Phd in Agricultural and Environmental Sciences

PHD IN AGRICULTURAL AND ENVIRONMENTAL SCIENCES

CYCLE XXXII

Coordinator Prof. Pietramellara Giacomo

CLIMATE RESILIENT CROPS IN HOT-SPOT REGIONS OF CLIMATE CHANGE

THE CASE OF QUINOA IN BURKINA FASO

Academic Discipline AGR 02

I declare that this PhD thesis is my own work and that, to the best of my knowledge, it contains no material previously published or written by another person, nor material which to a substantial extent has been accepted for the award of any other degree or diploma of the university or other institute of higher learning, except where due acknowledgment has been made in the text.

Correspondence: Jorge Alvar-Beltrán, Department of Agronomy, Food, Environmental and Forestry Sciences and Technology (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144, Florence, Italy. Email address: [email protected]; Tel.: +39-2755741

TABLE OF CONTENTS……………………………………………………………………… p.1 ABSTRACT……………………………………………………………………………………. p.5 RIASSUNTO……………………………………………………………………..…….………. p.6 ACKNOWLEDGEMENTS…………………………………………………………………… p.9 LIST OF FIGURES……………………………………………………………………………. p.11 LIST OF TABLES……………………………………………………….….…………………. p.13 LIST OF ACRONOMYS……………………………………………………………………… p.15 LIST OF ACRONOMYS: UNITS & OTHERS………………………………..……………. p.16

CHAPTER 1: GENERAL INTRODUCTION

1.1. Background information………………………………………………………….…..…….. p.17 1.1.1. Observed and projected regional changes in precipitation……………..…………… p.17 1.1.2. Observed and projected regional changes in temperature…………………..………. p.18 1.1.3. The vulnerability of Burkina Faso to climate change………………….……..……... p.19 1.1.4. Agricultural adaptation to climate change………………………….………..……… p.19 1.2. Justification of the topic and gaps in literature ………….………………………………… p.21 1.3. Research questions and problems addressed………………………………………………. p.22 1.4. Research aims and objectives………………...………………...………………………….. p.23 1.5. Structure of the research…………………………………………………………………… p.24 1.6. Contribution to the field……………………………………………………………………. p.25

CHAPTER 2: FARMER´S AWARENESS AND AGRICULTURAL ADAPTATION TO CLIMATE CHANGE IN BURKINA FASO´S DIFFERENT AGRO-CLIMATOLOGICAL ZONES (Submitted: Environmental Management Journal)

2.1. Introduction………………………………………………………………………………… p.27 2.1.1. Regional climate trends……………………………………………………………... p.28 2.2. Materials and Methods……………………………………………………………………... p.29 2.3. Results……………………………………………………………………………………… p.31 2.3.1. Climate characterization…………………………………………………………….. p.31 2.3.2. Agriculture in Burkina Faso………………………………………………………… p.34 2.3.3. Farmer’s perceptions on natural hazards……………………………………………. p.35 2.3.4. Agricultural adaptation to climate change…………………………………………... p.36 2.3.5. Vulnerability and awareness to climate change…………………………...………… p.39 2.4. Discussion…………………………………………………………………….…………...... p.41 2.5. Conclusions……………………………………………………………………………..….. p.43

CHAPTER 3: EFFECT OF DROUGHT AND NITROGEN FERTILISATION ON QUINOA (CHENOPODIUM QUINOA WILLD.) UNDER FIELD CONDITIONS IN BURKINA FASO (Published: Italian Journal of Agrometeorology)

3.1. Introduction…………………………………………………………………….…..……… p.45

3.2. Materials and Methods…………………………………………………….…….….……... p.46 3.3. Results…………………………………………………………………….…….…….…… p.48 3.4. Discussion...………………………………………..………………………….….….……. p.56 3.5. Conclusions…………………………………………………………………….….….…… p.57

CHAPTER 4: EFFECT OF DROUGHT, NITROGEN FERTILIZATION, TEMPERATURE AND PHOTOPERIODICITY ON QUINOA PLANT GROWTH AND DEVELOPMENT IN THE SAHEL (Published: Journal of Agronomy)

4.1. Introduction…………………………………………...…………………………………… p.59 4.2. Materials and Methods…………………………………………………………………….. p.61 4.2.1 Experimental set-up………………………………………………………………… p.61 4.2.2 Sampling and measurement………………………………………………………… p.62 4.2.3 Statistical analysis…………………………………………………………………... p.63 4.3. Results………………………………………………………………………...…………… p.63 4.4. Discussion.………………………………………………………………………………… p.72 4.5. Conclusions………………………………………………………………………………... p.75

CHAPTER 5: HEAT-STRESS EFFECT ON QUINOA (CHENOPODIUM QUINOA WILLD.) UNDER CONTROLLED CLIMATIC CONDITIONS: THE POTENTIAL OF QUINOA IN BURKINA FASO (Submitted: Journal of Agricultural Sciences)

5.1. Introduction……………………………………………………………………….……….. p.77 5.2. Materials and Methods………………………………………………………….…………. p.79 5.3. Results……………………………………………………………………………………... p.81 5.3.1. Soil analyses……………………………………………………………………...… p.81 5.3.2. Effect of HTS on yield and seed germination……………………………………… p.82 5.3.3. Spatio-temporal suitability of quinoa in Burkina Faso……..……………………… p.84 5.4. Discussion…………………………………………………………………………………. p.86 5.5. Conclusions…………………………………………………….………………………..… p.87

CHAPTER 6: IRRIGATION SCHEDULING OF DROUGHT-RESISTANT QUINOA IN BURKINA FASO WITH AQUACROP (Accepted: Irrigation Science Journal)

6.1. Introduction……………………………..………………………………………………… p.89 6.2. Materials and Methods………………………………………….……………………...…. p.91 6.2.1. Site description………………………………………………………………...…... p.91 6.2.2. Sampling and measurement……………………………….…………………...…... p.92 6.2.3. Soil and crop water aspects………………………………………………...……… p.92 6.2.4. AquaCrop model…………………………………………………………………... p.93 6.2.5. Statistical analysis…………………………………………………………...…….. p.93 6.3. Results…………………………………………………………………………………….. p.94 6.3.1. Field observations………………………………………………………………….. p.94 6.3.2. Calibration and validation of AquaCrop……………………………………...….… p.96 6.3.3. Simulation of eco-physiological parameters………………………………………. p.97 6.3.4. Statistical analysis of yield, biomass and canopy cover…………………………… p.99 6.4. Discussion……………………………………………………………..………...…....…... p.101 6.5. Conclusions……………………………………………………………………………….. p.102

CHAPTER 7: CAN GLOBAL WARMING HINDER THE POTENTIAL OF QUINOA IN THE SAHEL? (Submitted: European Journal of Agronomy)

7.1. Introduction…………………………………………………………………………...... p.105 7.2. Materials and Methods……………………………………………………………………. p.107 7.2.1. Crop modelling with AquaCrop and ArcGIS……………………………………… p.107 7.2.2. Generation of future climates…………………………………………………….... p.108 7.2.3. Soils grids………………………………………………………………………….. p.108 7.3. Results………………………………………………….………….……………………… p.109 7.4. Discussion……………………………………………….………….…………………….. p.116 7.5. Conclusions………………………………………………………….………………….… p.118

CHAPTER 8: GENERAL CONCLUSIONS

8.1. Past climatic trends, farmer’s awareness and agricultural adaptation……………..……… p.119 8.2. Future warming of Burkina Faso………………………………………………….……… p.120 8.3. Further research on climatic trends and agricultural adaptation…………………….……. p.120 8.4. Abiotic factors: lessons from quinoa experimentation…………………………....………. p.121 8.5. Quinoa in the Sahel: agronomic guidelines………………………………………………. p.123 8.6. SWOT (Strengths, Weaknesses, Opportunities y Threats)…………………….…………. p.124 8.7. Further research on quinoa………………………………………………………………... p.125 3

8.8. Recommendations for national and supra-national institutions……………...…………… p.126

REFERENCES...... p.127

APPENDICES Annex 1. Summary of activities…………………………………………………………… p.141 Annex 2. Technical guide for growing quinoa in Burkina Faso …..……………………… p.143 Annex 3. Results dissemination: quinoa workshop ……………………………...... …….. p.145 Annex 4. Surveys on farmer’s perceptions on climate change…………………………….. p.147 Annex 5. Field evidences……………………………………………………………….….. p.149 Annex 6. Published research paper n°1………...………….…………………………….… p.159 Annex 7. Published research paper n°2……………………………………………………. p.171

ABSTRACT Since the industrial revolution, there has been a continuous increase in atmospheric carbon dioxide

(CO2) concentrations to the highest levels in the last 800,000 years, over 400 ppm. This underscores human-induced climate change, and CO2-projections are not promising (Representative Concentration

Pathway (RCP) 4.5: 550 ppm of CO2; RCP 8.5: 1000 ppm of CO2 by 2100). Scientists foresee a point of no return in the climate system, where humankind and ecosystems are expected to face unprecedented changes in climate. Populations living in less developed countries are likely to suffer the most severe impacts of climate change (heatwaves, droughts, flooding…). With uncontrollable population growth, high-reliance on climate-sensitive sectors (agriculture), lack of governance, poor educational and health systems, and conflict, a sophisticated time-bomb is developing. The time-bomb will be in the form of populations facing starvation, causing conflict and displacing, even more, the population within and from the Sahel.

The Sahel is often portrayed as one of the world’s most vulnerable regions to climate change impacts, which are expected to severely affect Sub-Saharan agriculture and consequently human livelihoods. Many of the crops (maize, sorghum, millet, sugarcane, fonio and tef) grown at lower latitudes, have a C4 photosynthetic pathways which are more efficient in environments with higher solar radiation when compared to C3 crops. Nevertheless, C3 crops have a photosynthetic pathway that benefit more from increasing CO2 concentrations in the atmosphere. This is because the optimal CO2 atmospheric concentrations for enhancing the photosynthetic rate of C3 crops has not yet been reached. Modelling of C4 crops under changing climatic conditions has shown considerable yield losses of main crops (maize, millet and sorghum) within the region and for the coming decades. Trait improvement of C4 crops is time consuming with limited time for action, therefore alternative strategies to adapt to future climate are now imperative.

Different agricultural adaptive strategies may be available for the Sahel to face the detrimental impacts of climate change. Hence this research proposes a novel approach: to introduce a resistant to abiotic- stresses-C3-crop in a country suffering from high undernourishment rates, Burkina Faso. Field experimentation with Chenopodium quinoa Willd. has shown that quinoa is a very resilient plant, that can cope with drought-stress conditions (200-400 mm) and withstand the effect of heat-stress (38 °C), besides having low nitrogen nutrient requirements (25 kg N ha-1). Multimodel simulations under different climate scenarios (RCP 4.5 and RCP 8.5), predicting temperature increases of 2 °C to 5 °C, have shown that quinoa is capable of adjusting, with even yield enhancements, to the projected temperatures and CO2 concentrations. Although crop substitution may face social and research challenges, it is a more rapid solution for building climate-resilient communities than crop improvement. Key words: agricultural adaptation, climate change, Sahel, climate/crop-growth modelling

RIASSUNTO

A partire dalla rivoluzione industriale, si è assistito ad un costante aumento delle emissioni di CO2 in atmosfera che hanno superato la soglia record degli ultimi 800,000 anni delle 400 ppm. Ciò sottolinea la pericolosità del fenomeno del cambiamento climatico e, in questo senso, le proiezioni sull’aumento delle concentrazioni di CO2 in atmosfera non sono rassicuranti (RCP 4.5: 550 ppm di CO2; RCP 8.5: 1000 ppm entro la fine del secolo). La scienza ha definito un punto di non ritorno in cui l’umanità e gli ecosistemi naturali si troveranno a dover affrontare cambiamenti senza precedenti. In questo senso, le popolazioni appartenenti ai paesi meno sviluppati rischiano di soffrire maggiormente l’impeto del cambiamento climatico (ondate di calore, siccità, inondazioni…). In cima alla lista, l’incontrollata crescita demografica, la grande dipendenza dai settori strettamente legati all’andamento climatico (primo fra tutti l’agricoltura), la mancanza di governance, sistemi di educazione e sanitari scadenti e, ovviamente, le guerre, stanno lentamente unendo gli ingredienti per una sofisticata bomba ad orologeria. Questa bomba ad orologeria si tradurrà in un aggravamento della questione della fame nel mondo, causerà conflitti ed accentuerà il fenomeno delle migrazioni delle popolazioni all’interno e dall’areale del Sahel.

Il Sahel viene spesso indicato come uno degli areali maggiormente vulnerabili al fenomeno dei cambiamenti climatici, che si prevede investiranno l’agricoltura Sub-Sahariana e di conseguenza le condizioni di vita delle popolazioni umane. La maggior parte delle piante (mais, sorgo, miglio, canna da zucchero, fonio e tef) coltivate a basse latitudini, evidenziano un metabolismo C4 che le rende maggiormente efficienti in ambienti ad elevata radiazione solare, se confrontate con le piante C3. Tuttavia, le piante C3 evidenziano un metabolismo che può avvantaggiarsi maggiormente dell’aumento della concentrazione di CO2 in atmosfera. Questo perché non sono ancora stati raggiunti i livelli ottimali di concentrazione atmosferica di CO2 per massimizzare l’efficienza fotosintetica delle piante C3. A questo proposito, studi di modellizzazione su piante C4 (mais, sorgo, miglio) sottoposte a condizioni di cambiamenti climatici futuri hanno evidenziato notevoli riduzioni nelle produzioni. Un miglioramento delle caratteristiche delle piante C4 richiede notevoli sforzi in termini di tempo e lavoro, ma il tempo a disposizione è limitato. Per questo motivo, lo sviluppo di strategie di adattamento delle colture alle future condizioni climatiche risulta imperativo.

Ad oggi esistono differenti strategie di adattamento dedicate alla regione del Sahel per far fronte agli effetti dannosi del cambiamento climatico. Poiché è tempo di agire, questa ricerca propone un nuovo approccio: introdurre colture C3 resistenti agli stress abiotici in un paese che soffre fortemente di alti tassi di denutrizione come il Burkina Faso. Differenti sperimentazioni di campo su Chenopodium quinoa Willd. hanno dimostrato come la quinoa sia una coltura resistente alle condizioni climatiche estreme, in grado di far fronte a condizioni di siccità (200-400 mm di acqua irrigua) e sopportare gli stress termici (38 °C), oltre che evidenziare basse richieste in termini di concimazioni azotate (25 kg N

ha-1). Simulazioni riferite a differenti scenari climatici (RCP 4.5 and RCP 8.5), con incrementi medi di temperatura da 2°C a 5°C, hanno dimostrato come la quinoa sia in grado di adattarsi, e talvolta beneficiare (in termini di aumento delle produzioni), all’aumento delle temperature e della concentrazione di CO2. Sebbene la sostituzione delle colture appaia come una valida soluzione alle sfide sociali e di ricerca, riteniamo che fornire un piccolo spazio di azione possa rappresentare una rapida soluzione per la costituzione di comunità resistenti ai mutamenti climatici rispetto al miglioramento delle colture. Parola chiave: adattamento agricolo, cambiamento climatico, Sahel, modelli climatici/crescita

ACKNOWLEDGMENTS

This dissertation is dedicated to the climate-vulnerable communities around the world, to the poorer that have no voice, and to those living in less-developed countries that are certainly not responsible of climate change. Sorry on behalf of those who feel in the same way as I do, we are (people living in developed countries) accountable for it. As I am fully aware of my responsibility, I will continue to devote my energies on making your lives better.

To the people of Burkina Faso, for being so helpful, welcoming, honest, happy and lively. Despite the misfortunes of life, you continue to keep smiling and moving forward. You are a clear example of a society that does not consist of individuals, but of people that interrelate between each other.

Throughout the three-year PhD thesis, I have received knowledgeable and tactical guidance’s from thesis director, Prof. Simone Orlandini, and co-supervisor, Dr. Anna Dalla Marta, from University of Florence. Their support and confidence has been crucial for growing professionally in such short- period of time.

To the supervisory board from Burkina Faso, Prof. Jacob Sanou and Dr. Abdalla Dao, from Institut de l’Environnement et Recherches Agricoles (INERA), for accepting me in their team, facilitating and closely-following research activities in Burkina Faso.

Deep gratitude to my mentor, Dr. José Luis Camacho, from World Meteorological Organization, for its unconditional support since 2012. For having plenty of time to address my academic issues, for giving I wise indications as well as the opportunity to take the first steps in agro-meteorology.

