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Farmer preferences for adopting drought-tolerant maize varieties: evidence from a choice experiment in Nigeria. (Premilinary Draft-Not to be cited) Authors: Zainab Oyetunde-Usman and Apurba Shee Submitted for presentation in the lightning session of the 2021 AAEA & WAEA Joint Annual Meeting. Abstract Maize is an important food crop in sub-Saharan Africa and provides staple food needs for the majority of rural maize farm households, yet it is highly vulnerable to prolonged dry spells and drought stress which has impacted poor productivity and welfare. In Nigeria, a similar event unfolds in the maize production system despite the availability of the drought-tolerant maize varieties. Poor adoption of drought-tolerant maize varieties persistently derails the goals of improving food security and the welfare of the populace. This study questions the likelihood of farmers’ perceived preferences for various drought-tolerant maize traits and the implicit value they are willing to place on it, as a drawback to rapid adoption. This research thus adopts the DCE approach to elicit preference information on the attributes and the implicit price farmers are willing to pay using choice cards and household data from 320 maize farm households in Gwarzo and Rano local government areas in Kano State Nigeria. The Mixed logit and Hierarchical Bayesian model are comparatively used for more robust analysis in preference and willingness to pay space. The result shows that the most preferred attributes by farm households in the study areas include high drought-tolerant, yield, high nitrogen use efficiency, and early maturing attributes. The WTP estimates show that maize farmers place a high value on drought-tolerant attributes which is 1.11 times and 1.29 times more than the value farmers place on yield and high nitrogen use efficiency attributes respectively. Also, a very high negative turnoff is expressed for preference for non-resistance to the Striga attribute and the value farmers are willing to place on it. This study recommends policy needs to facilitate the design of maize varieties in a way that preference attributes are reflected, including creating targeted awareness on the needs of different interest groups. Introduction The underlying goal of agricultural technology interventions is to enhance rapid uptake among farm households to solve problems of low productivity, food insecurity, and poor welfare (Asfaw et al. 2012; Mathenge, Smale and Olwande 2014; Awotide et al. 2015; Abdoulaye, Wossen and Awotide 2018). However, low and slow adoption plagues agricultural technology adoption in various contexts across sub-Saharan Africa ( SSA) low (Abebe et al. 2013; Ward et al. 2018; Kagoya, Paudel and Daniel 2018). This also applies to the adoption of drought-tolerant maize varieties (DTMVs); a low-cost initiative to tackle climate risk among maize farm households in SSA. The DTMVs under the drought-tolerant maize for Africa (DTMA) project was introduced to 13 countries including Nigeria across SSA to provide resilience to drought and climate variation and also to improve farmers' welfare (Kostandini, La Rovere and Abdoulaye 2013). Among the objectives of the DTMA project was to increase maize yield under drought stress conditions and also to increase the average productivity of small farm-holders by 20-30% (Fisher et al. 2015). Evidence of the impact of DTMVs have been widely assessed across adopting countries in SSA and has been found positive and significant in increasing productivity, household welfare, and food security among adopters ( Bezu et al. 2014; Wossen, Abdoulaye, Alene, Feleke, et al. 2017; Abdoulaye, Wossen and Awotide 2018; Kassie et al. 2018). In particular to Nigeria, the positive impact of adopting DTMVs on productivity and welfare has been established and found to increase maize yields by 13.3%, reduced the level of variance by 53%, and downside risk exposure by 81% among adopters (Wossen et al. 2017). Despite this, significant variations exist in the adoption of DTMVs in drought-prone agricultural zones in Nigeria (Abdoulaye et al. 2018). The situation is unsettling with prevailing drought and desertification issues in agricultural areas zones in Nigeria (Medugu, Majid and Choji 2008; Eze 2018; Hassan, Fullen and Oloke 2019). There are also growing concerns on projected loss in maize yield by 20% or more in 2050 due to dryer and hotter temperature(Lobell et al. 2011). This might complicate the immediate welfare of the rural agricultural populace in which recent poverty indices show that, of the people that live below $1.90 per day, 84.6% lived in rural areas (World Bank Brief, 2020). Also, the food insecurity level may be worsened as Nigeria is currently ranked 98th out of 107 countries with a global hunger index of 29.2 and the food insecurity is classified as serious in the severity chart (GHI, 2020). Understanding the constraints or incentives that influence farm households’ decision to adopt DTMVs is key to enhancing adoption and in past studies, poor adoption of DTMVs