<figure><img src="image_33.png" /><figcaption>Figure 1</figcaption></figure>

Figure 1 - Conditional Densities for Probability of Treatment, by Category

In addition, Figure 2 presents the probability distribution of propensity scores (lowest to highest) and the region of common support for users and non-users. The data indicated that the common support condition was satisfied as there was overlap in the distribution of the propensity scores between the two groups. This is reflected in Figure 3, which shows the probability density functions of users and non-users. The results of the covariate balancing tests in Table 5 show that good balance was achieved after matching by reducing the percentage of balance bias up to more than 70% overall.
<figure><img src="image_34.png" /><figcaption>Figure 2 – Propensity Score Distribution of Herbicide Use, Users, and Non-users</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>Figure 3 – Probability Distribution of Herbicide Use</figcaption></figure>
<table><caption>Table 5
Covariates Balancing Test</caption><thead><tr><th></th><th>Standardized<br>Raw</th><th>Differences<br>Weighted</th><th>Variance<br>Raw</th><th>Ratio<br>Weighted</th></tr></thead><tbody><tr><td colspan="5"><strong>Not registered herbicides</strong></td></tr><tr><td>manager</td><td>0.538</td><td>0.538</td><td>2.662</td><td>2.660</td></tr><tr><td>village</td><td>-1.401</td><td>-1.401</td><td>0.451</td><td>0.451</td></tr><tr><td>subsidy</td><td>-0.283</td><td>-0.283</td><td>1.158</td><td>1.157</td></tr><tr><td>age</td><td>-0.192</td><td>-0.192</td><td>1.012</td><td>1.011</td></tr><tr><td>education</td><td>0.278</td><td>0.278</td><td>1.647</td><td>1.645</td></tr><tr><td>market</td><td>-0.301</td><td>-0.301</td><td>0.654</td><td>0.654</td></tr><tr><td>labor supply</td><td>0.334</td><td>0.334</td><td>1.216</td><td>1.215</td></tr><tr><td>weeding cost</td><td>0.732</td><td>0.732</td><td>12.777</td><td>12.768</td></tr><tr><td>individually-managed</td><td>0.559</td><td>0.559</td><td>2.745</td><td>2.743</td></tr><tr><td>seed</td><td>-0.069</td><td>-0.069</td><td>2.048</td><td>2.046</td></tr><tr><td>fertilizer</td><td>0.031</td><td>0.031</td><td>0.891</td><td>0.890</td></tr><tr><td>male labor</td><td>-0.141</td><td>-0.141</td><td>1.043</td><td>1.043</td></tr><tr><td>female labor</td><td>-0.192</td><td>-0.192</td><td>0.569</td><td>0.568</td></tr><tr><td>child labor</td><td>0.194</td><td>0.194</td><td>1.742</td><td>1.740</td></tr><tr><td>location</td><td>-0.033</td><td>-0.033</td><td>1.108</td><td>1.107</td></tr><tr><td>erosion control</td><td>-0.070</td><td>-0.070</td><td>0.999</td><td>0.999</td></tr><tr><td>machinery use</td><td>-0.313</td><td>-0.313</td><td>0.667</td><td>0.666</td></tr><tr><td>manure</td><td>0.020</td><td>0.020</td><td>1.016</td><td>1.015</td></tr><tr><td colspan="5"><strong>Registered herbicides</strong></td></tr><tr><td>manager</td><td>0.189</td><td>0.189</td><td>1.600</td><td>1.599</td></tr><tr><td>village</td><td>-0.824</td><td>-0.824</td><td>0.716</td><td>0.716</td></tr><tr><td>subsidy</td><td>-0.140</td><td>-0.140</td><td>1.099</td><td>1.098</td></tr><tr><td>age</td><td>-0.072</td><td>-0.072</td><td>1.022</td><td>1.021</td></tr><tr><td>education</td><td>0.180</td><td>0.180</td><td>1.427</td><td>1.426</td></tr><tr><td>market</td><td>-0.222</td><td>-0.222</td><td>0.751</td><td>0.750</td></tr><tr><td>labor supply</td><td>0.318</td><td>0.318</td><td>1.156</td><td>1.155</td></tr><tr><td>weeding cost</td><td>0.535</td><td>0.535</td><td>21.569</td><td>21.555</td></tr><tr><td>individually-managed</td><td>0.198</td><td>0.198</td><td>1.643</td><td>1.642</td></tr></tbody></table>
<table><thead><tr><th rowspan="2"></th><th>Standardized</th><th>Differences</th><th>Variance</th><th>Ratio</th></tr><tr><th>Raw</th><th>Weighted</th><th>Raw</th><th>Weighted</th></tr></thead><tbody><tr><td>seed</td><td>0.180</td><td>0.180</td><td>1.887</td><td>1.886</td></tr><tr><td>fertilizer</td><td>0.521</td><td>0.521</td><td>2.954</td><td>2.952</td></tr><tr><td>male labor</td><td>0.146</td><td>0.146</td><td>1.423</td><td>1.422</td></tr><tr><td>female labor</td><td>-0.185</td><td>-0.185</td><td>0.710</td><td>0.709</td></tr><tr><td>child labor</td><td>0.154</td><td>0.154</td><td>1.518</td><td>1.517</td></tr><tr><td>location</td><td>-0.172</td><td>-0.172</td><td>1.092</td><td>1.092</td></tr><tr><td>erosion control</td><td>-0.251</td><td>-0.251</td><td>0.797</td><td>0.796</td></tr><tr><td>machinery use</td><td>-0.095</td><td>-0.095</td><td>0.967</td><td>0.966</td></tr><tr><td>manure</td><td>0.324</td><td>0.324</td><td>1.138</td><td>1.137</td></tr></tbody></table>

Furthermore, Table 6 presents the critical values of gamma (Γ) for each of the four outcome variables. The critical gamma values indicated that yield and male labor were sensitive to unobserved characteristics at Γ = 1.4 and Γ = 1.6, respectively. These values suggested that the unobserved characteristic would need to increase the odds ratio by 40% for yield and 60% for male labor before we started questioning the estimated effects of treatment. The degree of vulnerability to unobserved factors for these two outcome variables was within an acceptable range and was not a critical issue (Rosenbaum, 2005). The critical gamma values for female and children labor were greater than two. This result implies that the treatment effects are more robust to the hidden biases arising from unobserved characteristics for these two outcome variables. Our estimates of treatment effects were overall robust to the influence of unobserved factors.

<table><caption>**Table 6**
Rosenbaum Bounds Analysis for the Main Outcomes</caption><thead><tr><th>Outcome variables</th><th>Gamma</th><th>CI+</th><th>CI-</th></tr></thead><tbody><tr><td rowspan="3">Yield</td><td>1</td><td>-401.395</td><td>-189.458</td></tr><tr><td>1.2</td><td>-512.604</td><td>-77.024</td></tr><tr><td><strong>1.4</strong></td><td><strong>-609.629</strong></td><td><strong>16.257</strong></td></tr><tr><td rowspan="2">Male labor</td><td>1</td><td>-7.902</td><td>-4.134</td></tr><tr><td>1.2</td><td>-10.132</td><td>-2.312</td></tr></tbody></table>

<footnote n="104">104 | Counterfeit Herbicides, Productivity and Family Labor Use on Farms in Mali: A Multivalued Treatment Approach</footnote>
<table><thead><tr><th>Outcome variables</th><th>Gamma</th><th>CI+</th><th>CI-</th></tr></thead><tbody><tr><td></td><td>1.4</td><td>-12.194</td><td>-0.802</td></tr><tr><td></td><td><strong>1.6</strong></td><td><strong>-14.034</strong></td><td><strong>0.524</strong></td></tr><tr><td rowspan="7">Female labor</td><td>1</td><td>-0.003</td><td>-0.003</td></tr><tr><td>1.2</td><td>-0.003</td><td>-0.003</td></tr><tr><td>1.4</td><td>-0.004</td><td>-0.003</td></tr><tr><td>1.6</td><td>-0.004</td><td>-0.004</td></tr><tr><td>1.8</td><td>-2.013</td><td>-0.004</td></tr><tr><td>2</td><td>-2.885</td><td>-0.004</td></tr><tr><td>2.2</td><td>-3.962</td><td>-0.004</td></tr><tr><td></td><td><strong>2.4</strong></td><td><strong>-4.878</strong></td><td><strong>0.553</strong></td></tr><tr><td rowspan="7">Children labor</td><td>1</td><td>-0.003</td><td>-0.004</td></tr><tr><td>1.2</td><td>-0.003</td><td>-0.004</td></tr><tr><td>1.4</td><td>-0.704</td><td>-0.004</td></tr><tr><td>1.6</td><td>-1.676</td><td>-0.003</td></tr><tr><td>1.8</td><td>-2.102</td><td>-0.002</td></tr><tr><td>2</td><td>-3.106</td><td>-0.003</td></tr><tr><td>2.2</td><td>-3.484</td><td>-0.003</td></tr><tr><td></td><td><strong>2.4</strong></td><td><strong>-3.896</strong></td><td><strong>0.549</strong></td></tr></tbody></table>

As described in the econometrics section, we applied RA, AIPW, and IPWRA to estimate the impacts of herbicide use on yield and intensity of adult male, adult female, and child labor with a multivalued treatment model (non-use, use of unregistered, and use of registered herbicides). For completeness, we used overall propensity score matching (PSM) to estimate the binary impact of herbicide use compared to non-use.

The results reported in Table 7 showed no significant impact of registered herbicide use on yield. In fact, herbicides reduced damage from weeds rather than enhancing the yield potential of the crop (Lichtenberg & Zilberman, 1986). Still, *ceteris paribus*, we might have expected a larger harvested output per acre if damages had been offset by use. On the contrary, the regression
indicates that the use of unregistered herbicide has a significantly negative effect on harvested yields, controlling for other covariates. Household members who managed plots where they applied unregistered herbicides expected an average yield loss of 218 kg/ha. Considering that the overall mean yield was only 1,141 ha, this amount is important in terms of magnitude. Overall, the binary PSM model confirms that the overall impact of herbicide use on crop yields is not significantly different from zero.

<table><caption>**Table 7**
Average Treatment Effects by Outcome and Model</caption><thead><tr><th></th><th>RA</th><th>AIPW</th><th>IPWRA</th><th>PSM</th></tr></thead><tbody><tr><td>ATE on Yield</td><td></td><td></td><td></td><td></td></tr><tr><td>not registered</td><td>-219.4*</td><td>-204.0**</td><td>-152.1</td><td></td></tr><tr><td>registered</td><td>66.1</td><td>108.2</td><td>137.3</td><td></td></tr><tr><td>use herbicide</td><td></td><td></td><td></td><td>1.876</td></tr><tr><td>ATE on men weeding labor</td><td></td><td></td><td></td><td></td></tr><tr><td>not registered</td><td>-4.686**</td><td>-5.441**</td><td>-6.284***</td><td></td></tr><tr><td>registered</td><td>-7.973***</td><td>-8.674***</td><td>-9.327***</td><td></td></tr><tr><td>use herbicide</td><td></td><td></td><td></td><td>-8.061***</td></tr><tr><td>ATE on women weeding labor</td><td></td><td></td><td></td><td></td></tr><tr><td>not registered</td><td>-1.055</td><td>-0.92</td><td>-1.318</td><td></td></tr><tr><td>registered</td><td>-1.397</td><td>-1.788*</td><td>-2.144**</td><td></td></tr><tr><td>use herbicide</td><td></td><td></td><td></td><td>-1.599</td></tr><tr><td>ATE on children weeding labor</td><td></td><td></td><td></td><td></td></tr><tr><td>not registered</td><td>-1.62</td><td>-1.785</td><td>-2.279*</td><td></td></tr><tr><td>registered</td><td>-1.913*</td><td>-2.241**</td><td>-2.706**</td><td></td></tr><tr><td>use herbicide</td><td></td><td></td><td></td><td>-1.342</td></tr><tr><td>N</td><td>1136</td><td>1136</td><td>1136</td><td>1136</td></tr></tbody></table>

Impacts on weeding labor appear to be stronger, as might be expected given that labor bottlenecks are the primary incentive for using this input. Among the households surveyed, adult male labor was by far the largest category. The labor of adult male household members was heavily demanded on the collective plots, particularly in land preparation and weeding. The

<footnote>* p < 0.10, ** p < 0.05, *** p < 0.01</footnote>
labor of adult female household members was deployed across a broad range
of household and farming activities; their individual plots were small in size,
and generally intercropped with legumes. The data shows low overall levels
of female labor use in weeding. Child labor efforts were also relatively minor
compared to those of adult males.

Consequently, the impacts of all categories of herbicide use on the labor
use intensity of adult males is strong and positive (corresponding to a
negative sign since the variable was labor days per hectare). Registered
herbicides had the greatest impact, almost twice as large as unregistered
herbicides (an average decrease of 7.9 vs. 4.6 days). The use of registered
herbicides was associated with lower amounts of weeding labor by women
and children in the household, reducing it by an average of 1.7 and 2.2 days,
respectively. This implies that children in households who use registered
herbicides are less likely to be asked to weed, which has important ethical
and, possibly, health implications.

Conclusions and Policy Implications

This study contributes in general to a sparse, emerging knowledge about
the use of herbicides by smallholder farmers in Sub-Saharan Africa and in
particular to information about counterfeit herbicides. We found that the
use of unregistered herbicides is associated with lower yields and that the
use of all herbicides, and in particular, registered products, reduces the
intensity of labor use (per ha) of male, female, and child labor in weeding.

Use of registered herbicides improves child welfare by reducing the
demand for their labor. Policies and regulations to support the use of
registered herbicides could be beneficial, but more research is needed on
health and environmental implications. Future research might further test
these hypotheses using techniques for quality directly through laboratory
testing, and a sample of farmers from more regions of Mali.

Clearly, the large volume of unregistered herbicides currently in use poses
a problem for farmers as well as potential problems for consumers and
the environment. These findings suggest a need for improved regulatory
practices. It is important to distinguish between the pre-registration review
system, which works very well, and the post-registration market monitoring,
which functions very poorly (Diarra & Haggblade, 2017; Haggblade et al., 2021). For over 20 years, Mali has participated in a regional regulatory review process. Since 1994, the Comité Permanent Inter-Etats de Lutte contre la Sécheresse dans le Sahel (CILSS) has operated a regional regulatory body, the Comité Sahelien des Pesticides (CSP), to review and certify all pesticide products sold throughout the Sahelian member countries, including Mali. Under these common rules, any pesticide passing CSP efficacy and safety reviews and registered for sale in one member country becomes automatically authorized for sale throughout all nine member countries. By centralizing this regulatory review process, the CSP provides a one-stop-shop for manufacturers and importers, facilitating the review process and enabling suppliers to reduce bureaucratic costs by standardizing and centralizing review procedures. This model economizes on scarce technical manpower and laboratory facilities by pooling talent from across the member countries. Specialists at the Food and Agriculture Organization of the United Nations (FAO) and across West Africa consider the CSP regulatory model to be "probably the most successful example of regional harmonization in Sub-Saharan Africa" (Traoré et al., 2011, p.16) and one increasingly emulated in other sub-regions of Africa (Haggblade et al., 2021).

In contrast, post-regulatory monitoring remains very weak. The growing numbers of unregistered and counterfeit herbicide products available on the market lead to mounting farmer concerns about product quality and safety. Yet the environmental impacts of herbicide use remain largely unmonitored in Mali (Haggblade et al., 2017b).

Looking forward, policy makers in Mali will increasingly require better monitoring of pesticide product quality and environmental impact. The CILSS model of regional regulatory review, which economizes on scarce scientific personnel and laboratory facilities, has proven efficient in vetting herbicide products prior to release. Regional sampling and studies across common Sahelian agro-ecological zones can perhaps offer parallel economies in environmental monitoring.
Acknowledgements

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

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

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

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

**Djiby Dia**, ISRA/BAME, Senegal

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

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

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

Abstract

This chapter aims to show the benefits of systematically using information
systems as a basis for agricultural decisions and strategies in the Senegalese
groundnut basin. The research involved two surveys conducted by ISRA/
Bame in 2016. The first, which focuses on market information, was
conducted on a sample of 105 cowpea farmers who use the Yeugglé Market
Information System (MIS) to obtain information on the current price in the
markets of Diourbel, Bambey, and Fatick from weekly telephone messages.
The second, based on a sample of 82 farmers in the regions of Diourbel,
Kaolack, and Kaffrine, focuses on Climate Information (CI). The approach is
based on a statistical analysis of variables related to the influence of market
and climatic information on production, marketing, and agricultural input investment decisions. The results show that for 49% of farmers, market information influences their choice of markets. For most farmers (85%), market information influences the decision of sale, period, and allows farmers to make trade-offs in time and space. Regarding climate information, the results show that 67% of respondents use it and that the national meteorological service is the main provider (52%), followed by the radio (6%), and the Yeugglé platform (4%).

Keywords: Information system, risks, groundnut basin, climate change, Senegal

Introduction

In economics, “the principle of rationality means that individuals act by making the best use of the resources available to them, given the constraints they face” (Simon, 1965, ). However, the rationality of agents basing their decisions only on prices and maximizing the difference between the value of inputs and the value of outputs is not a reality. In fact, agents do not and cannot behave in a way that maximizes utility, but instead adopt strategies that consider cognitive limits (i.e., the limits encountered in the knowledge and processing of information). Rationality is therefore linked to information mastery (Fraval, 2000).

According to neoclassicists, the “market game” allows economic agents to coordinate the best possible individual decisions. Indeed, economic and social activity is often characterized by situations where the well-being of an economic agent depends, not only on their own actions, but also on the decisions of other agents. Being aware of the existence of this type of interdependence, everyone takes it into account and incorporates it into their decision-making (Fraval, 2000).

In the agricultural sector, production and marketing decisions are often dependent on the level of information available to farmers. For that reason, Senegal, like most of the countries in the Sahel, has had a few Market Information System (MIS) experiments beyond that of the Food Security Commission (CSA), which was set up in the 1980s. These MISs, both public and private, do not all have the same objectives or targets. The channels and
frequencies of information dissemination may differ as some use traditional channels (e.g., radio, word of mouth) while others use second generation channels to transmit information through the website and/or cell phone.

In the groundnut basin of Senegal, cowpea producers use the *Yeuggle*¹ MIS to obtain information on the product's price. This MIS, like others, broadcasts weekly information on cowpea prices in the markets of Diourbel, Bambey, and Fatick via telephone short message services (SMS). It is managed by the cowpea sector table set up by the Support Program for Agricultural Sectors (PAFA). Like other MIS's, cowpea prices are collected weekly at urban and rural markets (Ngom, 2018).

Another experiment was developed on a large scale in the Groundnut Basin (which includes Diourbel, Fatick, and Kaffrine) in 2016 on the use of Climate Information (CI) and the impacts that it could have on farm management and more specifically on securing income. To this end, representatives of the PAFA network attended a training session on the concept of climate-smart agriculture with a particular focus on climate products.

This information impacts producers' production and marketing decisions, even if they also face the risk and uncertainty inherent in agricultural activity. Indeed, risk and uncertainty are two fundamental notions for understanding the strategies of rural households in Sub-Saharan Africa, particularly Senegal. They are so important that uncertainty is omnipresent in this context, whether it relates to climatic conditions, crop pest problems, the internal functioning of commodity chains (i.e., production, transport, marketing) or product prices. This raises the question of what influence the use of information can have on the economic decisions of producers? These decisions have the sole aim of maximizing their utility (i.e., consumers) or their profit (i.e., firms).

Overall, this chapter aims to show the benefits of using information systems in decision-making and on agricultural strategies of the farms in the Senegalese groundnut basin.

This chapter is structured as follows: first, we will analyze the influence of market information use on cowpea farmers' economic decisions. Next, we
focus on access to and use of climate information by farms. Finally, following
the conclusion, we provide recommendations.

Methodology

1. Sample

Three communes were chosen: Ndinguiraye (Bambey), Ndindy (Diourbel), and Niakhar (Fatick). In total, 14 villages belonging to these three communes benefit from the Yeugglé information system. We adopted a sampling rate of 50% to randomly select 7 villages through stratification by village. In each village, 15 producers were randomly selected. Thus, a group of 105 cowpea producers using the Yeugglé MIS were surveyed.

1.1 Market Information

We conducted a survey targeting cowpea producers in the Senegalese groundnut basin with a sample made up of cowpea producers who use the Yeugglé information system. This system is managed by the cowpea commodity chain sector setup within the framework of the Support Program for Agricultural Sectors (PAFA). Like other MISs, cowpea prices are collected weekly at the markets—mainly the markets of Diourbel, Bambey Serère, Ndiguiraye, and Gaouane—and are distributed by SMS to the producers of this commodity chain sector, which has become a national framework for sector consultation.