To Prof. Anne Gobin for her contributions to the climate-modelling section, and to Prof. Jim Salinger for reviewing the thesis and certainly improving it.

To Mr. Coulibaly Saturnin, now a life a friend. I really appreciate all your efforts on setting-up the quinoa-experimentation and help me monitoring the crop. You are one of the people that knows best how hard, but gratifying, this work has been. Thanks for making my life much easier at Bobo Dioulasso.

To Mr. Jean-Bosco Dibouloni, for being an outstanding friend and example of a hard-working person. Our end-less discussions about the developing world, dreams and projects to end up poverty will remain forever.

To the members of the “Traditional Cereals Department” Mr. Pare Pascal, Mme. Sib Jeanne and Dr. Zara Nikiema for being so kind, supporting and simplifying research activities in Burkina Faso.

To the interns of Farako-Bâ, Mr. Abdou Gnanda, Mr. Timothée Bayala, Mr. Guira Amidou, Mr. Louis Nebie, Mr. Mamadou Baillou for all the good moments inside and outside Farako-Bâ. Your passion and high commitment towards agronomy has marked me forever.

To the workforce of Farako-Bâ, Mr. Adolphe Millogo, Mr. Moussa Sanou, Mr. François Sanou, Mr Martin Sanou and Mr. René Kabore, for taking care of the crop management activities of this research. For their colossal battle under such harsh environmental conditions, and for silently-highlight the power of the working-class of Burkina Faso.

To the women of Farako-Bâ, Mme. Maimouna Sanou and Mme. Sylvie Kone, for being so patient and always willing to help during the seed-separation process. Your happiness was contiguous and made this activity much bearable.

To Mr. Théodore Dibouloni, for all the support and fun moments at Bobo Dioulasso.

To the DAGRI administration of the University of Florence for managing research collaborations between UNIFI and INERA, and everything that entails.

To the whole team of DAGRI, University of Florence, with special recognitions to Dr. Leonardo Verdi, Mr. Marco Mancini and Mr. Roberto Vivoli.

To my grandmother, which I lost along the way.

To my mother for giving me the opportunity to live and to my sister for inspiring me so much in life.

To my dad, for teaching and guiding me through the rigorous working path of life. For giving me numerous tips for facing academic challenges and for building my awareness on helping the poorer.

To all my friends. Special thanks to those who had the chance to visit me in Burkina Faso.

To Marina, my shining light and life companion. You are definitely the person to which I would like to dedicate this three-year work. For not letting me down, and for supporting me both emotionally and academically during hard moments. For having to listen and be patient during such long period and suffering circumstances. It was not an easy walk, but we made it. Thank you for being there.

LIST OF FIGURES

2.1. Spatial distribution of surveys conducted between February and April 2018

2.2. Mean annual temperature (°C) trends for Dori, Ouagadougou and Bobo Dioulasso for the period 1973-2017

2.3. Mean annual precipitation (mm) trends for Dori, Ouagadougou and Bobo Dioulasso for the period 1973-2017

2.4. Number of days per year with temperatures above 40 °C for Dori, Ouagadougou and Bobo Dioulasso for the period 1973-2017

2.5. Crop-mix among farmers surveyed in Burkina´s agro-climatic zones

2.6. Farmer´s responses on extreme weather events (questions 1-8) in the different agro-climatological zones

2.7. Farmer´s vulnerability (left)

2.8. Means of diffusion of weather and climate information services in Burkina Faso (right)

3.1. Meteorological observations at INERA Farako-Bâ research station during the growing period

3.2. Soil temperatures at 5 and 10 cm depth during the growing period

3.3. Daily evapotranspiration for c.v. Titicaca (left) and c.v. Negra Collana (right)

3.4. Full Irrigation (FI-100 % PET), Progressive Drought (PD-80 % PET), and Deficit irrigation (DI- 60 % PET) for Titicaca and Negra Collana for November and December sowing

3.5. Relationship (lineal regression) between seed yield per plant (g) and plant height (cm) at harvest for Titicaca and Negra Collana

3.6. Relationship (linear regression) between seed yield per plant (g) and canopy cover (%) for Titicaca and Negra Collana

4.1. Experimental field design with different levels of irrigation (Full Irrigation-FI, Progressive Drought-PD, Deficit Irrigation-DI and Extreme Deficit Irrigation-EDI) and N-fertilization (100, 50, 25 and 0 kg N ha-1) for the two year-experimentation (2017-2018 and 2018-2019).

4.2. Maximum and minimum temperatures recorded during the two year-experiment (2017-2018 and 2018-2019).

4.3. Emergence (E), two leaves (2L), four leaves (4L), eight leaves (8L), panicle formation (PF), flowering (FL) and milky seeds (MG) of quinoa at 5, 15, 18, 24, 30, 40 and 60 days after sowing (DAS), respectively, for different sowing dates (25-Oct., 4-Nov., 19-Nov. and 8-Dec.).

4.4. Canopy cover during the second year of experimentation, left (25-Oct.) and right (19-Nov.)

5.1. Relationship between seed yield (g) and high temperature stress (°C) during flowering.

5.2. Quinoa germination rates (%) under different heat-stress thresholds and room temperatures (°C).

5.3. Mean comparison under different heat-stress thresholds for the following crop parameters: a) seed yield per plant; b) seed germination rates; c) harvest index; d) total above-ground biomass. 11

5.4. Maximum mean monthly temperatures (MMMT’s) in the country’s different agro-climatic zones (Sahel-north, Soudano-Sahelian-central, and Soudanian-southernmost parts) from 1973-2017.

6.1. Observed mean absolute maximum and minimum temperatures, and rainfall for the 2017-2018 experiment (top) and for the 2018-2019 experiment (bottom) at INERA-Farako-Bâ research station.

6.2. Cumulative water supply under different irrigation schedules.

6.3. Differences between observed and simulated biomass and yield (kg ha-1)

7.1. Spatial variation of soil texture in Burkina Faso at soil-surface (0 cm depth).

7.2. Mean monthly temperature projection (October-March) until the end of the century using the ensemble of GCMs from CMIP5, with 1973-2017 as a baseline: (A) Dori (Sahel); (B) Ouagadougou (Soudano-Sahelian); and, (C) Bobo Dioulasso (Soudanian).

7.3. Spatio-temporal distribution of average quinoa seed yield (kg ha-1) in Burkina Faso’s agro- climatic zones, by combining main soil textures for different planting-periods under RCP 4.5

7.4. Spatio-temporal distribution of average quinoa seed yield (kg ha-1) in Burkina Faso’s agro- climatic zones, by combining main soil textures for different planting-periods under RCP 8.5.

8.1. Strengths, weaknesses, opportunities and threats (SWOT) of introducing quinoa in Burkina Faso.

A.1. PhD work-plan of activities

LIST OF TABLES

1.1. List of climate and agricultural initiatives being implemented in Burkina Faso

1.2. List of land management practices traditionally used in the Sahel

2.1. Lineal regression for different meteorological parameters (mean temperatures, total annual rainfall and days year-1 with temperatures higher than 40 °C) for the period 1973-2017

2.2. Number of farmers´ adopting/embracing soil and water conservation practices (SCW) and agricultural technological innovations

3.1. Main soil physic-chemical characteristics before sowing (average of 5 samples)

3.2. Main soil physic-chemical characteristics post-harvesting (average of 3 samples)

3.3. WUE (Water Use Efficiency in kg m-3); GYP (Seed yield per plant in grams); HI (Harvest Index in yield/biomass); TGW (Thousand seed weight in grams); CL1-2 (Chlorophyll content); CC (Canopy Cover in %); PH (Plant Height in cm) of quinoa under the different treatments. Factors: a) Variety (V) of Chenopodium quinoa Willd.; b) Irrigation level (I) (100 % PET; 80 % PET; 60 % PET); c) Fertilisation (F) (100 kg N ha-1; 50 kg N ha-1; 25 kg N ha-1).

3.4. Post-hoc Tukey´s pairwise comparison test for different crop parameters and factors of study (variety, irrigation and fertilisation)

3.5. ANOVA for different crop parameters and interactions between factors (variety, irrigation and fertilisation)

4.1. Soil main physic-chemical characteristics of the two-year experimentation (2017-2018 and 2018- 2019) (average of 5 samples)

4.2. Net irrigation under full irrigation (FI), progressive drought (PD), deficit irrigation (DI) and extreme deficit irrigation (EDI) for 2017-2018 (4-Nov. and 8-Dec.) and 2018-2019 (25-Oct. and 19- Nov.) 4.3. Cumulative growing degree days (CGDD in °C) for different phenological stages and photoperiodicity (minutes per day) for different sowing dates (25-Oct., 4-Nov., 19-Nov. and 8-Dec.)

4.4. Post-hoc Tukey-HSD pairwise comparison test for different crop parameters and interaction between factors of study (irrigation and fertilization), mean-values for each year experimentation (2017-2018 and 2018-2019).

4.5. Post-hoc Tukey-HSD pairwise comparison test for different crop parameters and factors of study (irrigation and fertilization), mean of all sowing dates (25-Oct., 4-Nov., 19-Nov. and 8-Dec.).

4.6. Relationship between different levels of irrigation and N-fertilization with the maximum canopy cover (CC as a percentage), yield (Y) and aboveground biomass (ABG) (kg ha-1) in the second year of experimentation (25-Oct. and 19-Nov.)

4.7. Post-hoc Tukey-HSD pairwise comparison test for the canopy cover (%) during the second year of experimentation

4.8. ANOVA test for the canopy cover and its interaction between irrigation and fertilizer application

5.1. Main soil physic-chemical characteristics before sowing

5.2. Evapotranspiration and water supply (mm season-1), yield (g plant-1), standing biomass (g plant-1) harvest index (%) and seed germination rates (%)

6.1. Different irrigation schedules, full irrigation (FI), progressive drought (PD), deficit irrigation (DI) and extreme deficit irritation (EDI), and sowing dates used for the calibration and validation of AquaCrop

6.2. Soil physic-chemical characteristics

6.3. Crop irrigation scheduling and crop potential evapotranspiration

6.4. Parameters used for the calibration of AquaCrop (Burkina Faso) and default values (Bolivia)

6.5. Observed and simulated biomass and yield for different sowing dates and irrigation schedules (FI, PD, DI and EDI)

6.6. Performance of AquaCrop calibration when comparing observed and simulated above-ground biomass and canopy cover

6.7. Performance of AquaCrop in biomass and yield simulation, average of all treatments

7.1. List of the ensemble of GCMs used in this study

7.2. Average quinoa yield changes (%) and standard deviation (SD) under RCP 4.5 and RCP 8.5 in Burkina Faso’s different agro-climatic zones for different time-periods in comparison with 2020.

7.3. Average biomass changes (%) and standard deviation (SD) under RCP 4.5 and RCP 8.5 in Burkina Faso’s different agro-climatic zones for different time-periods in comparison with 2020

LIST OF ACRONOMYS

ANAM Agence Nationale de la Météorologie BRACED Building Resilience and Adaptation to Climate Extremes and Disasters CESAO Centre for Economic and Social Studies of Western Africa CIA Central Intelligence Agency CMIP Coupled Model Inter-comparison Project CNSF Centre National des Semences Forestières DGM Direction Générale de la Météorologie DSSAT Decision Support System for Agrotechnology Transfer FEWSNET Famine for Early Warning Systems Network FAO Food and Agriculture Organization GCM Global Climate Models GDP Gross Domestic Product ICT Information Communication Technologies INERA Institut de l’Environnement et Recherches Agricoles IRD Institut de Recherche pour le Développement IPCC Intergovernmental Panel for Climate Change ISRIC International Soil Reference and Information Centre ITCZ Inter-tropical Convergence Zone NAPA National Adaptation Program of Action NOAA National Oceanic and Atmospheric Administration NDVI Normalized Difference Vegetation Index NPK Nitrogen, Phosphorus and Potassium OCHA United Nations Office for the Coordination of Humanitarian Affairs RCM Regional Climate Models RCP Representative Concentration Pathway RSS Remote Sensing Systems SWC Soil Water Conservation SWOT Strengths, weaknesses, opportunities and threats TCP Technical Cooperation Programme TWB The World Bank UNFCCC United Nations Framework Convention on Climate Change USDA United States Department of Agriculture WAAPP West Africa Agricultural Productivity Program WMO World Meteorological Organization

LIST OF ACRONOMYS: UNITS & OTHERS

ANOVA Analysis of Variance NMRSE Normalized root-mean-square error AGB Above ground biomass N2H4O3 Ammonium nitrate AS Anti-sense ppm Parts per million BAU Business as usual P Phosphorus B Branching P2O5 Phosphate PD Progressive Drought E Emergence PF Panicle formation °C Degrees Celsius PH Plant height Ca Calcium PET Potential Evapotranspiration CGDD Cumulative Growing Degree Days QTL Quantitative trait loci CL Chlorophyll content in leaves RIL Recombinant inbred lines cm Centimeters r2 Coefficient of determination cm3 Cubic centimeters r Pearson correlation coefficient CO(NH2)2 Urea SD Stem diameter CC Canopy Cover SNP Single-nucleotide polymorphism CO2 Carbon dioxide t Tones c.v. Crop variety TGW Thousand Seed Weight -1 CV Coefficient of Variation W h Watts per hour WUE Water Use Efficiency CWP Crop Water Productivity Y Seed yield d Wilmot’s index of agreement 2L Two leaves dS Diecisiemens 4L Four leaves DAS Days after sowing 8L Eight leaves DAT Days after transplanting DI Deficit Irrigation EDI Extreme Deficit Irrigation ENSO El Niño Southern Oscillation Fe Iron FI Full Irrigation g Grams Gt CO2 Gigatons of carbon dioxide GYP Seed yield per plant ha Hectares HI Harvest Index HTS Heat temperature stress K Potassium Kc Crop coefficient Ks Heat stress coefficient kg Kilograms m3 Cubic meters MAPE Mean absolute percentage error masl Meters above sea level MG Milky seeds Mg Magnesium Mg Million grams mg Milligrams Mj Mega joules Mm Millimeters mS Millisiemens N Nitrogen

CHAPTER 1: GENERAL INTRODUCTION

1.1. Background information

The global carbon budget, CO2 emissions from human activities and land use change balanced by the output (storage) of carbon sinks on land and oceans is positive, with increasing CO2 concentrations in the atmosphere and rising global temperatures. The growth of atmospheric CO2 concentration has been -1 -9 estimated at 6.2 GtCO2 yr in 2015 (gigatons of CO2, equivalent to 6.2 10 t of CO2 per year) -1 (Candela and Carlson, 2017). Moreover, at an average emission rate of more than 50 GtCO2 yr since

2010, it is very likely that the remaining carbon budget in the atmosphere, 420 GtCO2, is going to be exceeded before 2030 (Rogelj, 2018). This will result in a temperature increase of more than 1.5 °C by 2030 when compared to the preindustrial period (before 1850) (Hoegh-Guldberg et al., 2018). In fact, 1.5 °C has been considered a tipping-point at which an abrupt change will be made to sensitive ecological systems (glaciers, sea-level and grasslands, among others), while having negative consequences on human and economic systems (IPCC, 2018). However, a global warming of 1.5 °C will not be homogenous and temperatures will likely increase in Northern Hemisphere mid-latitudes by more than 3 °C (Hoegh-Guldberg et al., 2018).

1.1.1. Observed and projected regional changes in precipitation The Sahel is amongst the most ecologically sensitive regions to climate change. A steady decrease in the vegetation index due to high inter/intra annual variability, intensification of precipitation and human-induced land-use changes (from overgrazing, fires and agriculture) has accelerated soil erosion (Brooks, 2004; Giannini et al., 2008; Seddon et al., 2016; Taylor et al., 2017). For instance, in Burkina Faso, over 9 million ha of productive land is now degraded, and is expanding yearly by 0.36 million ha (Sacande et al., 2018). In recent studies, some authors acknowledge an average precipitation increase during the rainy season, over the period 1981 to 2014, in Western Sahel of 96 mm and up to 240 mm in northern Burkina Faso (Bichet and Diehiou, 2018). Maidment et al. (2015) have reported an increase in annual precipitation over the Sahel of 29 to 43 mm yr-1 decade-1 for the period 1983- 2014. While Giannini et al. (2013) reported that the partial recovery in precipitation during the last decades is the result from increases in daily rainfall intensity, rather than in frequency.

