at the effects of climate change on ticks and TBDs. Olwoch et al. (2008) applied a simple climate envelope model to investigate the effects of climate change on the distribution of *R. appendiculatus* and ECF in sub-Saharan Africa, based on climate anomalies for 2020s versus 1990s. They predicted a reduction in the range of the tick in the western arid regions in Angola, southern DRC and Namibia, given that these areas were already hot and dry and further increases in temperature would make them unsuitable under the future climate scenarios used. On the contrary, the study established that some areas in Botswana, eastern DRC, the Northern and Eastern Cape provinces of South Africa, and Zambia would become more suitable in the 2020s, because of increased rainfall and a rise in the minimum temperatures.

From a global perspective, a rise in temperature has the potential to expand the geographical range of about 50% of tick species, with 70% of these involving economically important tick species (Cumming and van Vuuren 2006). This mainly represents the northern expansion of the northern limits of ticks as has been observed in Sweden and Russia among other places.

#### *3.2.3 Inferences from the Case Studies*

These two cases show that climate change will cause local shifts in geographical ranges of most vector-borne diseases in both dry/hot and cool/wet areas due to at least two distinct processes. In the hot/dry areas, scenarios of higher rainfall and humidity would promote higher survival rates of vectors, while in the cool/wet areas, increasing temperatures would allow overwintering of these vectors. The key determinants of vectors' population dynamics include temperature, humidity and water availability, especially for mosquitoes. Although we point to potential shifts in disease risk, we believe climate change would affect transmission patterns of infectious diseases in multiple ways, including lowering the effectiveness of existing intervention strategies. No studies have been done to verify this issue but given that the rate of development of most arthropods would increase with temperature and lead to changing population dynamics, the frequency of application of some of the vector control measures such as acaricides might need to be reviewed. High temperatures also reduce the hosts' immune responses (Dittmar et al. 2014) and studies need to be done to determine whether this has implications on the effectiveness of the available vaccines which confer protection by priming the hosts' immune system.

#### **3.3 Mitigations and Adaptations**

Projections from simulation models suggest that global warming will continue to worsen if the current levels of greenhouse gas emissions are not reduced. It is, therefore, expected that the incidence and impacts of climate-sensitive diseases—including RVF and TBDs—will increase, particularly among the most vulnerable populations in developing countries. These diseases, though, can be mitigated by established control measures including quarantine, import bans, the identification and removal of suspicious animals and premises, surveillance and reporting, vaccination, disinfection, and compensation (Grace and McDermott 2012). However, the effectiveness of some of these measures in the face of climate change has not been determined. Moreover, their deployment is inadequate as the animal health systems in most of these countries have deteriorated.

Vector control and vaccination are often used to control RVF and TBDs. Vector control is however not a reliable measure for controlling RVF in livestock (Gachohi et al. 2017). This is because floods that trigger RVF epidemics maintain high mosquito population densities and insecticide-induced mortality rates would be much lower compared to the rates of development and emergence of new adults. Conversely, acaricides have been used successfully for many years to control TBDs but recent observations indicate that tick resistance to acaricides is threatening to limit the effectiveness of this measure. Alternative ways of managing TBDs are therefore being developed, such as the use of tick vaccines (specifically for *Boophilus spp.*), immunisation of animals through infection-and-treatment methods (ITM), breeding of TBD-resistant animals, and the strategic use of acaricides to balance the need to eliminate ticks versus the need to raise the endemic stability of TBDs in the livestock populations. There are ongoing studies to develop new vaccines to replace ITM.

RVF can be reliably controlled using livestock vaccination but its episodic occurrence, a predilection for remote, pastoral areas and lack of forward planning and pre-allocation of emergency funds in most animal health institutions cause a lot of delays in response. An assessment of emergency vaccination programmes that were implemented following the 2006/2007 outbreak in Kenya (Gachohi et al. 2012) as well as those deployed during the recent RVF scare in 2016/2017 showed that livestock vaccination was implemented late and at very low levels to attain sustainable herd immunity. It has now been realised that the administration of livestock vaccination as part of emergency response measures during periods of heightened RVF risk does not provide beneficial outcomes. They fall short of achieving critical levels of coverage that are required for the establishment of protective immunity. Research is underway to determine alternative vaccination strategies for RVF that might involve periodic vaccination in the high-risk areas in place of reactive or emergency vaccinations. In this case, reactive vaccinations can be used strategically to complement periodic vaccination following warnings for El Niño in East Africa or La Niña in South Africa.

Animal health programmes need to be underpinned by efficient surveillance systems which promptly detect and report disease occurrence patterns for action, and guide the prioritisation of interventions to geographical regions or periods where/ when interventions can yield desirable outcomes. There have been multiple uncoordinated efforts towards improving disease surveillance and the development of risk maps and contingency plans in the target areas to help in rationalising interventions. New surveillance systems based on citizen science methods and cloud computing offer great opportunities for identifying the distribution of these infectious diseases; they might also provide clues on how to deploy measures for multiple diseases at the same time. In addition, these systems can be programmed to provide input data for real-time disease forecasting, enabling decision-makers to plan more effectively for impending disease risks. This would require analysing such surveillance data with climate and land use/land cover data as predictors to generate dynamic risk maps.

#### **3.4 Conclusions and Implications for Development**

Climate change is expected to increase the risk of many vector-borne diseases, including those of RVF and TBDs. It is also likely to reduce the effectiveness of some of the control measures—such as vector control efforts—and hence decision makers need to be sensitized more on how to make the best use of the existing interventions, as more research is implemented to determine optimal control options. A key trend of recent decades has been the greater integration of human and veterinary medicine. One World One Health is a growing movement built around the premise that the health of humans, animals and the environment are inextricably linked, and that disease is best managed in broad and interdisciplinary collaborations. Such a multidisciplinary approach can improve targeting of interventions.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Chapter 4 Large Scale Crop Suitability Assessment Under Future Climate Using the Ecocrop Model: The Case of Six Provinces in Angola's**  *Planalto* **Region**

#### **4.1 Introduction**

The *planalto* midlands is a plateau that extends across central Angola, including the majority of the provinces of Huíla, Benguela, Cuanza Sul, Bié, Huambo and Malanje (see Fig. 4.1). The plateau ranges in altitude from 800 to 1600 m above sea level and extends eastwards from the escarpment above the semiarid coastal region towards the central highlands of the country. The Köppen-Geiger classification defines the climate of the interior plateau as "temperate with dry winters and warm/hot summers" (Köppen-Geiger abbreviations Cwa and Cwb, respectively), while the lowlands between the coast and plateau are classified as arid steppe (BSh). Collectively, the region represented by the arid lowlands of Huíla, Benguela, Cuanza Sul and the comparatively temperate highlands above supports a diverse and productive agriculture sector and is a major producer of economically important staple and cash crops.