1.2 Climate Information

With regard to climate information, the complexity and heterogeneity of the
farms as well as the size of the study area led us to adopt a "multi-stage"
sampling methodology, which consists of consecutive stages developed to
arrive at the selection of basic observation units (i.e., farms) where the
requirements of representativeness are respected. A reasoned choice was
made for the selection of the PAFA zone as well as for the villages, which
were dictated by the option of the millet/sorghum and sorrel (bissap) sectors, the focus of the PAFA. For the farms, all the members who participated in the training sessions were retained, as well as those with extensive experience in climate information at the Kaffrine level. The trainings were organized for the representatives of the PAFA network to share with them the concept of climate-smart agriculture with an emphasis on climate products. These were three-day workshops that trained farmers, defined the needed products, and identified a framework for transmission of climate information. These were held in Diourbel for the northern Groundnut Basin area and in Kaolack for the southern Groundnut Basin, with each workshop bringing together about 20 producers.

Our sample consisted of 82 farmers in three regions (Diourbel, Fatick and Kaffrine) as presented in Table 1. For the controls (i.e., no training in climate information), they were chosen at random from similar villages.

<table><caption>**Table 1**
Type of Actor</caption><thead><tr><th rowspan="2">Region</th><th colspan="5">Type of Actor</th></tr><tr><th>Participated in the Training</th><th>Extensive Experience on CI</th><th>No Training but CI</th><th>No Training and No CI</th><th>Total</th></tr></thead><tbody><tr><td>Diourbel</td><td>14</td><td>4</td><td>2</td><td>0</td><td>20</td></tr><tr><td>Fatick</td><td>17</td><td>2</td><td>11</td><td>9</td><td>39</td></tr><tr><td>Kaffrine</td><td>9</td><td>13</td><td>0</td><td>1</td><td>23</td></tr><tr><td>Groundnut Basin</td><td>40</td><td>19</td><td>13</td><td>10</td><td>82</td></tr></tbody></table>

*Source:* Author based on ISRA/BAME survey data, 2016. Farmers answered a questionnaire with three modules: farm characteristics; access, use and appreciation of climate information; and usefulness of climate information.

## 1.3 Approach Analysis

The approach is based on a statistical analysis of variables related to the influence of market and climate information in production, marketing, and agricultural input investment decisions. Additionally, as far as climate information is concerned, the comparative analysis of these two sites will
make it possible to: (i) define the added value of the adoption of climate information by farmers; (ii) observe how it can be integrated with other CSA technologies or practices; and (iii) identify the factors that facilitate or limit the diffusion of these technologies and practices.

# Teachings on the Influence of MIS Use on Cowpea Farmers' Economic Decisions

## 1. Market Information Does Not Always Influence the Point of Sale

For half of the cowpea producers (51%), the market information received does not influence the choice of the market where to make the sale. The reason given is the lack of means of transportation and the related costs to be borne. On the other hand, for 49% of the producers, this information influences the place of sale because it allows them to sell at more remunerative prices. Thus, as Egg et al (1996) points out, MIS are supposed to enable producers to make special trade-offs.

## 2. Market Information Guides the Timing of Sales

For the vast majority of cowpea producers (85%), the market information received influences the time of sale. Actually, the sale is made when the price of cowpea on the market is remunerative, showing that MIS allows farmers to make trade-offs by considering their opportunities. However, for 15% of producers, the time of sale is not influenced by the information received, but depends on social needs and to a lesser extent on the lack of storage infrastructure, hence the need to continue to focus on storage infrastructure for better management of opportunities.
### 3. Cowpea Price Trends in Reference Markets Drive Pricing

For the majority of cowpea producers (62%), market information influences their choice of selling price. The decision to sell is not made without first knowing the price trends of cowpeas in reference markets, which has a positive impact on their bargaining power vis-à-vis traders who are considered to be better informed. For slightly more than a third (38.1%), market information does not influence the selling price because producers generally engage in tied or loyalty transactions to avoid the risk of not finding any buyer for their produce. The uncertainty that results from the way a farmer finances their campaign can also influence the price. It can include the rising cost of capital, interest rate, limited availability, and degree of creditworthiness (Wade, 2009).

### 4. Privileging Group Sales Means that Market Information Does Not Influence the Quantity to be Sold

Market information does not influence the choice of the quantity to sell for most cowpea producers (75%). The reason given is that generally the sale of the crop is not done in a sequential manner but rather grouped together, which avoids post-harvest losses. On the other hand, for nearly 25% of these producers, market information influences the choice of the quantity to sell. The quantity sold is greater when the price is high. Conversely, it is low when the price is not remunerative. In other words, the higher the price, the greater the incentive for producers to sell a large quantity.

### 5. Market Information Influences the Choice of Crop

Market information influences the majority of producers (56%) in their choice of crop. It allows them to cultivate the crops for which prices are the most remunerative, beyond millet, which is a food crop in this zone. In fact, more and more food crops are being sold on the markets, which is why it
is important to be able to identify the most profitable crops for the zone in order to have a substantial cash income to meet the family's financial needs. Indeed, when the price of a good increases, suppliers are encouraged to increase their supply in order to increase their profit.

## 6. Market Information Influences the Choice of Plot Size

For the vast majority of cowpea producers (82%), market information influences the choice of plot size. Depending on the prices of the crops observed on the markets during the past raining season and during the dry season, producers decide to increase or maintain a given plot size for a given crop for the next season. This decision is influenced by market risk and environmental uncertainty. In fact, being in a situation of uncertainty, farmers favor flexibility by minimizing the investments (i.e., monetary or in time) that they devote to production. In the event that the crops are effectively destroyed, the loss of investment will be less important with this posture than if the feared event were not to occur (i.e., as if it were a certain universe). In a certain (favorable) universe, producers can safely make the investments required to maximize their production, which they know from the outset will be good at harvest time (Fraval, 2000).

## 7. Market Information Influences the Decision to Invest in Agricultural Inputs

For most cowpea farmers (94%), market information influences the decision to invest in agricultural inputs. Thus, producers prefer to invest in agricultural inputs for which prices are much more remunerative. In other words, prices guide producers towards the use of production factors. Nevertheless, they face production uncertainty because agriculture is affected by many uncontrollable events that are often related to climatic conditions (i.e., excessive or insufficient rainfall, extreme temperatures, hail), as well as diseases and pests. These factors have a direct impact on the quantity and quality of production (Wade, 2009). Such influences create an
increase in the quantity of inputs used (58%) and with particular attention
paid to the quality of inputs used (34%)

Access and Use of Climate Information

1. ANACIM: The Main Provider of Climate Information

The information on the climatic and meteorological forecasts are
broadcasted every day by the National Agency of Civil Aviation and
Meteorology (ANACIM) via radio and television. Their information includes
temperature, rainfall, and humidity. Sometimes, if there are abnormal
situations in sight (i.e., strong wind, heavy rains, etc.), the weather service
alerts producers, and even fishermen, so that they can make arrangements.
However, as part of its collaboration with projects in certain areas,
particularly in Kaffrine, in addition to training, ANACIM broadcasts weekly
climate information during production periods, particularly the rainy season,
via radio and SMS. The forecast period is generally 1, 3, and 10 days.

In the groundnut basin, 67% of producers are accustomed to receiving
climate information (Table 2). ANACIM is the main provider of this
information (52%), a trend that can be explained by the presence of the
CCAFS project in the area since 2010, which instilled in farmers the
importance of using climate information. Various annual training sessions
and the ANACIM's sharing of seasonal forecasts have finally convinced
farmers, who have confidence (94%) in the information provided by ANACIM.
This confidence is owed to the accuracy of past forecasts. However, 5% still
do not trust ANACIM and 1% do not follow its information.

Besides ANACIM, the radio, with 6%, ranks second because of its
widespread use in rural areas. The Yeugglé platform (4%), however, is
becoming increasingly important, particularly in Diourbel, where ¾ of the
"highly experienced CI" group get their information the platform, especially
after it integrated climate information into its services. These services are
followed by the decentralized services of the Ministry of Agriculture, the
sector tables (PAFA), television, and traditional news, each with 1%. Thus, the
sharing of climate information is more important when there is a regrouping
of actors during training workshops or other meetings.
<table><caption>Table 2
Habit of Receiving Climate Information</caption><thead><tr><th rowspan="3">Region</th><th rowspan="3">Type of Actor</th><th colspan="8">Habit of receiving climate information?</th></tr><tr><th colspan="4">Yes</th><th colspan="4">Source</th></tr><tr><th>Agriculture</th><th>ANACIM</th><th>Yeuglé Platform</th><th>Cowpea Sector Platform</th><th>Radio</th><th>Television</th><th>Traditional Information</th></tr></thead><tbody><tr><td rowspan="5">Diourbel</td><td>Participated in the Training</td><td>29%</td><td>57%</td><td>7%</td><td>7%</td><td>0%</td><td>6%</td><td>9%</td></tr><tr><td>Extensive Experience on CI</td><td>100%</td><td>25%</td><td>75%</td><td>0%</td><td>0%</td><td>6%</td><td>9%</td></tr><tr><td>No Training but CI</td><td>100%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>6%</td><td>9%</td></tr><tr><td>No Training and No CI</td><td>12%</td><td>82%</td><td>100%</td><td>0%</td><td>0%</td><td>6%</td><td>9%</td></tr><tr><td>Participated in the Training</td><td>64%</td><td>9%</td><td>11%</td><td>18%</td><td>0%</td><td>6%</td><td>9%</td></tr><tr><td rowspan="2">Fatic</td><td>No Training but CI</td><td>89%</td><td>11%</td><td>18%</td><td>0%</td><td>0%</td><td>6%</td><td>9%</td></tr><tr><td>No Training and No CI</td><td>89%</td><td>11%</td><td>18%</td><td>0%</td><td>0%</td><td>6%</td><td>9%</td></tr></tbody></table>
<table><thead><tr><th rowspan="3">Region</th><th rowspan="3">Type of Actor</th><th colspan="8">Habit of receiving climate information?</th></tr><tr><th colspan="8">Yes</th></tr><tr><th colspan="8">Source</th></tr><tr><th></th><th></th><th>Agriculture</th><th>ANACIM</th><th>Yeuglé Platform</th><th>Cowpea Sector Platform</th><th>Radio</th><th>Television</th><th>Traditional Information</th></tr></thead><tbody><tr><td rowspan="3">Kaffrine</td><td>Participated in the Training</td><td>11%</td><td>11%</td><td>67%</td><td>-</td><td>-</td><td>11%</td><td>-</td><td>-</td></tr><tr><td>Extensive Experience on CI</td><td>15%</td><td>-</td><td>77%</td><td>-</td><td>-</td><td>8%</td><td>-</td><td>-</td></tr><tr><td>No Training but CI</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Sample</td><td>No Training and No CI</td><td>100%</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td>33%</td><td>1%</td><td>52%</td><td>4%</td><td>1%</td><td>6%</td><td>1%</td><td>1%</td></tr></tbody></table>
## 2. Community-Based Learning Promotes the Use of Climate Information

The use of climate information is generally community-based, especially for the two groups that “attended the training” and “had a lot of experience on CI.” In short, these trainings, in addition to providing climate information, promote dialogue between actors for better decision making and especially for knowledge sharing. In contrast, for the other two groups—“no training but CI” and “no training and no CI”—the use of climate information is individualized with little sharing at the community level (Table 3).

Among those who received climate information (whether they were involved in the program or not), 84% actually used it. In Diourbel and Fatick, all of those who received climate information used it, while in Kaffrine, only 11% of those in the “attended the training” category did not. Several reasons were given by the latter, including not having received the information, not trusting the information because of its randomness, and that the information was not in the local language.

<table><caption>**Table 3**
Use of Climate Information</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">Type of Actor</th><th colspan="3">Did you use the climate and weather information you received to conduct your campaign?</th></tr><tr><th>Yes</th><th>No</th><th>Not Received</th></tr></thead><tbody><tr><td rowspan="3">Diourbel</td><td>Participated in the Training</td><td>93%</td><td>0%</td><td>7%</td></tr><tr><td>Extensive Experience on CI</td><td>100%</td><td>0%</td><td>0%</td></tr><tr><td>No Training but CI</td><td>100%</td><td>0%</td><td>0%</td></tr><tr><td rowspan="3">Fatick</td><td>Participated in the Training</td><td>100%</td><td>0%</td><td>0%</td></tr><tr><td>Extensive Experience on CI</td><td>100%</td><td>0%</td><td>0%</td></tr><tr><td>No Training but IC</td><td>82%</td><td>9%</td><td>9%</td></tr></tbody></table>
<table><thead><tr><th rowspan="2">Region</th><th rowspan="2">Type of Actor</th><th colspan="3">Did you use the climate and weather information you received to conduct your campaign?</th></tr><tr><th>Yes</th><th>No</th><th>Not Received</th></tr></thead><tbody><tr><td></td><td>No Training and No CI</td><td>11%</td><td>0%</td><td>89%</td></tr><tr><td></td><td>Participated in the Training</td><td>89%</td><td>11%</td><td>0%</td></tr><tr><td rowspan="2">Kaffrine</td><td>Extensive Experience on CI</td><td>100%</td><td>0%</td><td>0%</td></tr><tr><td>No Training and No CI</td><td>0%</td><td>0%</td><td>100%</td></tr><tr><td>Sample</td><td></td><td>84%</td><td>2%</td><td>13%</td></tr></tbody></table>

Source: Author based on ISRA/BAME survey data, 2016.

## 3. More Information Means More Choices on Crops and Varieties

Climate information has enabled farmers to better orient their agricultural activities, including the choice of crop (48%), the variety to be sown (35%), and what was needed to complete the cycle and the sowing periods (12%). Producers, through their experience, have some knowledge of the crops or varieties that are best suited to the given seasonal forecast. Sometimes they ask agricultural advisors for information. At this point, they can choose to plant more peanuts than another crop, for example. Thus, we note that climatic information allows farmers to implement a strategy in relation to their agro-climatic environment. Indeed, farmers would like to have information on the nature of the rainy season (54%), and therefore its course (seasonal forecast), along with the duration of the season (25%). In addition, they would also like to have daily information on rainfall in order to have better control over resource allocation. Finally, 1% would also like to have information at the beginning of the rainy season (Table 4).
<table><caption>Table 4
Types of Use of Climate Information</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">Type of actor</th><th colspan="8">Types of use of climate information</th></tr><tr><th>Crop Selection</th><th>Variety Selection</th><th>Plot Preparation</th><th>Ploughing Period</th><th>Sowing/Seeding Period</th><th>NPK Application Period</th><th>Weeding/ Hoeing Period</th></tr></thead><tbody><tr><td rowspan="3">Diourbel</td><td>Participated in the Training</td><td>54%</td><td>39%</td><td>0%</td><td>0%</td><td>8%</td><td>0%</td><td>0%</td></tr><tr><td>Extensive Experience on CI</td><td>50%</td><td>50%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td></tr><tr><td>No Training but CI</td><td>50%</td><td>50%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td></tr><tr><td rowspan="3">Fatick</td><td>Participated in the Training</td><td>18%</td><td>65%</td><td>0%</td><td>6%</td><td>12%</td><td>0%</td><td>0%</td></tr><tr><td>Extensive Experience on CI</td><td>0%</td><td>0%</td><td>50%</td><td>0%</td><td>50%</td><td>0%</td><td>0%</td></tr><tr><td>No Training but CI</td><td>22%</td><td>11%</td><td>0%</td><td>0%</td><td>44%</td><td>11%</td><td>11%</td></tr><tr><td rowspan="3">Kaffrine</td><td>No Training and No CI</td><td>100%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td></tr><tr><td>Participated in the Training</td><td>100%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td></tr><tr><td>Extensive Experience on CI</td><td>77%</td><td>23%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td><td>0%</td></tr><tr><td></td><td>No Training and No CI</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
<table><thead><tr><th rowspan="2">Region</th><th rowspan="2">Type of actor</th><th colspan="8">Types of use of climate information</th></tr><tr><th>Crop Selection</th><th>Variety Selection</th><th>Plot Preparation</th><th>Ploughing Period</th><th>Sowing/Seeding Period</th><th>NPK Application Period</th><th>Weeding/Hoeing Period</th></tr></thead><tbody><tr><td>Sample</td><td></td><td>48%</td><td>35%</td><td>1%</td><td>1%</td><td>12%</td><td>1%</td><td>1%</td></tr></tbody></table>
## 4. Consent to Pay (WTP) for Climate Information

All farmers in the area have heard of weather and climate information. Almost all (99%) agree with the usefulness of climate information, but 1% of them, and particularly the “no training and no CI” category (11% in Fatick), still doubt the usefulness of this tool. This doubt can be explained by attachments to traditional practices, but also by a lack of information on this tool.

As for the effective use of climate information for agricultural activities, we see the same trend with an affirmative response for the Kaffrine and Diourbel zones. However, in Fatick, 10% of the “no training but IC” category and 55.6% of the “no training and no IC” category claim not to have used weather information in their production. Thus, it would be interesting to know if farmers are willing to spend money to have this information.

## 5. Experience in Using Weather Information Favors Producers' WTP

The willingness to pay for weather information is quite ambiguous even though a slight majority (58%) are willing to pay for it (Table 5). However, the category with a large experience in CI is the most willing to pay for weather information, perhaps due to the experience these producers had in using climate information over the years. The “participated in the training” and “no training but IC” categories are also in favor of payment, but a significant portion (between 40 and 45%) remain hesitant to pay.
<table><caption>Table 5
Willingness to Pay for Climate Information</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">Type of Actor</th><th colspan="2">Are you willing to pay for weather information for your farming operation?</th></tr><tr><th>Yes</th><th>No</th></tr></thead><tbody><tr><td rowspan="3">Diourbel</td><td>Participated in the Training</td><td>57.1%</td><td>42.9%</td></tr><tr><td>Extensive Experience on CI</td><td>75%</td><td>25%</td></tr><tr><td>No Training but CI</td><td>50%</td><td>50%</td></tr><tr><td rowspan="4">Fatick</td><td>Participated in the Training</td><td>58.8%</td><td>41.2%</td></tr><tr><td>Extensive Experience on CI</td><td>100%</td><td>0%</td></tr><tr><td>No Training but CI</td><td>50%</td><td>50%</td></tr><tr><td>No Training and No CI</td><td>56.6%</td><td>44.4%</td></tr><tr><td rowspan="3">Kaffrine</td><td>Participated in the Training</td><td>56.6%</td><td>44.4%</td></tr><tr><td>Extensive Experience on CI</td><td>61.5%</td><td>38.5%</td></tr><tr><td>No Training and No CI</td><td>0%</td><td>100%</td></tr><tr><td>Sample</td><td></td><td>58%</td><td>42%</td></tr></tbody></table>

Source: Author based on ISRA/BAME survey data, 2016.

Two main reasons may explain this trend: not knowing payment methods (44%) and lack of means (44%) (Table 5). We also note the weak structuring of farmers' organizations (7%) and others who think they can get the information through their acquaintances (4%) (Table 6).
<table><caption>Table 6
Reasons Given for Not Paying for Climate Information</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">Type of Actor</th><th colspan="4">Main reasons for not wanting to pay for climate information</th></tr><tr><th>Availability of other sources to capture information</th><th>Lack of knowledge of payment methods</th><th>Lack of resources</th><th>Lack of organized structure</th></tr></thead><tbody><tr><td rowspan="3">Diourbel</td><td>Participated in the Training</td><td>0%</td><td>25%</td><td>50%</td><td>25%</td></tr><tr><td>Extensive Experience on CI</td><td>0%</td><td>100%</td><td>0%</td><td>0%</td></tr><tr><td>No Training but CI</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Fatick</td><td>Participated in the Training</td><td>13%</td><td>38%</td><td>50%</td><td>0%</td></tr><tr><td>Extensive Experience on CI</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>No Training but CI</td><td>0%</td><td>0%</td><td>67%</td><td>33%</td></tr><tr><td rowspan="4">Kaffrine</td><td>No Training and no CI</td><td>0%</td><td>67%</td><td>33%</td><td>0%</td></tr><tr><td>Participated in the Training</td><td>0%</td><td>50%</td><td>50%</td><td>0%</td></tr><tr><td>Extensive Experience on CI</td><td>0%</td><td>60%</td><td>40%</td><td>0%</td></tr><tr><td>No Training and No CI</td><td>0%</td><td>100%</td><td>0%</td><td>0%</td></tr><tr><td>Sample</td><td></td><td>4%</td><td>44%</td><td>44%</td><td>7%</td></tr></tbody></table>

Source: Author based on ISRA/BAME survey data, 2016.

Farmers are willing to pay for different products: seasonal forecast, decadal information, daily information, and for agrometeorological advice (Table 7). Respondents in the “extensive CI experience” and “participated in the training” categories are the most willing to pay for all of these products, especially in Kaffrine. Reasons why include the fact that prices fluctuate
greatly and this variability is explained by the fact that the service is not well appreciated.

As noted above, the information that is really sought concerns the nature of the rainy season, its duration, and other daily information. The vectors for accessing this information are quite diversified but focus on the latest generation of communication tools (e.g., SMS, email, telephone, television, etc.) and information provided through the sharing workshops. Depending on the area, the actors, and the communication channel selected, the prices offered vary in relation to their income level. Thus, farmers understand the importance of climate information for the proper conduct of their agricultural activities and are willing to invest in this tool to secure their production.