There are some discrepancies between studies, as those using longer datasets (1950-2013) show a large precipitation decrease (25 % reduction) with higher precipitation variability in southernmost parts of the country, i.e. Bobo Dioulasso (de Longueville et al., 2016). It is important to highlight that in the last two or three decades rainfalls have moderately recovered, particularly in northern parts of the country, i.e. Dori and Ouahigouya (de Longueville et al., 2016). In addition, Bichet and Diehiou (2018) have also observed an intensification of precipitation, to more than 240 mm day-1 between 1981 and 2014. This has been corroborated during the rainy season of 2009 in Ouagadougou, with downpours of 263 mm in couple of hours (Taylor et al., 2017). Bichet and Diehiou (2018) assert that 17

dry-spells are now shorter but more frequent than before, while others associate the shift in dry-spells (to June-July) to the warming of the tropical-Atlantic ocean (Salack et al., 2014).

Moreover, in the latest IPCC (2018) report, changes in precipitation patterns for West Africa at 1.5 °C and 2 °C global warming are: an increase in the total amount of precipitation, as well as on the number of days with precipitation >10 mm and >20 mm, intensification of yearly 1-day and 5-day precipitation; with a decrease in consecutive wet days and days with precipitation >1 mm (Hoegh- Guldberg et al., 2018). Some of these parameter-trends (increase in amount and intensification of precipitation) are in accordance with that observed by Bichet and Diedhiou (2018); but contrast with those estimations made by James et al. (2015) showing a precipitation decrease over western Sahel. Some have pointed out that projected precipitation increase during the rainy season could be the result of stronger moisture flux convergence during late-rainy season (Monerie et al., 2016).

1.1.2. Observed and projected regional changes in temperature Very little amount of research has been conducted on observed temperature changes in the Sahel. This is because of a poor weather observation network, as well as short period of data collection with frequent gaps. Some studies have reported a constant warming trend of 0.30 °C decade-1 over the Saharan zone and a 0.22 °C decade-1 over tropical regions, using 1979-2015 as the reference period (Vizy and Cook, 2017). In Burkina Faso, between 1950 and 2013, statistics show a positive trend of the hottest day of the year and hot-day frequency, both parameters spread throughout the territory; except for Ouahigouya and Fada N’Gourma (de Longueville et al., 2016).

Projected changes in temperature for West Africa at 1.5 °C and 2.0 °C global warming are: an increase in mean temperatures, summer days, tropical nights, maximum and minimum absolute temperatures, proportion of days with temperatures higher than the 90th percentile of maximum day-temperatures, and on the duration of warm spells; with a decrease in cold-spell duration and on the proportion of days with a minimum night-temperature lower than the 10th percentile (Hoegh-Guldberg et al., 2018). Other modelling studies on future African climate suggest a greater temperature increase than for previous studies. For instance, in Burkina Faso between June-August, temperatures are expected to increase by 1-2 °C under RCP 2.5, 2-4 °C under RCP 4.5 and between 5-7 °C under RCP 8.5; whereas from December-February, the estimated rise is 1-2 °C under RCP 2.5, 2-3 °C under RCP 4.5 and between 5-6 °C under RCP 8.5 (Dike et al., 2015). For these reasons, the timing of the climate departure (when the coldest year in the future will be warmer than the hottest year of the past) for the Sahel has been placed between 2030 and 2040 (Mora et al., 2013). All of the previous trends are far more concerning in a country holding one of the highest annual mean temperature in the world, with a value of 28.3 °C (CRU, 2011).

1.1.3. The vulnerability of Burkina Faso to climate change Burkina Faso has 0.26 % of the total world population (approximately 20 million) but contributes to

0.03 % of the total CO2 emissions (TWB, 2014; CIA, 2017). However, according to the climate change vulnerability index, in 2017, half of the African countries were considered at extreme risk of suffering the impacts of extreme weather events, among which many were in the Sahel region (Maplecroft, 2017). Social, economic, health and environmental factors are responsible for exacerbating the vulnerability of the population to climate change; particularly in a region highly dependent on agriculture, having a low purchasing power, and experiencing unprecedented population growth. Similarly, 150 million people are currently living in the Sahel (comprising the countries of Burkina Faso, Chad, The Gambia, Mali, Mauritania, Niger, Senegal, and northern Cameroon), while 300 million are expected to inhabit the region by 2045 (OCHA, 2016). Approximately 80 % of the population relies on agricultural activities, 15 % suffer from food insecurity, 22.5 % of the children under the age of 5 are underweight, the GDP per capita is of 2300 €, while 4.5 million are now displaced because of conflict (OCHA, 2016, CIA, 2017). More frequent and intensified environmental shocks, including prolonged droughts, heat waves and floods, and its impacts on agricultural activities have led to the livelihood disruption of the most vulnerable.

A study conducted in Burkina Faso (MECV, 2007) noted the role of agriculture to the national economy, and giving reasons for why agriculture is the most vulnerable sector to climate change related-impacts because of degree of exposure, duration, severity of climate-impact and resource importance to the economy. Improving livelihoods and strengthening the resilience to climate change has now been identified as a top priority for policymakers and researchers.

1.1.4. Agricultural adaptation to climate change In 2015, Burkina Faso developed a National Adaptation Program of Action (NAPA) on climate change that included axes of action for different sectors: agriculture, animal production, environment and natural resources, energy, health, infrastructure and housing, as well as other horizontal issues (UNFCCC, 2015). The total costs of short, medium and long-term adaptation (1 to 15 years) to climate change were estimated at 5.9 billion € (GDP in 2017 was of 11.4 billion €), of which 2 billion € were required for agricultural adaptation (UNFCCC, 2015; CIA, 2017).

Despite the increasing internal/external efforts made during the last years to increase agricultural research spending (60 million € in 2014, of which 12 million € where managed by INERA), this remains low for impacts of changes in climate and under resourced for that required for successful agricultural adaptation (2 billion € for the next 15 years, equivalent to 133 million € per year) (UNFCCC, 2015; ASTI, 2017). Agricultural research activities in Burkina Faso are highly dependent on external donors so fluctuates considerably from year-to-year. Because of the five year-project

funded by The World Bank (16 million €), through the West Africa Agricultural Productivity Program (WAAPP), INERA was able to release a total of 17 (between 2012-2014) climate-resilient and highly performing crop varieties in Burkina Faso, being the following: 6 varieties of cowpeas, 4 of rice, 2 of millet, sorghum and tomatoes, and 1 of cotton (TWB, 2016). For maize, the most productive crop in the country (with a production of 1.53 x 106 tonnes in 2017), followed by sorghum and millet (1.36 and 0.83 x 106 tonnes) research is underway, with a total of 6 hybrids being tested in the Soudanian and Soudano-Sahelian zones (SEMAFORT, 2018).

At the NAPA, the following short/medium term agricultural adaptation strategies deserve mentioning: cultivation of early varieties, drought-resistant crops, application of water and soil conservation methods, improve access to climate information, apply water-saving irrigation techniques, among others. Some of the techniques used in the Sahel for the restoration of degraded lands by decreasing water run-off, reducing soil erosion, increasing infiltration and sedimentation are provided in Table 1.1 and Table 1.2. Recent technological innovations have helped build climate resilience in the Sahel. For example, the BRACED project (Building Resilience and Adaptation to Climate Extremes and Disasters) has increased resilience among 5 million farmers in the Sahel by anticipating (through preparedness, planning and risk information), adapting (increasing incomes and changing livelihoods that depend on natural resources), absorbing (improving nets and substitutable assets) and transforming (strategic thinking and policy, empowerment and innovation) agriculture to climate change (BRACED, 2018).

Table 1.1. List of climate and agricultural initiatives being implemented in Burkina Faso

Initiative Location Reference Access to improved cereal seeds (drought- National INERA (2019) tolerant, thermic-resistant and short cycle varieties) Performing maize and sorghum varieties resistant Regional SEMAFORT (2019) to abiotic and biotic stresses Access to improved maize, rice and cowpea seeds West and south-western regions NAFASO (2019) at reasonable prices Community radios spreading climate information North, Central-North, East BRACED (2018) to farmers and rising awareness provinces (> 700 villages) Communication of climate information to end- National scale ANAM (2017) users using SMS, TV, radio and social media Early warning and analysis of food insecurity National, regional and local scale FEWSNET (2019) within the country Rain measurement from cellular phone networks National scale IRD (2016)

Table 1.2. List of land management practices traditionally used in the Sahel

Technique Description Reference Vallerani systems Water harvesting catchments using a tractor for sowing Mekdaschi & Liniger (2013) shrubs and trees.

Contour ridges Water micro-catchment techniques using ridges spacing 1-2 Younan & Simpson (2014) m for agricultural purposes.

Planting trenches Soil-stabilization technique through tree cover restoration Younan & Simpson (2014)

Contour bunds Water micro-catchment technique using soil bunds Bado et al. (2016)

Contour stone bunds Water micro-catchment technique using stone bunds Barbier et al. (2009)

Contour line cultivat. Permanent ridges around the crops to remove excessive Younan & Simpson (2014) water Vegetative strips Established of vegetation by reducing soil erosion Dörlochter, S. (2012) Mulching Reduce the evaporation of remaining moisture from the soil Bado et al. (2016) Sand dune stabilization Combination of mechanical and biological measures such as Younan & Simpson (2014) live fences and sowing grass

Farmer managed Regeneration of living sumps and emergent seedling Liniger et al. (2011) natural regeneration

Zaï Planting pits to rehabilitate barren land, improve infiltration Barbier et al. (2009) and nutrient availability for plants

Zaï (stone bunds) More resistant than the previous, but requiring lots of stones Bado et al. (2016)

Semicircular bunds Semicircular embankments, with tip of bunds facing uphill Bado et al. (2016)

Agroforestry Agricultural areas interspersed by self-generating and local Liniger et al. (2011) (parkland systems) tree species

1.2. Justification of the topic and gaps in literature This agronomic experiment with quinoa falls within NAPA’s specific objective n°3: “adapting crop types to climate and abandoning of certain crops in favour of those which are more resistant to climate shocks” and “research focusing on technological innovations aimed at helping farmers to cope with climate change” (UNFCCC, 2015). In fact, quinoa is well-known for its resilience to drought-stress conditions, to thermic-variability, for being a halophyte plant and standing soils with different pH levels (Geerts et al., 2009a; Adolf et al., 2013; Bertero, 2015). The investigation on different irrigation schedules and drought-stress tolerance of quinoa adds on the research conducted by Garcia et al. (2003), Geerts et al. (2008a), Azurita-Silva et al. (2015). The research approach is highly pertinent, as it is providing a set of water conservation strategies using technological innovations in a region where water-resources are extremely limited.

This study also expands the coverage of crop-modelling in Africa using different climate scenarios. In this line, Challinor et al. (2007) affirmed the lack of studies examining crop-responses to changing

climatic conditions within the region due to poor climate data. Studies on AquaCrop modelling in Sub- Saharan Africa have mainly targeted vegetables and only one focused on maize (Karunaratne et al., 2011; Sam-Amoah et al., 2013; Darko et al., 2016; Akumaga et al., 2017). While the experiment conducted under controlled-climatic conditions and examining the effect of heat-stress at flowering has answered some of the uncertainties emerging from different FAO-TCP reports (Breidy, 2015; Djamal, 2015; Hassan 2015; Saeed, 2015).

Although quinoa is not a tree, field experimentations with drought-tolerant crops fall within national/international efforts on combating the effects of climate change and desertification in the Sahel. This is the case of the Great Green Wall, Africa’s flagship on combatting desertification, advocating to plant a continuous band of trees from Senegal to Djibouti as a mean of reducing desertification in the region (Berrahmouni and Sacande, 2014; O’Connor and Ford, 2014).

1.3. Research questions and problems addressed The issues tackled in this thesis can be framed into three categories of research questions: (i) Uses of qualitative research methods to evaluate: what are the ways farmers perceive changes in climate and how they are adapting to it? In order to identify farmer’s needs, we must understand how changes in climate are affecting them. Once the climatic problem is contextualised and understood, then; (ii) How can we support farmers in the adaptation to climate change, particularly during the dry season when food insecurity rates are at its highest? This is addressed by giving the farmers the agronomic solutions to adapt and cope with changes in climate; for example, by introducing climate-resilient crops, such as quinoa in the Sahel. Simple to say, hard to do so; why? This is when field experimentation in Burkina Faso takes place. This research question is addressed by conducting a thorough agronomic investigation on how the crop adapts to multiple abiotic stresses (drought, heat-stress, N-poor soils etc.). By monitoring quinoa’s responses to different sowing dates, irrigation and N-fertilisation levels determination can be made if a crop is suitable for a giving zone. If the crop is well-adapted, then an understanding of how the crop will adapt to projected changes in climate leads to the next research question;

(iii) How quinoa will adapt to increasing CO2 emissions and increasing temperatures in a region with high precipitation variability? In this section, climate modelling and crop growth modelling is used to have a better understanding of crop’s phenological and physiological responses. Other research questions that come together with the previous are then addressed. For example, how will the crop respond to new environmental conditions that differ to that of the present? In which time of the year and in which parts of the country will it be suitable to grow quinoa in the future? All of the previous are crucial for determining crop-calendars, but also for providing researchers and policymakers with agronomic guidelines and tools for scaling up the crop.

1.4. Research aim and objectives The main aim of this research is to analyse and evaluate the adaptability of a climate-resilient and highly nutritional crop, Chenopodium quinoa (Willd.), in a country experiencing from natural hazards and facing high undernourishment rates. The specific objectives (SO) of this thesis are the following:

For chapter 2 . SO1: Evaluate farmer’s level of awareness on extreme weather events. . SO2: Identify the soil and water conservation practices adopted throughout the country. . SO3: Assess the degree of vulnerability to climate change. . SO4: Gather the existing information communication technologies (ICT’s) for the delivering of weather and climate information services.

For chapters 3 and 4 . SO1: Assess the suitability of quinoa’s short cycle versus long cycle varieties. . SO2: Determine the concentration of nitrogen fertilization required for quinoa’s best growth. . SO3: Determine quinoa’s water requirements under the agro-climatic conditions of the Sahel. . SO4: Create a crop-calendar for quinoa in Burkina Faso.

For chapter 5 . SO1: Test the effect of heat-stress at quinoa flowering. . SO2: Test the effect of heat-stress at quinoa seed-germination.

For chapters 6 and 7 . SO1: Calibrate AquaCrop for quinoa using field experimentation results (2018-2019) . SO2: Validate AquaCrop using field experimentation results (2017-2018) . SO3: Simulate yield and biomass responses to different irrigation schedules . SO4: Model future temperatures, using CMIP-5, for different climate scenarios . SO5: Simulate yield and biomass responses to different climate scenarios

For Appendices . SO1: Prepare technical guidance’s for quinoa in Burkina Faso. . SO2: Results dissemination with stakeholders’ across-scales (researchers, agro-business sector, focal points in agriculture) of two year-experimentation and on climate change aspects. . SO3: Quinoa tasting with stakeholders across-scales (researchers, agro-business sector, focal points in agriculture). . SO4: Raise farmer’s awareness, through focus groups, on climate change causes and consequences.

1.5. Structure of the research This thesis displays the research material collected and analysed at the University of Florence (UniFi), Italy, and at Institut de l’Environnement et Recherches Agricoles (INERA), Burkina Faso, between November 2016 and October 2019. The thesis is divided as follows: Chapter 1: Introduction; Chapters 2-7: quinoa research-experiments and climate modelling; Chapter 8: conclusions and further research.

Chapter 1 gives an outlook of past and future regional warming, and on the changes of precipitation patterns and its impacts on human and ecological systems in the Sahel. A glimpse of agricultural adaptation and technological innovations within the region is displayed. The aims of the research, justification of the topic, questions being addressed and gaps filled within the literature are discussed.

Chapter 2 frames the context of agriculture and climate change in Burkina Faso. Through surveys conducted in all agro-climatic zones (Sahel, Soudano-Sahelian and Soudanian zone) the knowledge and awareness of Burkinabe farmers to extreme weather events and methods of agricultural adaptation are assessed. From surveys, a better understanding is acquired on how climate change is being perceived, how are farmer’s adapting to it, and which policies are being implemented to increase the resilience amongst the most vulnerable.

Chapter 3 explores the effect of different abiotic factors, water and fertilization, on two varieties of quinoa during the 2017-2018 dry-season. Different irrigation schedules and N-fertilisation levels are part of the trial to evaluate the adaptability of short/long cycle varieties of quinoa to environmental stresses.