There is a risk that climate change will undermine the potential contributions of these crops toward national objectives for sustainable development and food security. However, stakeholders are unable to plan for or respond to the risks posed by climate change to agricultural productivity, food security and socioeconomic development, due to the absence of more detailed information to assess the scope and scale of climate-change impacts.

This study assessed the likely impact of climate change on the future suitability of Angola's *planalto* region on two staple crops commonly grown in the region,

O. Crespo

R. Hunter (\*)

African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa e-mail<EMAIL_ADDRESS>
Climate System Analysis Group, University of Cape Town, Cape Town, South Africa e-mail<EMAIL_ADDRESS>
**Fig. 4.1** Elevation map of the study region

namely cassava (*Manihot esculentum*) and maize (*Zea mays*), which are respectively the first and second most important staple crops by area of cultivation and total production. Using a model-based approach, this study assessed the impacts of climate change on the spatial extent of areas classified as climatically suitable for maize and cassava, between the "historical baseline" period (i.e., the present) and a future date (2050). The goal of these analyses is to improve decision-making and spatial planning regarding which crops, cultivars and farming practices should be promoted as part of a strategy for climate-resilient agricultural and socio-economic development in the *planalto*.

#### **4.2 Materials and Methods**

#### *4.2.1 Sources of Climate Data*

Baseline climate data for the study area was derived from Worldclim historical data, which provides average monthly climate data for minimum, mean and maximum temperature and for precipitation for the period 1960–1990 at a spatial resolution of about 1 km2 (a resolution grid of 30 arc-sec) (Hijmans et al. 2005). Interpolations of observed data for the period 1960–1990 are henceforth referred to as the "historical baseline" period.

The future effects of climate change in the study area were computed based on analysis of 29 general circulation models (GCMs) downloaded from the AgMERRA dataset (Ruane et al. 2015). Future climate changes in 2050 for monthly mean temperature (Tmean), monthly minimum temperature (Tmin), and monthly mean precipitation (Precip) were computed assuming the scenario of RCP 8.5 (high emission pathway).

#### *4.2.2 Analysis of Crop Suitability*

The influence of future climate change predictions on crop suitability was assessed using the Ecocrop suitability model developed by the Food and Agriculture Organisation (Ecocrop 2010), based on the methodologies described in Ramirez-Villegas et al. (2013). The Ecocrop model calculates the relative suitability of a crop in response to a range of climate variables such as temperature, rainfall; and growing period, thereby generating a suitability index score ranging from 0 (totally unsuitable) to 1 (optimal/excellent suitability) as an output. It should be noted that this study did not undertake any additional ground-truthing or calibration of the range of climate parameters preferred for either crop, and therefore the default EcoCrop parameters were assumed. Suitability index scores were calculated for the range of climate variables reported for the historical baseline period (WorldClim data) and future (GCM predictions for 2050).

#### **4.3 Results and Discussion**

#### *4.3.1 Projected Climate Changes*

By 2050 a clear trend of warming is projected across the entire study region throughout all months of the year, with predictions of increases of Tmin and Tmean of approximately 1–2.5 °C. The mean and minimum monthly temperature (Tmean and Tmin) is predicted to increase by 1.5–2 °C in the eastern and southern interior of the country (including large areas of Bié, Huambo, Huíla and Malanje provinces), and increases of about 1 °C predicted for the coastal, central and northern regions of the country. Figure 4.2 depicts the spatial distribution of Tmin and Tmean (left and centre, respectively), with anomalies between the two time periods indicated by red shading (bottom row).

With respect to predicted effects of climate change on rainfall, it is projected that the onset of the rainy season (typically September–October) in 2050 will be charac-

**Fig. 4.2** Predicted effects of climate change in study region on average monthly minimum temperature (Tmin) (left), average monthly mean temperature (Tmean) (centre), and mean monthly precipitation (Precip) (right) by the year 2050 for the month of October. Top row depicts baseline (current) climate, centre row depicts predicted future (2050) climate and bottom row indicates anomalies between the two time periods

terised by reduced mean monthly precipitation across the entire region compared to the baseline. Anomalies in monthly rainfall (indicated by red shading in the centre of Fig. 4.2, bottom right) are particularly acute in the northern, central and western extents of the study area at the onset of the rainy season. The trend of reduced rainfall at the onset of the rainy season is projected to continue for the month of November in the central and southern extent of the study area (including the entire extent of Huíla province and majority of Bié and Huambo), whereas the majority of Cuanza Sul and Malanje provinces are projected to benefit from increased rainfall in November by 2050. The majority of the study area is projected to benefit from increased rainfall by 2050 during the midsummer months from December to February, with the exception of the southernmost extent of Huíla province. In March and April, the last months of the traditional maize-growing season, rainfall across the study area is projected to follow two distinct trends: (i) reduced rainfall in the southern and eastern areas, particularly Huíla, Bié and the south-eastern extent of Huambo; and (ii) increased rainfall in the central, western and northern areas, particularly Malanje, northern Huambo, and the highland interior of Cuanza Sul and Benguela provinces. No major changes to rainfall are projected for the dry winter months of May to August.

The predicted spatial and temporal shifts in temperature and precipitation are likely to result in diverse effects on crop productivity between different crops and regions. Increased temperatures are expected to increase crop water demand, which may lead to increased crop stress or reduced productivity. In certain areas, however, the increased temperatures may increase productivity and extend the length of growing season for some crops, particularly where supplementary irrigation is available or the duration or volume of rainfall received increases (such as in the centre and north of the study region). Climate change is projected to impact the distribution, timing and volume of rainfall, most notably showing a delayed onset of rainfall season or reducing the mean precipitation received during the growing season.

These projected climate changes are likely to result in long-term changes to the timing of various agricultural activities such as field preparation and sowing of seed. In the southern and eastern parts of the study area, notably Huíla and south-east Bié, climate change is expected to reduce precipitation across all months of the growing season, which will reduce the productivity of traditional agricultural approaches and force farmers to adopt new practices and crops. Drought-sensitive crops are likely to be increasingly unreliable or unproductive in the latter areas. In contrast, the central and northern extent of the study region is expected to benefit from increased rainfall during the middle and late summer months, which may extend the growing season or improve the yield potential of certain crops.