<table><caption>Table 7
Types of Climate Information and Willingness to Pay</caption><thead><tr><th>Region</th><th>Type of Actor</th><th>Types of Ready-to-pay Climate Information</th><th>Minimum Price</th><th>Average Price</th><th>Maximum Price</th></tr></thead><tbody><tr><td rowspan="4">Diourbel</td><td rowspan="3">Participated in the Training</td><td>Seasonal Forecasts</td><td>100</td><td>167</td><td>280</td></tr><tr><td>Decadal information</td><td>100</td><td>125</td><td>150</td></tr><tr><td>Daily information</td><td>50</td><td>50</td><td>75</td></tr><tr><td></td><td>Agro-meteorological advice</td><td>100</td><td>150</td><td>200</td></tr><tr><td rowspan="2">Extensive Experience on CI</td><td>Seasonal Forecasts</td><td>25</td><td>50</td><td>70</td></tr><tr><td>Agro-meteorological advice</td><td>2000</td><td>2500</td><td>2500</td></tr><tr><td rowspan="6">Fatick</td><td rowspan="2">Participated in the Training</td><td>Seasonal Forecasts</td><td>500</td><td>750</td><td>750</td></tr><tr><td>Daily information</td><td>200</td><td>300</td><td>500</td></tr><tr><td rowspan="3">Extensive Experience on CI</td><td>Seasonal Forecasts</td><td>18</td><td>20</td><td>23</td></tr><tr><td>Daily information</td><td>15</td><td>20</td><td>25</td></tr><tr><td>Agro-meteorological advice</td><td>500</td><td>500</td><td>1000</td></tr><tr><td rowspan="2">No training but CI</td><td>Seasonal Forecasts</td><td>667</td><td>833</td><td>1000</td></tr><tr><td>Decadal information</td><td>1750</td><td>1875</td><td>2000</td></tr></tbody></table>
<table><thead><tr><th>Region</th><th>Type of Actor</th><th>Types of Ready-to-pay Climate Information</th><th>Minimum Price</th><th>Average Price</th><th>Maximum Price</th></tr></thead><tbody><tr><td rowspan="5">Participated in the Training</td><td>No Training and No CI</td><td>Daily information</td><td>500</td><td>600</td><td>600</td></tr><tr><td></td><td>Seasonal Forecasts</td><td>1900</td><td>2650</td><td>4000</td></tr><tr><td></td><td>Decadal information</td><td>513</td><td>765</td><td>1025</td></tr><tr><td></td><td>Daily information</td><td>500</td><td>500</td><td>1000</td></tr><tr><td></td><td>Agro-meteorological advice</td><td>750</td><td>1000</td><td>1500</td></tr><tr><td rowspan="5">Kaffrine</td><td rowspan="4">Extensive Experience on CI</td><td>Seasonal Forecasts</td><td>2168</td><td>3188</td><td>3821</td></tr><tr><td>Decadal information</td><td>353</td><td>228</td><td>541</td></tr><tr><td>Daily information</td><td>38</td><td>58</td><td>58</td></tr><tr><td>Agro-meteorological advice</td><td>2365</td><td>751</td><td>3504</td></tr><tr><td>No Training but CI</td><td>Decadal information</td><td>1000</td><td>1000</td><td>1000</td></tr><tr><td>No Training and no CI</td><td>Daily information</td><td>500</td><td>600</td><td>600</td></tr></tbody></table>

Source: Author based on ISRA/BAME survey data, 2016.

## Conclusion

This chapter aimed to show the benefits of using information systems in decision-making and on agricultural strategies of the farms in the Senegalese Groundnut Basin using surveys conducted in that basin. The methodological approach conducted statistical analysis of variables related to the influence of market and climatic information on production, marketing, and agricultural input investment decisions.

The analyses show that for almost half of the cowpea producers (49%), this information influences their choice of markets. For most cowpea producers (85%), the market information received also influences the decision of when to sell the crop. This allows producers to make trade-offs in time and space.
For the majority of cowpea producers (62%), market information influences their choice of selling price. However, it does not influence the choice of the quantity to sell for most cowpea producers (75%). For more than half of the cowpea producers (56%), market information influences the choice of which crop to grow. In addition, market information influences the choice of plot size and the decision to invest in agricultural inputs for 82% and 94% of cowpea producers respectively.

The analyses show that climate information has enabled various actions to be taken to ensure that agricultural activities run smoothly. The availability of climatic information has made it possible to better orient the choice of crops and the sowing period, as well as the preparation of soil and the period for spreading fertilizer. In addition, the majority (67%) of respondents are accustomed to using climate information, and ANACIM (52%) is the main provider of this information, followed by the radio (6%) and the Yeugglé MIS (4%). The use of climate information is generally community-based and is effectively used by 84% of those who have received it. This climatic information allowed farmers to choose the crop to be cultivated (48%), the variety to be grown (35%) in order to complete the cycle, and the sowing periods (12%). Thus, almost all (99%) agree that climate information is useful, but only a small majority (58%) agree to pay for it. The main reasons given were not knowing how to pay (44%) and lack of means (44%).

However, the price of the product is not the only criterion influencing producers' economic decisions. They are also confronted with the risk and uncertainty of markets and environmental factors. Agricultural activity depends on climate, cultivation practices, government policies, and global markets, all of which have a definite impact on agricultural supply in the long and short terms. However, there is no optimal reference situation that agents can use to take measures that will allow them to get closer to that optimal situation. With respect to uncertainty, the producer can seek to: i). transfer it by concluding a purchase or sale contract; ii). limit its effects by choosing several complementary productions, in order to stabilize income through diversification; iii). reduce it by improving knowledge of the environment, by resorting to futures markets for example. (Wade, 2009). Thus, the producer adopts mechanisms that Robison and Barry (1987) describe as managerial.

In terms of recommendations, we propose: an extension of agricultural
information systems to all agro-ecological zones. This information system
will consider all the information needed by the actors in the value chain
(i.e., market information, climatic, and agronomic information). It will also
be necessary to promote the establishment of consultative frameworks to
discuss information and technologies related to climate change and their
repercussions for the development of agro-sylvo pastoral activities
(agriculture, livestock and forestry).

References

Egg J., Galtier F., & Gregoire, E. (1996). Systèmes d'information formels et informels: La régulation des marchés des céréales au Sahel. In Jean-Paul Minvielle, *Cahiers des Sciences Humaines*, 32(4), 845-868.

Fraval, P. (2000). Éléments pour : L'analyse économique des filières agricoles en Afrique sub-saharienne. Bureau des Politiques Agricoles et de la Securite Alimentaire.

L'Agence Nationale de la Statistique et de la Démographie (ANSD) a procédé
à la publication des résultats définitifs du Recensement Général de la
Population et de l'Habitat, de l'Agriculture et de l'Elevage (RGPHAE).
(2014). Recensement général de la population de l'Habitat, de l'Agriculture,
de l'Elevage. https://www.ipar.sn/Recensement-General-de-la-
Population-et-de-l-Habitat-de-l-Agriculture-et-de-l.html?lang=fr

Ngom, (2018). Déterminants et impact des systèmes d'information de marché sur les revenus agricoles des producteurs de niébé du Bassin Arachidier Sénégalais.

Robison, L. J., & Barry, P. J. (1987). *The Competitive Firm's Response to Risk*. MacMillan.

Simon, H. A. (1965). *Administrative behavior* (2nd edition). The Free Press.

Stiglitz, J. E. (2001) Information and the change in the paradigm in economics. *The American Economic Review*, 92(3), 460–501.

Wade, I. (2009). *Système d'information de marché, coordination et gestion des risques dans les filières agricoles : Cas des produits maraichers au Sénégal*.
[Doctoral Dissertation, Université Montpellier I].
https://www.bameinfopol.info/IMG/pdf/These-
IWADE_20-01-09_complete.pdf

ENDNOTES

[1]Yeugglé means to inform in wolof.
# 6. Consumer Willingness to Pay for Millet-based Food Attributes in Niger

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

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

## Abstract

This chapter assesses urban consumers' preference for quality attributes of value-added food products derived from cereals. We combine qualitative and quantitative methods to assess the preferences of 205 randomly sampled consumers and derive their willingness to pay for those quality attributes. We account for taste and preference heterogeneity inherent in consumers' responses to a change in quality attributes. Consumers are sensitive to health quality attributes as measured by the expiration date, the presence of a micronutrient, and the product's country of origin. The resulting market demand for quality-improved products suggests the marketing of novel attributes through a promotion strategy that includes quality labeling to improve product differentiation.

**Keywords:** labeling, expiration date, fortification, choice experiment

## Introduction

Several studies show that nutrition and safety information positively affect consumers' food choices, depending on whether they are price conscious or not (Chowdhury et al., 2011; Drichoutis et al., 2005; Lusk and Schroeder, 2004; Nayga et al., 1998). The introduction of new or improved food quality attributes has a primary goal of a market differentiation of the product
from competing ones and creates new market segments (Henson & Reardon, 2005; Roy & Srivastava, 2022). Indeed, income growth and relative price changes also induce a gradual shift toward processed foods in developing countries (Barrett et al., 2017). Successful introduction of an improved or a new food product requires a deep understanding of consumers' preferences and valuation of intrinsic and extrinsic attributes. Hence, knowing consumers' willingness-to-pay (WTP) for food attributes is critical to achieve effective policy intervention. Furthermore, it provides information about the market demand of those attributes to food processors for strategic marketing and segmentation (Caswell & Mojduszka, 1996).

Another important consideration is that consumers consider price premiums the most decisive limiting factor cereal products demand in West Africa (Naseem et al., 2013). For example, high premiums combined with limited availability might restrict market growth, especially in populations that need these micronutrients (O'Donovan & McCarthy, 2002). Therefore, WTP estimates can analyze the marketability of new products and develop Pareto-improving public policy where warranted (Van Loo et al., 2011).

Several studies investigated consumers' preferences for food quality attributes in developing countries. For example, biofortification of micronutrients (e.g., zinc, pro-vitamin A, or iron) translates to substantial premiums in Uganda, Ethiopia, and Senegal (Chowdhury et al., 2011). Fiamohe et al. (2015) conducted a study in West Africa on extrinsic and intrinsic characteristics finding that Togolese consumers are willing to pay a premium of up to 46% of the actual price for cleanliness and 53% for whiteness in locally produced rice.

This chapter presents the results of a framed choice experiment to determine the relative values of food product fortification and marketing information. The study assesses consumers' preferences for intrinsic (i.e., the expiration date on products, the dietary composition of iron in food) and extrinsic (i.e., family-friendly promotion and country-of-origin) characteristics of high-quality millet flour-based agglomerated cereal products. This chapter explicitly models consumers' marginal rate of substitution of income for quality attributes, accounting for preference heterogeneity across consumers, using mixed multinomial logit models in both preference and WTP spaces proposed by Train and Weeks (2004) and Scarpa et al. (2008). We applied these models to consumer data elicited from
a collective conjoint-based choice experiment conducted in the capital of
Niger. Our contribution to the literature and policy debate is twofold. First,
in the global debate on the implications of date labeling for food waste,
we evaluate consumers' valuation of expiration dates. Second, we assess
the relevance of accounting for preference heterogeneity in evaluating
consumer valuation of quality attributes. Additionally, we provide empirical
evidence for a plausible market-driven micronutrient supply through
fortified food products in developing countries.

Choice Experiment Methods

We evaluated a random sample of 205 consumers WTP for quality attributes of value-added cereal food product locally known as Dèguè which is an agglomerated food product, similar to couscous, but of a larger diameter.¹ Traditionally it has been marketed as a homogenous food commodity and developed for subsistence purposes. However, some small firms, owned mainly by women, use modern technologies to produce high-quality products, especially in urban areas. Quality characteristics of those products are not made explicit to consumers in a way that can be directly analyzed through market purchases to determine relative demand for these quality attributes. As such, this experiment has been conducted to determine consumers' preferences for selected quality attributes embedded in these food products.

Packages of five hundred grams (500 g) of Dèguè with varying quality
attributes are the unit of analysis. Because the products with these quality
attributes were either new or unavailable in the local market, demand for
the attributes was uncertain. Consequently, this study represents a classical
use of Choice Experiments (CE) to determine relative preference for the
product attributes and estimate the associated marginal WTP (Louviere,
1991). Revealed preference data — groceries or scanner data — often estimate
WTP in high or middle-income countries. Such data are not readily available
in Niger, where grocery stores with electronic scanning systems are at an
early stage of development. Also, Lusk and Schroeder (2004) noted the
difficulty of finding survey participants in such studies adding to the
challenge of collecting demographic characteristics from each purchaser of
Dèguè in a store setting. It may not be possible to identify market niches by classifying consumers with the same preference structure.

Hence, random participants who were going to, or returning from the market, were recruited on the spot. To include a random factor during sampling, we approached every fifth male or female passer-by with an estimated age between 18 and 65. Following the methods of Demont et al. (2012), whenever we approached a group, we selected a maximum of one participant so that none of the participants knew each other. Subjects were offered 2000 FCFA ($4) cash to participate in “Consumers’ willingness to pay for quality attributes of cereal value-added food products,” a study conducted in a schoolroom near the market site. We assigned participants a random identification number for anonymity and directed them to the experiment site. One and a half hour sessions occurred once or twice per day, depending on the difficulty of recruiting participants, between July 4 and July 10, 2017. The experiment was conducted in the five districts of Niamey to ensure the spatial representativeness of the sample to the city population.

Prior to the focus group discussion on May 30, 2017, we conducted a literature review and reviewed expert interviews to identify the main drivers of consumer food choices. As a result, ten attributes were selected for the focus group discussion, including: price and brand, food safety, ecological certification, production process, health-related characteristics, and nutritional attributes. Through a series of pre-tests and expert advice, subsequent refinement led to four customized and improved attributes (Figure 1).

Upon arrival at the session, subjects completed a short demographic questionnaire and examined a packaged sample of Dèguè made locally by processors in town without labels. An information sheet and cheap talk script were read aloud, after which subjects responded to a series of eight repeated questions on product choice. Each question or choice set appeared on the board, read aloud in French, and translated into the two major languages of Hausa and Djerma, which are spoken in the town.

Three package options (Figure 2) were presented to the subjects in each set, including a “None of these” option. Subjects were asked to indicate which package was preferred (or none) in each scenario. Package price ranged
between 450 FCFA, 500 FCFA, 550 FCFA, and 600 FCFA. During focus group discussions, the price levels were chosen to straddle the range of prices available in the market.

<table><caption>Figure 1</caption><thead><tr><th rowspan="2">Attribute</th><th rowspan="2">Description</th><th colspan="4">Attribute levels</th></tr><tr><th>1</th><th>2</th><th>3</th><th>4</th></tr></thead><tbody><tr><td>Limit date of consumption</td><td>Limit Date of Consumption (DLC) "Date of Expiration"</td><td>No</td><td>Yes</td><td></td><td></td></tr><tr><td>Product Nutritional Content</td><td>Highlight the content of the product in Iron relative to the daily value requirement.</td><td>0% of Daily Value</td><td>25% of Daily Value<br>25%</td><td>75% of Daily Value<br>75%</td><td>100% of Daily Value<br>100%</td></tr><tr><td>National origin claim</td><td>Highlight the origin of the product and the pride related to the country</td><td>None</td><td>Made in Niger</td><td></td><td></td></tr><tr><td>Family "friendly" product</td><td>Whether the image associated with the product is suitable for all family members</td><td>None</td><td>Family</td><td></td><td></td></tr><tr><td>Product price (per 500g)</td><td>The buying price of the product per 500g in FCFA</td><td>450</td><td>500</td><td>550</td><td>600</td></tr></tbody></table>

<caption>Figure 1 - Attributes and Levels for the Choice Experiment</caption>

In this experimental design, there are three options of packages (including none) built using four price levels, micronutrient content levels, country-of-origin declaration, and a modern family image (See Figure 1). Subjects would have to be shown 22 x 22 x 22 x 42 x 42 = 16,384 different choice sets if presented with every package at every combination of price, micronutrient level, date of expiration, the origin of the product, and family image. An orthogonal fractional of the full factorial design was generated to reduce the number of questions respondents had to answer. In this design, attributes are uncorrelated between packages. The resulting design consisted of 64 sets of scenarios or choice sets, which were then formed into eight blocks of eight choice sets each. An example choice card is presented in Figure 2.
<table><caption>Figure 2</caption><thead><tr><th>Attribute</th><th>Package A</th><th>Package B</th><th>Package C</th></tr></thead><tbody><tr><td>Expiration Date</td><td>Yes</td><td>No</td><td></td></tr><tr><td>Origin</td><td>Made in Niger</td><td></td><td></td></tr><tr><td>Image</td><td></td><td></td><td>None</td></tr><tr><td>Micronutrient: Iron</td><td>0 % Daily Value</td><td>75% Daily Value</td><td></td></tr><tr><td>Price</td><td>550</td><td>450</td><td></td></tr><tr><td>I would Choose (Check (√) only one choice)</td><td></td><td></td><td></td></tr><tr><td>Rank</td><td></td><td></td><td></td></tr></tbody></table>

<caption>Figure 2 - Sample Choice Set Presented to Participants</caption>

## Empirical Methodology

Consider a consumer's utility maximization problem under a budget constraint, with an exogeneous level of a good's quality ($q$). In the case of agribusiness applications, $q$ is most applicable as a measurement or index of a good's quality (Lusk & Hudson, 2004). The consumer chooses the market good ($x_m$) that maximizes utility, producing the traditional Marshallian demand curve $x_m(p, y, q)$, where $p$ is the market price of the good and $y$ is income. The resulting indirect utility function is $V(p, y, q)$. Now assume that an agribusiness food processor and retailer consider improving the quality of an existing product from $q_0$ to $q_1$. A measurement of the consumer's value on this improvement can be derived by determining the magnitude of a WTP such that the following equality holds: $V(p, y - \text{WTP}, q_1) = V(p, y, q_0)$. WTP measurement is often conducted using a Choice Experiment (CE) composed of hypothetical sets of questions for novel products or attributes without an existing market.

In each CE question or choice set, respondents had to choose between two packages and the “none of these” option. Let $V_{njt}$ be the deterministic
component of n'th consumer's utility of choosing package j at choice occasion t. For simplicity, the utility derived from alternative j chosen by consumer n at choice occasion t is separable in price and quality (non-price attributes) x_njt,

$$U_{njt} = -a_n price_{njt} + \beta_n x_{njt} + u_{njt}, \quad j=A, B, C \quad (1)$$

where scalar a_n is the price coefficient, and vector β_n represents the marginal utility associated with the quality attributes. Both sets of parameters vary randomly by consumer n = 1, ..., N, u_njt is the stochastic component of utility that is Gumbel distributed with a variance Var(u_njt) = μ_n²(π²/6), and μ_n is the scale parameter for consumer n. Train and Weeks (2004) demonstrated that the scale of utility is irrelevant to consumer behavior. The utility can then be divided by μ_n without changing behavior, which results in an i. i. d. new error term ε_njt of type-one extreme value, distributed with constant variance (π²/6):

$$U_{njt} = \lambda_n price_{njt} + c_n x_{njt} + \varepsilon_{njt} \quad (2)$$

where λ_n = -α_n/μ_n and c_n = β_n/μ_n. Train and Weeks (2004) call this specification the “utility model in preference space.” It can be seen from Equation 2 that λ_n and c_n are correlated unless the scale parameter does not vary over individuals since μ_n appears in the denominator in both expressions. Specifying the coefficients to be independent implies that the random term is homoscedastic, which may not be a realistic assumption. By using the fact that the WTP is given by w_n = -β_n/α_n = c_n/λ_n, Equation 2 can be written as:

$$U_{njt} = \lambda_n[price_{njt} + w_n x_{njt}] + \varepsilon_{njt} \quad (3)$$

This model is what Train and Weeks (2004) and Scarpa et al. (2008) called “utility in WTP space.” Models (2) and (3) are, of course, behaviorally equivalent. However, the key thing to note is that a standard assumption regarding λ_n and c_n in the preference space (equation 2) can lead to an unusual distribution for the WTP. For instance, if the price coefficient has any positive density setting over zero, then the distribution of this ratio has undefined moments (Daly et al., 2012). In addition, if λ_n and c_n are both normally distributed, it implies that w_n is a ratio of two normal distributions, which does not have defined moments. This choice of distribution is unlikely if we were to specify the distribution for the WTP directly as we did in the
WTP space model. We estimated the coefficients in the preference space and WTP space models using maximum-simulated likelihood. We followed Scarpa et al. (2008) as described in the next section.

## Estimation Method

This section presents the method of estimating coefficients in equations 2 and 3. We adopt the mixed logit specification under repeated choices by consumers with continuous taste known as panel mixed logit developed by Revelt and Train (1998). In our study, consumer n faces a choice among j alternatives (package A, Package B, or C-Neither) in each T choice situation. For the simplicity of the exposition, let $\theta_n$ represent the random terms entering utility, which are $\lambda_n$ and $c_n$ in the preference space (equation (2)) and $\lambda_n$ and $w_n$ in the WTP space (equation (3)).