Chapter 4 is an overall assessment of the two-year (2017-2018 and 2018-2019) quinoa-trials in Burkina Faso. By studying the behaviour of this plant to different sowing dates, irrigation schedules, N-fertilisation levels a complete evaluation of the suitability of this crop is performed. The emerging findings are combined and a set of agronomic recommendations are provided.

Chapter 5 study’s the effect of heat-stress on quinoa and its viability and potential for the introduction in the Sahel region. By performing a trial under controlled climatic conditions, quinoa’s response to high temperature stresses in quinoa’s most critical phenological phases, seed-germination and flowering, is measured. Evidences emerging from controlled climatic conditions are then extrapolated within a region where quinoa is currently being introduced.

Chapter 6 simulates quinoa’s responses to drought-stress conditions and validates the data emerging from two years of field observations in Burkina Faso. By calibrating a crop growth model, AquaCrop, developed by FAO (version 6.1), a set of irrigation management strategies are proposed for making a better use of water resources during the dry season.

Chapter 7 models the projected temperature increase during the dry season in Burkina Faso until 2100. By estimating how global warming, using two climate scenarios (RCP 4.5 and RCP 8.5) and a total of 43 global climate models (GCM’s), will affect the performance of quinoa, the crop’s spatiotemporal suitability is determined.

Chapter 8 gathers the main conclusions of chapters 2 to 7, provides a SWOT analyses and identifies possible pathways for further research.

1.6. Contribution to the field - First research paper on quinoa in sub-Saharan Africa - First research paper using AquaCrop model with quinoa outside its ecosystem of origin - First research paper showing the effect of heat-stress at quinoa-flowering and its effect on yield losses - First research paper using the ensemble of CMIP-5 (43 GCM’s) in Burkina Faso

CHAPTER 2: FARMER´S AWARENESS AND AGRICULTURAL ADAPTATION TO CLIMATE CHANGE IN BURKINA FASO´S DIFFERENT AGRO-CLIMATOLOGICAL ZONES

Abstract The Sahel is considered a hot-spot of climate change and is portrayed among the most vulnerable regions in the world. Farmers in Burkina Faso have been exposed to climate variability and have traditionally cope and adapt their agricultural activities to continuous changes in climate. This study analyses the indigenous knowledge and adoption of agricultural adaptation strategies to climate change along Burkina Faso´s different agro-climatic zones (Soudanian, Soudano-Sahelian and Sahelian zone). In this research, 150 one-to-one surveys have been conducted, concluding that farmers are aware of changing climatic patterns: increasing temperatures, greater rainfall variability and intensity, and shifts in wet season onset and offset. There is a good correlation between empirical evidence and climatic trends, but perceptions can be distorted with time. Soudanian farmers are setting new clues of changing climatic patterns which have not yet been reported in literature; particularly when describing changes in annual rainfall distribution. Adaptation to climate change highly varies along the country, with soil and water conservation strategies (SWC) widespread among Sahelian farmers, basing their adaptation on a traditional heuristic approach. In contrast, the cropping systems of Soudanian farmers is modernizing with market-oriented purposes. The high exposure and risk of farmers throughout the country makes them see themselves as highly vulnerable to forthcoming climate change. The lack of agrometeorological information services and early warning systems has simply exacerbated farmer’s vulnerability to climate change impacts. Policymaking for developing agricultural adaptation should be tailored according to the means and needs of each agro- climatological zone. Key words: agriculture; climatic trends; natural hazards; agricultural adaptation strategies; Sahel

2.1. Introduction The Sahel is considered a hot-spot of climate change with unprecedented changes expected to happen, while being portrayed as one of the world’s most vulnerable regions because of its low societal adaptive capacity (Boko et al., 2007; Diffenbaugh and Giorgi, 2012; Niang et al., 2014). Development challenges, including unsustainable population growth, endemic poverty, fragile governmental institutions, lack of governance, poor educational and health systems, as well as degraded environments are among the factors exacerbating human’s susceptibility to climate change related impacts. Vulnerability among climate-reliant and sensitive groups its often greater, i.e. agriculture and livestock, for which much research has been conducted. Some of this research has sought to better understand indigenous knowledge on climate impacts, likewise farmer’s adoption of adaptation strategies for coping with climate variability. Some of this research is Maddison´s (2007) work on farmer’s perceptions and adaptation to climate change in 11 different African countries, Mertz et al. (2009) on agricultural adaptation in rural Sahel, or Nyong’s et al. (2007) work on local knowledge and on adaptation and mitigation of climate change in the Sahel. In Burkina Faso many studies have been 27

conducted on the typology of farmer’s adaptation, awareness of climate hazards and climatic trends (Adesina and Baidu-Forson, 1995; Roncoli et al., 2001; West et al., 2008; Barbier et al., 2009; Ouédrago et al., 2010; Zorom et al., 2013; Fonta et al., 2015; Sanfo et al., 2017). The previous investigations remain site-specific, particularly lacking of a thorough understanding on how farmers perceive and adapt to natural hazards within the country´s different agro-climatic zones. Additionally, there is not yet a detailed study on early warning systems and communication fluxes of climatic and weather information among stakeholder’s across-scales. In addition, Ouédrago’s et al. (2010) research on farmer´s perceptions on rainfall variability and agricultural adaptation is, up to now, the largest and most complete work conducted in Burkina Faso. In spite of the 1530 agricultural-holding sample size, this study does not provide a sound correlation between agricultural adaptation and climatic trends, nor on the adoption of SWC strategies.

This research aims to compile farmer’s knowledge on climate change by analyzing the adoption of agricultural adaptation strategies besides of assessing the access to climate and weather information services in different climatic zones (Soudanian, Soudano-Sahelian and Sahelian zones). The comparison of both observed (quantitative climatic data from land-based weather stations) and perceived (qualitative climatic data from farmer´s perception) climatic information allows to identify the common points between the two. This research adds value to the existing literature on climate change, adaptation and vulnerability that remains, to some extent, site-specific by gathering farmer´s perceptions in each of the different agro-climatological zones. This study also supports policymaking by contextualizing the climatic issue at a national/regional level, and on addressing agricultural adaptation strategies at a regional/local level.

2.1.1. Regional climate trends The Sahel is the semi-arid region that lies between the Sahara Desert and Africa´s tropical zone. It is characterized for having warm mean annual temperatures and a distinguished wet season from May to October, being the result of the displacement of the Inter-tropical Convergence Zone (ITCZ) towards the north. In the African continent, observed temperature trends show, with a very likely degree of confidence, an increase of mean annual temperatures of 0.5 ºC, or greater, over the last 50 to 100 years (Nicholson et al., 2013). Nevertheless, the rate of temperature rise differs along the continent. For instance, in West Africa and the Sahel region, temperatures have augmented by 0.5 ºC to 0.8 ºC between 1979 and 2010 (Collins, 2011), with a higher number of warm days and nights between 1961 and 2000 (New et al., 2006; Mouhamed et al., 2013). Despite of the uncertainty and differences between scenarios and General Climate Models (GCMs), temperature projections show an increase of 3 ºC to 6 ºC by the end of the century (Fontaine et al. 2011; Monerie et al., 2012). Though, the occurrence of droughts in arid and semi-arid regions is considered a normal pattern, shifts in wet and dry periods on annual and decadal timescales are indicators of a high climate variability within this

region (Tschakert, 2007). In this line are Yabi´s and Afouda´s (2012) findings, asserting that West Africa has been affected by unprecedented rainfall variability within the last decades. While others affirm that rainfall in Western Sahel has decreased over the past century, with a very likely degree of certainty (Niang et al., 2014). On the other side, recent research and climate modelling has suggested the greening of the Sahel during the last decade, being the result of consecutive years of rainfall recovery (Hickler et al., 2005; Olsson et al., 2005; Giannini et al., 2008; Ouedrago et al., 2014). According to these studies, the rainy season is nowadays longer when compared to the dry period of 1968-1995, with a recent increase in rainfall variability during the rainy season (Descroix et al., 2015). Some discrepancies emerge among researchers when examining rainfall projections in West Africa and have been highlighted in the AR5-IPCC report (2014). Uncertainties on rainfall projections in West Africa remain very high, as GCMs are incapable of including convective rainfalls (Roehrigh et al., 2013). Moreover, Regional Climate Models (RCMs), unlike GCMs, are capable of predicting more intense but less frequent rainfalls in West Africa and the Sahel, while heat-waves are expected to become more frequent (Vizy and Cook, 2012). All of these multiple drivers, changing climatic patterns, unsustainable land management and demographic pressure, among others, have elevated the exposure and risk, and consequently the vulnerability of the population living in the Sahel.

2.2. Materials and methods This study was carried out between February and April 2018 in each of Burkina Faso´s different agro- climatological zones: Sahelian (400-600 mm year-1), Soudano-Sahelian (600-900 mm year-1) and Soudanian (more than 900 mm year-1). The spatial distribution of surveys was as follows: 38 surveys in the Sahel, 58 surveys in the Soudano-Sahelian, and 58 surveys in the Soudanian belt (Figure 2.1). The criterion used for the survey size sample was according to access to specific areas, in terms of security, and population size within the agro-climatological zone. In total, 150 one-to-one surveys each including 17 questions, were conducted to examine the ways farmers perceive and cope with changing climatic trends. Depending on the region, the survey was carried out either in French, Mooré, Dyula and Fula languages. Surveys-data was collected either at farmers’ fields, workshops organized at INERA Farako-Bâ research station and at agricultural associations. Farmers were selected randomly, regardless the age and sex. Validation of the survey was done by local experts, just by selecting a small-subset of participants in order to avoid leading questions. The different surveyors were trained with survey techniques and familiarized with questions prior to the data collection process. Emerging results were compiled according to the different climatic zones in order to be compared and contrasted. The statistical tool used for analyzing the surveys was the percentage of the total number of respondents per climatic zone; whereas the ratio of the standard deviation to the mean (coefficient of variation-CV) was used to determine the inter-annual rainfall variability with respects to the mean rainfall. The time-frame used to analyze farmer´s perceptions on extreme weather events

was 10 years or more. Additional farmer-observations were included in each of the surveys to improve farmer’s assertions on extreme weather events.

The first section of the survey was focused on background information of the farmer; that included: place of residence, age, sex, literacy, main crop, field size and crop yield. The second section was on the farmer´s perceptions on changing climatic trends (questions 1 to 9); including information on temperature changes, dry spells within the rainy season, changes in rainfall intensity, changes in inter- annual rainfall variability, shifts in the onset and offset of the rainy season, changes in Harmattan wind patterns and changes in dust storm frequency, as well as on the farmer´s level of awareness on changing climate. The scope of the third section was on climate change impacts (questions 10 to 12); including questions on migration, drivers for migration and vulnerability to climate change. The final part of the survey was on agricultural adaptation to changing environmental conditions (questions 13 to 17), with specific questions on soil and water conservation strategies, agricultural technological innovations and on existing ICT´s for delivering climate and weather information services to end- users.

To compare qualitative data with quantitative information, the Burkina´s Direction Générale de la Météorologie (DGM) has provided this research with daily weather data on rainfall, maximum/minimum and mean temperatures for the period 1973-2017 (45 years). In total, three synoptic automatic weather stations, each located in one of the country’s different agro-climatic zones, were used to analyze changing climatic trends: Sahel (Dori; 14º01´N 0º01´W; 280 masl; climate-data coverage 88 %), Soudano-Sahelian (Ouagadougou airport; 12º21´N 1º30´W; 310 masl; climate-data coverage 95 %) and Soudanian climate (Bobo Dioulasso airport; 11º09´ 4º19´W; 450 masl; climate- data coverage 93 %). Existing climatic-gaps have been addressed using information provided by the National Oceanic and Atmospheric Administration (NOAA).

Figure 2.1. Spatial distribution of surveys conducted between February and April 2018

2.3. Results For a more comprehensive analysis of the results, this section has been divided into five. The first part, climate characterization, has merely focused on the 45year dataset provided by DGM and NOAA. Whereas the following sections, from second to fifth, on the emerging findings of the surveys (agriculture in Burkina Faso, farmer’s perceptions on natural hazards, agricultural adaptation to climate change, and vulnerability and awareness to climate change).

2.3.1. Climate characterization Mean temperatures have steadily increased during the period 1973-2017 (Figure 2.2 and Table 2.1). Major temperatures changes are observed in the southernmost parts of the country where temperatures have risen at a rate of 0.3 ºC per decade. Similar temperature increasing trends are observed towards the Sahel, with a 0.15 ºC rise per decade, but at a slower pace if compared to the Soudanian region. Alike pattern is observed for daily maximum temperatures, with a significant increase in the number of days with temperatures higher than 40 ºC (Figure 2.4 and Table 2.1). Indeed, for the 2013-2017 period temperatures have exceeded 40 ºC during 60 days per year in Ouagadougou and 6 days per year in Bobo Dioulasso. When comparing these values to the reference climatological period (1980-2010), temperatures have exceeded 40 ºC during 36 days per year in Ouagadougou, whereas 3 days per year in Bobo Dioulasso. Dori, within the Sahel region, is already experiencing on average 100 days per year with temperatures higher than 40 ºC (period 1980-2010).

In regards to the rainy season, great inter-annual rainfall variability is reflected when calculating the coefficient of variation (CV). During the periods 1973-1982, 1983-1992, 1993-2002 and 2003-2012, the inter-annual rainfall variability (CV) in Dori was of 12.5 %, 17.0 %, 15.3 % and 21.1 %, respectively. For the same periods, CV in Ouagadougou was of 12.7 %, 8.8 %, 9.4 % and 18.3 %, respectively. For Bobo Dioulasso CV was of 5.6 %, 10.5 %, 9.2 % and 10.1 %, respectively. Overall, CV values show an increasing inter-annual rainfall variability over time in central and northern parts of the country; whereas for the southernmost parts, even if inter-annual rainfall variability remains higher when compared to the other regions, there is not an increasing trend over time. Moreover, rainfall decrease is extraordinary in central parts of the country, with a 10 % rainfall reduction per decade in Ouagadougou (Figure 2.3 and Table 2.1). In the Sahel, rainfall decrease has stagnated and slightly recovered during the 2013-2017 period, however it has experienced an overall rainfall reduction of 30 % between 1973-1982 and 2003-2012. For Bobo Dioulasso, the total amount of annual rainfall has not significantly changed over time.

Changes in other climatic patterns, such as frequency and 24 h maximum rainfall per year, can be observed throughout the whole country. If the climatological periods, 1973-1982 and 2003-2012, are compared, the number of rainy days per year has dropped, from 68 to 59 in Bobo Dioulasso (13 % reduction), from 46 to 44 in Ouagadougou (4 % reduction in Ouagadougou), and from 31 to 26 in Dori (16 % reduction). Regarding the 24 h maximum rainfall per year, it has gone up from 86 mm day-1 to 89 mm day-1 in Bobo Dioulasso during 1973-1982 and 2003-2012; whereas, for the period 2013-2017 this value has been as high as 120 mm day-1. For the same periods (1973-1982 and 2003-2012), the 24 h maximum rainfall per year has increased from 59 mm day-1 to 66 mm day-1 in Ouagadougou, and from 36 mm day-1 to 49 mm day-1 in Dori. As a result, these values certainly reflect greater rainfall intensity; especially, towards the north.