#### *4.3.2 Effects of Climate Change on Distribution of Crop Suitability*

Changes in the total spatial extent of suitable area were calculated for both crops for the period from the present to 2050. Figure 4.3 provides an example of the approach used to depict spatial distribution of crop suitability, where the relative proportion of each colour-shaded area indicates the spatial extent of each corresponding category of crop suitability. Modelled distribution of suitability for cassava and maize is depicted in Figs. 4.4 and 4.5, respectively, where current distribution of crop suitability is depicted on the left, projected future distribution of suitability is depicted on the right, and the anomalies (i.e. changes) between the two periods are depicted in the centre.

#### **<IP_ADDRESS> Cassava**

Cassava is an important contributor to the diet and livelihoods of Angola's rural farmers and urban consumers, and is a particularly efficient crop in terms of calories generated per input cost. In addition, cassava is considered to be relatively tolerant of low rainfall conditions, and is increasingly promoted as a climate-resilient crop

**Fig. 4.3** Example demonstration of spatial variability in crop suitability index scores, where the relative proportion of each colour-shaded area indicates the spatial extent of each corresponding category of crop suitability

**Fig. 4.4** Changes to spatial distribution of areas suitable for production of cassava (Manihot esculentum) in the 'historical' (left) and 'mid-century 2050' (right) scenarios as a result of climate change. Changes between the two-time periods are depicted in the centre

**Fig. 4.5** Changes to spatial distribution of areas suitable for production of maize (*Zea mays*) in the 'historical' (left) and 'mid-century 2050' (right) scenarios as a result of climate change. Changes between the two-time periods are depicted in the centre

which has the potential to contribute meaningfully to pro-poor economic development (Theodory et al. 2014).

Previous studies of climate-change effects on cassava have suggested that productivity of cassava will not be negatively impacted by climate change and may enjoy slight increases in certain areas of Africa, particularly in Angola (Liu et al. 2008). Jarvis et al. (2010) also reported that climate change will result in a net increase in the range of suitable areas for cassava production (although noting that, despite a net increase in suitable area, other areas are predicted to decline in suitability as a result of temperature increases). The results of this study support the findings of the latter studies, suggesting that the main effect of climate change on cassava production will be to increase the spatial extent and relatively suitability (i.e., localised suitability index score) of existing cassava production zones. EcoCrop analyses predict that the extent of areas suitable for cassava production will increase in the interior highlands above the coastal escarpment, stretching northward from the border of Huambo and Benguela, through Cuanza Sul and northwards into Malanje. This expected improvement in the region's suitability for cassava may be attributable to the projected increase in Tmean from 20 to 21.5 °C, where the optimum temperature range for cassava is 20–29 °C. The trend towards increased suitability for cassava in the latter areas is projected to remain consistent from October through the rest of the summer months.

The suitable range for cassava production is limited by the arid low-lying southern interior of Huíla and western lowlands of coastal Benguela and Cuanza Sul, which are considered to be poorly suited for cassava production in both the baseline and future scenarios. Potential opportunities and adaptation options for such arid regions may include: (i) promotion of sweet potato as a perennial starch-rich alternative to cassava; (ii) adoption of relatively drought-tolerant cereals such as sorghum and millet; and (iii) promotion of increased crop diversification, including combinations of sweet potato, cassava, legumes and drought-tolerant cereals.

It should be emphasised that the potential benefits of cassava as a climateresilient subsistence crop are unlikely to be realised without addressing existing structural barriers in the cassava value chain. Market accessibility for cassava farmers in remote areas is hindered by the short shelf life of unprocessed cassava. Therefore, it is recommended that efforts to promote the cultivation of cassava should be supported by simultaneous investments in capacity-building for improved post-harvest storage, processing and value-adding.

#### **<IP_ADDRESS> Maize**

Maize is an important staple crop across Southern Africa and is broadly considered to be prone to climate risk such as drought, irregular rainfall and heat stress. Increased temperatures and an increased frequency of severe drought events pose major concerns to cereal production in sub-Saharan Africa, as do expected increased incidence of diseases, pests and parasitic plants (ADB 2015). Past studies have suggested that projected temperature increases could reduce the productivity of major cereal crops, including maize, by 20–30% by 2050 (Liu et al. 2008; Schlenker and Lobell 2010).

In agreement with past studies, EcoCrop analyses predict that climate change will result in minor but widespread decreases in the crop suitability index score for maize in the *planalto* region by 2050, particularly at the onset of the rainy season in October. In Huíla province, the absolute spatial extent of areas which are suitable for maize production are predicted to decrease considerably as a result of climate change, to the extent that the entire province is likely to become poorly suited to the crop by 2050. Of the remaining five provinces, the maize-suitable production areas are predicted to remain unchanged in absolute spatial extent but will undergo decreases in crop suitability index score.

In addition to the effects of increasing temperature, a major challenge that will affect maize farmers in Angola is the projected delay in onset of the rainy season as a result of climate change. The trend towards reduced suitability for maize production is likely to be attributable to the predicted decreases in rainfall over the growing season; EcoCrop's parameters specify a minimum seasonal rainfall of 400 mm and optimum rainfall of at least 600 mm. Analysis of GCMs suggest that onset of rainfall is likely to shift from October/November to December/January by 2050. For households practicing rain-fed maize cultivation, the delayed rains will increase the duration of the "lean" season, when households are reliant on the previous season's harvest. Households therefore will need to adopt new strategies to ensure that food reserves (and adequate seed for planting) can last through this longer lean season. It is unclear whether a long-term shift in the onset of Angola's rainy season will result in a delay to the planting season, or whether changing rainfall patterns will shorten the effective growing season. It is recommended that Angola urgently promote the development and adoption of locally adapted, improved maize cultivars that are more tolerant to heat and drought stress and that can grow to maturity within the confines of a shortened or variable growing season, as well as the promotion of comparatively drought-resilient cereals such as millet and sorghum.

#### **4.4 Implications for Development**

The approach and results presented in this chapter demonstrate the use of downscaled climate projections and crop suitability models as a useful but broad-level means of assessing the possible effects of climate change on the temporal and spatial distribution of crop suitability. This is particularly important in countries such as Angola where agronomic data and climate measurements are not readily available. In this case study of six provinces in the *planalto* region of Angola, the diverse impacts of climate change on the crops analysed cannot be easily generalised across the entire study area and indicate the need for detailed local-level studies and strategies for intervention.

The semi-arid regions in the south of Huíla and in the western lowlands of Benguela and Cuanza Sul have climates that are at the limit of the suitable range of the crops analysed. As a result, the spatial range of suitability for heat- and droughtsensitive crops such as maize is projected to be reduced in the low-lying, coastal and southerly parts of the study area by 2050. Climate change will also reduce the duration—or delay the onset—of the growing season for rain-fed crops such as maize across most of the study region. The negative effects of climate change on staple crops such as maize has the potential to undermine the wellbeing of rural households and jeopardise long-term objectives for economic development in climatevulnerable regions such as Angola. In the affected regions, the primary options for adaptation include the promotion of both climate-resilient cultivars of maize and of alternative crops such as cassava, millet or sorghum.