Consumer n chooses package j in period t if $U_{njt} \ge U_{nkt} \forall j \ne k$. Denote the consumer's chosen alternative in choice occasion t as $y_{nt}$. The consumer sequence of choice over the T_n choice occasions t as $y_n=\langle y_{n1},...,y_{nTn} \rangle$. Hence, conditional on $\theta_n$, consumer's n sequence of choice probability is:

$$L(Y_n|\theta_n) = \prod_{t=1}^{t=8} \frac{\exp(\theta_n z_{njt})}{\sum_{k=1}^{3} \exp(\theta_n z_{nkt})} \quad (4)$$

The unconditional probability of consumer n's sequence of choice is the integral over all values of $\theta_n$ weighted by its density

$$P_n(Y_n|\Omega^*) = \int L(y_n|\theta_n) f(\theta_n|\Omega^*) d\theta_n \quad (5)$$

where $f(\cdot)$ is the density of $\theta_n$, which depends on parameters $\Omega^*$ to be estimated. The goal is to estimate $\Omega^*$, which is the population parameter that describes the distribution of individual parameters (Revelt & Train, 1998) using the log-likelihood function $LL(\Omega) = \sum_n \ln P_n(Y_n|\Omega^*)$. The exact maximum likelihood is not possible since the integral of equation (4) cannot be calculated analytically. Hence $P_n(Y_n|\Omega^*)$ is approximated via simulation. The simulation is done by first drawing a $\theta^r$ values of $\theta_n$ from $f(\theta_n|\Omega^*)$ with $\Omega^*$ fixed at a given initial value.

With a given R number of draws: $P_n^\mu(Y_n|\Omega^*) = \frac{1}{R} \sum_{r=1}^R L_{n,j}(\theta^r)$ is an unbiased estimator of $P_n(Y_n|\Omega^*)$. Finally, substitute $P_n^\mu$ into the Log-Likelihood
function to yield the simulated Log-Likelihood:
$$SLL = \sum_n \sum_j d_{njt} \ln P_n^\mu (\hat{y}_n | \Omega^*)$$ where $d_{njt} = 1$ if consumer n chooses alternative j at choice situation t.

In this study, we first estimated a range of models in both preference and WTP space and afterward selected the “best” model among a set of non-nested models using the Akaike likelihood Ratio Index proposed by Ben-Akiva and Swait (1986). We compute Akaike Likelihood Ratio Index (ALRI) for each model to measure how well the models best fit the data. We chose the model with significantly higher ALRI1.

# RESULTS

## 1. Descriptive Statistics

Table 1 presents sampled consumer distributions concerning gender, age groups, marital status, education, and their corresponding average household monthly expenditure group. The table also compares our sample structure with our target population at the city and national levels, even though our survey was exclusively in Niamey. These comparisons were chosen because the generalization of the findings will critically depend on the sample representativity of the targeted population: consumers between 18- and 60-years old living in the city of Niamey. Our sample distribution was also compared to the national level to understand similarities and differences between the sample and the target population. Specifically, our sample is stratified concerning the town districts and has similar spatial distributions as our targeted population. Overall, the randomization appeared to be well-conducted since our sample is relatively representative of consumers in Niamey. In addition, this similarity is extended to the national level despite some noticeable dissimilarities for education and marital status variables. When the city sample is compared to the national level population, consumers without any level of education are underrepresented in our sample.

Specifically, 17.5% of our sample had no education, which is low compared to the 47.7% for the city of Niamey and 73.4% for the national level. The
reticence in participating in the survey may concern a lack of trust in the
recruiter and the delivered message. Finally, a noticeable fact is a
discrepancy about the top and bottom classes of participants' household
food expenditures in Niamey. The low-income class is overrepresented,
while the higher class is underrepresented in our sample. This situation is
probably due to the incentives we proposed (US $4), which is 25% of the
average monthly food expenditure in our sample. This may be a significant
incentive for the lower-income group, but not for the higher-income group.

Like Niamey and the national level, our typical consumer is relatively
young, between 18 and 34 years old, married, with a monthly food
expenditure of fewer than 12,000 FCFA (USD 22), and lives in a household
with 6-8 members. Specifically, about two-thirds of the participants have a
monthly food expenditure level of fewer than 16,000 FCFA (USD 29).²

Regarding food poverty, 36.5% of the participants live in households with a per capita food expenditure lower than the town threshold, calculated to be 9,925 FCFA (USD 18) per capita per month by official statistical services (INS, 2011). Comparatively, the chronic food insecurity estimated by the National Institute of Statistics was 33.5%.
<table><caption>Table 1
Average Sample Socioeconomic and Demographic Characteristics by Class</caption><thead><tr><th></th><th>Sample</th><th>Niamey</th><th>Diff. Sample-Niamey<sup>a</sup></th><th>t-stat</th><th>National</th><th>Diff. Sample National<sup>b</sup></th><th>t-stat</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th></th><th>(4)</th><th>(5)</th><th></th></tr></thead><tbody><tr><td>Gender</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Male</td><td>42.27</td><td>48.91</td><td>-6.64***</td><td>-2.70</td><td>49.37</td><td>-7.1***</td><td>2.60</td></tr><tr><td>Female</td><td>57.73</td><td>51.09</td><td>-8.82**</td><td>-2.70</td><td>50.63</td><td>-8.36***</td><td>2.60</td></tr><tr><td>Age Group</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>18-24 years</td><td>25.26</td><td>27.27</td><td>-2.01*</td><td>-1.92</td><td>23.99</td><td>1.27</td><td>-0.86</td></tr><tr><td>25-34 years</td><td>27.32</td><td>29.74</td><td>-2.42*</td><td>-.032</td><td>28.12</td><td>-0.8</td><td>0.16</td></tr><tr><td>35-44 years</td><td>24.23</td><td>17.5</td><td>6.73*</td><td>1.91</td><td>18.35</td><td>5.88</td><td>1.64</td></tr><tr><td>45-54 years</td><td>14.95</td><td>12.99</td><td>1.96</td><td>0.40</td><td>13.87</td><td>1.08</td><td>0.07</td></tr><tr><td>55-64 years</td><td>6.19</td><td>7.81</td><td>-1.62</td><td>0.30</td><td>9.06</td><td>-2.87</td><td>-0.30</td></tr><tr><td>65 years or +</td><td>2.06</td><td>4.99</td><td>-2.93</td><td>0.31</td><td>6.61</td><td>-4.35</td><td>-0.84</td></tr><tr><td>Marital Status</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Single</td><td>33.51</td><td>35.11</td><td>-1.6</td><td>-0.56</td><td>19.26</td><td>14.25***</td><td>4.77</td></tr><tr><td>Married</td><td>60.82</td><td>55.68</td><td>5.14</td><td>1.51</td><td>71.78</td><td>-10.96***</td><td>-2.87</td></tr><tr><td>Divorced</td><td>4.12</td><td>3.8</td><td>0.32</td><td>0.09</td><td>2.64</td><td>1.48***</td><td>-15.37</td></tr><tr><td>Widow</td><td>1.55</td><td>5.37</td><td>-3.82*</td><td>-1.74</td><td>6.01</td><td>-4.46***</td><td>-60.37</td></tr></tbody></table>
<table><thead><tr><th></th><th>Sample</th><th>Niamey</th><th>Diff. Sample-Naimey<sup>a</sup></th><th>t-stat</th><th>National</th><th>Diff. Sample National<sup>b</sup></th><th>t-stat</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th></th><th>(4)</th><th>(5)</th><th></th></tr></thead><tbody><tr><td colspan="8"><strong>Education</strong></td></tr><tr><td>None</td><td>17.53</td><td>47.7</td><td>-30.17***</td><td>-10.62</td><td>73.41</td><td>-55.88</td><td>-19.64</td></tr><tr><td>Primary</td><td>24.23</td><td>22.04</td><td>2.19***</td><td>-26.40</td><td>13.47</td><td>10.76</td><td>-15.58</td></tr><tr><td>Junior High</td><td>24.23</td><td>14.54</td><td>9.69***</td><td>2.52</td><td>7.43</td><td>16.8</td><td>5.06</td></tr><tr><td>Senior High</td><td>10.31</td><td>2.72</td><td>7.59**</td><td>2.84</td><td>1.07</td><td>9.24</td><td>7.16</td></tr><tr><td>Professional</td><td>5.67</td><td>4.5</td><td>1.17</td><td>-1.38</td><td>1.91</td><td>3.76</td><td>3.93</td></tr><tr><td>Koranic</td><td>8.25</td><td>0.8</td><td>7.45</td><td>-0.39</td><td>0.23</td><td>8.02</td><td>3.11</td></tr><tr><td>Higher Education</td><td>8.25</td><td>7.6</td><td>0.65</td><td>0.40</td><td>2.42</td><td>5.83</td><td>2.87</td></tr><tr><td>Other</td><td>1.53</td><td>0.1</td><td>1.43***</td><td>3.39</td><td>0.06</td><td>1.47</td><td>3.59</td></tr><tr><td colspan="8"><strong>Per capita monthly household food expenditure (FCFA)<sup>c</sup></strong></td></tr><tr><td>Less than 4000</td><td>10.4</td><td>0.66</td><td>9.74</td><td>0.40</td><td>1.89</td><td>8.51***</td><td>2.77</td></tr><tr><td>4000-6000</td><td>10.98</td><td>1.76</td><td>9.22**</td><td>2.00</td><td>7.47</td><td>3.51</td><td>-1.46</td></tr><tr><td>6000-8000</td><td>12.14</td><td>5.29</td><td>6.85</td><td>1.50</td><td>13.30</td><td>-1.16**</td><td>-2.33</td></tr><tr><td>8000-10000</td><td>12.14</td><td>9.14</td><td>3.00</td><td>0.43</td><td>16.39</td><td>-4.25*</td><td>-2.77</td></tr><tr><td>10000-12000</td><td>4.62</td><td>9.03</td><td>-4.41</td><td>0.71</td><td>14.78</td><td>-10.16*</td><td>-1.76</td></tr><tr><td>12000-14000</td><td>6.94</td><td>10.46</td><td>-3.52</td><td>0.32</td><td>12.53</td><td>-5.59</td><td>-0.54</td></tr></tbody></table>
<table><thead><tr><th></th><th>Sample</th><th>Niamey</th><th>Diff. Sample-Naimey<sup>a</sup></th><th>t-stat</th><th>National</th><th>Diff. Sample National<sup>b</sup></th><th>t-stat</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th></th><th>(4)</th><th>(5)</th><th></th></tr></thead><tbody><tr><td>14000-16000</td><td>9.25</td><td>11.23</td><td>-1.98**</td><td>2.33</td><td>9.15</td><td>0.1</td><td>3.05</td></tr><tr><td>More than 16000</td><td>33.53</td><td>52.42</td><td>-18.89***</td><td></td><td>24.48</td><td>9.05</td><td>1.38</td></tr></tbody></table>
Note. *** p<0.01, **p<0.05, * p<0.1, with p the p-value of the one-sample t-test. <sup>a</sup> is the difference between Niamey and the sample estimate, <sup>b</sup> represents the difference between national and the sample estimate, <sup>c</sup>

Per capita monthly household food expenditure (FCFA). Niamey and National level information are official statistics by the National Institute of Statistics.

## 2. Estimated Model Parameters

In this section, we compare results from the estimated choice models. The models are estimated using maximum simulated likelihood with 1000 Halton draws. Table 2 presents estimation results for both preference and WTP spaces models as discussed in the methodology. In both spaces, models are estimated across random utility coefficients. The first two columns are the estimation results of models in preference space (Equation 2) without and with the correlation between estimated coefficients, respectively. The two last columns display estimation results of models in the WTP space, again without and with the correlation between the estimated coefficients.

<table><caption>**Table 2**

Estimation Results of Utility Parameters in Preference and WTP Space With and Without Correlation Among Coefficients</caption><thead><tr><th rowspan="2"></th><th colspan="2">Preference Space</th><th colspan="2">WTP Space</th></tr><tr><th>Without correlation (1)</th><th>With correlation (2)</th><th>Without correlation (3)</th><th>With correlation (4)</th></tr></thead><tbody><tr><td>Price</td><td>-0.006***<br>(0.0005)</td><td>-0.006***<br>(0.000551)</td><td>-5.090***<br>(0.0986)</td><td>-4.930***<br>(0.0896)</td></tr><tr><td>Expiration date</td><td>1.638***<br>(0.108)</td><td>1.709***<br>(0.123)</td><td>276.2***<br>(20.40)</td><td>242.6***<br>(17.81)</td></tr><tr><td>Micronutrient (less than 25% of DR)</td><td>0.387***<br>(0.0461)</td><td>0.400***<br>(0.0526)</td><td>65.20***<br>(7.789)</td><td>63.60***<br>(8.428)</td></tr><tr><td>Product origin</td><td>0.752***<br>(0.107)</td><td>0.737***<br>(0.121)</td><td>122.0***<br>(15.12)</td><td>138.3***<br>(16.61)</td></tr><tr><td>Family image</td><td>0.129<br>(0.109)</td><td>0.0908<br>(0.122)</td><td>19.1<br>(18.12)</td><td>41.07**<br>(18.60)</td></tr></tbody></table>
<table><thead><tr><th rowspan="2"></th><th colspan="2">Preference Space</th><th colspan="2">WTP Space</th></tr><tr><th>Without correlation<br>(1)</th><th>With correlation<br>(2)</th><th>Without correlation<br>(3)</th><th>With correlation<br>(4)</th></tr></thead><tbody><tr><td>L* at Convergence</td><td>-1136.574</td><td>-1104.79</td><td>-1135.927</td><td>-1093.286</td></tr><tr><td>Akaike Information Criterion</td><td>2299.148</td><td>2265.58</td><td>2299.853</td><td>2256.572</td></tr><tr><td>ρj2</td><td>-1.24</td><td>-1.18</td><td>-1.24</td><td>-1.16</td></tr><tr><td>Pr (ρ42-ρj2)</td><td>0</td><td>0</td><td>0</td><td>a</td></tr><tr><td>Pr (ρ22-ρj2)</td><td>0</td><td>a</td><td>0</td><td>b</td></tr><tr><td>Observations</td><td>4272</td><td>4272</td><td>4272</td><td>4272</td></tr></tbody></table>

Note. Standard errors in parentheses, *** p<0.01, **p<0.05, * p<0.1, a
Probabilities are not calculated because of the auto-comparison. b The
statistics are not computed because
ρ22-ρ42 is negative in this case.

<table><caption>**Table 3**
Marginal Willingness to Pay for All Consumers by Gender and Income Classes in FCFA Using Utility Model in WTP Space</caption><thead><tr><th></th><th>Observation</th><th>Expiration Date</th><th>Micronutrient</th><th>Origin</th><th>Family Image</th></tr></thead><tbody><tr><td>All Classes</td><td>4272</td><td>242.6***<br>(17.81)</td><td>63.60***<br>(8.428)</td><td>138.3***<br>(16.61)</td><td>41.07**<br>(18.60)</td></tr><tr><td>Gender</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Male</td><td>2,520</td><td>203.4***<br>(19.05)</td><td>62.24***<br>(10.39)</td><td>147.8***<br>(20.01)</td><td>44.53*<br>(23.52)</td></tr><tr><td>Female</td><td>1,752</td><td>317.4***<br>(40.31)</td><td>70.82***<br>(16.17)</td><td>132.4***<br>(28.66)</td><td>49.69<br>(35.92)</td></tr><tr><td>Monthly Income Class<sup>a</sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Less than 60</td><td>888</td><td>297.6***<br>(40.51)</td><td>32.40*<br>(17.63)</td><td>145.2***<br>(33.89)</td><td>66.29<br>(44.67)</td></tr><tr><td>60-120</td><td>1368</td><td>226.2***<br>(46.50)</td><td>55.05***<br>(19.22)</td><td>203.3***<br>(45.11)</td><td>92.84*<br>(51.36)</td></tr><tr><td>More than 120</td><td>2,016</td><td>225.2***</td><td>84.89***</td><td>104.0***</td><td>30.75</td></tr></tbody></table>
<table><thead><tr><th></th><th>Observation</th><th>Expiration Date</th><th>Micronutrient</th><th>Origin</th><th>Family Image</th></tr></thead><tbody><tr><td></td><td></td><td>(24.08)</td><td>(12.17)</td><td>(22.20)</td><td>(24.07)</td></tr><tr><td><strong>Age</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Less than 28 years old</td><td>1,704</td><td>249.8***<br>(21.71)</td><td>41.08***<br>(10.54)</td><td>114.7***<br>(20.46)</td><td>28.05<br>(23.10)</td></tr><tr><td>28-38 years old</td><td>1,104</td><td>221.9***<br>(18.38)</td><td>89.24***<br>(10.78)</td><td>151.4***<br>(30.60)</td><td>77.59**<br>(35.44)</td></tr><tr><td>More than 38 years old</td><td>1,464</td><td>266.7***</td><td>51.34***</td><td>185.1***</td><td>44.61</td></tr><tr><td>(35.42) (15.64)<br>(32.04) (40.10)</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note. Standard errors in parentheses, *** $p < 0.01$, ** $p < 0.05$, * $p < 0.1$, with *p* the *p*-value. <sup>a</sup>Income class in thousands of FCFA

The fit of alternative models of heterogeneity was compared using the Akaike Likelihood Ratio Index because the models estimated are non-nested. In addition, based on this index, a test procedure developed by Ben-Akiva and Swait (1986) was implemented to test the statistical difference of fit between models. The general result is that the model in WTP space with correlation outperforms all others because the probability of incorrectly specified functional form is almost equal to zero, as shown in the lower panel of Table 3. The result strongly suggests the suitability of its structure in explaining consumers' preferences in our sample case and is the basis of subsequent model interpretation. Also, the fact that preference heterogeneity exists is evident by the improvement in the likelihood of the model with random parameters from that with fixed random parameters. Appendix A provides details on the model selection procedure.

### 3. Robustness Analysis

We further test model performance by bifurcating the total sample into two subsets containing 80 and 20 percent of the sample. We drew one hundred iterations with replacement and models estimated using 80 percent of the sample. The resulting parameters predicted the results for the 20 percent
held aside. Then we compared the predicted choice with the actual choice to calculate the out-of-sample prediction success rate. We did this to test the model's predictive ability and to determine a preferred model based on comparing success rates.

The cumulative probability distribution curves allowed us to illustrate the distribution of correct predictions for the out-of-sample selection for each of the four models. These results are presented in figure 3. A distribution to the right indicates a higher prediction rate for a given probability. We used these tests to determine whether models with correlation (or not) better predict choice and whether models in preference space out predict those in willingness-to-pay space. Overall, models have had little difference in terms of predictive power since the prediction rate is around 74 percent for all four models; however, those with a correlation between coefficients have had a slightly higher success rate (Table D.1). As a result, models equally perform well in an out-of-sample setting. No curve first-order or second-order dominates another, indicating that no model better predicts the out-of-sample choice.
<figure><img src="image_36.png" /><figcaption>Figure 3</figcaption></figure>

Figure 3 - Cumulative Distribution Functions (CDF) of Out of Sample Prediction Success Rates

Figure 4 presents the posterior distributions of parameter estimates. These graphs emphasize the range of heterogeneity of consumer valuation of the product's intrinsic and extrinsic traits under alternative specifications. Since there are interesting spread and multimodal distributions, we attempt to explain these phenomena by disaggregating models into market segments to enhance understanding of the robustness of these models.
Figures 4a-4e Individual Posterior Means Distributions of Marginal WTPs Across Sample Respondents

<figure><img src="image_37.png" /><figcaption>Figure 4a Distributions of WTP for Product Origin for estimated models</figcaption></figure>

<figure><img src="image_38.png" /><figcaption>Figure 4b Distributions of WTP for Micronutrient for estimated models</figcaption></figure>
<figure><img src="image_39.png" /><figcaption>Figure 4c Distributions of WTP for expiration date in both WTP and preference spaces without correlation</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>Figure 4d Distributions of WTP for expiration date in both WTP and preference spaces with correlation</figcaption></figure>
<figure><img src="image_41.png" /><figcaption>Figure 4e Distributions of WTP for Family Image</figcaption></figure>

4. Parameter Interpretation and Heterogeneity

All models produced reasonable estimates. The price coefficient is negative, while consumers strongly prefer the expiration date, micronutrient information, and the product's origin. The family image attribute is less critical, although significant. Also, all standard deviations associated with the marginal utilities of attributes are statistically significant at the one percent level, denoting significant preference heterogeneity of consumers concerning attributes. The expiration date, which informs consumers about a product's safety for consumption, increases utility by 1.7 (Column 3). As a result, consumers are willing to pay a premium of 242.6 FCFA for this attribute or information. Initially, the result of 242.6 FCFA seemed to be an implausibly large value since it is roughly 1/3 of the average producer price of the product. Nevertheless, this high value may reveal a consumer's WTP to avoid expenditure on an outdated and unhealthy product. Several studies highlight this strong social desirability of food safety and calls for labelling policy (Liu et al., 2020; Wongprawmas & Canavari, 2017).
The second-most important attribute is micronutrients—the percentage of daily iron requirements or daily value—needed by a human body. The willingness to pay for 25% of the Recommended Dietary Allowance (RDA) of iron is 65 FCFA. Since micronutrients have three levels, WTP for one additional daily requirement level will be linear because our experimental design does not allow for estimating nonlinear effect by integrating dummy variables representing each level. As a result, the willingness to pay for 50%, 75%, and 100% of daily requirements could be estimated as equal to 127 FCFA, 191 FCFA, and 254 FCFA, respectively. Nevertheless, 25% of RDA for 500g of the product is a technological threshold since iron taste is distinguishable above that level, possibly confounding consumer taste and preference.