Figure 2.2. Mean annual temperature (°C) trends for Dori, Ouagadougou and Bobo Dioulasso for the period 1973-2017

Figure 2.3. Mean annual precipitation (mm) trends for Dori, Ouagadougou and Bobo Dioulasso for the period 1973-2017

Figure 2.4. Number of days per year with temperatures above 40 °C for Dori, Ouagadougou and Bobo Dioulasso for the period 1973-2017

Table 2.1. Lineal regression for different meteorological parameters (mean temperatures, total annual rainfall and days year-1 with temperatures higher than 40 °C) for the period 1973-2017

Location Mean temperature (°C) Total annual rainfall Number of days (mm) per year ≥ 40 °C Dori y = 0.015x + 29.14 y = -2.70x + 467.42 y = -0.10x + 100.64

Ouagadougou y = 0.011x + 28.44 y = -5.80x + 800.64 y = 0.66x + 22.72

Bobo Dioulasso y = 0.033x + 26.93 y = -0.40x + 992.22 y = 0.14x - 0.06

2.3.2. Agriculture in Burkina Faso Survey information confirms that Burkina Faso has a traditional land tenure system were women have very little access to land, especially in rural areas. However, women play an important role in agricultural clusters for vegetable cropping (i.e. outskirts of Ouagadougou and Bobo Dioulasso). Survey background information shows that majority of the respondents were aged between 30 and 49, with an overall rate of illiteracy of 57 %. The land is predominantly used for smallholder farming for subsistence agriculture of cereals (maize, millet, sorghum and rice), where 60 % of the farmers- surveyed owned less than 5 ha; except for maize growers in Hauts-Bassins and Boucle du Mouhoun regions, having fields greater than 20 ha for market-oriented purposes (Figure 2.5). Moreover, the main crops vary within the different agro-climatological zones. From south to north, crop distribution in Burkina Faso is as follows: Soudanian agro-climatic zone (maize and rice are the most cultivated cereal crops, while cotton placed third). Crop water requirements during the growing season of maize, rice and cotton varies between 800 mm up to 1300 mm; hence explaining its distribution within the country´s wettest zones. In the Soudano-Sahelian agro-climatic zone crop-mix is equally distributed, with maize, millet, sorghum and sesame as the main crops. Crop water requirements are of 450 mm to 800 mm within this zone, being maize the crop with highest water requirements. Some cotton production it’s also found within the southernmost parts of the Soudano-Sahelian belt. In the Sahel, less water demanding cereal crops are grown, being millet and sorghum the most widely distributed. Additionally, observed crop yields considerably vary among farmers within the same region. For instance, maize yields in Hauts-Bassins ranged from 500 kg ha-1 up to 4000 kg ha-1. Those farmers having higher yields acknowledged using better performing seeds. A similar pattern has been observed with rice growers in Cascades region, with yields ranging from 500 kg ha-1 up to 2000 kg ha-1.

Figure 2.5. Crop-mix among farmers surveyed in Burkina´s agro-climatic zones

Soudanian Soudano-Sahelian Sahelian

2.3.3. Farmer’s perceptions on natural hazards Locals’ understanding of changing climatic trends can be shaped by recent extreme weather events, and in some cases perceived evidences can be argued. To make this clear, some authors have affirmed that migration to the cities reduces the access to word of mouth information on past climatic events, typically of rural farming families; besides of having a poor reference baseline to compare present climatic information (Pahl et al., 2014). Having clarified the previous, the first eight questions of the surveys have been focused on natural hazards (Figure 2.6). Regarding farmer’s survey-responses, there is a consensus on rising temperatures among Burkina´s different climatic zones; with a higher percentage of farmers acknowledging so towards the northern parts of the country (95 %). In the Soudanian climate, a smaller but still significant number of farmers have affirmed so (89 %).

Other signals of climate change are those concerning rainfall behavior, including seasonal distribution and characteristics of rainfall. There is a consensus among farmers along the country, acknowledging that dry spells during the rainy season have lengthen (above 86 % of all respondents), with a shifting and shortening of the rainy season. In particular, dry-spells play a crucial role on the success of the growing season, the longer and more recurrent they are, the more severe its consequences are for rainfed agriculture. In particular, in Hauts-Bassins region, with a Soudanian climate, farmers assert that periods without rain during the rainy season can last up to two weeks. For the Plateau Central region, Soudano-Sahelian climate, farmers continue to keep close in mind the poor harvest of the 2017 growing season, with dry spells as long as three weeks in July. Meteorological data from a close by weather station, at Ouagadougou airport, corroborates the previous.

Furthermore, farmers refer to greater rainfall intensity towards central and northern parts of the country; whereas within the southernmost parts, rainfall is perceived as less intense (57 %). Overall farmers agree upon the fact that rains are less frequent but more intense than before. However, within the Soudanian climate, only one-third of the respondents believe that rainfalls are nowadays more intense. In regards to rainfall distribution there are spatial differences among farmer’s appraisals. In particular, Sahelian farmers generally admit a delay of wet-season onset as well as a premature offset (76 % and 92 %, respectively). Slight discrepancies emerged among respondents in Soudano-Sahelian climate, where just two-thirds of the respondents agree on the shortening of the rainy season. Although Soudanian farmers perceive in general a shortening of the rainy season, sporadic but significant rains are now occurring in March. This unusual observation could suggest a shift in rainfall distribution from a unimodal towards a bimodal precipitation regime, typical of tropical climates. In fact, meteorological data from Farako-Bâ INERA research station at Hauts-Bassins region supports the previous, with rainfall records as high as 30 mm day-1 and 40 mm day-1 in March 2018. Recent rainfall anomalies cannot be considered a new climatic pattern, but deserve further research and a longer temporal baseline.

Moreover, most farmers in the Soudanian climate are witnessing a greater inter-annual rainfall variability (71 %), while decreasing towards the north, where only 46 % of the respondents affirm so. For the question on Harmattan winds, which are prevailing north-east winds associated with high pressure systems during the dry season in the Sahel, most of the farmers consider present winds stronger than before, particularly towards the north. Relationships between different questions and possible responses among regions have also been reported. For example, within the Soudano-Sahelian and Sahelian climate (in Plateau Central, Est and Sahel region), farmers perceive an intensification of rainfalls to blame on stronger Harmattan winds. Also, farmers perceive a relationship between stronger Harmattan winds and more frequent dust storms (questions 7 and 8).

Figure 2.6. Farmer´s responses on extreme weather events (questions 1-8) in the different agro- climatological zones

Note: questions 1-8 most given answer, where numbers as a percentage of total respondents within climatic zone

2.3.4. Agricultural adaptation to climate change Farming agricultural practices differ according to the needs and means both required by and available to farmers within the different regions. For that, the driving forces for different types of adaptation 36

strictly depend on the exposure and risk that farmers face to specific natural hazards in a given place, as well as on the access to finance, knowledge-sharing hubs, agricultural raw materials, among other aspects. Generally speaking, it is more likely to find non-organic and intensive agriculture with market-oriented purposes within the Soudanian climate, than a traditional and subsistence-based agriculture typical of the Sahel.

In this section, SWC practices have been examined as well as the innovation strategies that farmers are adopting in each of Burkina´s agro-climatological zones (Table 2.2). Soil conservation practices can equally be water preserving, and for each of the following questions (questions 13 to 15) farmers could provide multiple responses. Despite of the similarities between soil and water conservation practices they have been divided into two: 1) traditional methods preventing soil degradation, including soil erosion and reduction of fertility; 2) water conservation strategies and water harvesting methods. In Burkina Faso, the most extended practice for maintaining soil fertility is manure from zebu, applied once before sowing. The amount of fertilizer application strictly depends on the purchasing power parity of the farmer, rather than on the type of the crop. Secondly, the most extended practice is minimum tillage, 5-15 cm depth, through traditional hand hoeing or furrow-tilling and donkey-draft in order to minimally disrupt the soil´s surface while allowing water to infiltrate instead of running-off. For this question, Sahelian farmers affirm to practice dromedary-draft minimum tilling or simply no-tilling, as soil texture within this region is predominantly sandy. Thirdly, crop rotation, i.e. maize-cotton or maize-sorghum, is a very common practice among Soudano-Sahelian and Soudanian farmers. Instead, in the Sahel, farmers most likely grow vegetable crops, or perform agro-forestry and silvopastoral systems in order to reduce evapotranspiration, increase nutrient input and enhance soil stability. Farmers in the Soudano-Sahelian and Soudanian climate also perform agro-forestry, but through agrisilvicultural systems where cereals crops are combined with trees; for instance, Mangifera indica, Acacia albida, Anacardium occidentale or Vitellaria paradoxa. Another commonly adopted practice among Soudanian farmers is to leave stalk- leftovers from cereal crops to improve the organic matter and structure of the soil. On the contrary, Sahelian farmers prefer storing the stalk on barns or roof tops for feeding their livestock during the dry season. Additionally, ancestral planting pits, known locally as zaï, are used for restoring soils and for increasing crop yields. This agronomic practice is particularly found in northern and drier parts of the country, where at least one-third of the farmers assert practicing so. Notwithstanding, towards the south it is more likely to find farmers that place stone bunds for reducing water surface runoff, increase water infiltration and diminish soil erosion. Finally, raised-bed cropping and cultivation tables to remove excess of water are predominantly adopted by Soudanian and Soudano-Sahelian farmers which grow vegetables or pseudo-cereals, i.e. Amaranth, in small agricultural clusters.

In Burkina Faso, water conservation infrastructures are very limited and at least half of the farmers’ surveyed claim to have no access to water; particularly within the Soudano-Sahelian and Sahelian zones where ground-water is less abundant. Water is often acquired from traditional water-wells, embankments or nearby dams where smallholder farmers can withdraw water manually or use their own diesel-propelled-pumps; i.e. smallholder farmers close by to water reservoirs in Ouahigouya and Ouagadougou. Another sustainable practice is ground-levelling, improving water coverage in rice crops within Burkina´s wettest zones, i.e. Cascades region. Soudanian farmers have better access to water because of a denser network of water-channels for irrigation than in other regions. Indeed, farmers belonging to agricultural associations generally have better access to water storage systems, such as polytanks or basins; whereas those farmers using drip irrigation systems remain scarce.

Throughout the country, the most common tool used for mechanized-laboring are ploughs, with two- thirds of total respondents; particularly within Soudanian and Soudano-Sahelian zones, where intensive agriculture is widespread. However, these are still animal-drawn with very few farmers using tractors. The use of non-organic products, fertilizers, herbicides, pesticides and insecticides is broadly applied among Soudanian farmers, sometimes lacking of an application know-how or just neglecting the environmental consequences that can occur from using them (volatilization and leaching). The need to tackle pest, diseases and weeds for achieving higher yields in regions tightly dependent on markets, makes Soudanian farmers the most likely to non-organic agriculture. Indeed, almost every Soudanian farmer has affirmed using herbicides, Glyphader (agrochemical containing Glyphosate), and fertilizers in the form of urea, NPK and phosphate; as well as fungicides and insecticides such as Cypercal and Caiman Rouge (agrochemicals containing Cypermetrine, Endosulfan, Permethrine and Thirame), among others. The use of the previous slightly decreases towards the Soudano-Sahelian zone and drops in the Sahel, where traditional and sustainable agriculture is extensively practiced. In fact, some of the farmers belonging to agricultural associations in Plateau Central region, Soudano- Sahelian climate, have acknowledged spraying bio-fertilizers in vegetable crops, Nourrisol (rich in NPK, CaO, MgO, Fe, Zn, Cu, and Mn). Approximately half of the farmers, in both the Soudanian and Soudano-Sahelian zone, assert using better performing seeds, that includes climate and pest-diseases resistant seeds. For instance, maize or black-eyed-pea seeds provided by the Institut de l’Environnement et Recherches Agricoles (INERA), Centre National des Semences Forestières (CNSF) or at agricultural associations. Whereas the rest of the farmers either select from their own seed-pool, exchange with other farmers or acquire them at local markets. There are no farmers that have machinery for harvesting, except one in the Sahel, nor sprinkler irrigation or greenhouses. Finally, around 10 % of the farmers, most of them in the Sahel, do not have access nor purchasing power to any of the previous techniques.

Table 2.2. Number of farmers´ adopting/embracing soil and water conservation practices (SCW) and agricultural technological innovations

Question Agronomic practice Soudanian Soudano-Sahelian Sahelian (56 farmers) (56 farmers) (38 farmers)

Soil conservation Zaï 4 3 11 Half-moons 1 1 2 Stone bunds 14 10 6 Straw 29 11 2 Windbreakers 6 2 1 Manure/Compost 48 54 35 Agroforestry 27 33 25 Rotation 32 46 18 Minimum tillage 39 36 34 Crop association 13 18 24 Cultivation tables 0 5 0 Raised bed cropping 7 13 0 Furrow 30 42 18 None 0 0 0

Water conservation Water well 8 17 11 Water basin 1 6 0 Reservoir 14 6 16 Programmed irrigation 1 0 0 Embankments 12 2 0 Water tanks 0 5 0 Drip irrigation system 1 0 0 Ground levelling 15 3 0 Others 8 0 0 None 22 33 19

Technological innov. Better performing seeds 24 26 3 Sprinkler irrigation 0 0 0 Mechanical tilling 42 41 20 Greenhouses 0 0 0 Mechanized harvesting 0 0 1 Pesticides 23 10 0 Insecticides 41 37 19 Herbicides 52 33 7 Fertilizers 46 33 19 Others 1 5 3 None 1 0 12

Note: Questions 13-15: multiple-response questions

2.3.5. Vulnerability and awareness to climate change Assessing the perception of human´s vulnerability to the effects of climate change is crucial to better understand their ability to face, resist and recover from climate change negative impacts. Vulnerability can be defined as the sensitivity of a farmer to a given natural hazard; being the degree of sensitivity the extent to which an extreme weather event can affect its productivity. In Burkina Faso, the combination of low adaptive capacity and lack of institutional support and will, has left farmers responsible of self-adapting to climate change impacts. Survey results show an increase in vulnerability towards the northern parts of the country (Figure 2.6). In the Sahel farmers are more 39

exposed and at higher risk of facing longer dry-spells, prolonged heat-waves and the effects of a shortening of the rainy season. For this reason, the majority of the Sahelian farmers (92 %) considered themselves as highly vulnerable (score of 5 out of 5) to climate change impacts; whereas towards the south, this value drops down to 73 % (Soudano-Sahel) and to 33 % (Soudanian). Therefore, there is an increasing vulnerability trend at higher latitudes. Overall, 94 % of the famers affirm having a vulnerability score higher than 3, meaning that most of the farmers in Burkina Faso see themselves as, to some extent, susceptible to climate impacts. The previous question has been followed by one on the factors triggering migration. Overall, 34 % of respondents live in a different place to that of birth, while one-quarter have migrated due to environmental reasons such as: lack of rainfall (5 respondents), soil degradation (4), extreme heat (2), excess rainfall (1) and desertification (1). The main factors inducing migration of the remaining respondents were: marriage, recent war in Ivory Coast, demographic pressure and other non-specified reasons.

Public and private efforts seeking to create climate-resilient communities are negligible, with very little interaction and poor communication flow between stakeholders across scales: farmers, researchers, ministries and private sector. Most of the respondents acknowledge receiving climate and weather information services through the local radio (two-thirds of the respondents) and via TV weather-programs (half of the respondents) (Figure 2.7). Another widespread method for communicating weather information is through the word of mouth at markets or fields. Efforts for improving ICT´s, through SMS, are being made; for instance, by diffusing weather alerts, seasonal weather forecasts, as well as agricultural and livestock tips for farmers. The Agence Nationale de la Météorologie (ANAM) is promoting this initiative, and has been reported in the following regions: Hauts-Bassins, Plateau Central, Boucle du Mouhoun, Centre Ouest, Centre and Nord. For this initiative, more detailed weather information is provided through a written message or voicemail in the local language. Very few farmers (2 respondents) have received climate flyers and none of them affirm having access to agrometeorological bulletins.

Regarding the clustering of farmers in agricultural associations, less than one-third of the farmers assert belonging to one; particularly in central parts of the country, while farmers in the Sahel are not organized nor structured. Nonetheless, half of the Sahelian farmers have received agricultural trainings, at least once in their life, through workshops or at agricultural events. Soudano-Sahelian and Soudanian farmers acknowledged receiving some type of capacity building and knowledge transfer on agronomic techniques, but very few (only 8 respondents) have affirmed so within the Sahel region.

Figure 2.6. Farmer´s vulnerability (left) Figure 2.7. Means of diffusion of weather and climate information services in Burkina Faso (right)

2.4. Discussion The emerging findings of this research are in harmony with those recorded at land-based weather stations. Regarding rainfall trends, precipitation has decreased along the country´s different climatic zones, particularly within central and northern parts. Farmers within this region have affirmed that water is now scarcer, and that rains are less frequent but more intense than before. In this line is West et al. (2008) study in Central Plateau region, with farmer’s reporting that rainfall is steadily decreasing, while variability is now increasing. Along with the previous, is the research conducted by Ouédrago et al. (2012) on rainfall trends. In this study, farmers have pointed out a rainfall reduction within the last 20 years, particularly in central parts of the country. Moreover, when comparing observed and perceived rainfall trends, farmer´s insights are in line with those values recorded at the different weather stations (NOAA, 2018).