However, despite the apparent threats posed by the declining productivity of certain crops in response to climate change, this study also indicates that climate change may create new opportunities for agricultural development through promotion of climate-resilient staples and alternative crops. In addition to crop-specific considerations, adaptation options for Angola's agriculture sector may include promotion of rural finance, food processing, development of irrigation infrastructure, increased access to extension services, development of early-warning systems and development of rural transport infrastructure.

These analyses provide a demonstration of the applications of crop suitability models for the identification of potential climate vulnerabilities related to food security, as well as identification of potential climate-resilient subsistence crops to be promoted as a strategy to adapt to changing climate conditions. Modelled approaches such as those applied in this study can be further strengthened through the inclusion of measures for calibration and incorporating field-level measurements and local crop performance data.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Chapter 5 Understanding the Role of Soils and Management on Crops in the Face of Climate Uncertainty in Zimbabwe: A Sensitivity Analysis**

**Patricia Masikati, Katrien Descheemaeker, and Olivier Crespo**

#### **5.1 Introduction**

More than 50% of agricultural land in Africa is degraded and yields of the main staple crops have been at the lower end of the global range for decades (UNCCD 2014; Folberth et al. 2013). To meet the demands of a growing population, agricultural land has expanded into forests. This, coupled with unsustainable agricultural practices has led to increased land degradation (Lisk 2009; GGCA 2012). Africa is one of the most vulnerable continents because of its highly sensitive social and ecological systems and its limited institutional and economic capacity to respond appropriately to these emerging threats (Lisk 2009; GGCA 2012; Perez et al. 2015). Although climate change affects a number of development sectors, the risk to agriculture stands out since the sector represents a significant part of the economies of many African countries (Vermeulen et al. 2012). There is no doubt that climate change will amplify drivers of land degradation and pose increased threats on smallholders' livelihoods of which the majority are women (GGCA 2012; UNCCD 2014).

Degradation of agricultural land is causing annual yield reductions of 0.5–1% suggesting productivity loss of at least 20% in the next 40 years. In addition, climate

P. Masikati (\*)

World Agroforestry Center (ICRAF), Lusaka, Zambia e-mail<EMAIL_ADDRESS>
K. Descheemaeker Plant Production Systems, Wageningen University, Wageningen, the Netherlands

O. Crespo Climate System Analysis Group, University of Cape Town, Rondebosch, South Africa change impacts are projected to reduce yields by up to 25% (Ioras et al. 2014; Asseng et al. 2015; Rurinda et al. 2015). Agriculture-based livelihood systems that are already vulnerable to food insecurity will face immediate risk if such yield reductions would occur. Although there has been progress made to understand the impact of climate change and variability on different crops in Africa, there is limited knowledge on how crop-soil systems respond to climate change. Characteristics of different soils vary; for example, clay soils with high organic matter have low thermal conductivity as well as high water holding capacity (Makinen et al. 2017). In contrast, sandy soils, which are predominant in smallholder farming systems, have high thermal conductivity and low water holding capacity (Moyo 2001; Nyamangara et al. 2001). However, the levels of fertility of sandy soils within and across farms greatly depend on the soil-fertility management practices used (Tittonell et al. 2007; Zingore et al. 2011). Soil-climate combination also plays a key role. The magnitude of crop responses to climate is highly sensitive to the soil type (Folberth et al. 2016; Makinen et al. 2017). Farmers in Nkayi, Zimbabwe, have already experienced this; during years of above-average rainfall, farming on clay soils generated a better harvest than on sandy soils, while the reverse is also true.

Empirical and quantitative information regarding the dependency of yield responses to agro-climatic variables on soil type is needed for designing effective climate-smart adaptation methods and enhancing the resilience of smallholder farming systems in the region (Piikki et al. 2015; Folberth et al. 2016; Makinen et al. 2017). Crop models are important tools that can be used to unravel the importance of soil type on crop responses to climate change and variability. However, model choice is also important as different model configurations, operation time steps, physiobiological processes, and others determine the model outputs (Asseng et al. 2015). Here we use the Decision Support System For Agrotechnology Transfer (DSSAT) model and the Agricultural Production Systems Simulator (APSIM) model (McCown et al. 1996; Jones et al. 2003; Hoogenboom et al. 2010; Holzworth et al. 2015). The two models simulate the dynamics of phenological development, biomass growth and partitioning, water and nitrogen cycling in an atmosphere-cropsoil system driven by daily weather variables that include rainfall, maximum and minimum temperatures and solar radiation (Hoogenboom et al. 2010; Holzworth et al. 2015). We use the two models to (1) assess the sensitivity of maize and groundnuts to individual climatic factors such as rainfall, temperature and CO2 concentration, under three soil types differentiated by levels of organic carbon and plant available soil water (2) simulate the combined impacts of future climate (2040– 2070) on the two crops across the three soil types. Both soil fertility and climate are important issues in smallholder farming systems and will have different impacts on plant production and crop yields under future climate change. Production may increase or decrease depending on plant response to the interactions between climate and soil type, hence the importance to assess these impacts to inform adaptation decision-making.

#### **5.2 Methods**

#### *5.2.1 Study Site*

Nkayi district is located in the northwestern part of Zimbabwe. Soils in the area are predominantly sandy. Soil organic carbon varies between 0.4% and 0.8% in the top layers while the water holding capacity varies from 52 to 102 mm (Moyo 2001). Maize is the major staple cereal while groundnuts are generally considered women crops that can improve both household income and nutrition. Groundnut is a multipurpose crop that can be used to improve soil fertility as well as the quality of livestock feed, especially during the dry season. Nkayi has a short growing season and limited water availability. Yields are not necessarily linked with higher nitrogen input due to interactions between nitrogen-induced growth and its effects on water use and water availability at different growth stages, especially during grain filling.

Historical changes in climate in the district show increasing temperature trends and recent projections show increases of approximately 1–2 °C in the near future, 2–3 °C in the mid-century, 2–5 °C by end of century (Masikati et al. 2015). Projections (medium confidence) show that rainfall change direction and amplitude are uncertain, yet averages would remain within or close to baseline variability. Seasonality seems to remain unchanged with possible rainfall reduction at the beginning of the rainy season (Masikati et al. 2015). These projected changes will have different impacts on plant production and crop yields as production may increase or decrease depending on the interactions between crops, climate (CO2, temperature and rainfall) and soil type.