Table 3 and Figure 5 present the marginal WTP for attributes by income classes, and gender, using the utility model in WTP space with the correlation between the marginal utility of attributes. The product's origin is the third attribute consumers strongly prefer. They are willing to pay a premium of 138 FCFA for the product to be made in their country rather than an uncertain place of origin. Women have a strong preference for iron content and the product's expiration date, making them more inclined to pay a higher premium for those attributes. This situation is not surprising because the team nutritionist highlighted the benefit for pregnant and lactating women, and their babies, in the “Cheap Talk Information” given to the participants.
<figure><img src="image_42.png" /><figcaption>Figure 5</figcaption></figure>

Figure 5 – Marginal Willingness to Pay for All consumers by Gender and Income Classes in FCFA

Men strongly prefer “Made in Niger” and the expiration date, which translates to higher WTP than women. Also, younger consumers are even more ethnocentric than older ones based on their willingness to pay a higher premium for products that carry an image of the Nigerien flag. The willingness to pay for the expiration date is higher for low-income classes and lower for higher-income classes. This finding suggests that risk-averse consumers are willing to pay a higher premium to purchase a product with a more certain quality as defined by the expiration date in WTP space.

Finally, consistent with Bennett's law on the relationship between income and food quality, WTP for micronutrients is significantly higher for higher-income classes of consumers. Indeed, Bennett's law reflects the seemingly universal desire for variety in diet and diversification towards higher quality.
Discussion and Conclusion

Our study focuses on urban consumers in one of the poorest nations in the world. The hypothetical market setting provides information on a range of intrinsic and extrinsic attributes of the food product. Our experiment's design provided signals to consumers on product freshness, as described by the date of expiration, nutritional content, as described by the micronutrient density, the national origin of the product, and family friendliness as described by the family image. Specifically, we inferred consumer demand for product safety through a statistically significant WTP for a product that bears a date of expiration compared to a product that does not have such information. In these treatments, consumers showed a strong preference for the quality attributes compared to the situations where they were uncertain on attributes. Also, consumers strongly prefer products made in their home country.

These results reveal how consumers may react when they are certain about product information when making food choices. This finding indicates the value of transforming credence attributes into search attributes through labeling. The uncertainty reduction drives a higher willingness to pay for product-related attributes. Furthermore, consumers are willing to pay a price premium for iron fortification. These results are consistent with theoretical predictions derived by Falconi and Row (1990) in their study on the economics of food safety. They show that consumers' beliefs, the certainty of beliefs, and the presence of information (signals), are important determinants of demand for goods as the demand for health drives them.

The attributes and the information provided to participants enhanced those beliefs. In addition, the effectiveness of how information is supplied to consumers has been crucial on consumers' WTP. For example, the low level of micronutrient WTP may be related to consumer-limited knowledge of its health benefit. Consumers widely understand the date of expiration or product origin, which is not the case with micronutrients. Hence, participants may be uncertain about any associated health benefit. Aside from these results, findings are heterogeneous across socioeconomic characteristics, such as gender and income class, which segments the market.
Because market demand is high for health-related attributes such as date of expiration, public policy could be undertaken to create an enabling environment for the supply of such attributes. This policy could first sensitize food processors on the economic benefit of following quality standards and informing consumers on those attributes. This information could be provided to consumers using harmonized and high-quality labels and packaging. High-quality labels are critical in developing consumer trust in the claimed attributes. Furthermore, governments could set guidelines on the type of label that food processors should adopt. Several countries already implement such guidelines in West Africa following FAO and WHO (2010) Codex Alimentarius Commission standards.

In addition, our study suggests that micronutrient demand is higher in wealthier income classes and for women. This finding is consistent with Abdulai and Aubert (2004), who found that in Tanzania, the diets of high-income households were richer in all micro-and macronutrients. Likewise, Ecker and Qaim (2011) generally found that higher household incomes were associated with a more diversified diet in Malawi as measured by the number of different food items consumed.

Our findings suggest that micronutrient demand, especially by women and higher-income classes, in addition to necessary policies, may reduce malnutrition. Those critically in need of such micronutrients are less willing to pay for them mainly because of financial constraints and a lack of information on short and long-term health benefits. Government intervention may be necessary to achieve the optimal provision of such attributes. This kind of intervention could target those most in need, such as women in low-income classes. However, this approach could be costly for the entire population, mainly when based on income. Indeed, such an intervention would require records of all consumers, ultimately deciding who could benefit from the intervention or who could not. Instead of income targeting, gender targeting seems easier to implement and could have the highest return per dollar invested because pregnant and non-pregnant women of childbearing age are the most in need of iron.

Finally, our study found that consumers are likely to be ethnocentric, especially younger consumers, when purchasing processed food products. This is consistent with findings by Jin et al. (2015) in their study on consumers' preference for a product image representing a country of origin.
One explanation of this result is that with increased globalization and economic development, consumers in low-income countries may have realized that locally produced products are becoming increasingly competitive with imported products. This finding is important for food processors from both high and low-income nations because they have the opportunity to exploit and support the country associated with their companies. Therefore, such research remains of significant relevance to international agribusiness (Chabowski et al., 2013).

This study has some limitations that could be considered in future research. First, the quality issue of alternatives in each choice set is essential for avoiding any problem of price endogeneity. For example, a high price of an alternative could be interpreted as a signal of a higher quality product. Omitting the quality aspect in choice experiments could cause biased and inconsistent estimates, potentially inducing sign flipping of coefficients, especially the price. One solution is to build the quality aspect in the choice experiment by selecting choice sets for which price will be positively correlated with quality. In addition, to better manage the choice experiment, especially in developing countries, it may be helpful to have fewer attributes, and limited levels since the number of choice sets could impose power requirements of a larger sample size to detect effects.

## Acknowledgements

This study was made possible by the support of the American People provided to the Feed the Future Innovation Lab for Collaborative Research on Sorghum and Millet through the United States Agency for International Development (USAID). The contents are the authors' sole responsibility and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by the United States Agency for International Development (USAID) under Cooperative Agreement No. AID-OAA-A-13-00047.
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# Appendix A. Model Selection Test Procedure

In order to test the appropriateness of alternative specifications, we applied the test on non-nested choice models, which was based on the AIC proposed by Ben-Akiva and Swait (1986). The test proceeded as follows. Assume that there are two models (model 1 and model 2). Model 1 explains choices using $K_1$ variables and model 2 explains the same choices using $K_2$ variables. Assume that the models are different either by their functional form or by the number of variables. The fitness measured is defined as follows:

$$ \rho_j^2 = 1 - \frac{L_j - K_j}{L(0)} $$

where $L_j$ is the log (simulated) likelihood for model j = 1,2. The denominator $L(0)$ is the log-likelihood for a model—multinomial logit in our case—with constant only. Under the null hypothesis that model 2 is the true model, its fitness measure will be higher. More importantly, the difference between the two fitness measures of both models is:

$$ Pr(|\rho_2^2 - \rho_1^2 > Z) \leq \Phi(-\sqrt{-2ZL(0)}) $$

where Z is the difference of the fitness measures between models. This probability was calculated for all the pairwise combinations of models 2 and 3 estimated with and without correlation. Using the above definition, we calculated that the probability that model 2 under correlation of the coefficients is incorrectly specified functional form is almost equal to zero, as shown in Table 2.

# Appendix B. Survey Materials

## Appendix B.1. Questions on Choice Experiment

Imagine you are purchasing a package of Dèguè at your local store or market. You can choose between two packages of Dèguè that Package A and B. Package A has a date of expiration on it so that you know if it is expired or not. Package B did not have this information that is you do not know if it is expired or not. In addition, on package A has a Family picture on while package B does not.
Package A has zero percent of iron daily requirement in that it does not contain iron micronutrient to satisfy your body daily requirement. By contrast, Package B contains iron that represents 75% (three quarters) of your body daily requirement. But to have Package A you must pay 550FCFA and to have package B you must pay 450. If you choose package A, check the corresponding box; if you decide to choose package B check its corresponding Box. If you do not prefer package A nor B then check the box corresponding to package C.

## Appendix B.2. Information on Micronutrient (Iron)

Iron is important and effects red cell production in quantity and quality, the productivity that comes from having good quality blood, the relationship of a healthy pregnant woman to have a healthy baby, role in the prevention of anemia allows the brain to function properly, allows a good growth and learning skills of the child, prevent certain diseases of the liver and pancreas, etc. Without enough iron, your body can't produce enough of a substance in red blood cells that enables them to carry oxygen. As a result, iron deficiency anemia may leave you with the following symptoms: extreme fatigue, weakness, pale skin, chest pain, fast heartbeat or shortness of breath, cold hands and feet, headache, dizziness or light-headedness, poor appetite, especially in infants and children with iron deficiency anemia. One of the causes of a lack of iron is in your diet. Your body regularly gets iron from the foods you eat. If you consume too little iron, your body can become iron deficient over time. Examples of iron-rich foods include meat, eggs, leafy green vegetables, and iron-fortified foods. For proper growth and development, infants and children also need iron from their diets. Women, infants, and children are at greater risk of iron deficiency anemia. Infants, especially those who were low birth weight or born prematurely, who don't get enough iron from breast milk or formula, may be at risk of iron deficiency. Children need extra iron during growth spurts. If your child isn't eating a healthy, varied diet, he or she may be at risk of anemia.
Appendix B.3. Cheap Talk Script

We are here today to conduct a study on your preferences for millet products. In particular, we want to know how you will react if we give you information on the Dèguè. We will give you information about two packets of Dèguè and ask you what you will choose. Your answers will be anonymous. At the end of the study, you will be paid an amount of 2000 FCFA for your time.

In addition, the information we gather from this study will be very useful in designing an appropriate labeling and promotion strategy that will improve the sales of women producing this product and also the nutrition of the population. Therefore, it is important that you understand the experience and think carefully before making your answers. In a recent study, several different groups of people were asked whether they would purchase a new food product similar to the one you are about to be asked about. This purchase was hypothetical for these people, as it will be for you. No one actually had to pay money when they indicated a particular preference. The results of this study were that over 80% of people said they would buy the new food. However, when a store actually put the same new food on their shelf, but where payment was real and people really did have to pay money if they decided to purchase the new food, the results were that only 43% of people actually bought the new food. That's quite a difference, isn't it? We call this "hypothetical bias." Hypothetical bias is the difference that we continually see in the way people respond to hypothetical purchase questions as compared to real situations.

Why this is so? In my opinion when you go to the market and wants to buy a product, you have a limited budget to make purchases for you or your household. You really think twice before spending your money, so you don't waste your money buying something that you or your household doesn't need. This is what is happening in the real life. But in the hypothetical life you may think that you really don't lose money or since you don't have to eat the product you are more inclined to make a decision that may not represents what you will do in real market. So, if I were in your place, I would ask myself: if I was really shopping at the local store or market and I had X FCFA to purchase a package of Dèguè, do I really want to spend X FCFA to have package A or Y to have package B? If the answer is yes in either of the cases, check the corresponding box and left blank the other cases. If the answer is no in both of the cases,
check the last box. In any case, I ask you to respond to each of the following purchase questions just exactly as you would if you were really in a local store or market and were going to face the consequences of your decision: which is to pay money if you decide to buy a food.

## Appendix C. Estimation outputs

<table><caption>**Table C.1.**
Cholesky Matrix from MSL Estimates in Preference Space</caption><thead><tr><th></th><th>Expiration Date</th><th>Micronutrient</th><th>Origin</th><th>Family Image</th></tr></thead><tbody><tr><td>Expiration Date</td><td>0.811***<br>(0.139)</td><td></td><td></td><td></td></tr><tr><td>Micronutrient (25 % of DR)</td><td>-0.595***<br>(0.158)</td><td>0.207**<br>(0.0844)</td><td></td><td></td></tr><tr><td>Origin</td><td>-0.0457<br>(0.213)</td><td>0.0904<br>(0.153)</td><td>0.196<br>(0.188)</td><td></td></tr><tr><td>Family Image</td><td>0.178<br>(0.654)</td><td>0.240<br>(0.149)</td><td>-0.973***<br>(0.192)</td><td>-0.105<br>(0.534)</td></tr></tbody></table>

Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1, with p the p-value.

<table><caption>**Table C.2.**
Cholesky Matrix from MSL Estimates in WTP Space</caption><thead><tr><th>Parameter</th><th>ln(λn)</th><th>Expiration Date</th><th>Micronutrient</th><th>Origin</th><th>Family Image</th></tr></thead><tbody><tr><td>ln(λn)</td><td>-116.5***<br>(19.95)</td><td></td><td></td><td></td><td></td></tr><tr><td>Expiration Date</td><td>41.38*<br>(21.69)</td><td>-33.23***<br>(11.56)</td><td></td><td></td><td></td></tr><tr><td>Micronutrient (25 % of DR)</td><td>3.163<br>(27.68)</td><td>0.438***<br>(0.105)</td><td>-25.73<br>(21.30)</td><td></td><td></td></tr><tr><td>Origin</td><td>1.407<br>(16.65)</td><td>-82.36***<br>(31.01)</td><td>0.291**<br>(0.134)</td><td>40.46***<br>(10.92)</td><td></td></tr><tr><td>Family Image</td><td>-85.62***<br>(30.37)</td><td>0.128<br>(0.131)</td><td>-42.21<br>(46.82)</td><td>0.0163<br>(0.178)</td><td>0.0518<br>(0.182)</td></tr></tbody></table>

Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1
# Appendix D. Model robustness check using out of sample prediction rate

<table><caption>Table D.1. Models' Out-of-Sample Performance</caption><thead><tr><th rowspan="2"></th><th colspan="2">Without correlation</th><th colspan="2">With Correlation</th></tr><tr><th>Preference space</th><th>WTP space</th><th>Preference space</th><th>WTP space</th></tr></thead><tbody><tr><th>Out of sample prediction success rate</th><td>73.8%</td><td>73.8%</td><td>74.1%</td><td>74.1%</td></tr><tr><th>Chi2</th><td>146.27</td><td>146.27</td><td>150.00</td><td>146.27</td></tr><tr><th>p-value</th><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr></tbody></table>

## ENDNOTES

[1] In some countries, “Dèguè” comprises agglomerated cereal and dairy products such as yogurt or sour milk. Our study focuses on the cereal product and none of the other co-products.

[2] The exchange rate of USD 1 in 2017 was 550 FCFA.
PART II

ADVANCED PHENOTYPING
AND CROP MODELLING FOR
ADAPTATION TO DRYLANDS
<figure><img src="image_43.png" /></figure>
# 7. UAV Method Based on Multispectral Imaging for Field Phenotyping

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

**Adama Ndour**, ICRISAT, Mali

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

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

**Delphine Luquet**, CIRAD, France

**Gregory Beurrier**, CIRAD, France

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

## Abstract

In many countries, particularly in West Africa, there is a strong social demand for increased cereal production. Responding to this demand involves the improvement of cereal varieties. Modern varietal breeding programs in the sub-region need to establish the relationship between plant genotype and phenotype to select high-yielding stress-tolerant plants and to enhance agricultural production. However, in most cases, accurate phenotyping of large mapping populations is a limiting factor. The Regional Study Centre for the Improvement of Drought Adaptation (CERAAS) has developed a robust drone-based data collection and spatial modelling process to better measure cereal crops' traits for the benefit of plant breeding programs. Herein, we report an unmanned aerial vehicle (UAV) driven crop characteristics analysis throughout the crop cycle. We present a fully automatic pipeline based on a multispectral imaging system for the indirect measurement of agronomic and phenological characters of crops in agricultural field trials. The pipeline is made up of different stages including
image acquisition, georeferencing, generation of orthoimages, creation of masks to delimit individual plots, and calculation of proxies. The incorporation of the UAV into agricultural field experiments has the potential to fast-track the genetic improvement of adaptation to drought.

**Keywords:** UAV, multispectral, field phenotyping, sorghum

**Introduction**

If the world's population and food demand continue to grow, food production will need to increase 60% by 2050. It is urgent to develop new strategies to feed future generations. Over the last few decades, many breeding programs have focused on the improvement of major traits for crop varieties such as yield, disease resistance, and resistance to other environmental constraints (Cuenca et al., 2013). Nowadays, breeding methodologies employ innovative digital tools such as artificial intelligence, bioinformatics, genomics, and statistical advances to enable the speedy creation of cultivars (Vardi et al., 2008). A fundamental condition to new breeding methods such as genomic selection is the development of a training population with an exceedingly high genetic diversity (Aleza et al., 2012). Therefore, carrying out large-scale plant phenotyping experiments is critical; the fast and precise collection of phenotypic data is especially important to explore the association between genotypic and phenotypic information.

In Africa, sorghum is the second major staple cereal and constitutes the only viable food for the most food-insecure populations of the world (Hariprasanna & Rakshit, 2016). However, its genetic improvement relies mostly on manual phenotyping. Traditional phenotyping techniques are often expensive, labor-intensive, and time-consuming (Cruz et al., 2017; Luvisi et al., 2016). Using unmanned aerial vehicles (UAV) equipped with sensors has recently been considered as a cost-effective alternative tool for rapid, accurate, non-destructive, and noninvasive high-throughput phenotyping (Pajares, 2015). However, the measurements of plant traits using UAVs are carried out through vegetation indices obtained by image processing. Many studies demonstrated the efficient use of UAV to monitor plant biomass (Lussem et al., 2019), crop health status, nitrogen content,
plant water need estimates (Romero et al., 2018), or even to help in the
detection of plant diseases (Abdulridha et al., 2018). Unlike satellites, UAVs
represent a relatively low-cost method for image acquisition with high
resolution and they are increasingly used for agricultural applications. Hunt
et al. (2010) established a good correlation between leaf area index (LAI) and
normalized difference vegetation index (NDVI) by using UAV multispectral
imaging for crop monitoring. Ribera et al. (2018) deployed UAV trichromatic
imagery to count the number of leaves in sorghum. Nebiker et al. (2008)
reported the successful application of UAV imagery to evaluate grapevine
crop health.

The use of high-performance sensors for plant imaging has resulted in
the generation of enormous amounts of image data that required processing
to extract useful information. Here, we present our full image processing
pipeline to store, preprocess, and analyze sorghum UAV images in a holistic
way to extract the spectral indices that correlate the most with structural
and physiological variables measured. The pipeline provides valuable
information about key priority traits for breeding programs, and it can be
used as a decision support tool.

UAV Image Data Acquisition

For this study, images were collected with a hexacopter UAV (FeHexaCopterV2, MikroKopter, Germany) at an altitude of 50 m and a constant speed of 4.5 m.s⁻¹. This UAV can fly by either remote control or autonomously with Global Positioning Systems. The UAV's support software (MikroKopter tools, MikroKopter, Germany) implements a flight plan, monitors the flight, and allows information such as drone position. An Airphen multispectral camera (Hyphen, France) with 6 spectral bands (blue = 450 nm, green = 532 nm, green-edge = 568 nm; red = 675 nm; red-edge = 730 nm; NIR = 850 nm) combined with a thermal infrared camera (Flir Ltd, USA) was used. In addition, a RGB SONY ILCE-6000 digital camera (Sony, Japan) with a 6000 x 4000-pixel sensor equipped with a lens of 60 mm focal length was used. To reduce the effects of ambient light conditions, we limited data capturing missions to clear and cloudless days.

Both the RGB and the Airphen multispectral cameras acquired images
continuously at 1 Hz frequency. The Hexacopter tools were used to design the flight plan so that it covered all the area and ensured 80% of overlapping both across and along the track. We used a 2.5 m² carpet reference panel placed horizontally on the ground at 1.5 times the height of the closest plants, as recommended by Ahmad et al. (2021). Besides, 6 circular panels of 50 cm diameter were placed in the 4 corners of the field as ground control points (GCPs) (Kääb et al., 2014). The exact positions of these GCPs were defined with GPS GNSS (Global Navigation Satellite System) equipment, providing an accuracy of 2 cm.

Regarding phenotyping in RGB, we calculated proxies from the literature such as the Brightness Index, the Soil Color Index, etc. These indices were shown to have a positive correlation with measured traits.

<figure><img src="image_44.png" /><figcaption>Figure 1 - Field-based UAV Aerial Phenotyping Platform and Methodology Used to Extract the Multispectral Image</figcaption></figure>

Note. MS is the multispectral camera, LWIR is the thermal camera, and RGB is the red-green-blue camera. D1 to D6 are the flight dates.

Flight dates are optimized according to the traits of the measured plants. Some traits cannot be measured at certain stages of plant development. For example, if the leaf area is 2 cm, the determination of biomass becomes extremely difficult. Thus, the flight date D1 is used to create a Digital Elevation Model (DEM), which is the field's reference height (h0). Flight dates D2-D3-D4 are used to measure a plant's agro-morphological traits, such as biomass, Leaf Area Index (LAI), and plant height. Flight dates D5-D6 are
used to assess varieties' performances at the maturity stage (yield estimation and panicle number). Specific flights are carried out for the characterization of stresses including water, nitrogen, and thermal stresses. The thermal imaging camera is used more during stress characterization to calculate temperature distribution according to cereal varieties.