Furthermore, the research of Ingram et al. (2002) in Burkina Faso shows that farmers are aware of the following: climatic variability, more frequent water-deficit years, and on a disruption of the rainy season onset and offset. Similar results have been obtained in our research, particularly on those climate indicators regarding dry spells, likewise on the onset and offset of the rainy season. In more detailed, farmers have made intriguing observations on the rainy season onset. In fact, and even though farmers affirm that the rainfall season onset is now later (May-June), some Soudanian farmers are aware of unusual rains occurring in March. Ibrahim et al. (2012) have determined that the rainy season starts once 5 % of the total rainfall has fallen, while finishing once 95 % of the total amount has been reached. In Burkina Faso, April is the starting month for the rainy season in the southernmost parts of the country, city of Gaoua. When following this criterion, the rainy season starts in Bobo Dioulasso once 50 mm have fallen, normally in April. Astonishingly, between 2014 and 2018, the onset of the rainy season has occurred twice in March, while in the rest of the data-set (1973-2013) it has occurred only three times. Pre-onset of the rainy season has been thoroughly examined, with the 41

ITCZ establishing at 5 ºN during mid-May, while moving abruptly to 10 ºN during late-June (Laux et al., 2008). However, a shifting of the annual rainfall distribution, from unimodal to bimodal, has not yet been discussed within the scientific literature; therefore, deserving further scientific consideration, as rainfall is crucial for agricultural planning operations in West Africa, especially land preparation and sowing.

Moreover, the amount of scientific research and discussion in northern Sahel has intensified after the devastating droughts and famines occurring during the 70´s and 80´s. In this line, recent and interesting research (Olsson et al., 2005; Giannini et al., 2008, among others) show large-variations of the Normalized Difference Vegetation Index (NDVI) within the Sahel region, with rainfall increases in the late 90´s (Hulme, 2001). The climatological data-set from Dori, Sahel region, is in harmony with those findings highlighted in literature, showing a rainfall increase of 13 % between the 80´s and 90´s. Since then, rainfall variability has shoot, with years wetter than average while other years considerably drier. In the Sahel, farmer’s observations do not perfectly match with rainfall records, where only 51 % acknowledge greater rainfall variability within the last 10 years.

Much of this research´s findings on agricultural adaptation have already been pointed out within the scientific literature (Adesina and Baidu-Forson, 1995; Roncoli et al., 2001; Zougmoré et al., 2004; Nyong et al., 2007; Mertz et al., 2009; Ouédrago et al., 2010; Nielsen and Reenberg, 2010, among others). For instance, this research’s and Sawadogo’s et al. (2010) results have shown that traditional agricultural management practices in the Sahel aim to reduce water surface runoff (zaï, stone bunds and half-moon practices), besides of increasing soil’s fertility using organic fertilization (manure). Similar observations, Barbier et al. (2009), have been made in Yatenga province, where farmers are using stone bounds (60 %), apply compost (56 %), and adopt zaï´s (49 %) as part of the soil restoration practices. Whereas very few farmers acknowledge using mineral fertilization on rainfed crops (21 %) or better-performing seeds (9 %). Regarding the latter, this research’s results show that only 8 % of the farmers in the Sahel have access to them. Moreover, demographic pressure is of growing concern, as land is limited and continuously degrading. In fact, Burkina Faso is the world´s eighth country with the highest population growth (CIA, 2017). Similar considerations have been reflected in Yatenga province, Sahelian climate, where three-quarters of the farmers have started recovering exhausted fields (Barbier et al., 2009). On the contrary, the Soudanian zones are characterized for having a more intense and productive agriculture, with widespread use of mineral fertilization. However, mechanization of agriculture continues to be non-existent, except for sugarcane plantations within the Cascades region close to Banfora.

The use of climate and weather information services in Burkina Faso are not yet widely distributed and means of communication need to be improved. Despite of the use of 10-day agrometeorological and public-access bulletins prepared by the DGM and available for the whole country, they continue to remain unknown by end-users. Up to now, probably the most effective observed measure are SMS’ seeking to build climate-resilient communities by providing farmers with real-time weather information. Finally, another agro-meteorological information service is the Famine for Early Warning Systems (FEWS Net, USAID), combining both Geographical Information Systems and agro- meteorological modelling.

2.5. Conclusions This research´s results on farmer’s perceptions on temperature rise is unique, as most of the research conducted on indigenous knowledge in Burkina Faso is based on rainfall trends, but none on temperature trends. However, actual occurrence of extreme weather events can be under-reported or over-reported, and therefore findings emerging from farmer’s perceptions need to be treated carefully. This study also examines and compares local based knowledge with climatic trends in Burkina´s different agro-climatological belts: Soudanian, Soudano-Sahelian and Sahelian. Harmony between farmer’s perceptions and land-based weather station records has been continuous, reflecting that farmers are aware of changes in climate and are consequently taking actions to adapt; in particular, through water and soil conservation strategies. Nevertheless, perceptions differ among the country´s different agro-climatological zones. Sahelian farmers are among those perceiving the greatest temperature change, longest dry spells, greatest changes in wet season onset and offset, stronger Harmattan winds and more frequent dust storms; whereas Soudanian farmers are certainly more aware of inter-annual rainfall variability as well as on changes in intra-annual rainfall distribution. In fact, the Sahel is well-known for being a hot-spot for climate change, aspect reflected in farmer’s vulnerability appraisals and perceptions on extreme weather events. This has resulted in a steady crop reduction, leading to food insecurity and famine. Within northern parts of the country, traditional based agriculture and SWC strategies are certainly not enough for increasing farmer’s resilience. In general, Sahelian farmers require a greater governmental support, that can be achievable through capacity building on agro-meteorological aspects, deployment of agricultural techniques, or by enhancing the delivery of weather and climate information services (seasonal forecasts, weather alerts, and changes in climatic patterns). Accurate monitoring of the Sahel is crucial together with highly performing forecasts that can provide farmers with useful real-time based information for carrying out agricultural activities. Policymaking must be inclusive and consider empirical local evidences (farmer´s perceptions), besides climatic-data from ANAM, on forthcoming national climate and agricultural policies. To increase success, policymaking on agricultural adaptation strategies must be flexible and tailored to farmer needs and means. This will allow farmers to adapt to diverse climate scenarios in the different agro-climatic zones.

CHAPTER III: EFFECT OF DROUGHT AND NITROGEN FERTILISATION ON QUINOA (CHENOPODIUM QUINOA WILLD.) UNDER FIELD CONDITIONS IN BURKINA FASO

Abstract Chenopodium quinoa (Willd.) is an herbaceous C3 crop originating in the Andean Altiplano. Quinoa possesses a great deal of genetic variability, can adapt to diverse climatic conditions, besides of having seeds with high nutritional properties. An experiment conducted in Burkina Faso has determined the response of two quinoa varieties (c.v. Titicaca and Negra Collana) to different planting dates (November versus December), irrigation levels (Potential evapotranspiration-PET, 100, 80 and 60 % PET), and N fertilization rates (100, 50 and 25 kg N ha-1). Main research findings have shown that quinoa can be highly performing under drought stress conditions and low nitrogen inputs, besides of coping with high temperatures typically of the Sahel. The highest yields (1.9 t ha-1) were achieved when sown in November at 60 % PET and 25 kg N ha-1. For this location, short cycle varieties, such as c.v. Titicaca, were recommended in order to avoid thermic stress conditions occurring prior to the onset of the rainy season (May-October). Key words: Sahel; agro-meteorology; extreme climatic conditions; abiotic stress; water management

3.1. Introduction Climate change affects agricultural productivity that needs to adapt to satisfy food demand. Agricultural adaptation becomes crucial in hot-spot regions of climate change, often matching with those having highest undernourishment rates and greatest population growth, i.e. Sahel, in Africa. Among scientists, quinoa (Chenopodium quinoa Willd.) is considered a climate resilient and superfood crop, while being promoted in regions vulnerable to climate change. It is a highly nutritional and gluten free crop, having a balanced composition of essential amino-acids sometimes scarce in legumes and cereals (Repo-Carrasco et al., 2003); as well as for been rich in Ca, Fe, and Mg, with high content of vitamins A, B2 and E (Adolf et al., 2013).

Moreover, quinoa is well-known for its resilience to abiotic stresses being drought-tolerant, halophyte, pH versatile, and resistant to thermic variability. Most of the scientific research is focused on its adaptability to saline levels, being as high as those found in sea water (Jacobsen et al., 2003, Razzaghi et al., 2011, Hirich et al., 2014a; Riccardi et al., 2014; Fghire et al., 2015). In fact, its salt tolerance is the result of osmotic adjustment, osmo-protection, sodium exclusion and xylem loading, potassium retention, gas exchange, stomatal control and water use efficiency (Adolf et al., 2013). As a C3 crop, quinoa´s crop water productivity (CWP), expressed in kg of biomass produced per m3 of water applied, is generally low, lying between 0.3-0.6 kg m-3 in the Bolivian Altiplano while exceeding 1 kg m-3 in Morocco and Italy (Geerts et al., 2009a, Hirich et al., 2014a, Riccardi et al., 2014). Indeed, quinoa´s transpiration rate is similar to that of reference evapotranspiration, hence having low water requirements, around 400 mm (Steduto et al., 2012). Moreover, rapid stomata closure, restricting shoot 45

growth and accelerated leaf senescence makes quinoa highly adaptable to drought stress conditions (Azurita-Silva et al., 2015). In addition, it’s capable of maintaining its turgidity with very low water potentials, while optimizing water use through minimum leaf gas exchange (Jensen et al., 2000; Jacobsen et al., 2003). It can also increase its assimilation efficiency by improving the ratio of photosynthetic rate over transpiration up to 2 (Vacher, 1998; Geerts et al., 2008a). Other morphological and anatomical responses are the presence of calcium oxalate crystals in leaf vesicles, reducing leaf-transpiration, besides of having a thick plant cuticle and sunken stomata (Azurita-Silva et al., 2015). Furthermore, the wide geographical distribution of quinoa has given the plant a great genetic variability, besides of increasing its coping-capacity under extreme climatic conditions (Ceccato et al., 2015). Indeed, temperature is the environmental factor affecting the most crop´s cycle duration, germination, development and seed formation (Hirich et al., 2014b, Bertero, 2015; Hassan, 2015). Further research on nitrogen (N) suggests that greater N-fertilisation can result in a significant yield increase, but having no effect on seed size or weight (Shams, 2012; Benlhabib et al., 2015; Piva et al., 2015). Soils with higher clay content are the most suitable for growing quinoa, as N-uptake, organic matter, soil´s water holding capacity is highest (Razzaghi et al., 2012).

To promote quinoa´s consumption in West Africa, the Food and Agriculture Organization of the United Nations (FAO) has developed Technical Cooperation Programs (TCP/SFW/3404 and TCP/RAF/3602) together with the Ministries of Agriculture. The aim of this research was to investigate the adaptability and performance of two quinoa varieties when sown at different dates, under decreasing levels of irrigation and different N-fertilisation conditions.

3.2. Materials and methods The experiment was carried out during the dry season, from November 2017 to May 2018, at Institut de l’Environnement et Recherches Agricoles (INERA), Farako-Bâ´s research station (11º05´N; 4º20´W). The area of study is within Burkina´s Soudanian agro-climatic belt, with a tropical savannah- wet and hot climate. The onset of the wet season is in May and offset in October, with a total amount of rainfall exceeding 900 mm year-1; where mean annual temperatures can attain 28 ºC. The experimental field was organized in a randomized split-split block design with a multiple factor analysis of variance (ANOVA): 3 levels of irrigation according to the potential evapotranspiration (PET) (Full irrigation-FI: 100 % PET; Progressive Drought-PD: 80 % PET; Deficit Irrigation-DI: 60 % PET), 3 levels of N fertilisation (100, 50 and 25 kg N ha-1), two quinoa varieties (c.v. Titicaca-short cycle-85 days, and c.v. Negra Collana-long cycle-150 days), and 3 repetitions. Quinoa seeds were sown in 54 plots, each of 7.5 m2, in 50 cm row distance and 10 cm space between plants (200000 plants ha-1), at a rate of 10 kg seeds ha-1. The ANOVA was done using IBM SPSS software and Tukey´s HSD test using Minitab 2018.

Sowing was carried out in two dates: 4/11/2017 (hereinafter November) and 8/12/2017 (hereinafter December). The harvesting of November´s sowing was done at the beginning of February for Titicaca (89 days after sowing-DAS) and end of March for c.v. Negra Collana (139 DAS). Whereas the harvesting for December´s sowing, it was carried out beginning March for Titicaca (82 DAS) and end of May for c.v. Negra Collana (159 DAS). Prior to sowing the soil was amended with compost (50.2 -1 - % organic matter) at a rate of 5 t ha , as well as with phosphate (26.7 % P2O5) at a rate of 400 kg P ha 1. Nitrogen fertilisation, in the form of urea (46.2 % N), was split into two doses and was applied 25 and 40 DAS. Weed removal was carried out manually every 3/4 weeks to avoid weed interference with actual crop water requirements. Seeds were treated with fungicides/insecticides (Permethrin 25 g kg-1 + Thirame 250 g kg-1) at a rate of 25 g per 10 kg of seeds, and through foliar application (Cypermethrin) at a rate of 1 litre ha-1.

Prior to sowing and post-harvesting, soil samples were extracted at 0-20, 20-40 and 40-60 cm for the determination of its main physic-chemical characteristics. Leaf chlorophyll was recorded at 30 and 68 DAS using a Leaf Chlorophyll Meter SPAD 502 Plus with a total of 25 observations per plot. The canopy cover was measured at 40 DAS (sowing November) and 56 DAS (sowing December) using the Canopeo app. developed by the University of Oklahoma. The rest of the parameters, including plants height (10 per plot); biomass and seed yield (12 per plot); 1000 seed weight (3 per plot); branching, panicle size, panicle width, stem diameter (5 per plot); root depth and root length (1 per plot), were done at physiological maturity.

Daily evapotranspiration was calculated using the following formula (Hargreaves and Samani, 1985): 0.5 ETo = 0.0023 (T mean + 17.78) Ro (T max – T min) (Eq. 1)

Where: Ro = solar radiation at a given month and latitude (Allen et al., 1998); T mean = mean daily temperature; T max = daily maximum temperature; T min = daily minimum temperature.

Moreover, crop evapotranspiration (ETc = Kc*ET0) was calculated using the crop´s coefficient (Kc) for quinoa´s different phenological phases (Garcia et al., 2003): 0.52 at emergence, 1.0 at maximum canopy cover and 0.70 at physiological maturity. Net irrigation requirements were estimated using ETc daily data and adjusted according to the level of irrigation: ETc*1.0 (FI); ETc*0.80 (PD) and ETc*0.60 (DI). In fact, ETc was adapted according to the growing cycle of both quinoa varieties. A water-counter was placed at the entrance of each irrigation block to estimate the amount of water applied. The drip irrigation flow rate was of 1.05 l hour-1, varying according to the water pressure, maximum 1 bar, and the frequency of water application, between 2 to 4 times per week depending on the growing stage of the plant.

3.3. Results The experimental field was characterised for having a sandy-loam texture in the first soil layer (0-20 cm) with high infiltration rate, low water holding capacity and very poor organic matter content, below 0.5 % (Table 3.1 and 3.2). Mineral nitrogen (ammonium and nitrate) was negligible (0.03 %), while soil pH was slightly acidic (pH 6.5). As a result of low soil carbon content and mineral nitrogen, the C/N ratio remained low (8.8 units of C per 1 unit of N), but had slightly increased after organic amendment up to 10-12 C units per 1 N unit at 0-20 cm depth. As a result of phosphate fertilisation, P within the first layer had boosted from 4 mg kg-1 prior to sowing, up to 84-106 mg kg-1 after harvesting. Finally, bulk densities were of 1.66 g cm-3.