has been attributed to differences in topography, plot, and socioeconomic conditions (Fisher and Carr 2015; Holden and Fisher 2015; Katengeza, Holden and Lunduka 2019). This approach, although significant for policy designs focusing on observable factors of farm households, may not be enough as several underlying behavioural attributes and perception to adoption may have been overlooked. To highlight, the design of DTMVs incorporates several other attributes in varying forms that can pique the interest of maize farmers differently and can inform their decisions to adopt or not. This suggests that it is not enough to judge the adoption of DTMVs based on households’ characteristics without considering choices of DTMVs attributes that maximise households’ utility. How to promote DTMVs that better reflects farm households’ constraints and preferences is a question relevant in the face of increasing climate risks and given projections of increasing population and the need to meet food needs and improve farm households’ welfare. A discrete choice experiment is a popular approach to eliciting preference information and a large body of literature reports the use of DCE in various contexts in applied behavioral studies in fields such as management, hospitality, health, transport (Jagger and Jumbe 2016; Alemu and Olsen 2018; Penn and Hu 2020; Hu et al. 2020; Potoglou et al. 2020; Chen 2021; Nthambi, Markova-Nenova and Wätzold 2021) inclusive of a broad presence in the field of agriculture and natural resources management (Ward et al. 2014; Ward and Singh 2015; Ward et al. 2016; Owusu Coffie et al. 2016; Joshi, Khan and Kishore 2019; Krah et al. 2019a; Oyinbo et al. 2019; Teferi et al. 2020; Shee, Turvey and Marr 2020). In this context, the DCE has been applied to assessing trait preferences in improved seeds; for example, Ward et al. 2014 employed DCE to examine farmers’ preferences for drought-tolerant traits in rice and explore heterogeneity in these preferences in India. Within the context of managing weather risks and access to credit for farmers; Teferi et al. 2020 use DCE to investigate the preferences and willingness to pay for frost and yellow resistance grain yield, grain colour, plant height, and length of maturity traits for wheat in Ethiopia. The use of DCE extends to examining preferences in climate-smart agricultural techniques (Schaafsma, Ferrini and Turner 2019; Krah et al. 2019a). Within the framework of managing agricultural risks, Shee et al. 2020) applied DCE to examine the demand and supply side preferences for insurance-linked credit among farm households in Kenya. Based on this background, this study uses the DCE experiment approach to elucidate information on maize farm households’ preferences for DTMVs. Given that the price effect may play a role in the demand for improved maize varieties, we explore the implicit price they are willing to pay for defined attributes. Attempt to elicit information on trait preferences in DTMVs is still quite scarce. A recent one is in Kassie et al. 2017, which examined taste parameter preferences and the implicit price maize farm households are willing to pay for in Zimbabwe. However, estimation is limited to the conventional approach. This study differs by comparatively employing both the conventional classical approach and the Bayesian estimation approach in preferences and willingness-to-pay space. This paper further contributes to better understanding the characteristics of the trait in DTMVs that are valuable to farmers to guide plant breeders and policymakers in the design and promotions of DTMVs in Nigeria. The objectives are as follows: (1) to elicit information on farmers' preferences for DTMVs (2) examine the heterogeneity in DTMVs preferences (3) estimate the willingness to pay for DTMVs attributes. The next section provides some background information generally on maize, DTMVs, and their attributes in Nigeria. The third section describes the data and empirical framework, the results and discussion were presented in section four and the last section concludes the paper. 2.0 Context of the Study In Nigeria, maize is the second most widely grown crop after Cassava based on the land areas covered and production indices (FAOSTAT, 2019). It is cultivated on about 4.9 million hectares of land (FAOSTAT, 2019) and available in most of the country especially in the Savanna zone due to the presence of high radiation which is favourable for its growth (Bello et al. 2014). In West Africa, Nigeria contributes 43% of maize grown (Tegbaru et al. 2020), and based on recent production statistics, Nigeria is the second-largest maize producer in Africa with over 100 million tonnes after South Africa (FAOSTAT, 2019). Maize as a staple food crop is consumed in many forms as a main dish, infant foods and snacks, and a core ingredient in animal feed (Ekpa et al. 2018; Adewopo 2019). Despite its importance, a major concern is a lag in productivity which has not kept pace with the rate of growth in the area and with population growth.

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