After the flights, images were uploaded to Agisoft software (Agisoft LLC, St. Petersburg, Russia) to create a [geo-referenced multi-layer orthoimage of the flight for each date](#). A subsample of microplots was designed in both sites and georeferenced using FieldImageR package (Matias et al., 2020). The plot-level reflectance data and vegetation indices were calculated using R (Hijmans & van Etten, 2016). The entire process of spectral index extraction is fully automated, and the outputs are directly obtained in a CSV file.

# Workflow of the Image Processing Pipeline

## 1. Generation of the Orthomosaic Image

All the image datasets collected from every flying date for both cameras were processed separately to generate mosaics of the entire plantation. The RGB imagery was assembled using Agisoft PhotoScan software fully automated scripting API by applying three consecutive phases of superimposed image alignment, field geometry construction, orthoimage, point cloud, and dense surface model (DSM) generation using structure-from-motion algorithms. The final ortho-product is a three-band orthomosaic. Multispectral images were assembled using Agisoft PhotoScan and the multi-band imaging plugin Airphen. The final product was a six-band orthomosaic and a DSM.

## 2. Radiometric Calibration

Depending on the lighting conditions, sensor configuration, sun position, and measurement angle, the luminance measured by the multispectral sensor occasionally differed from the energy reflected by the crop due to radiometric distortions. To ensure radiometric consistency between the different drone images, radiometric distortions and inconsistencies were
accurately processed for subsequent analysis of the images. Radiometric correction consists of converting a digital number of multispectral images into reflectance by absolute or relative calibration (Liang, 2008). In this pipeline, we calibrated the reflectance using a reference surface: the carpet located on the ground at a distance from the plot which was imaged at each flight, and the radiometric calibration tool (Agisoft PhotoScan and the Airphen plugin) which used known reflectance indices of the carpet from laboratory measurements.

### 3. Geometric Correction

Due to the drone's speed, altitude, and the angle of sensor view, geometric distortions are possible. As a result, the pixels recorded in the different images might not project onto the same geographical grid due to these distortions. Thus, corrections must be made to increase the spatial coincidence between the images. Firstly, we carried out geometric correction through multiband co-registration to modify and adjust the image coordinate system to decrease geometric distortions and make pixels in different pictures coincide to the similar map grid points. The co-registration process is simply based on internal GPS from raw image metadata. Ortho-rectification was then completed using the GCPs to increase the accuracy of the generated orthomosaic.

### 4. Extraction of Spectral Vegetation Indices

Automated scripts were developed to load RGB and MS orthoimages. Then we used RGB orthomosaic to segment and separate sorghum plants from the soil background by converting mosaics from RGB to HSV colour space and by performing thresholding operations over green pixels to create a sorghum mask. Escadafal's (1993) modified HUE index was used for effective soil masking of both MS and RGB images. Figure 2 illustrates the output of the soil masking operation. This process is important for reducing bias since the spectral signature of soil mixed with vegetation layers tend to introduce strong outliers.
<figure><img src="image_45.png" /><figcaption>Figure 2</figcaption></figure>

Figure 2 – Image Obtained After Using the Soil Masking Technique to Extract Both Soil and Vegetation Layers with the Traditional HUE Index Thresholding

We extracted calibrated reflectance in red, green, and NIR bands using that mask raster. Modified scripts from RSToolbox (Leutner et al., 2017) and FieldImageR (Matias et al., 2020) libraries were used to derive the following well-known spectral indices for crop physiology and biomass monitoring: NDVI, GNDVI, MSAVI2, RVI, CTVI, and NDWI (Table 1). In total, automated extraction of 15 proxies with 7 spectral bands from drone imagery was operated. In addition, the GPS coordinates of each plot were extracted using the QGIS geographic information system software (Menke et al., 2016) and exported as spatial vector data. The extraction of the average values of each
vegetation index was performed according to the GPS coordinates extracted on QGIS using the features of the sf and raster packages.

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

Popular Vegetation Indices are Used in Drone Phenotyping with the RGB Camera.</caption><thead><tr><td>Vegetation index</td><td>Formula</td></tr></thead><tbody><tr><td>Normalized Ratio Vegetation Index</td><td>NRVI = <sup>R/N-1</sup>&frasl;<sub>R/N+1</sub></td></tr><tr><td>Normalized Difference Water Index</td><td>NDWI = <sup>G-N</sup>&frasl;<sub>G+N</sub></td></tr><tr><td>Ratio Vegetation Index</td><td>RVI = <sup>R</sup>&frasl;<sub>N</sub></td></tr><tr><td>Green Leaf Index</td><td>GLI = <sup>2R-G-B</sup>&frasl;<sub>2R+G+B</sub></td></tr><tr><td>Green Normalized Difference Vegetation Index</td><td>GNDVI = <sup>N-G</sup>&frasl;<sub>N+G</sub></td></tr><tr><td>Normalized Difference Vegetation Index</td><td>NDVI = <sup>N-R</sup>&frasl;<sub>N+R</sub></td></tr><tr><td>Visible Atmospherically Resistant Index</td><td>VARI = <sup>G-R</sup>&frasl;<sub>G+R-B</sub></td></tr><tr><td>Soil Color Index</td><td>SCI = <sup>R-G</sup>&frasl;<sub>R+G</sub></td></tr><tr><td>Brightness Index</td><td>BI = <sup>&radic;<span style="text-decoration: overline;">R<sup>2</sup>+G<sup>2</sup>+B<sup>2</sup></span></sup>&frasl;<sub>3</sub></td></tr><tr><td>Spectral Slope Saturation Index</td><td>SI = <sup>R-B</sup>&frasl;<sub>R+B</sub></td></tr><tr><td>Overall Hue Index</td><td>HUE = <sup>arctan(2(B-G-R))</sup>&frasl;<sub>30.5(G-R)</sub></td></tr><tr><td>Difference Vegetation Index</td><td>DIV = s * (N - R)</td></tr><tr><td>Corrected Transformed Vegetation Index</td><td>CTVI = <sup>NDVI+0.5</sup>&frasl;<sub>&radic;(NDVI+0.5)</sub></td></tr></tbody></table>

Figure 3 shows different combinations of orthomosaic bands from the multispectral sensor on field trials of water-stressed and irrigated plots. For each combination, important information can be deduced as parts of the test subjected to water stress or experimental units that are less developed. Figure 3(c) is an overlay of the shapefile of the experimental units with the generated orthomosaic. On each experimental unit, vegetation indices were calculated and further analysis on the spatial modelling on crop characteristics was conducted.
<figure><img src="image_46.png" /><figcaption>Figure 3</figcaption></figure>

Figure 3 - Outputs of an RGB Image Showing Spatial Variation Based on Some Vegetation Indices (VIs) on Soil-free Orthomosaic Data

Note. 3(a) is a True-Color map, while 3(d) is a false-color. 3(c) shows the shapefile generated from each variety.

## 5. Regression Analysis

Two approaches have been developed in our image processing and analysis pipeline. The first approach uses statistical modelling and machine learning regression to link the agronomic traits to vegetation indices, especially the NDVI. Nevertheless, more than 15 other vegetation indices have been determined including the Soil Color Index (CSI), the Simple Ratio Vegetation Index (SRI), the Green Normalized Difference Vegetation Index (GNDVI), and the Modified Soil Adjusted Vegetation Index (MSAVI). The leaf area index calibrated with data from previous sorghum tests with measurements of NDVI derived from the drone images was estimated according to the statistical model proposed by Gano et al. (2021). An exponential regression law with a coefficient variation of 0.92 was used to estimate the LAI.

Figure 4 illustrates the spatial-temporal evolution of biomass, NDVI, and LAI of plants grown under water stress and non-stress conditions. This time series plot allowed us to make a rapid survey of how crops are sensitive to stress conditions. It appears from the figure that biomass, as well as LAI, shows a similar trend as the NDVI. This correlation is also noted for other vegetation indices such as GNDVI. Most importantly, water stress decreased the biomass of all the three varieties tested. However, the magnitude of this
decrease was not homogeneous across varieties. For example, the variety V3 stands better water stress.

<figure><img src="image_47.png" /><figcaption>Figure 4</figcaption></figure>

Figure 4 – Time Series Analysis of Plant Traits (biomass, NDVI, LAI) in 3 Sorghum Varieties (V1-V3) at Different Dates of Crop Cycle

In this report, the regression models developed showed an excellent correlation between LAI and vegetation indices such as NDVI, CTVI, and GNDVI (0.76 < R² < 0.96). Figure 5 illustrates regression analysis and indirect estimation of the LAI. The logarithmic transformation of the LAI shows a linear correlation between the estimated vegetation indices. These indices also have a strong linear dependence of the order of 0.99. From a modeling point of view using one of the indices would give the same result in terms of LAI prediction.
<figure><img src="image_48.png" /><figcaption>Figure 5</figcaption></figure>

Figure 5 - Regression Analysis of Linear Dependency Between Drone Vegetation Index & in Situ LAI

For the second approach, we used a densely connected neural network to estimate the LAI based on drone vegetation indices and the RGB bands. Our network consisted of four hidden layers with the succession of two drop-out and batch normalization layers. The hidden layers consisted of 128, 64, 128, and 11 units respectively. The drop-out rate was 40% (first layer) and 30% (second layer). We used the Mean Absolute Error as metrics and the Mean Standard Error as loss. For the optimizer, the Gradient Descent with the Root Mean Squared Propagation was used. Deep learning with the above-mentioned vegetation indices showed a better linear relationship with an error of 2% and a coefficient of determination of 0.99. However, deep learning with RGB optical bands produced a 10% error with an R² of 0.7. Figure 6 shows the deep learning regression model for LAI estimation with the deep learning model architecture and the output of the loss function for two different regression models (drone RGB and multispectral).
<figure><img src="image_49.png" /><figcaption>Figure 6</figcaption></figure>

Figure 6 - A Densely Connected Neural Network for LAI Estimation

The use of statistical regression or deep learning approaches depends on the volume of field data and the experimental data acquisition protocol. However, statistical regression models are more likely to produce bias and prediction errors compared to deep learning models. It is important to note that for large-scale phenotyping, it is much easier to implement statistical models.

Conclusion

In this study, we evaluated the use of multispectral UAV imagery coupled
with a fully automated image processing pipeline for the phenotyping of
cereal crops. To optimize the computation, we developed 6 proxies from
the RGB camera and around 10 other proxies for the multispectral camera.
The generation of the shapefile from the experiments is now simplified and
allows an easier extraction of the vegetation indices. However, due to the
high resolution of the images, the computation time is still long with the
processors at our disposal. With this process, we were able to accurately
estimate agro-morphological traits using machine learning regression or
deep learning architecture.

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8. Agro-physiological Responses
of 10 West Africa Sorghum
Varieties to Early Water Deficit
Assessed by UAV and Ground
Phenotyping

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

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

**Bassirou Sine**, ISRA/CERAAS, Senegal

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

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

Abstract

Sorghum is a staple food for many in the Sahel. However, it often faces early-
stage water deficit resulting in production decrease. Research is focusing on
developing early drought tolerant varieties. This study assessed the effects of
early drought stress on 10 elite varieties of West African sorghum collection
tested over 2 years (2018-2019) in Bambey (Senegal). Water stress was applied
by withholding irrigation 25 days after sowing for one month, followed by
optimal irrigation until maturity. Soil moisture and agro-physio-
morphological traits were monitored. Results showed highly significant
effects of early drought stress on sorghum plants growth. The combined
analysis of variance revealed highly significant differences (P ≤ 0.01) between
varieties in the different environments for most traits studied. Under water
deficit, the genotypic adaptation was linked to the capacity of varieties
to increase the dead leaves weight and the roots length density and to
reduce photosynthesis rate, stomata conductance, and leaf transpiration. The analysis of spectral indices across water treatments revealed significant variation. However, the differential responses between varieties remained the same. Fadda (V1), Nieleni (V2), Soumba (V8) and 621B (V9) showed promising behavior under drought stress and could be suitable for further use in West Africa.

Keywords: early drought tolerance; genetic variability; root adaptation; sorghum

Introduction

Plant adaptation to water deficit is one of the key factors that will determine the severity of climate change on food production, because in the next decade, water availability will be greatly affected by climate change (Molden, 2007). Currently, agriculture uses 75% of the world's total water consumption and that is likely to increase (Molden, 2007). Food production increases at only 1.2% for the four key global crops—maize, rice, wheat, and soybean—and future demands will cause it to double (Ray et al., 2013). This is also valid for sorghum (Sorghum bicolor (L.) Moench), which is a staple food for millions of people in arid and semiarid tropical regions (Agrama et al., 2003). Its production in Africa has been estimated at 29.83 million tons from 30.54 million hectares of land (FAOSTAT, 2019).

However, the production remains unstable despite cultural practices farmers implemented to adapt and cope with the growing needs and population rise (Chaléard, 2010; Sanchez, 2002). Indeed, the latter has caused strong pressure on the arable land leading to soil depletion.

Actions to promote food security in the Sudano-Sahelian zones include the promotion of local cereal production through the identification of high-performance varieties and the breeding of new varieties. In Senegal, sorghum is one of the most important local cereals grown in the various agro-ecological zones of the country. However, climate change has increased the risk of rainfall shortage during July—the beginning of the growing season (Salack et al., 2012). This could be the cause of the low production (291.171 tons in 2019) (DAPSA, 2019). Early-stage water deficit is experimented with in many research studies by withholding water supply at
the beginning of the crop growth cycle, about 3 weeks after germination, for 3 to 4 weeks (Debieu et al., 2018; Qazi et al., 2014; Zegada-Lizarazu & Monti, 2013). This causes a decrease in leaf appearance, transpiration, and photosynthesis through leaf senescence (Craufurd & Peacock, 1993; Dwivedi et al., 2008; Tari et al., 2013). The combined effects of drought and rewatering are still not well-known, especially regarding sorghum. The ability to recover from early water deficit during rewatering can explain in some cases the yield difference between varieties at harvest (Zegada-Lizarazu et al., 2013). The drought recovery index (DRI) represents rewatering-induced recovery of the growth traits related to biomass, height, etc., and would be suitable for investigating drought tolerance. According to Perrier et al., (2017), recovery capacity is an important trait for future phenotyping, genetic and breeding studies while its process and genotypic variability are poorly understood. Late maturity genotypes appeared to be more tolerant to early drought because the stress occurs before the panicle initiation phase, when plants have more ability to recover after rewatering. However, these genotypes no longer suit the farming systems of semiarid regions because of the shortened rainy season. Thus, the solution would be to identify improved short-cycle varieties capable of adapting to early season water stress. This requires a characterization of sorghum behavior under early season water stress and the identification of relevant selection criteria to facilitate the decision on the choice of varieties for cultivation and breeding programs.

Root response is of prime importance to crop productivity under drought stress. This is because the root size, architecture, and distribution determine the ability of plants to access and take up the water for proper physiological functioning of shoots (Henry, 2013; Taiwo et al., 2020). Sorghum's ability to adapt to water shortage is also due to its root system, which can extend into the deep soil layers. The investigation of this trait through the measurement of root length density (RLD) trait revealed useful (Masi & Maranville, 1998). Researchers have been working to identify specific root traits targeted for plant improvement under drought and limited nutrients conditions (Comas et al., 2013; Girma et al., 2020; Lynch et al., 2014). Recently, greater focus was given to root system architecture, especially the RLD distribution in the soil, which is a key factor for water and nutrient uptake (Chopart et al., 2008; Gregory, 2006). However, field assessment of RLD is not obvious. Among other methods, the trench profile method for mapping root intersection in a soil profile was identified as more efficient and feasible to provide
information on roots distribution (Böhm, 1976; Chopart et al., 2008; Tardieu,
1988).

Another method investigated in this study is the use of vegetation indices
(VIs) calculated from Unmanned Aerial Vehicle (UAV) multispectral images
to phenotype sorghum varieties under drought. Previous findings have
promoted VIs related to normalized difference vegetation index (NDVI) as
an important multi spectral index to track the agro-physiological dynamics
of key traits such as biomass, leaf area index (LAI), yield, etc. (Foster et al.,
2017; Magney et al., 2016; Samborski et al., 2015). However, breeders are not
aware and have less understanding about the application of the different
reflectance bands ratio on monitoring crops development and adaptation.
Higher level of NDVI is associated with faster growth rate, higher biomass
accumulation during the vegetative stage, and a longer grain filling period by
delaying leaf senescence during the ripening phase thereby increasing yield
(Babar et al., 2006).

The objectives of this study were to investigate the effects of early water
deficit in sorghum to determine the main adaptation mechanisms and
simultaneously highlight an interesting method and criteria for the agro-
physiological characterization of sorghum in water deficit conditions.

Material and Methods

1. Plant Material

The plant material used in this study consisted of 10 elite varieties of
sorghum from Senegal, Mali, and Nigeria (West Africa) (see Table 1). They
differ in terms of phenology (from 90 days to 128 days cycle duration), plant
architecture (120 cm to 450 cm height), response to inputs (hybrid vs. open
pollinated varieties caudatum or guinea), and yield (2 t/ha to 4.5 t/ha)
(Dembele et al., 2020). These varieties are widely cultivated by the farmers
because of their adaptability and agronomic characteristics (Dembele et al.,
2021; Gano et al., 2021; Ndiaye et al., 2018; Ndiaye et al., 2019).
<table><caption>Table 1
Characteristics of the Varieties Studied</caption><thead><tr><th>Variety</th><th>Code</th><th>Type</th><th>Cycle (days)</th><th>Height (cm)</th><th>Potential yield (t/ha)</th><th>Panicle form</th><th>Photoperiod-sensitivity</th><th>Isohyet (mm)</th><th>Origin</th></tr></thead><tbody><tr><td>Fadda</td><td>V1</td><td>Guinea (hybrid)</td><td>128</td><td>200-300</td><td>4.5</td><td>noncompact</td><td>mean</td><td>700-1000</td><td>Mali</td></tr><tr><td>NIELENI</td><td>V2</td><td>Guinea (hybrid)</td><td>115</td><td>300</td><td>4</td><td>semicompact</td><td>low</td><td>700-800</td><td>Mali</td></tr><tr><td>IS15401</td><td>V3</td><td>Guinea-Caudatum</td><td>115</td><td>400-450</td><td>2</td><td>semicompact</td><td>high</td><td>900-1200</td><td>Mali</td></tr><tr><td>PABLO</td><td>V4</td><td>Guinea (hybrid)</td><td>125</td><td>400</td><td>4</td><td>noncompact</td><td>mean</td><td>700-1000</td><td>Mali</td></tr><tr><td>CSM63E</td><td>V5</td><td>Guinea</td><td>90</td><td>400</td><td>2</td><td>noncompact</td><td>low</td><td>600-1000</td><td>Mali</td></tr><tr><td>SK5912</td><td>V6</td><td>Caudatum</td><td>170</td><td>200</td><td>2.5-3.5</td><td>semicompact</td><td>high</td><td>700-900</td><td>Nigeria</td></tr><tr><td>GRINKAN</td><td>V7</td><td>Caudatum</td><td>90</td><td>120</td><td>4</td><td>semicompact</td><td>non</td><td>500-800</td><td>Mali</td></tr><tr><td>SOUIMBA</td><td>V8</td><td>Caudatum</td><td>115</td><td>250</td><td>2.5</td><td>semicompact</td><td>low</td><td>600-1000</td><td>Mali</td></tr><tr><td>621B</td><td>V9</td><td>Caudatum</td><td>105</td><td>175</td><td>2.5-3</td><td>semicompact</td><td>non</td><td>600-900</td><td>Senegal</td></tr><tr><td>F2-20</td><td>V10</td><td>Caudatum</td><td>110</td><td>210</td><td>3-5.3</td><td>semicompact</td><td>low</td><td>600-900</td><td>Senegal</td></tr></tbody></table>
## 2. Methods

### 2.1 Trial Conditions

Trials were conducted at Bambey (14°42'N; 16°28'W) (Senegal) in the Centre National de la Recherche Agronomique (CNRA) on a sandy soil (sand = 94.2%, silt = 3.5%, clay = 2.3%) with a previous cowpea crop. Three trials were carried out between 2018 and 2019 during the dry seasons, which allowed control of the water supply by irrigation. The experimental design is a randomized complete block design, with irrigation as the main factor, and varieties randomized in three replications in each main block. The two water treatments (well-watered [ww] and drought stress [ds]) were placed 10 m apart to avoid involuntary irrigation. A sprinkler method of water supply was provided twice weekly with 25 mm per irrigation (50 mm per week) until physiological maturity. The amount of irrigated water was calculated to cover the weekly average evapotranspiration of sorghum that varied between 25 mm to 37 mm (see Figure 1D). One irrigation was applied before and after sowing to promote good seedling emergence. Fertilizers were applied after sowing and at 21 days after sowing (DAS) as NPK and urea respectively following standard recommendation (i.e., 150 kg ha⁻¹ of NPK [15-15-15] and 100 kg ha⁻¹ of urea). Water stress was applied by withholding irrigation in the drought stress environment for 1 month from the 25th DAS.