#### *5.2.2 Climate Data*

The best available historical weather record was gap-filled with AgMERRA data to create a 30 yearlong daily climate data set for Nkayi district (Ruane et al. 2014). To assess sensitivity of maize and groundnuts to different climatic factors; temperature (minimum and maximum, CO2 and rainfall) we used increments as shown in Table 5.1. To assess the second objective, we use two climate scenarios generated under two Representative Concentration Pathways (RCP), RCP4.5 and RCP8.5 for mid-century (2040–2070) (Ruane and McDermid 2017) (Fig. 5.1a, b) The two

**Table 5.1** Factors and levels considered for sensitivity analyses

**Fig. 5.1** (**a**) Mid-century temperature and precipitation changes in Nkayi, Zimbabwe, from 29 General Circulation Models (GCMs) under RCP4.5 and RCP 8.5 (**b**) Sub-setting of (GCMs) represented by different letters with different colored dots showing average changes in precipitation and temperature as predicted by GCMs showing projected cool/wet, cool/dry, hot/wet, hot/dry and middle conditions

selected were the hot/wet and hot/dry scenarios in view of likely increases of temperature while rainfall change direction is uncertain.

#### *5.2.3 Crop Model Setup and Sensitivity Tests*

DSSAT and APSIM models were used to test the effects of climate change on crop production (McCown et al. 1996; Jones et al. 2003; Hoogenboom et al. 2010; Holzworth et al. 2015). Both DSSAT and APSIM are models that have been developed to simulate biophysical processes in crop farming systems in relation to economic and ecological outcomes of management practices in current or future farming systems (Hoogenboom et al. 2010; Holzworth et al. 2015; Steduto et al. 2009). For Nkayi the models have been calibrated (Masikati et al. 2014, 2015) and can be used with confidence in conducting ex-ante climate impact assessments on crop production systems.

For this study we assess the impacts of single climate factors (CO2, temperature and rainfall) at varying levels (Table 5.1) and also the combined effects on maize and groundnut. Model simulations were done on three soil types which differed in soil physical and chemical characteristics (Table 5.2): poor, average and better soils representing about 29%, 59% and 12% of farms in the district, respectively. Current farmer management practices were used and these are defined in Table 5.3. Outputs from the models, which were considered for the current analyses, include grain and stover crop yields. Planting was set to be done automatically after the model detected that the set soil moisture conditions were met. For this study the sowing window was set between 1 November and 31 December, and planting was done when at least 15 mm of rain was received in three consecutive days.

#### **5.3 Results**

#### *5.3.1 Maize Response to CO2, Temperature, Rainfall and Fertilizer*

Maize sensitivity to CO2 was evaluated at different concentrations with 60 kg N/ha and without nitrogen fertilizer. Without fertilizer, only the APSIM model simulates slight yield increases in response to increasing CO2 concentrations on better soils. However, when fertilizer is added both models simulate increases of maize yields with increasing CO2 across all soil types. Maize sensitivity to CO2 levels differed between the two models and across soil types (Fig. 5.2). Both maize grain and stover show incremental yields up to the maximum level evaluated here, 720 parts per million (ppm), which is more than double the current CO2 levels.

**Table 5.2** Soil initial conditions used for evaluation of APSIM and DSSAT crop models. Soil samples were collected from experimental sites in December 2008 from the Nkayi district

Source: Masikati (2011)

a*NO3-N* nitrate-nitrogen, *LL 15* crop lower limit, *DUL* drained upper limit, *SAT* saturation, *PAWC* plant available water capacity

**Table 5.3** Treatments used to assess the sensitivity of maize and groundnuts crops to different climate factors in Nkayi, Zimbabwe

a ICRISAT (2008) and Masikati (2011)

In response to temperature the two models show divergent results on both maize grain and stover on poor soil. The APSIM model showed a slight increase for grain yields and a decrease for stover yields, however, the DSSAT model showed the opposite. Temperature increase of up to 2 °C would see a slight decrease of grain yields on poor soils, while the same temperature increase would substantially reduce grain yields on average and better soils with higher impact on the latter. Both models show almost no effects of temperature increases on maize stover across soil types.

In response to rainfall, a 25% reduction in rainfall, show yield reductions across all soil types, however impacts are higher on average and better soils. For example at 25% rainfall reduction yield losses on poor soils simulated by the APSIM model are 68 kg/ha, while on the average and better soils are 138 and 487 kg/ha, respectively. Simulated average grain yields for current rainfall are 434, 759 and 2110 kg/ ha for poor, average and better soils, respectively. Conversely rainfall that was higher than the defined baseline was not beneficial to maize grown on poor soils. On better soils, maize yield increases were simulated only up to about 25% rainfall increases but after that there is a yield plateau.

On all soil types maize yields show positive response to increases in fertilizer application rates. Maize yields reach a plateau at about 60–70 kg N/ha for all soil types, however, from 30 kg N/ha, the rate of yield increases on better soils is low compared to the other two soil types. Although increases are simulated across soils with increasing rates of fertilizer higher yield gains were simulated for poor than the other two soils at application rate of 30 kg N/ha. Grain yields gains with application of 30 kg N/ha from base yields simulated by the APSIM model were 1314, 1190 and 466 kg/ha for poor, average and better soils, respectively. The average base yields were 434, 759 and 2110 kg/ha. Above 60 kg N/ha, there is a yield plateau, meaning that the water environment at Nkayi becomes the limiting factor to achieving higher average yield.

#### *5.3.2 Groundnuts Response to CO2, Temperature, Rainfall and Fertilizer*

Groundnuts show high response to CO2 concentrations on all soil types (Fig. 5.3). The two models show similar trends although yields from the APSIM model are higher than those simulated by the DSSAT model. Both grain and stover yields

**Fig. 5.2** Sensitivity of maize grain and stover to temperature, CO2, rainfall change and fertilizer application rates on different soil types, in Nkayi Zimbabwe. APSIM\_1, APSIM\_2 and APSIM\_3 show simulations by the APSIM model for the three soil types: 1 = poor; 2 = average; 3 = better. The same applies for the DSSAT model

57

**Fig. 5.3** Sensitivity of groundnut grain and stover to temperature, CO2 and rainfall change on different soil types, in Nkayi Zimbabwe. APSIM\_1, APSIM\_2 and APSIM\_3 show simulations by the APSIM model for the three soil types: 1 = poor; 2 = average; 3 = better. The same applies for the DSSAT model

increase up to the highest CO2 level evaluated here. The APSIM model simulated higher increases on the better soils while the DSSAT model simulated similarly large increases on the average soil.