Table 3.1. Main soil physic-chemical characteristics before sowing (average of 5 samples) Soil layer (cm) Parameter Units 0-20 20-40 40-60 Sand % 67.2 54.6 41.3 Silt % 17.6 16.5 15.7 Clay % 15.2 28.9 43.0 Texture Sandy-Loam Sandy-Clay-Loam Clay

pH (H2O) 6.51 5.95 6.05 C % 0.28 0.23 0.23 Organic matter % 0.48 0.39 0.39 N % 0.032 0.026 0.027 C/N 8.8 8.7 8.4 P available mg/kg 4.0 1.70 1.02 K available mg/kg 79.73 74.97 58.70

Table 3.2. Main soil physic-chemical characteristics post-harvesting (average of 3 samples)

Soil horizon (cm) and Nitrogen fertilisation (kg N ha-1) 0-20 20-40 40-60

100kgN 50kgN 25kgN 100kgN 50kgN 25kgN 100kgN 50kgN 25kgN Parameter Units Bulk density g/cm3 1.55 1.66 1.63 ------C % 0.43 0.41 0.51 0.40 0.38 0.46 0.38 0.39 0.40 Org. Matter % 0.75 0.71 0.89 0.68 0.66 0.79 0.66 0.66 0.69 N % 0.035 0.038 0.051 0.032 0.034 0.044 0.029 0.033 0.037 C/N 12.5 10.8 10.0 12.4 11.2 10.5 13.4 11.7 10.9 P total mg/kg 95.8 97.6 85.0 83.8 91.6 90.0 93.5 106.6 103.5 K total mg/kg 711.9 879.6 993.4 1101.7 1123.5 1405.3 1535.5 1741.3 1881.7

Mean daily temperature during the growing period was 28.6 ºC (Figure 3.1). The value of 40 ºC was trespassed 14 times, especially in March and April. In addition, longer cycle varieties (c.v. Negra Collana) were affected to a larger extent than short cycle varieties (c.v. Titicaca) by maximum temperatures at flowering (> 39 ºC). Finally, soil temperatures, at 5 and 10 cm depth, have shown that roots (average depth, 6.5 cm, for both varieties) were thermic-stressed throughout the whole growing period (Figure 3.2).

Figure 3.1. Meteorological observations at INERA Farako-Bâ research station during the growing period

Figure 3.2. Soil temperatures at 5 and 10 cm depth during the growing period

Estimated ETc (Figure 3.3) was lowest at plant emergence and two leaves stage (± 3 mm day-1), while steadily increasing at a rate of +0.5 mm week-1 during the vegetative stage up to 6-7 mm day-1. The plateau phase of maximum water requirements for c.v. Titicaca was reached after 6 weeks (ETc = ±6 mm day-1). Once leaf senescence took place, ETc started to decline, thus depleting during pasty seed formation and physiological maturity of the plant, 10-13 weeks (ETc = ± 5.5 mm day-1). For c.v. Negra Collana, with longer cycle, the ETc reached its maximum after 10 weeks, just after flowering. It remained on the plateau phase (ETc = ± 6.5 mm day-1) until 18 weeks, then decreased to ± 4.5 mm day-1 during pasty seed formation and physiological maturity, 19-23 weeks.

Figure 3.3. Daily evapotranspiration for c.v. Titicaca (left) and c.v. Negra Collana (right)

Quinoa´s water requirements (Figures 3.4) under field conditions vary considerably depending on: cultivar, phenological phase, evapotranspiration rate, type of soil texture and efficiency of the irrigation system. FI results have shown that c.v. Titicaca´s water demand was 403 mm, whereas for c.v. Negra Collana 811 mm (average of both sowing dates). Under PD, the amount of water supplied to c.v. Titicaca was 323 mm, whereas for c.v. Negra Collana 614 mm. For DI, the amount of water supplied was 231 mm and 437 mm to c.v. Titicaca and c.v. Negra Collana, respectively.

Figure 3.4. Full Irrigation (FI-100 % PET), Progressive Drought (PD-80 % PET), and Deficit irrigation (DI-60 % PET) for c.v. Titicaca and c.v. Negra Collana for November and December sowing.

Note: bars showing Potential Evapotranspiration (PET); lines irrigation applied, and clouds weekly precipitation Note: total PET (columns) and irrigation applied (lines) do not always match at the end of the growing period

The statistical analysis of results, Table 3.3, have shown that water use efficiency (WUE, expressed in kg biomass per m3 of water applied) was higher under PD and DI, meaning that quinoa was performing under drought-stress conditions (except for c.v. Negra Collana sown in December). For the seed yield per plant (GYP), there was significant difference (p<0.001) between the two varieties and for both sowing dates, being up to 10 times higher for c.v. Titicaca than for c.v. Negra Collana. Moreover, yields have depleted by half between November and December, from 2.69 to 1.22 g plant-1 (average of both varieties). In fact, extreme temperatures during flowering, higher than 39 ºC, have resulted in high seed abortion in plants. For c.v. Titicaca sown in November under DI and 25 kg N ha-1 fertilisation was the most performing, with yields of 9.5 g plant-1 (equivalent to 1.9 t ha-1). However, for the sowing in December, higher yields (4.05 g plant-1, equivalent to 0.8 t ha-1) were observed under PD and 50 kg N ha-1. Harvest index (HI, as a ratio of harvested seeds to total dry matter) have shown statistical significant differences (p<0.001) between the two varieties, 0.38 and 0.05 HI for c.v. Titicaca and c.v. Negra Collana (average of both sowing dates), respectively. In addition, statistical significant differences (p<0.001) between quinoa varieties were observed when analysing the weight of thousand seeds (TGW) for both sowing dates; having c.v. Titicaca seeds doubled the weight of c.v. Negra Collana seeds, 1.94 and 1.00 g, respectively.

Chlorophyll content (CL), N in the leaf, has shown statistical significant differences (p<0.001) amongst quinoa varieties, with higher N values for c.v. Titicaca sown in November. This was probably the consequence of N redistribution from leaf to storage organs, hence leading to leaf senescence and fostering seed filling. Canopy cover (CC) had varied between quinoa varieties, with 3 times more vegetation coverage for c.v. Titicaca than for c.v. Negra Collana. Quinoa sown in December has shown statistical significant differences (p<0.05) among the heights of the two varieties, 44 and 39 cm for c.v. Negra Collana and c.v. Titicaca, respectively (average of both sowing dates). Strong relationships, using Pearson correlation coefficient (r), were observed between plant height and GYP (Figure 3.5), with values of 0.88 and of 0.63 for c.v. Titicaca and c.v. Negra Collana, respectively. Figures 3.5 and 3.6 show the notable enhancement of GYP (5 g per plant-1, equivalent to 1 t ha-1) once the plant exceeded 50 cm height. On the other hand, the relationship between GYP and CC (Figure 3.6) was robust, showing a correlation coefficient higher than 0.7 for both varieties and sowing dates. In fact, greater canopy was responsible of an increase in light interception, enhancing assimilation and plant growth.

Table 3.3. WUE (Water Use Efficiency in kg m-3); GYP (Seed yield per plant in grams); HI (Harvest Index in yield/biomass); TGW (Thousand seed weight in grams); CL1-2 (Chlorophyll content); CC (Canopy Cover in %); PH (Plant Height in cm) of quinoa under the different treatments. Factors: a) Variety (V) of Chenopodium quinoa Willd.; b) Irrigation level (I) (100% PET; 80% PET; 60% PET); c) Fertilisation (F) (100 kg N ha-1; 50 kg N ha-1; 25 kg N ha-1). μ, σ, CV represents mean value, standard deviation and coefficient of variation of three repetitions, respectively.

Factors NOVEMBER DECEMBER a b c V I F WUE GYP HI TGW CL1 CL2 CC PH WUE GYP HI TGW CL1 CL2 CC PH 60 25 1.69 9.56 0.48 2.40 55.3 21.9 6.26 59.4 0.50 1.27 0.32 1.91 47.9 37.7 7.44 33.5 60 50 0.72 3.73 0.46 2.41 53.2 23.2 5.13 44.8 0.53 1.30 0.31 1.68 45.5 32.0 5.55 33.0 60 100 0.21 0.85 0.25 2.01 50.3 21.1 1.39 28.7 0.23 1.29 0.29 1.78 41.6 30.6 6.06 32.4 Titicaca 80 25 0.90 9.04 0.48 2.06 43.2 33.4 5.04 52.7 0.44 2.75 0.30 1.82 44.9 48.4 7.62 38.7 80 50 0.68 5.47 0.45 1.87 45.4 31.4 2.33 45.1 0.81 4.05 0.36 1.87 41.6 54.6 8.66 44.8 80 100 0.08 0.56 0.35 1.92 50.4 29.2 0.87 25.7 0.55 2.70 0.35 1.84 45.4 44.3 6.59 38.6 100 25 0.29 4.29 0.41 2.02 39.1 45.7 1.97 34.7 0.44 2.98 0.43 1.83 46.7 31.8 6.14 39.8 100 50 0.43 5.16 0.38 1.85 44.6 35.5 1.74 44.0 0.42 2.19 0.42 1.67 45.8 35.0 4.68 34.0 100 100 0.17 2.42 0.38 1.75 40.6 25.4 1.36 36.4 0.35 1.42 0.32 1.68 46.1 30.4 6.56 36.3 μ - - 0.57 4.57 0.41 2.03 46.9 29.7 2.90 41.3 0.48 2.22 0.35 1.79 45.1 38.3 6.59 36.8 σ - - 0.57 3.61 0.08 0.28 6.35 9.46 2.58 13.1 0.30 1.71 0.07 0.22 4.75 10.3 3.33 10.1

CV - - 0.32 13.0 0.01 0.08 40.3 89.4 6.67 171.8 0.09 2.91 0.01 0.05 22.6 104.9 11.1 101.0

60 25 1.43 2.32 0.12 0.87 42.6 33.3 2.29 54.2 0.08 0.04 0.02 0.77 37.1 41.9 2.06 26.8 60 50 0.96 0.91 0.07 0.84 41.8 43.6 1.22 47.3 0.02 0.04 0.02 0.77 36.2 45.7 1.96 35.0 60 100 0.24 0.20 0.03 0.88 43.0 42.8 0.35 26.4 0.10 0.05 0.02 0.71 31.6 51.3 2.18 33.4 Negra 80 25 0.64 1.46 0.10 1.38 39.5 32.5 1.48 60.5 0.18 0.10 0.02 1.07 38.9 55.4 3.21 49.8 Collana 80 50 0.47 0.69 0.09 1.38 39.9 39.4 0.95 44.0 0.22 0.05 0.01 0.99 44.5 50.7 2.78 49.3 80 100 0.07 0.08 0.05 1.13 48.1 38.6 0.42 21.9 0.20 0.09 0.01 1.11 43.0 51.3 3.84 49.4

100 25 0.37 1.11 0.05 1.32 30.5 44.9 0.64 54.9 0.24 0.68 0.06 1.02 41.1 47.0 2.42 52.3 100 50 0.22 0.38 0.05 0.98 36.2 44.0 0.86 54.3 0.28 0.48 0.05 1.03 42.9 52.2 2.25 51.6 100 100 0.05 0.13 0.04 0.85 42.8 41.5 0.22 30.8 0.29 0.51 0.05 0.99 41.4 47.0 4.11 48.2 μ - - 0.50 0.81 0.07 1.07 40.5 40.1 0.94 43.8 0.18 0.22 0.03 0.94 39.6 49.2 2.76 44.0 σ - - 0.49 0.82 0.04 0.26 5.78 5.47 0.85 15.7 0.11 0.28 0.02 0.27 4.85 7.75 1.49 12.7 CV - - 0.24 0.67 0.00 0.07 33.4 29.9 0.72 244.9 0.01 0.08 0.00 0.07 23.5 60.1 2.23 161.3 μ - - 0.54 2.69 0.24 1.58 43.7 34.9 1.92 42.5 0.33 1.22 0.19 1.36 42.3 43.7 4.67 40.4 σ - - 0.53 3.21 0.18 0.55 6.83 9.29 2.15 14.4 0.27 1.57 0.17 0.49 5.49 10.5 3.21 11.9 CV - - 0.28 10.3 0.03 0.31 46.6 86.2 4.61 206.1 0.07 2.48 0.03 0.24 30.1 111.1 10.3 141.8

Table 3.4. Post-hoc Tukey´s pairwise comparison test for different crop parameters and factors of study (variety, irrigation and fertilisation)

WUE GYP HI TGW CL1 CL2 CC PH

Factor Level NOV. DEC. NOV. DEC. NOV. DEC. NOV. DEC. NOV. DEC. NOV. DEC. NOV. DEC. NOV. DEC.

Variety Titicaca 0.57 0.48A 4.57A 2.22A 0.40A 0.35A 2.03A 1.79A 46.9A 45.1A 29.6B 38.3B 2.90A 6.59A 41.3 36.8B Negra 0.50 0.18B 0.81B 0.22B 0.07B 0.03B 1.06B 0.94B 40.5B 39.6B 40.1A 49.2A 0.94B 2.75B 43.8 44.0A

60 0.89A 0.24 2.93 0.67 0.23 0.16B 1.57 1.27 47.7A 40.0B 31.0B 39.8B 2.78A 4.21 43.5 32.4B Irrigation 80 0.47B 0.40 2.88 1.62 0.25 0.18B 1.60 1.45 44.4A 43.0AB 34.1AB 50.8A 1.85AB 5.45 41.6 45.1A 100 0.26B 0.34 2.25 1.37 0.22 0.22A 1.46 1.37 39.0B 44.0A 39.5A 40.6B 1.31B 4.36 42.5 43.7A

25 0.89A 0.31 4.63A 1.30 0.27A 0.19 1.67A 1.40 41.7B 42.8 35.3 43.7 2.95A 4.82 52.7A 40.2 Fertilisation 50 0.58B 0.38 2.72B 1.35 0.25A 0.20 1.55AB 1.33 43.5AB 42.7 36.2 45.0 2.04AB 4.31 46.6A 41.3 100 0.15C 0.29 0.70C 1.01 0.19B 0.17 1.41B 1.35 45.8A 41.5 33.1 42.5 0.77B 4.89 28.3B 39.7 Note: capital letter (significant difference between set of groups) Note: “A” is the group with highest value when compared to the other sets of groups “B” or “C” (in all cases statistically significant different) Note: NOV. corresponds to the sowing in November and DEC. to the sowing in December

Table 3.5. ANOVA for different crop parameters and interactions between factors (variety, irrigation and fertilisation)

WUE GYP HI TGW CL1 CL2 CC PH

Source NOV. DEC. NOV. DEC. NOV. DEC. NOV. DEC. NOV. DEC. NOV. DEC. NOV. DEC. NOV. DEC.

V ns *** *** *** *** *** *** *** *** *** *** *** *** *** ns * I *** ns ns ns * ** ns ns *** * *** *** * ns ns **

F *** ns *** ns *** ns ** ns * ns ns ns *** ns *** ns V x I ns ns ns ns ns ns *** ns * * ** * ns ns ns ns V x F ns ns *** ns ** ns ns ns * ns ** ns ns ns ns ns I x F ** ns * ns *** ns ns ns * ns * ns ns ns ns ns V x I x F ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns 2 R 0.77 0.53 0.82 0.59 0.97 0.95 0.93 0.81 0.77 0.57 0.76 0.65 0.65 0.45 0.71 0.44 -3 Abbreviations: WUE (Water Use Efficiency in kg m ); GYP (Seed yield per plant in grams); HI (Harvest Index in yield/biomass); TGW (Thousand seed weight in grams); CL1- 2 (Chlorophyll content); CC (Canopy Cover in %); PH (Plant Height in cm); NOV. (November sowing); DEC. (December sowing) Note: a Variety (V) of Chenopodium quinoa Willd. ; b Irrigation level (I) (100 % PET; 80 % PET; 60 % PET); c Fertilisation (F) (100 kg N ha-1; 50 kg N ha-1; 25 kg N ha-1) Note: *** extremely significant (p<0.001); ** very significant (p<0.01); * significant (p<0.05); ns: not significant (p>0.05) Note: R2 is the proportion of variance in the dependent variable (crop parameter) which can be explained by the independent variables (V, I, F) 54

Figure 3.5. Relationship (lineal regression) between seed yield per plant (g) and plant height (cm) at harvest for c.v. Titicaca and c.v. Negra Collana

Note: r shows Pearson correlation coefficient Note: Negra Collana (sowing date: December) was removed from the graphs due to high seed abortion in plants

Figure 3.6. Relationship (linear regression) between seed yield per plant (g) and canopy cover (%) for c.v. Titicaca and c.v. Negra Collana

Note: r shows Pearson correlation coefficient Note: c.v. Negra Collana (sowing date: December) was removed from the graphs due to high seed abortion in plants

3.4. Discussion Despite of the amount of research examining quinoa´s water requirements under water-stress conditions, there were no studies displaying such low water inputs than those observed in this research (231 mm c.v. Titicaca and 437 mm c.v. Negra Collana, average of both sowing dates under DI). Furthermore, this study´s average WUE results (0.53 kg m-3 c.v. Titicaca and 0.34 kg m-3 c.v. Negra Collana) were similar to those recorded in Bolivia (0.21-0.45 kg m-3) (Geerts et al., 2008b), but lower to those observed in Italy and Morocco (0.6 and 1.7 kg m-3, respectively) (Hirich et al., 2014a, Hirich et al., 2014c, Riccardi et al., 2014). In fact, drought stress conditions at key phenological stages (pre- flowering, flowering and pasty seed formation) have had a negative effect both on seed yield per plant and WUE (Geerts et al., 2008a). GYP results were in harmony with those modelled in AquaCrop showing that quinoa can be highly performing under DI (Geerts et al., 2009a, Cusicanqui et al., 2013). C.v. Titicaca´s harvest index (HI) results (0.38, average of both sowing dates) were lower to those observed in Morocco (0.57-0.67), but higher than those of Iraq (0.28) (Hirich et al., 2014c, Hassan, 2015).