### 2.2 Weather Conditions

Figure 1 shows the climate conditions (vapor pressure deficit [VPD], solar radiation, relative humidity, and temperature) during both experiments. There was a high evaporative demand during these dry seasons as shown by the VPD data that reached 5 KPa during the day. The dry season is characterized by high temperature (above 30°C) and high solar radiation (800 w.m⁻²) during the day. These climate conditions are representative of the dry season, allowing control over water supply.
<figure><img src="image_50.png" /><figcaption>Figure 1</figcaption></figure>

Figure 1 - Climate Conditions During the Experiments in Bambe y in 2018 and 2019.

Notes. (A) Daily averages of vapor pressure deficit (VPD) and solar radiation; (B) Temperature and relative humidity; (C) Minimum and maximum VPD; (D) Potential evapotranspiration. Although climate conditions were measured in both years, only representative annual graphics were presented. (A) and (B) were recorded during 2018 experiment and (C) and (D) during the 2019 experiment.

2.3 *Monitoring Water Stress*

Water stress in the field was monitored by measuring the volumetric soil moisture (Diviner 2000, Sentek Pty Ltd) once a week during the irrigated period and twice a week during the stress period to assess the fraction of transpirable soil water (FTSW) (Debieu et al., 2018; Sinclair & Ludlow, 1986). Diviner probe tubes of 1.6 m length were used to record water stock at every 10 cm of depth. In this work, a total of 1.6 m depth water stock was considered to assess the FTSW as this depth represents the sorghum's root activity zone. Soil moisture measurements allowed us to follow the level of stress down to FTSW = 0.3 at the end of the stress period. The average of the three most contrasted varieties was used to evaluate the general trend. We also monitored the predawn leaf water potential once a week using the
pressure chamber (PMS Instrument Co., Corvallis, Oregon, USA) according
to the protocol of Peyrano et al. (1997). The plant's water potential was
measured before sunrise, when there is a balance between plant and soil for
water potential.

## 2.4 Assessment of Agro-physiological Traits

Plants' agro-physiological traits—such as number of leaves appeared (NLA) and plant height after stress (PHS)—were measured on three tagged plants per plot. The photosynthesis rate (Pn), leaf temperature (Tleaf), transpiration (Tr), and stomata conductance (C) were measured on the last ligulate leaf using the CI340 handheld photosynthesis system (CID Bio-science, USA). Biomass production was evaluated by estimating plant dry weight (DWP) and dead leaves weight (DLW) using six plants per plot at seven different dates (before the stress, at the end of the stress, and after recovery [2 weeks after rewatering]). At physiological maturity, grain yield, plant height (PHT) and straw dry biomass (SDW) were measured. The specific leaf area (SLA) was calculated as the ratio between plant leaf area and biomass.

Moreover, other morphological traits were measured weekly during drone
flights on three randomly tagged plants per plot, from crop emergence (25
DAS) to flowering-maturity stage (89 DAS). Seven sampling weeks' data were
used for LAI and biomass. LAI was measured using the SunScan septometer
(Delta-T Devices, Cambridge, England). Nondestructive measurements (LAI)
were performed just before UAV flights. Plants were sampled, dried outdoors
for 2 weeks, and oven-dried for 3 days before biomass measurements were
taken using ad-venturer pro precision balance (OHAUS Corporation, Pine
Brook, New Jersey, NJ, USA).

To better study the recovery performance and classify varieties, we
introduce a new parameter that we have named the DRI. The DRI represents
the relative recovery of the growth traits (NLA, PHS, DWP, Pn) after a freely
defined time of drought stress followed by rewatering. This approach was
inspired by the drought factor index (DFI) used by Strauss et al. (2006) for the
detection of dark chilling tolerance in soybean genotypes and by Oukarroum
et al., (2007) for probing the response of barley cultivars under drought
stress conditions. DRI was calculated by applying the formula:
$$DRI = \log A + 2 \log B \quad (1)$$

in which A is the relative trait measured at the end of the drought period and B is the relative trait measured 2 weeks after rewatering. The relative trait is calculated as trait drought over trait control. The principle of the DRI is that recovery efficiencies should play an important role on the production capacity of some genotypes. Varieties with DRI near to zero have good recovery and varieties with DRI around -1 have bad recovery index (Oukarroum et al., 2007).

## 2.5 Roots Phenotyping

Root measurements were done on two tagged plants per plot. We used the described methods, to count the number of adventitious roots and estimate RLD from intersections between the roots and the face of a soil trench profile (Chopart et al., 2008; Dusserre et al., 2009; Faye et al., 2019). The trench profiles were dug perpendicular to the rows of seedlings and at two distances (20 cm then 10 cm) from the plant stem. Iron grids of 60 cm length by 30 cm width, in relation to the spacing between rows and between plants, were used to count root impacts. Square meshes of 10 cm side length were made inside the grids to facilitate the measurement of the number of impacts. At the end of the stress period, the plants were dug up for additional measurements on the tilling tray, such as the number of adventitious roots.

## 2.6 UAV Data Capture and Image Analyses

Time series flights were done at the altitude of 25 m and a constant speed of 2.2 m.s⁻¹ with the hexacopter drone (FeHexaCopterV2, MikroKopter Company, Moormerland, Germany). Seven UAV weekly flight data, from emergence to maturity, were recorded. Nine grey colored ground control points (GCPs) were uniformly distributed over the entire field area with fixed position for all the flights throughout the experiment. These were surveyed using Precis BX305 Real Time Kinematics (RTK) GNSS unit (Tersus GNSS Inc., Shanghai, China). The GCPs were made of painted PVC disks of 60 cm diameter where the central disk of 40 cm diameter was 20% gray level and the outer disk 60% gray level color. These gray levels were selected to avoid saturation and allow automatic target detection on the images. The
UAV system, equipped with six motors, can perform user-defined waypoint flights with a differential global navigation satellite system (GNSS) receiver. The UAV support software (Mikrokopter tools, Mikrokopter Company, Moormerland, Germany) that implements the flight plan also monitors the flight and records information such as drone position. The flight was performed with an RGB ILCE-6000 digital camera (Sony Corporation, New York, NY, USA) with a 6000×4000-pixel sensor equipped with a lens of 60 mm focal length. To minimize the blurring effect and noise in the images, the camera was set on speed priority (1/1250 sec) and auto ISO mode. Another flight was performed with an Airphen multispectral camera (hi-phen, Avignon, France, https://www.hiphen-plant.com/) equipped with a lens of 8 mm focal length and acquiring 1280×960-pixel images. The Airphen consists of six individual cameras equipped with filters centered on 450 nm, 530 nm, 560 nm, 675 nm, 730 nm, and 850 nm, with a spectral resolution of 10 nm. For each camera (RGB and MS), the flight lasted about 15 minutes with a break of approximately 10 minutes in between to prepare the second flight. The cameras captured images at 1-second intervals and recorded them in JPG and Tiff format on the SD memory card. The drone did round trips spaced by 4 m that allowed a side and forward overlapping fraction of 0.75. To reduce the effects of ambient light conditions, such as plant shadow that can greatly affect spectral measures especially between rows at maturity stages, the flights were limited to clear and cloudless days between 10:00 a.m. and 12:00 a.m. UTC.

An automatic image-processing pipeline was designed to generate radiometrically calibrated and geometrically corrected multiband orthoimages using Agisoft PhotoScan digital photogrammetric software version 1.4.0 (Agisoft LLC, St. Petersburg, Russia, https://www.agisoft.com/downloads/installer) (see Part 2 Chapter 1, Mbaye et al. in this book). Radiometric calibration included automatic correction of vignetting effects (Iqbal et al., 2018). Real reflectances were computed using a reference target positioned to the ground during UAV flights. This target was previously spectrally characterized in controlled conditions. Geometric correction involved firstly, multiband coregistration to modify and adjust the images' coordinate system to decrease geometric distortions and make pixels in different pictures coincide with the corresponding map-grid points. The coregistration process was based upon the internal GNSS from raw image metadata. Orthorectification was then performed using GCPs to increase
the accuracy of the generated orthoimage. As Agisoft Photoscan manages multilayer images, we used the 450 nm band for tie point searching. For a better plots segmentation, we uploaded the RGB orthoimage in QGIS (Geographical Information SYSTEMS, version 3.10.0, QGIS Development Team, open source 2019, https://www.qgis.org/fr/site/forusers/download.html) and designed the plots boundaries. The created shapefile with the GNSS coordinates of each plot was exported as spatial vector data. The extraction of the average values of the varieties' vegetation index in each plot was performed according to the GNSS coordinates of plots, extracted on QGIS and MS orthoimage. The computation was performed using R software (version 3.6.0) libraries (sf, raster, rgdal, RSToolbox and uavRst) (R Core Team, 2020). Four vegetation indices (NDVI, CTVI, MSAVI2 and SR) were used to estimate LAI and biomass during the dry seasons of 2018 and 2019. These vegetation indices are single values computed by grid calculation.

They are invariant to the difference in illumination conditions, slope, seasons, etc. They represent a quick way to distinguish green leaves from other objects and to estimate the relative biomass present on the image, therefore, distinguishing stressed vegetation from nonstressed (Li et al., 2018; Shi et al., 2016; Steven et al., 2015; Zhang et al., 2017).

## 2.7 Statistical Analyses

The raw data were analyzed using R software version 3.6.0 (www.r-project.org). An analysis of variance was performed for each environment (ww and ds) to verify statistical differences between varieties. Subsequently, a combined analysis of variance was performed to test the effects of water stress and years on varieties. The homogeneity between residual variances was tested using Bartlett's test (Bartlett, 1937). RLD was modeled on the basis of measurements of root intersections density (RID) on a vertical perpendicular plane within a sorghum row because this method is most commonly used for studying roots in a soil profile. Relationships between RLD and RID were evaluated taking the slope, the standard error of the slope (SE), the intercept, and the regression ($r^2$) into account. Ordinary least squares linear regression models were applied. Regression models were developed to predict LAI and biomass using vegetation indices. The performance of regression models in estimating LAI and biomass were
evaluated by calculating the root mean squared error (RMSE), the coefficient of determination ($r^2$) and p-values at the probability level of 0.05.

# Results

## 1. Water Stress

During the irrigation period, FTSW and predawn leaf water potential showed a very low variation and revolved around 0.7 and -1.5 bars, respectively (see Figure 2). However, when the plots in the ds treatment were let to dry down, FTSW and predawn leaf water potential decreased progressively and reached 0.3 and -5 bars respectively, showing an effective drought stress experience that occurred between 35 DAS and 55 DAS. Thereafter, after resuming irrigation, these parameters increased again and stabilized around the initial values with a slight drop (0.6 and -2 bars for FTSW and predawn leaf water potential, respectively).
<figure><img src="image_51.png" /><figcaption>Figure 2</figcaption></figure>

Figure 2 – Monitoring of Water Stress Parameters in Well-watered (ww) and Drought Stress (ds) Treatments

Notes. (A) Evolution of soil moisture stock (%) in 2018 and (B) in 2019; (C)
Fraction of transpirable soil water in 2018 and (D) in 2019; (E) Predawn leaf
water potential (bar) in 2018 and (F) in 2019.
## 2. Effects of Early Water Deficit on Growth, Recovery, and Yield

Table 2 presents the effects of water deficit on agro-physiological traits assessed in the experiments. The results showed highly significant differences (P ≤ 0.01) between the different environments for all the characters under study. The early water deficit led to a reduction of leaf appearance (NLA) (-9.18% in 2018 and -6.75% in 2019); PHS (-16.37% and -48.99%); Pn (-12.45% and -27.75%); C (-18.37% and -35.32%); and Tr (-26.37% and -25.92%). After maturity and harvest, we observed a decrease in yield (-22.78% and -28.15%), SDW (-18.25% and -27.79%) and PHT (-15.01% and -23.23%). However, we noticed an increase in the DLW (+43.29% and +15.10%) and Tleaf (+1.29% and +7.64%).

<table><caption>Table 2
Average Performance and Statistical Parameters of Some Agro-, Physio- and Morphological Traits of Sorghum Genotypes Under Well-watered (ww) and Drought Stress (ds) Conditions of 2018 and 2019 Field Trials</caption><thead><tr><th rowspan="2">Traits</th><th colspan="4">Year 2018</th><th colspan="4">Year 2019</th></tr><tr><th>Mean ww</th><th>Mean ds</th><th>ΔWS</th><th>Signif.</th><th>Mean ww</th><th>Mean ds</th><th>ΔWS</th><th>Signif.</th></tr></thead><tbody><tr><td>NLA</td><td>15.33a</td><td>13.92b</td><td>-9.18</td><td>***</td><td>15.30a</td><td>14.26b</td><td>-6.75</td><td>***</td></tr><tr><td>PHS</td><td>143.89a</td><td>120.33b</td><td>-16.4</td><td>***</td><td>130.55a</td><td>66.59b</td><td>-49</td><td>***</td></tr><tr><td>DLW</td><td>13.65b</td><td>19.56a</td><td>43.29</td><td>***</td><td>14.22b</td><td>16.37a</td><td>15.1</td><td>***</td></tr><tr><td>PHT</td><td>174.83a</td><td>148.58b</td><td>-15</td><td>***</td><td>165.75a</td><td>127.24b</td><td>-23.2</td><td>***</td></tr><tr><td>SDW</td><td>453.38a</td><td>370.61b</td><td>-18.3</td><td>***</td><td>427.35a</td><td>308.60b</td><td>-27.8</td><td>***</td></tr><tr><td>Yield</td><td>3271.85a</td><td>2526.21b</td><td>-22.8</td><td>***</td><td>2419.84a</td><td>1738.64b</td><td>-28.2</td><td>***</td></tr><tr><td>Pn</td><td>39.32a</td><td>34.43b</td><td>-12.5</td><td>***</td><td>41.38a</td><td>29.90b</td><td>-27.8</td><td>***</td></tr><tr><td>C</td><td>184.94a</td><td>150.97b</td><td>-18.4</td><td>***</td><td>179.14a</td><td>115.88b</td><td>-35.3</td><td>***</td></tr><tr><td>Tr</td><td>7.43a</td><td>5.47b</td><td>-26.4</td><td>***</td><td>7.04a</td><td>5.22b</td><td>-25.9</td><td>***</td></tr><tr><td>Tleaf</td><td>39.02b</td><td>39.53a</td><td>1.29</td><td>**</td><td>39.16b</td><td>42.15a</td><td>7.64</td><td>***</td></tr></tbody></table>

NLA: number of leaves appeared; PHS: plant height after stress (cm); DLW: dead leaves weight (g); PHT: plant height (cm); SDW: Straw dry weight (g); Yield: grain yield (kg/ha); Pn: photosynthesis rate; C: stomata conductance; Tr: leaf transpiration; Tleaf: leaf temperature; Signif: significance at p:s 0.001 (***); and p:s 0.01 (**); ΔWS: delta water stress (%). For a given trait,
numbers followed by the same letters are not significantly different
between water treatments.

Figure 3 represents the monitoring of C, SLA per plant, plant height, NLA on the main stem. DWP, and Pn of varieties under both conditions (ww and ds). The results showed that the number of leaves on the main stem, plant height, and dry weight per plant gradually increased in a similar way between varieties, but the advent of water stress induced a drop. These results indicated that water stress causes significant reduction in biomass production and shoots growth in sorghum. The SLA of the plant, which reflects the thickness of the leaves, the C, and the (Pn) initially increased to reach their maximum at the 30th DAS, before gradually decreasing until maturity. In the water-stressed environment, the drop in SLA, C, and Pn was greater than in the nonstressed conditions. After the end of the stress period, these traits rebounded but without recovering to the SLA, C, and Pn values of the nonstressed environment. In terms of height and biomass, the varieties have all lost pace despite the high plasticity found in sorghum.
<figure><img src="image_52.png" /><figcaption>Figure 3</figcaption></figure>

Figure 3 - Evolution of Agro-physiological Traits of Sorghum Varieties Under Well-watered (22) and Drought Stress (ds) Conditions

Note. (A) Dry weight per plant, (B) Specific leaf area (SLA) per plant, (C) Plant height, (D) Stomata conductance, (E) Number of leaves, (F) Photosynthesis rate

Sorghum varieties that exhibited the smallest values of DRI had more problems recovering. We noted that varieties had good recovery in the NLA and Pn with a DRI of -0.11 and -0.04 respectively. However, varieties' recovery on PHS and the DWP was more difficult with a DRI of -0.62 and -0.65 respectively (see Table 3). The results indicated a best recovery on plant height and dry weight for the variety V3 and V1 respectively; varieties V4, V5, and V6 revealed the worst recovery on the same traits.
<table><caption>Table 3
Drought Recovery Index of Sorghum Varieties on Some Growth Trait</caption><thead><tr><th colspan="5">Drought Recovery Index (DRI)</th></tr><tr><th>Varieties</th><th>NLA</th><th>PHS</th><th>DWP</th><th>Pn</th></tr></thead><tbody><tr><td>V1</td><td>-0.04</td><td>-0.54</td><td>-0.32</td><td>-0.22</td></tr><tr><td>V2</td><td>-0.1</td><td>-0.43</td><td>-0.6</td><td>-0.24</td></tr><tr><td>V3</td><td>-0.12</td><td>-0.33</td><td>-0.75</td><td>-0.08</td></tr><tr><td>V4</td><td>-0.13</td><td>-0.81</td><td>-0.98</td><td>0.07</td></tr><tr><td>V5</td><td>-0.1</td><td>-0.68</td><td>-0.64</td><td>0.35</td></tr><tr><td>V6</td><td>-0.09</td><td>-0.8</td><td>-0.97</td><td>-0.16</td></tr><tr><td>V7</td><td>-0.13</td><td>-0.55</td><td>-0.93</td><td>-0.05</td></tr><tr><td>V8</td><td>-0.11</td><td>-0.77</td><td>-0.57</td><td>0.12</td></tr><tr><td>V9</td><td>-0.12</td><td>-0.62</td><td>-0.44</td><td>-0.18</td></tr><tr><td>V10</td><td>-0.12</td><td>-0.69</td><td>-0.32</td><td>0</td></tr><tr><td>MEAN</td><td>-0.11</td><td>-0.62</td><td>-0.65</td><td>-0.04</td></tr></tbody></table>

NLA: number of leaves appeared; PHS: plant height after stress; DWP: dry weight per plant; Pn: photosynthesis rate