In response to temperature changes, both models show negative effects of increased temperature and positive effects of decreased temperatures. Higher yield reductions were simulated for better soils than for the other two soils types. Both models show high yield reductions with temperature increases of about 2 °C, however at higher temperature increases, for example at +6 °C, the DSSAT model simulates slight stover yield increases across all soil types.

Reduction in rainfall by about 50% shows substantial reductions in both grain and stover yields. Both stover and grain yields continue to increase as rainfall increases, however, the increases are very small.

#### *5.3.3 Combined Effects of Climate Factors on Maize*

We evaluated the combined effects of climate elements on maize grain and stover yield. Hot/wet and hot/dry climate scenarios for both RCP4.5 and 8.5 were used. The effects were simulated by both the APSIM and DSSAT model on three soil types. The two models show divergent effects of combined climate elements on both maize grain and stover. The APSIM model shows more stover yield reductions while the DSSAT model shows more grain reductions across soils and climate scenarios. Yield reductions are more pronounced on better soils than on the other two soil types, while the hot/wet climate scenario shows more positive effects than the hot/dry scenario. Climate effects are more distinct for grain than for stover and this is more pronounced for RCP8.5 than RCP4.5. Generally, the hot/dry conditions show substantial reductions with probability of 35, 40 and 70% of getting reduced grain yields as simulated by the APSIM model while the DSSAT model shows 85, 75 and 85% on poor, average and better soils respectively under RCP4.5 hot/dry conditions. Generally, maize production will decrease under future climate scenarios though the degree of impact differs among soil types.

#### *5.3.4 Combined Effects of Climate Factors on Groundnuts*

In contrast to maize, groundnuts mostly showed positive effects with yield increases of more than 50% for stover in some instances. The APSIM model generally simulated positive grain yields under RCP4.5 and reductions at a probability of 19, 30 and 50% for poor, average and better soils, respectively under RCP8.5 hot/dry conditions. Stover yields showed positive yield increases for both RCP4.5 and RCP8.5 under hot/wet conditions. However, reductions were simulated for hot/dry conditions mainly for RCP8.5. The DSSAT model shows more negative effects on grain yields than stover yields across soil types and climate scenarios. Grain yield reductions are more pronounced for average and better soils under hot/dry climate scenarios for both RCP4.5 and 8.5. Stover yields on average soil are mostly affected showing about 40% probability of getting negative yields. Although groundnuts seem to be benefitting on average, however there are years when yield changes are negative.

#### **5.4 Discussion**

#### *5.4.1 Maize and Groundnut Response to CO2, Temperature, Rainfall and Fertilizer*

Temperature increases in areas such as Zimbabwe where crops are grown near thresholds can be detrimental to rain-fed crop production. Increased temperatures can negatively affect crop yields by accelerating crop phonological stages hence less time for biomass accumulation (Asseng et al. 2015). In this study, increased temperatures show negative effects on both maize and groundnut yields across soil types with higher yield reductions simulated on the better soils. Although simulated yield reductions were higher on better soils, average grain and stover yields were always higher than those for poor soils.

Responses to CO2 can vary by crop species (Asseng et al. 2015). In our study, maize showed minimal increases of about 5% for both the APSIM and DSSAT model with fertilizer, however, groundnuts showed average increases of about 23%. Asseng et al. (2015) reported that C4 (e.g., maize, sorghum, millet) and C3 (e.g., wheat, groundnuts, potatoes) plants when CO2 is increased to 500–550 (ppm), grain yield can be increased by 10–20% and by <13% for C3 and C4, respectively. Responses to CO2 also depend on soil water and nutrient availability with highest responses being reported under soil water limiting conditions (Kang et al. 2002). However low soil fertility can reduce the possible positive effects of elevated CO2 on yields (Yang et al. 2006). This was also simulated in the current study where there were minimal to no benefits at all with increases in CO2 across all soil types when no fertilizer was added. However positive responses were simulated with application of 60 kg N/ha with higher increases simulated on better soils. Both stover and grain yields increased as CO2 concentrations increased up to the 720 ppm level. Increases of CO2 in the atmosphere is one of the most certain aspects of climate over the coming decades and leguminous crops such as groundnuts have the potential to benefit from this. Leguminous crops fix the atmospheric nitrogen, release high-quality organic matter in the soil and allow sequestration of carbon in soil. If used as feed (provided the quality is not affected), leguminous crops could reduce methane emissions from livestock. These multiple benefits provide both mitigation and adaptation benefits to farmers.

Rainfall variability can have both positive and negative impacts on agriculture depending on the environment. Reduced rainfall by about 25% can be detrimental to crop yields while increases by similar magnitude would not be as beneficial in low input systems and more importantly on poor soils. Rainfall distribution also plays an important role, as lack of rainfall at crop critical growth stages such as anthesis can substantially reduce grain yield.

Smallholder farming systems are low input systems with an average nitrogen application rate of 3 kg/ha and zero fertilizer application for legumes such as groundnuts. General fertilizer recommendations for different soil types are up to 110, 110–140 and 140–180 kg N/ha for better, average and poor soils, respectively, which is beyond what most farmers can afford (FAO 2006; Vanlauwe and Giller 2006). However, in our current study both models simulate yield plateau at 60 kg N/ ha for better soils and at around 70 kg N/ha for poor soils. The biophysical and socio-economic situation needs to be considered for establishing recommendations and these should be location-specific and dynamic because soil changes depending on how it is managed.

#### *5.4.2 Combined Effects of Climate Factors on Maize and Groundnuts*

Temperatures are projected to increase in Nkayi district, however, rainfall is likely to change by −15% to +10%. Average annual rainfall for Nkayi is about 650 mm per year and the projected reductions and increases can lead to 552 and 715 mm per year, respectively. Variability will be high especially under hot/dry conditions as shown by the variations in yields reductions (Figs. 5.4 and 5.5). Yield variability is higher for maize grain than stover while for groundnuts high variability is only simulated by the DSSAT model for both grain and stover. Increased temperature effects supersede the other factors and will be mostly detrimental to maize while high response to CO2 exhibited by groundnuts negate the negative effects of increased temperature. Increased temperature reduces crop yields by accelerating crop phonological stages, hence, reducing the time for biomass accumulation. Another adverse effect of high temperature is heat and/or water stress, which at the critical crop growth stages, such as anthesis or grain filling, also reduces crop yields. It will be important to assess which effect will be more prominent and this information could be used when developing adaptation strategies. Important is also to assess the particular times when crops are water stressed so that farmers can adjust water and nutrient management, planting and sowing dates, plant densities and cultivar choice. Climate-smart agricultural practices such as agroforestry that make use of water more efficiently and have the potential to induce microclimatic conditions can be recommended in areas affected by heat stress (Mbow et al. 2014).