### 3. Adaptation Responses to Early Water Deficit

The combined analysis of variance revealed highly significant differences (P ≤ 0.05) between varieties; environment; and the interactions between variety (V), environment (E), and year (Y), (V\*E, V\*Y, E\*Y and V\*E\*Y) (see Table 4). Variety V7 showed the smallest number of leaves and PHS; V6 and V3 recorded respectively the highest values for these traits. Varieties V8 and V4 had the highest DLW in both years; V1, V6, and V9 recorded the smallest values. At harvest, variety V5 exhibited the highest plant height and SDW but the lowest grain yield. V7 and V9 exhibited the lowest plant height and SDW respectively. V1 and V10 recorded the highest grain yield under ww conditions in 2019 and 2018 respectively; V1 and V9 recorded the highest one under ds conditions in 2019 and 2018 respectively.
<table><caption>**Table 4a**
Performance of 10 Sorghum Varieties Under Two Water Treatments (Well-watered and Drought Stress) for Agro-morphological Traits Measured During 2018 and 2019 Field Trials</caption><thead><tr><th></th><th colspan="2">NLA</th><th colspan="2">PHS</th><th colspan="2">DLW</th></tr><tr><th></th><th>ww</th><th>ds</th><th>ww</th><th>ds</th><th>ww</th><th>ds</th></tr></thead><tbody><tr><td><strong>V</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>V1</strong></td><td>14.6cd</td><td>13.5bc</td><td>133.7bc</td><td>111.7bcd</td><td>5.7e</td><td>13.9cd</td></tr><tr><td><strong>V2</strong></td><td>15.3bc</td><td>13.6bc</td><td>125.7bc</td><td>101.6bcd</td><td>16.8ab</td><td>28.7a</td></tr><tr><td><strong>V3</strong></td><td>15.5abc</td><td>13.2bc</td><td>204.3a</td><td>179.3a</td><td>12.6bcd</td><td>25.8a</td></tr><tr><td><strong>V4</strong></td><td>15.5abc</td><td>13.8bc</td><td>200.6a</td><td>137.5b</td><td>10.7cde</td><td>17.3bc</td></tr><tr><td><strong>V5</strong></td><td>15.0bc</td><td>13.5bc</td><td>186.3a</td><td>193.5a</td><td>6.0e</td><td>16.6bc</td></tr><tr><td><strong>V6</strong></td><td>16.6a</td><td>15.9a</td><td>108.5cd</td><td>91.2cd</td><td>7.8de</td><td>9.9de</td></tr><tr><td><strong>V7</strong></td><td>13.5d</td><td>12.4c</td><td>92.5d</td><td>86.2cd</td><td>14.2bc</td><td>19.2b</td></tr><tr><td><strong>V8</strong></td><td>16.0ab</td><td>14.4ab</td><td>147.5b</td><td>111.8bcd</td><td>22.4a</td><td>26.9a</td></tr><tr><td><strong>V9</strong></td><td>15.7abc</td><td>14.9ab</td><td>98.4d</td><td>74.0d</td><td>18.3ab</td><td>8.6e</td></tr><tr><td><strong>V10</strong></td><td>15.1bc</td><td>13.8bc</td><td>141.0b</td><td>116.1bc</td><td>21.4a</td><td>28.3a</td></tr><tr><td><strong>Grand mean</strong></td><td>15.3a</td><td>13.9b</td><td>143.8a</td><td>120.3b</td><td>13.6b</td><td>19.5a</td></tr><tr><td><strong>ANOVA</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>V</strong></td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td><strong>E</strong></td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td><strong>V×E</strong></td><td>ns</td><td>ns</td><td>**</td><td>**</td><td>***</td><td>***</td></tr><tr><td><strong>V1</strong></td><td>15.7ab</td><td>13.7a</td><td>120.4a</td><td>77.5a</td><td>13.7d</td><td>15.1e</td></tr><tr><td><strong>V2</strong></td><td>16.5a</td><td>14.2a</td><td>140.6a</td><td>83.5a</td><td>14.9ab</td><td>16abcd</td></tr><tr><td><strong>V3</strong></td><td>15.0ab</td><td>13.8a</td><td>163.1a</td><td>103.1a</td><td>11.9f</td><td>15.7de</td></tr><tr><td><strong>V4</strong></td><td>15.2ab</td><td>14.3a</td><td>107.3a</td><td>38.3a</td><td>15.3a</td><td>16.1cd</td></tr><tr><td><strong>V5</strong></td><td>15.5ab</td><td>14.3a</td><td>154.6a</td><td>68.5a</td><td>14.8ab</td><td>16.3bcd</td></tr><tr><td><strong>V6</strong></td><td>16.0a</td><td>15.3a</td><td>107.1a</td><td>60.8a</td><td>12.6e</td><td>16.1cd</td></tr><tr><td><strong>V7</strong></td><td>13.5b</td><td>14.1a</td><td>106.5a</td><td>55.3a</td><td>14.5bc</td><td>16abcd</td></tr><tr><td><strong>V8</strong></td><td>15.8ab</td><td>14.4a</td><td>124.5a</td><td>49.0a</td><td>15.1ab</td><td>17.2a</td></tr><tr><td><strong>V9</strong></td><td>15.1ab</td><td>14.4a</td><td>99.4a</td><td>53.5a</td><td>15.0ab</td><td>17.1ab</td></tr><tr><td><strong>V10</strong></td><td>14.3ab</td><td>13.7a</td><td>149.4a</td><td>66.3a</td><td>14.0cd</td><td>16.7abc</td></tr><tr><td><strong>Grand mean</strong></td><td>15.3a</td><td>14.2b</td><td>130.5a</td><td>66.5b</td><td>14.2b</td><td>16.3a</td></tr><tr><td><strong>ANOVA</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th></th><th colspan="2">NLA</th><th colspan="2">PHS</th><th colspan="2">DLW</th></tr><tr><th>V</th><th>ww</th><th>ds</th><th>ww</th><th>ds</th><th>ww</th><th>ds</th></tr></thead><tbody><tr><th>V</th><td>*</td><td>ns</td><td>ns</td><td>ns</td><td>***</td><td>***</td></tr><tr><th>E</th><td>***</td><td></td><td>***</td><td></td><td>***</td><td></td></tr></tbody></table>

V×E ns ns ***

<table><caption>Table 4b</caption><tbody><tr><th>Y</th><td>ns</td><td>***</td><td>***</td></tr><tr><th>V×Y</th><td>*</td><td>***</td><td>***</td></tr><tr><th>E×Y</th><td>ns</td><td>***</td><td>***</td></tr><tr><th>V×E×Y</th><td>ns</td><td>**</td><td>*</td></tr></tbody></table>

<table><thead><tr><th></th><th colspan="2">PHT</th><th colspan="2">SDW</th><th colspan="2">YIELD</th></tr><tr><th>V</th><th>ww</th><th>ds</th><th>ww</th><th>ds</th><th>ww</th><th>ds</th></tr></thead><tbody><tr><th>V1</th><td>172.5cd</td><td>148.9cd</td><td>427.6bc</td><td>358.7abc</td><td>4183.4abc</td><td>3182.3b</td></tr><tr><th>V2</th><td>162.8d</td><td>140.4cde</td><td>470.2abc</td><td>349.3abc</td><td>3715.9d</td><td>3359.3b</td></tr><tr><th>V3</th><td>191.2bc</td><td>193.6ab</td><td>498.7ab</td><td>409.0abc</td><td>2001.5e</td><td>1817.6de</td></tr><tr><th>V4</th><td>205.6b</td><td>161.3bc</td><td>530.0ab</td><td>449.8ab</td><td>1606.4ef</td><td>1540.3e</td></tr><tr><th>V5</th><td>235.3a</td><td>215.6a</td><td>603.2a</td><td>461.4a</td><td>1473.1f</td><td>1168f</td></tr><tr><th>V6</th><td>159.8d</td><td>133.6cde</td><td>408.0bc</td><td>424.4ab</td><td>3926abcd</td><td>2598.3c</td></tr><tr><th>V7</th><td>127.2e</td><td>108.9e</td><td>315.0c</td><td>294.5bc</td><td>3812.1cd</td><td>2006.9d</td></tr><tr><th>V8</th><td>168.1d</td><td>126.0de</td><td>496.8ab</td><td>339.9abc</td><td>3922bcd</td><td>3132.8b</td></tr><tr><th>V9</th><td>152.2d</td><td>117.1de</td><td>321.5c</td><td>259.3c</td><td>4246.0ab</td><td>3851.7a</td></tr><tr><th>V10</th><td>169.3cd</td><td>140.0cde</td><td>451.0abc</td><td>359.3abc</td><td>4349.3a</td><td>2643.02c</td></tr><tr><th>Grand mean</th><td>174.8a</td><td>148.5b</td><td>453.3a</td><td>370.6b</td><td>3271.85a</td><td>2526.21b</td></tr></tbody></table>
<table><thead><tr><th></th><th colspan="2">PHT</th><th colspan="2">SDW</th><th colspan="2">YIELD</th></tr><tr><th>V</th><th>ww</th><th>ds</th><th>ww</th><th>ds</th><th>ww</th><th>ds</th></tr></thead><tbody><tr><td colspan="7">ANOVA</td></tr><tr><td>V</td><td>***</td><td>***</td><td>***</td><td>**</td><td>***</td><td>***</td></tr><tr><td>E</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td></tr></tbody></table>

V×E * Ns ***

<table><tbody><tr><td colspan="7">Year 2019</td></tr><tr><td>V1</td><td>173.2cd</td><td>127abcd</td><td>494.2bc</td><td>377.3abc</td><td>3876.8a</td><td>2424a</td></tr><tr><td>V2</td><td>149.4de</td><td>120.1bcd</td><td>404.5bcd</td><td>259.0bc</td><td>2127.7cd</td><td>2070.3abc</td></tr><tr><td>V3</td><td>216.7ab</td><td>170.4abc</td><td>329.6cd</td><td>323.0abc</td><td>1886.4d</td><td>1752.5bc</td></tr><tr><td>V4</td><td>204.6bc</td><td>183.2ab</td><td>791.2a</td><td>399.4ab</td><td>1233.9e</td><td>1124.5de</td></tr><tr><td>V5</td><td>250.8a</td><td>198.2a</td><td>564.5b</td><td>428.4a</td><td>771.2f</td><td>817.4e</td></tr><tr><td>V6</td><td>137.9ef</td><td>88.4d</td><td>413.2bcd</td><td>274.0abc</td><td>3429.2ab</td><td>1582bcd</td></tr><tr><td>V7</td><td>109.2f</td><td>93.4d</td><td>380.8bcd</td><td>246.5c</td><td>3166.7b</td><td>2027.6abc</td></tr><tr><td>V8</td><td>143def</td><td>106.1cd</td><td>330.4cd</td><td>267.8abc</td><td>2514.8c</td><td>2087.6ab</td></tr><tr><td>V9</td><td>131.4ef</td><td>101.8cd</td><td>303.7d</td><td>272.0abc</td><td>2105.5cd</td><td>2037abc</td></tr><tr><td>V10</td><td>140def</td><td>102.6cd</td><td>382.6bcd</td><td>278.3abc</td><td>3085.8b</td><td>1462.9cd</td></tr><tr><td>Grand mean</td><td>165.7a</td><td>127.2b</td><td>427.3a</td><td>308.6b</td><td>2419.84a</td><td>1738.64b</td></tr><tr><td colspan="7">ANOVA</td></tr><tr><td>V</td><td>***</td><td>***</td><td>***</td><td>**</td><td>***</td><td>***</td></tr><tr><td>E</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>V×E</td><td>*</td><td>**</td><td>**</td><td>***</td><td>***</td><td>***</td></tr><tr><td>Y</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>V×Y</td><td>***</td><td>**</td><td>**</td><td>***</td><td>***</td><td>***</td></tr><tr><td>E×Y</td><td>ns</td><td>ns</td><td>ns</td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>V×E×Y</td><td>ns</td><td>ns</td><td>ns</td><td>ns</td><td>ns</td><td>ns</td></tr></tbody></table>
ww: well-watered; ds: drought stress; NLA: number of leaves appeared; PHS: plant height after stress; DLW: dead leaves weight; PHT: plant height at harvest; SDW: Straw dry weight; Yield: grain yield; V: variety; E: environment; Y: year; *** significant at *p* = 0.001; ** significant at *p* = 0.01; * significant at *p* = 0.05; ns: not significant. The means with the same letters are not significantly different. The bold values indicate the highest and lowest value measured.

Physiological traits like photosynthesis rate, stomata conductance, leaf transpiration, and leaf temperature revealed a wide range of genetic variability among the varieties under both ww and ds conditions (see Table 5). The effects of variety, environment, and their interaction (V×E) were highly significant in both years. Under ww conditions, variety V1 recorded the highest photosynthesis rate, stomata conductance, and leaf transpiration; V5, V9, and V2 recorded respectively the lowest values for the same traits in 2018. However, the occurrence of drought stress induced various responses in these varieties. Variety V10 recorded the lowest photosynthesis rate in 2018 but the highest rate in 2019, showing variation in the behavior of this variety from year to year. Overall, a decrease of photosynthesis rate, stomata conductance, and leaf transpiration—and an increase of leaf temperature—were the physiological responses of the studied varieties to early water deficit (see Table 5).
<table><caption>Table 5
Performance of 10 Sorghum Varieties Under Two Water Treatments (well-watered and drought stress) for Physiological Traits Measured During 2018 and 2019 Field Trials</caption><thead><tr><th></th><th colspan="2">Pn</th><th colspan="2">C</th><th colspan="2">Tr</th><th colspan="2">Tleaf</th></tr><tr><th></th><th>ww</th><th>ds</th><th>ww</th><th>ds</th><th>ww</th><th>ds</th><th>ww</th><th>ds</th></tr></thead><tbody><tr><td>V</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>V1</td><td>43.14a</td><td>31.92abc</td><td>213.06a</td><td>119.35cd</td><td>9.34a</td><td>5.06ab</td><td>36.34cd</td><td>40.70ab</td></tr><tr><td>V2</td><td>41.57ab</td><td>33.29abc</td><td>189.14ab</td><td>101.85d</td><td>7.00b</td><td>4.00b</td><td>34.80d</td><td>39.12abc</td></tr><tr><td>V3</td><td>39.88ab</td><td>39.66a</td><td>179.04ab</td><td>169.84ab</td><td>7.12b</td><td>6.42a</td><td>40.30ab</td><td>39.83abc</td></tr><tr><td>V4</td><td>38.99ab</td><td>38.61a</td><td>169.08b</td><td>140.07abcd</td><td>7.38b</td><td>5.58ab</td><td>40.77a</td><td>39.77abc</td></tr><tr><td>V5</td><td>35.64b</td><td>26.05bc</td><td>169.11b</td><td>128.31bcd</td><td>7.13b</td><td>4.45ab</td><td>39.87ab</td><td>39.08bc</td></tr><tr><td>V6</td><td>42.17ab</td><td>39.83a</td><td>202.09ab</td><td>181.85a</td><td>7.56ab</td><td>6.16a</td><td>39.32ab</td><td>40.94a</td></tr><tr><td>V7</td><td>40.69ab</td><td>37.81a</td><td>194.62ab</td><td>181.45a</td><td>6.39b</td><td>5.89a</td><td>40.40ab</td><td>39.54abc</td></tr><tr><td>V8</td><td>38.16ab</td><td>38.02a</td><td>182.93ab</td><td>183.21a</td><td>7.68ab</td><td>6.36a</td><td>40.83a</td><td>38.51c</td></tr><tr><td>V9</td><td>36.05ab</td><td>35.74ab</td><td>166.88b</td><td>166.72ab</td><td>7.50ab</td><td>6.53a</td><td>39.34ab</td><td>39.02bc</td></tr><tr><td>V10</td><td>37.70ab</td><td>22.96c</td><td>186.72ab</td><td>147.17abc</td><td>7.22b</td><td>4.61ab</td><td>37.83bc</td><td>38.75c</td></tr><tr><td>Grand mean</td><td>39.32a</td><td>34.43b</td><td>184.94a</td><td>150.97b</td><td>7.43a</td><td>5.47b</td><td>39.02b</td><td>39.53a</td></tr><tr><td>ANOVA</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>V</td><td>*</td><td>***</td><td>**</td><td>***</td><td>**</td><td>***</td><td>***</td><td>**</td></tr><tr><td>E</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>**</td><td>**</td></tr></tbody></table>
<table><thead><tr><th></th><th>Pn</th><th>C</th><th>Tr</th><th>Tleaf</th></tr></thead><tbody><tr><th>V</th><td>ww</td><td>ww</td><td>ww</td><td>ww</td></tr><tr><th></th><td>ds</td><td>ds</td><td>ds</td><td>ds</td></tr></tbody></table>

V×E

***

***

***

***
<table><thead><tr><th></th><th>Pn</th><th>C</th><th>Tr</th><th>Tleaf</th></tr></thead><tbody><tr><td>V</td><td>ww</td><td>ww</td><td>ww</td><td>ww</td></tr><tr><td>V1</td><td>47.03a</td><td>22.68c</td><td>193.03a</td><td>78.41c</td><td>7.28ab</td><td>4.00b</td><td>38.75b</td><td>42.86ab</td></tr><tr><td>V2</td><td>42.37abc</td><td>29.28abc</td><td>189.03a</td><td>108.90bc</td><td>7.25ab</td><td>5.17ab</td><td>39.98a</td><td>42.72ab</td></tr><tr><td>V3</td><td>41.56abc</td><td>35.05ab</td><td>191.45a</td><td>140.03ab</td><td>6.70abc</td><td>5.73ab</td><td>40.44a</td><td>41.68bc</td></tr><tr><td>V4</td><td>37.76bc</td><td>34.51ab</td><td>137.08b</td><td>135.79ab</td><td>6.12c</td><td>5.40ab</td><td>38.30bc</td><td>43.11a</td></tr><tr><td>V5</td><td>41.08abc</td><td>27.06abc</td><td>167.77ab</td><td>96.88bc</td><td>7.24ab</td><td>4.96ab</td><td>39.94a</td><td>40.73c</td></tr><tr><td>V6</td><td>38.79abc</td><td>26.58abc</td><td>165.52ab</td><td>94.97bc</td><td>6.89abc</td><td>5.23ab</td><td>40.50a</td><td>42.12ab</td></tr><tr><td>V7</td><td>43.72ab</td><td>35.59ab</td><td>194.77a</td><td>177.36a</td><td>7.65a</td><td>6.18a</td><td>37.36c</td><td>42.34ab</td></tr><tr><td>V8</td><td>34.15c</td><td>27.81abc</td><td>164.44ab</td><td>98.39bc</td><td>6.56bc</td><td>4.78ab</td><td>40.00a</td><td>40.62c</td></tr><tr><td>V9</td><td>43.24ab</td><td>25.57bc</td><td>190.44a</td><td>93.53bc</td><td>7.14abc</td><td>4.64ab</td><td>37.63bc</td><td>43.27a</td></tr><tr><td>V10</td><td>44.10ab</td><td>36.41a</td><td>197.92a</td><td>134.17ab</td><td>7.61a</td><td>6.11a</td><td>38.67b</td><td>42.06ab</td></tr><tr><td>Grand mean</td><td>41.38a</td><td>29.90b</td><td>179.14a</td><td>115.88b</td><td>7.04a</td><td>5.22b</td><td>39.16b</td><td>42.15a</td></tr><tr><td>ANOVA</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>V</td><td>**</td><td>***</td><td>***</td><td>***</td><td>***</td><td>*</td><td>***</td><td>***</td></tr><tr><td>E</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>VxE</td><td>***</td><td>***</td><td>***</td><td>***</td><td>**</td><td>**</td><td>***</td><td>***</td></tr></tbody></table>
<table><thead><tr><th></th><th colspan="2">Pn</th><th colspan="2">C</th><th colspan="2">Tr</th><th colspan="2">Tleaf</th></tr><tr><th></th><th>ww</th><th>ds</th><th>ww</th><th>ds</th><th>ww</th><th>ds</th><th>ww</th><th>ds</th></tr></thead><tbody><tr><td>V</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Y</td><td>ns</td><td></td><td>***</td><td></td><td>**</td><td></td><td>***</td><td></td></tr><tr><td>V×Y</td><td>***</td><td></td><td>***</td><td></td><td>***</td><td></td><td>*</td><td></td></tr><tr><td>E×Y</td><td>***</td><td></td><td>***</td><td></td><td>ns</td><td></td><td>***</td><td></td></tr><tr><td>V×E×Y</td><td>***</td><td></td><td>***</td><td></td><td>**</td><td></td><td>**</td><td></td></tr></tbody></table>
ww: well-watered; ds: drought stress; Pn: photosynthesis rate; C: stomata conductance; Tr: leaf transpiration; Tleaf: leaf temperature; SDW: Straw dry weight; V: variety; E: environment; Y: year; \*\*\* significant at p = 0.001; \*\* significant at p = 0.01; \* significant at p = 0.05; ns: not significant. The means with the same letters are not significantly different. The bold values indicate the highest and lowest value measured.

The RLD was estimated by RID along the soil profiles using the geometrical model for both ww and ds treatments at the end of the stress. Our results showed a strong and significant effect of water deficit on the number of total roots (NTR) and RLD profiles (see Table 6).

Varieties V1 and V8 exhibited the highest NTR in ww and ds conditions respectively, while V2 and V10 exhibited the lowest in ww and ds conditions respectively. Among varieties, V4 exhibited the lowest RLD (0 cm – 120 cm) in both the ww and ds environment. Under drought stress, V1 and V8 recorded the strongest RLD in the shallow horizon (0 cm – 50 cm) and deep horizon (60 cm – 120 cm) respectively (see Table 6). From the data presented in Table 6, the global trend of the varieties' root system's responses to early drought stress is highlighted on Figure 4. Drought stress induced significant reduction of RLD in the 0 cm – 50 cm soil horizon; it increased in the 60 cm –120 cm deep soil layers.
<table><caption>Table 6
Average Performance of Sorghum Varieties for Root Traits Under Well-watered and Drought Stress Conditions</caption><thead><tr><th></th><th>NTR</th><th>RLD [0-120cm]</th><th>RLD [0-50cm]</th><th>RLD [60-120cm]</th></tr></thead><tbody><tr><td>V</td><td>ww</td><td>ds</td><td>ww</td><td>ds</td></tr><tr><td>V1</td><td>63.00a</td><td>28.66ab</td><td>1922.80a</td><td>2764.00a</td></tr><tr><td>V2</td><td>32.00f</td><td>22.33cd</td><td>1760.68bc</td><td>654.07f</td></tr><tr><td>V3</td><td>40.33c</td><td>26.00bc</td><td>1751.29bc</td><td>2435.41f</td></tr><tr><td>V4</td><td>50.33b</td><td>31.66a</td><td>1467.24e</td><td>715.29e</td></tr><tr><td>V5</td><td>62.66a</td><td>28.66ab</td><td>1781.89bc</td><td>719.73e</td></tr><tr><td>V6</td><td>40.00cd</td><td>28.66ab</td><td>1739.46bc</td><td>873.33a</td></tr><tr><td>V7</td><td>36.00e</td><td>25.33bc</td><td>1827.72b</td><td>1421.00d</td></tr><tr><td>V8</td><td>42.00c</td><td>30.66a</td><td>1991.88a</td><td>1807.34b</td></tr><tr><td>V9</td><td>37.00de</td><td>25.00bcd</td><td>1625.93d</td><td>2266.67a</td></tr><tr><td>V10</td><td>37.00de</td><td>21.33d</td><td>1696.45cd</td><td>1271.11e</td></tr><tr><td>Grand mean</td><td>44.03a</td><td>26.83b</td><td>1758.61b</td><td>1502.8a</td></tr><tr><td>ANOVA</td><td></td><td></td><td></td><td></td></tr><tr><td>V</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>E</td><td>***</td><td>***</td><td>***</td><td>***</td></tr><tr><td>VxE</td><td>***</td><td>***</td><td>***</td><td>***</td></tr></tbody></table>