#### **5.5 Implications for Development**

Crop models are important tools that can be used to understand disaggregated effects of climate elements on crop production. However, models do differ in the way they are constructed and in their responses to different effects of climatic factors on crop production. We used two crop models DSSAT and APSIM and both models generally agreed on the effects of different climatic factors on maize and groundnuts. It is only the magnitude of the effects that vary, for example, reductions on maize grain yields are more pronounced in the APSIM model while the DSSAT

5 Understanding the Role of Soils and Management on Crops in the Face of Climate…

**Fig. 5.4** Probability of exceedance for maize grain and stover yield reductions simulated by APSIM and DSSAT models for RCP4.5 and 8.5 for hot/wet and hot/dry climate scenarios. 8.5\_ HW\_1, 8.5\_HW\_2, 8.5\_HW\_3 = RCP8.5, hot/wet for poor, average and better soils, respectively. HD represent hot/dry; HW represent hot/wet

model shows more pronounced reduction of maize stover yields. Both models show yield benefits under elevated CO2 concentration for groundnuts negating the effects of increased temperatures when evaluating the combined effects of the climatic factors. However, yield increases for both groundnut grain and stover are more

**Fig. 5.5** Probability of exceedance for groundnut grain and stover yield reductions simulated by APSIM and DSSAT models for RCP4.5 and 8.5 for hot/wet and hot/dry climate scenarios. 8.5\_ HW\_1, 8.5\_HW\_2, 8.5\_HW\_3 = RCP8.5, hot/wet for poor, average and better soils, respectively. HD represents hot/dry, HW represent hot/wet

pronounced in the DSSAT model than in the APSIM model. Soils play an important role in determining outputs of crop-climate interactions; they can buffer or aggravate climatic impacts. Better soils exhibited higher responses to positive influences such as increased rainfall and CO2 concentrations compared to poor soils. Better soils would be more important in future farming systems.

**Acknowledgements** In this paper we use the Agricultural Model Intercomperison Project approach (AgMIP, www.agmip.org), using the case study of the crop-livestock Intensification Project (CLIP). UKAID and USDA funded the research. We are grateful to communities in Nkayi district, Matabeleland North and Zimbabwe national stakeholders for their contributions. The research contributes to CGIAR research program CCAFS.

#### **References**

Climate Change Impacts, Adaptation, and Mitigation, vol 3. Imperial College Press, London, pp 90–112

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Part II Adaptive Germplasm Delivery Systems**

## **Chapter 6 Role and Challenges of the Private Seed Sector in Developing and Disseminating Climate-Smart Crop Varieties in Eastern and Southern Africa**

**Biswanath Das, Francois Van Deventer, Andries Wessels, Given Mudenda, John Key, and Dusan Ristanovic**

#### **6.1 Introduction**

CC poses a significant risk to crop production across sub-Saharan Africa (SSA), with ESA particularly vulnerable to the projected changes. Temperature increases are estimated to rise at a rate above the global average during the twenty-first century and it is predicted that by 2050 will significantly change the cropping duration for key staple crops (Cairns et al. 2013; Schlenker and Lobell 2010; Niang et al. 2014; James and Washington 2013; Challinor et al. 2016). Meanwhile, precipitation is projected to increase in parts of eastern Africa but decrease significantly in southern Africa. The combined heat and drought stress in parts of ESA is projected to reduce yields of staple cereals by as much as 30% within two decades (Niang et al. 2014; Lobell et al. 2008).

Smallholder, subsistence farmers constitute over 70% of the population in ESA and account for over 75% of agricultural output (AGRA 2017). They are the group most vulnerable to CC and require urgent, scalable access to CS crop varieties with adaptive characteristics that can tolerate future climes. These include; tolerance to combined heat and drought stress, waterlogging and lodging stress, post-harvest storability, maintenance of nutritive value in warmer climes, and adaptation to new and shifting incidences of pests and diseases. To deliver CS crop varieties in CC

F. Van Deventer · A. Wessels Syngenta, Midrand, South Africa

J. Key Syngenta, Basel, Switzerland

D. Ristanovic Plant Breeding Consultant, Lusaka, Zambia

B. Das (\*) · G. Mudenda Syngenta, Lusaka, Zambia e-mail<EMAIL_ADDRESS>
affected areas of ESA will largely depend on increasing the rate of genetic gain (genetic improvement through artificial selection) for CS traits and the establishment of scalable, competitive seed delivery systems that ensure improved varieties reach smallholder farmers in the shortest time (Atlin et al. 2017).

Smallholder farmers' adoption of improved crop varieties in SSA is amongst the lowest in the world (estimated to be 20% by the Alliance for a Green Revolution in Africa (AGRA) 2017), yet the formal seed sector has grown significantly following deregulation of the seed industry regionally in the early 1990s. The emerging private seed sector provides a unique and timely opportunity to promote the development and dissemination of improved, CS crop varieties through certified, scalable seed systems that can potentially impact millions of livelihoods in SSA. In this chapter, the specific roles and constraints for the private sector in ESA in developing and disseminating improved, CS crop varieties are discussed, with particular emphasis on maize (*Zeae maydis*), the staple food crop and primary source of daily calorie intake in the region.

#### **6.2 The Emerging Private Seed Sector in ESA**

In most of ESA, the plant breeding and seed industries were dominated by public institutions until the mid-1990s, when the seed sector was deregulated. Since then, dozens of private, local seed companies have been established, and several global multinational seed corporations have entered the ESA seed market. The primary focus of seed companies in ESA is maize, the driver of the global seed industry by virtue of acreage and potential for hybridization. The effect of deregulating the seed sector in ESA is highlighted in Fig. 6.1a, b, which show maize variety releases in Zambia and Kenya respectively. Both countries have emerged as leading centers for the seed industry in SSA and serve as important bellwethers of regional trends. In both cases, deregulation of the seed industry has led to a marked increase in the total number of seed companies and, subsequently, maize variety releases. However, the majority of these variety releases have been licensed from existing public breeding pipelines, and it is estimated that less than 25% of seed companies in the region (estimated to be 80 in total) have invested in proprietary germplasm improvement (Langyintuo et al. 2008).

Variety releases of other important staple crops in ESA have not emulated maize, in large part due to low commercialisation opportunities for the private sector. Total variety releases for maize, sorghum (*Sorghum bicolour*), common bean (*Phaseolus vulgaris* L.) and cassava (*Manihot esculentum*) are shown in Fig. 6.2a, b for Zambia and Kenya respectively. Even though variety releases of these crops have doubled since deregulation, the cumulative number of releases (for sorghum, common bean and cassava) is still less than 30% that of maize, and dominated by the public sector (over 80% of releases). In Zambia, cassava is an important secondary staple crop, yet only seven varieties (all publically bred) have been released since 1970, the latest in 2001. These crops are important components of food and

**Fig. 6.1** (**a**) Maize variety releases in Zambia (1964–2016) by the public and private sectors. (Source: SCCI 2017). (**b**) Maize variety releases in Kenya (1964–2016) by the public and private sectors (KEPHIS 2017)

nutritional security in ESA, where they will play a critical role in diversified, CS agricultural systems. Market incentives are urgently required to better integrate these open pollinated and vegetatively propagated crops into scalable, certified seed systems in the region.

**Fig. 6.2** (**a**) Annual releases for maize, sorghum, common bean and cassava varieties in Zambia since 1970. (Source: SCCI 2017). (**b**) Annual releases for maize, sorghum, common bean and cassava varieties in Kenya since 1970 (KEPHIS 2017)

#### **6.3 Low Rates of Variety Turnover and Agricultural Research and Development Investment in ESA**

Despite the growth of the seed industry in ESA since the 1990s, rates of variety turnover remain slow, and investment into agricultural research and development is extremely low. A handful of established varieties also continue to dominate markets in most countries (Abate et al. 2017). In Kenya, H614D (a variety released in 1986 by the state parastatal) accounts for over 40% of area cultivated to improved maize varieties while in Zambia, the three most widely grown maize varieties were released almost two decades ago, shortly after the deregulation of the seed sector (Smale and Olwande 2014; Smale et al. 2015). The average age of commercial

**Fig. 6.3** Average maize yields (1961–2016) in Kenya, Zambia, United States and South Africa (FAO 2018)

maize varieties in ESA is estimated to be 13 years. South Africa is an exception; with the most competitive seed industry in the region, the life of the typical maize variety here spans 3–4 years, similar to the United States. Most smallholder farmers in ESA are therefore not cultivating the best available varieties for their environment, and in many cases are persevering with obsolete cultivars that were developed under climatic, agronomic and pest conditions distinct from current and future climes. This has contributed to modest yield gains for maize in many countries in ESA (Fig. 6.3).

Reasons for slow rates of maize variety turnover in ESA are several and complex. The majority of smallholder farmers in the region grow maize in unpredictable, rain-fed conditions, and are risk averse to investing in inputs and new technologies. Average yields throughout the region are low and genetic gains in yield through crop improvement (usually 1% per year in well managed breeding programmes) are frequently overshadowed by seasonal variations in on-farm climatic conditions and crop management. The incentive for smallholder farmers to invest in new agricultural technologies is further reduced by limited access to grain markets, poor storage and transport infrastructure, as well as counterfeit seed and fertilizer. Without strong demand for new varieties, seed companies are reluctant to withdraw established, well-known varieties and invest in launching and marketing new products.

In addition to low rates of variety turnover, investment in agricultural research and development is very limited in ESA. Low income countries (including most of those in ESA) account for less than 3% of global agricultural research and development expenditure, despite being some of the most vulnerable to CC (Pardey et al. 2016). Of this expenditure in ESA, 90% is by the public sector, which continues to dominate the development of new technologies, including crop varieties (Beinteman and Stads 2011). By comparison, private sector investment in agricultural research and development in member countries of the Organization for Economic Co-operation and Development (OECD) regularly accounts for over 70% of total expenditure (OECD 2018) and the role and costs of developing new agricultural technologies has been assumed by a vibrant private sector, driven by competition for market share. Private sector investment in agricultural research and development remains low in ESA in part due to small, fragmented markets and a lack of commercial incentive in the region. Given the projected impacts of CC in ESA, increased investment in crop improvement is vital, as are mechanisms to drive faster rates of variety turnover to ensure farmers have sustained access to the latest genetics.

#### **6.4 Driving Genetic Gain for CS Traits Through Public-Private Partnerships (PPP)**

Increasing rates of genetic gain will be fundamental to ensuring plant breeders are able to react quickly to changing dynamics caused by CC, many of which are difficult to predict (e.g., shifting incidence and severity of pests and disease). Driving genetic gain for CS traits will require access to appropriate germplasm, reliable phenotyping platforms for traits of interest, and adoption of modern breeding methods that reduce breeding cycle time. Given the current levels of investment in agricultural research and development in ESA, driving genetic gains for CS traits is unlikely to be achieved in the near term without the combined efforts of PPPs.

Effective PPPs will utilize the public sector's experience and capacity in the region whilst exploiting the emerging private sectors access to regional markets and expertise in commercial plant breeding, particularly in the case of regional or international companies. Public research institutions in ESA, for example, have developed germplasm adapted to local conditions and are strategically positioned to establish long term regional phenotyping networks for key CS traits, such as drought or emerging disease tolerance (e.g., the maize lethal necrosis (MLN) screening facility in Kenya, developed by the Kenya Agricultural and Livestock Research Organization (KALRO) and the International Maize and Wheat Improvement Center (CIMMYT)).

Conversely, the emerging private sector offers a sustainable route to market whilst assuming the costs and responsibility for seed production, quality, purity and distribution. Currently, most small and medium scale enterprise (SME) seed companies in ESA rely on this model to license and commercialise publically developed varieties, although significant bottlenecks persist in accessing foundation seed and legal services to enter mutually beneficial licensing agreements (Cramer, this volume).

The entry of multinational corporation (MNC) seed companies into the ESA seed market provides an additional opportunity to develop PPPs around technology

**Table 6.1** Strength rating (low, medium or high) of selected drivers of genetic gain within the private and public sectors in ESA

transfer and optimisation of breeding pipelines. MNCs have led the global development of applied breeding technology in genomics, phenomics and mechanisation, and can therefore complement ongoing public breeding efforts with modern technology to drive genetic gain. Technologies such as doubled haploids,1 marker assisted selection,2 precision phenotyping tools and data management platforms have transformed plant breeding in mature seed markets to develop products quickly in response to customer requirements. MNCs also have access to global sources of elite germplasm for a range of traits that will become more important in ESA as a result of CC (in terms of tolerance to drought, new pests and diseases). PPPs between public institutions and MNCs are likely to focus on germplasm exchange, the creation and release of joint products, the provision of technological services, and shared phenotyping platforms. The relative strengths of MNCs and public breeding pipelines in ESA in terms of driving genetic gain are shown in Table 6.1.

<sup>1</sup>Artificial doubling of haploids to develop homozygous lines in one generation rather than six generations as required by conventional breeding

<sup>2</sup>Use of genetic markers to drive selection for a trait of interest