Moreover, recent research in Algeria, Lebanon, Mauritania, Yemen and Iraq have suggested that 35 ºC was the critical threshold at flowering, if exceeded quinoa plants would become sterile (Breidy, 2015, CNRADA, 2015, Djamal, 2015, Hassan; 2015, Saeed, 2015). Nonetheless, this research has proven that c.v. Titicaca can stand temperatures above 35 ºC during flowering and still be highly performing (up to 1.9 t ha-1). In regards to c.v. Negra Collana, long cycle variety, the effect of temperatures above 39 ºC has resulted in a very low number of plants with seeds. This is because pollen viability is a function of pollen moisture content which is strongly dependent on vapour pressure deficit (Hatfield and Prueger, 2015). At high temperatures, vapour pressure deficits were highest resulting in pollen desiccation and low pollen viability. In this line, further research would be required to better understand the effect of temperature on plant fertility.

In contrast with other studies, this research did not bring to light any relevant information on yield enhancement with increasing nitrogen fertilisation (Kaul et al., 2005; Shams, 2012). But was in harmony with other investigations (Moreale, 1993), showing that N-fertilisation does not play a crucial role on crop growth nor seed yield, and that quinoa´s N uptake was of 25 kg N ton-1 of seeds produced (1:40 ratio). In addition, the combination of high temperatures and soil moisture in sandy-loam soils during fertilisation could have resulted in urea volatilization (ammonia losses) and hydrolysis. Overall, this investigation has shown that quinoa can adapt and be highly performing in poor structured (sandy- loam texture) and low fertility soils (<0.5 % organic matter and 0.03 % N), typically of the Sahel.

3.5. Conclusions This research confirms that quinoa is a climate resilient crop that can cope with high temperatures and drought-stress conditions. It has a good adaptation to slightly acidic, poor structured and low fertile soils, besides of having low N-requirements. Moreover, c.v. Titicaca yields could attain 900 kg ha-1 if sown in November (average of all treatments), and could be exceeded if appropriate agronomic practices are followed. For the time being, it will be important to prioritize the use of short-cycle varieties (c.v. Titicaca, 85 days), rather than long cycle varieties (c.v. Negra Collana, 150 days). By sowing short cycle varieties in November, the effect of extreme temperatures occurring in mid- February until the onset of the rainy season will be diminished. In fact, quinoa´s sowing could be advanced by several weeks towards northern parts of the country. Moreover, organic amendment is highly recommended at the rate of 1 t ha-1 or higher prior to sowing; besides of a two-time mineral fertilisation in the form of ammonium nitrate, rather than urea, at the rate of 50 kg N ha-1. Mechanised tilling, at 10-20 cm depth, would be advised, and if irrigated frequent soil aeration would be recommended to avoid soil adhesion that allows effective root development. For that, sowing in furrows would also be supported. Furthermore, research on plant-breeding should target higher- temperature and wind tolerant varieties capable of standing the warmest months and winds occurring during the Harmattan. This could potentially broaden the spatial distribution and sowing time across the country, as well as to other hot-spot regions of climate change. Overall, this research has allowed settling a provisional quinoa crop-calendar, besides of describing ideotype cultivars and suitable agro- meteorological zones for quinoa production in Burkina Faso. For that, quinoa regional programmes implemented by FAO, TCP/SFW/3404 and TCP/RAF/3602 (Burkina Faso, Cameroun, Niger, Senegal, Chad, Togo and Ghana), need to be further supported and its production scaled-up.

CHAPTER 4: EFFECT OF DROUGHT, NITROGEN FERTILIZATION, TEMPERATURE AND PHOTOPERIODICITY ON QUINOA PLANT GROWTH AND DEVELOPMENT IN THE SAHEL

Abstract Drought, heat stress, and unfavourable soil conditions are key abiotic factors affecting quinoa’s growth and development. The aim of this research was to examine the effect of progressive drought and N- fertilization reduction on short-cycle varieties of quinoa (c.v. Titicaca) for different sowing dates during the dry season (from October to December). A two-year experimentation (2017–2018 and 2018–2019) was carried out in Burkina Faso with four levels of irrigation (full irrigation—FI, progressive drought—PD, deficit irrigation—DI and extreme deficit irrigation—EDI) and four levels of N-fertilization (100, 50, 25, and 0 kg N ha−1). Plant phenology and development, just like crop outputs in the form of yield, biomass, and quality of the seeds were evaluated for different sowing dates having different temperature ranges and photoperiodicity. Crop water productivity (CWP) function was used for examining plant’s water use efficiency (WUE) under drought stress conditions. Emerging findings have shown that CWP was highest under DI and PD (0.683 and 0.576 kg m−3, respectively), while highest yields were observed in 2017–2018 under PD and its interaction with 25 to 50 kg N ha−1 (1356 and 1110 kg ha−1, respectively). Mean temperatures close to 25 °C were suitable for optimal plant growth, while extreme temperatures at anthesis limited the production of seeds. Small changes in photoperiodicity from different sowing dates were not critical for plant growth. Key words: Burkina Faso; Chenopodium quinoa Willd.; heat stress; irrigation; crop water productivity

4.1. Introduction Sustainable irrigation strategies that can increase crop water productivity are of growing interest in arid and semi-arid regions (Debaeke and Aboudrare, 2004). This is the case of the Sahel, with a rapid growing population and an agricultural system extremely dependent on weather; 98 % of its agriculture is rainfed, in addition to experiencing larger inter/intra annual rainfall variability (Giannini, 2008; OCHA, 2016). In Burkina Faso, one-third of the country’s surface area is degraded, with an estimated annual expansion of degraded land of 3600 km2 (FAO, 2019). In this region, changes in weather patterns are expected to have a negative impact on the yields of major cereals (Roudier et al., 2011, Berg et al., 2013). Some studies conducted in Burkina Faso estimate a yield reduction of 10 % for sorghum, close to 17 % for maize, and up to 23 % for millet by 2050, and is expected to worsen with changing climatic conditions (Jones and Thornton, 2003; Salack, 2006; Schlenker and Lobell, 2010); all of the previous crops having a C4 photosynthetic pathway. Even though C4 crops have the most efficient form of photosynthesis (Sage and Zhu, 2011), crop’s with C3 photosynthetic pathways can benefit more from increasing CO2 concentrations (Kimball, 1983; Poorter, 1993). This is because optimal CO2 atmospheric concentrations for enhancing the photosynthetic rate of C3 crops have not yet been reached (Kimball, 1983; Poorter, 1993). 59

Quinoa is an herbaceous crop belonging to the C3 group of plants, besides of having a high nutritional value (Koziol, 1992; Jacobsen et al., 2003). In the last decades, there has been an intensification of scientific research, and in 2013, the International Year of Quinoa was declared— all of which, doubling the number of countries growing quinoa since then (Bazile et al., 2016). In the Sahel region, several countries (Senegal, Mauritania, Mali, Burkina Faso, Niger, Chad, and Sudan) are now growing quinoa for research purposes, as well as seeking to promote quinoa for local consumption (Bazile et al., 2016). The latest scientific efforts have been focused on quinoa’s adaptability under adverse environmental conditions. Its resilience to abiotic stresses is the result of a wide genetic variability (Bhargava et al., 2007), giving the plant an excellent salt-tolerance, up to 40 dS m−1, and drought- resistance, with water requirements as low as 230 mm for c.v. Titicaca (Razzaghi et al., 2011; Adolf et al., 2013; Azurita-Silva et al., 2015; Alvar-Beltrán et al., 2019a). The crop water productivity (CWP; amount of biomass in kg per volume of water supply in m3) of quinoa is low, and can be increased if: water losses from evaporation are reduced, if the negative effect of drought stresses at specific phenological phases are avoided, and if unfavorable conditions during crop growth, (i.e., pests and diseases) are diminished (Geerts and Raes, 2009). In addition, quinoa can adapt to most types of soils, but differences in the literature arise when assessing crop nitrogen-uptake. Some studies argue that maximum yields are reached at 80 kg N ha−1, but yields can differ according to the soil type, being highest among sandy–clay–loam soils (Kaul et al., 2005; Razzaghi et al., 2012). On the contrary, some authors have acknowledged that increasing N-fertilization does not determine quinoa growth nor yield (Moreale, 1993).

In this paper, the effect of drought and nitrogen fertilization on plant’s phenology and physiological responses of short-cycle varieties is evaluated. A crop’s performance during the dry season in the Soudanian belt and its adaptability to unfavourable soil and climatic conditions was monitored. Moreover, different sowing dates were tested and some agricultural planning practices for farmers were proposed. Finally, as a climate resilient and C3 crop, this research aims to look beyond the traditional grown C4 cereals (e.g., maize, sorghum, and millet) and has selected quinoa for enhancing food security and nutrition in Burkina Faso under changing climatic conditions.

4.2. Materials and methods 4.2.1 Experimental set-up This experiment was carried out between 2017 and 2019 at Institut de l’Environnement et Recherches Agricoles (INERA) Farako-Bâ research station (11°05′ N; 4°20′ W), Burkina Faso. The study area was characterized for having a tropical savannah-wet and hot climate, with a well-defined dry season (from November to April). In the first year, 2017–2018, the experimental design was organized in a split-block design with a multiple factor analysis of variance (ANOVA), with three levels of irrigation according to the potential evapotranspiration (PET) (FI 100 % PET, progressive drought (PD) 80 % PET, and deficit irrigation (DI) 60 % PET) and three levels of N-fertilization (100, 50, and 25 kg N ha−1), while each treatment having three replicates (Figure 4.1). For the second year, 2018–2019, the experimental set-up was as follows: two levels of irrigation (FI 100 % PET and extreme deficit irrigation (EDI) 50 % PET), two levels of N-fertilization (100 and 0 kg N ha−1), with each treatment having four replicates (Figure 4.1). Between 2017–2018 and 2018–2019 slightly different irrigation levels and N-fertilization levels were used in order to identify and minimized the gaps between inputs (crop N-fertilization and irrigation requirements) and outputs (losses in the form of volatilization, leaching and water-surface runoff).

Throughout the two years of experimentation, 2017–2018 and 2018–2019, maize was the crop grown during the rainy season whereas quinoa during the dry season. Moreover, for the first year of experimentation, quinoa (c.v. Titicaca) was sown on the 4th November (hereinafter, 4-Nov.) and 8th December (hereinafter, 8-Dec.); whereas for the second year, the sowing date was the 25th October (hereinafter, 25-Oct.) and 19th November (hereinafter, 19-Nov.). The sowing rate was 10 kg of seeds per hectare, with 50 cm distance between rows and 10 cm between plants, with a total density of 200,000 plants ha−1. Prior to the sowing of quinoa, the field was amended with 5 t ha−1 of compost (50.2 % organic −1 matter) as well as phosphate (26.7 % P2O5) at a rate of 400 kg ha . N-fertilization was applied twice, 2 to 3 weeks and 4 to 5 weeks after sowing, at a same concentration rate.

Figure 4.1. Experimental field design with different levels of irrigation (full irrigation (FI), progressive drought (PD), deficit irrigation (DI), extreme deficit irrigation (EDI)), and N-fertilization (100, 50, 25 and 0 kg N ha−1) for the two year-experimentation (2017 – 2018 and 2018 – 2019).

4.2.2 Sampling and measurement Maximum and minimum air temperatures, maximum and minimum relative humidity, and daily rainfall values were obtained from an agro-meteorological station placed nearby to the experimental field. Irrigation was measured using a water-counter placed at the entry of each irrigation block and −1 was drip-irrigated at a rate of 1.05 l h . Crop’s daily potential evapotranspiration (PETc) was calculated using the following formula (Hargreaves and Samani, 1985): 0.5 ETo = 0.0023 (T mean + 17.78) Ro (T max – T min) (Eq. 1) −2 −1 Where: Ro = solar radiation (MJ m day ) at a given month and latitude (Allen et al., 1998); T mean = mean daily temperature (°C); T max = daily maximum temperature (°C); T min = daily minimum temperature (°C).

The different phenological phases were measured and analyzed separately according to the different sowing dates, being the following: emergence (E), two leaves (2L), four leaves (4L), eight leaves (8L), panicle formation (PF), flowering (FL), and milky seeds (MG) at 5, 15, 18, 24, 30, 40, and 60 days after sowing (DAS), respectively with a total of 100 samples per plot. The phenological phase was determined once 50 % of the plants had reached a given stage. Regarding plant’s morphology, the plants’ height (10 measurements per plot), number of branches (10 per plot), panicle length (10 per plot), stem diameter (5 per plot), root depth and horizontal length (1 per plot) were all measured at physiological maturity. In addition, the plants’ performance was measured for the following crop

parameters: seed yield per plant (12 measurements per plot) and thousand seeds weight (3 per plot). The crop water productivity (CWP) was defined as the ratio between biomass production and water applied (kg m−3). The harvest index (HI) was calculated as the ratio between yield and biomass, as a percentage. For the second year, the canopy cover was measured throughout the growing cycle using a mobile-application, Canopeo, developed by the Oklahoma University (Patrignani and Ochsner, 2015). Measurements were taken at 60 cm distance from the top of the canopy with an image cover of 75 × 50 cm. During the 2018–2019 experimentation, the canopy cover was measured six times: at 24, 34, 40, 49, 70, and 85 DAS for the sowing on the 25-Oct., and at 24, 34, 40, 60, 75, and 84 DAS for the sowing on the 19-Nov. The calculation of the cumulative growing degree-days (CGDD in °C) was done using the following formula, and where Tbase was equal to 3 (Jacobsen and Bach, 1998):

GDD = ((Tmax + Tmin)/2)-Tbase (Eq. 2) Finally, prior to sowing, five soil samples were extracted from each experimental field at 0–20 cm and 20–40 cm depth. The main physic-chemical properties of the soil, texture, pH, N, P, K, C, and organic matter were analysed.

4.2.3 Statistical analysis Since no major differences were observed between different sowing dates on plant’s physiology and phenology, sowing dates were combined together in a two-year experimentation (2017-2018 and 2018-2019). For this reason, sowing-date and year were not considered as independent factors, whereas irrigation and N-fertilization were the only independent factors of statistical interest (Table 4.4 and 4.5). The analysis of variance (ANOVA) and Tukey-HSD post-hoc test were used to estimate the mean variation between and within groups for different crop parameters, as well as to examine the effect of irrigation and N-fertilization and its interaction on a wide-range of crop parameters. The ANOVA and Tukey-HSD post-hoc test was done using Minitab 18 and IBM SPSS software. Finally, the Pearson correlation coefficient (r) was used to test the association or correlation between two variables.

4.3. Results The two-year experimental field was characterized for being sandy–loam in the first layer (0–20 cm) and sandy–clay–loam in the second layer (20–40 cm), besides having slightly acidic properties (Table

4.1). Mineral nitrogen (in the form of ammonium-NH4 and nitrate-NO3) in the soil was very low with N-content lower than 0.036 % at all depths (0–20 cm and 20–40 cm) and for the two years. The organic matter found in the second year of experimentation had higher values (0.60 %) when compared to that of the first year (0.48 %). The ratio of C/N was higher for the second year (9.8) than for the first year (8.8). The P-availability within the first soil layer was considerably higher for the

second year-experiment (44 mg kg−1) when comparing to that of the first year (4 mg kg−1); while similar K-availability (between 79 and 90 mg kg−1) was found for both years.

Table 4.1. Soil main physic-chemical characteristics of the two-year experimentation (2017-2018 and 2018-2019) (average of 5 samples). 2017-2018 2018-2019 Parameter Unit 0-20cm 20-40cm 0-20cm 20-40cm Sand % 67.2 54.6 75.3 59.5 Silt % 17.6 16.5 14.8 12.7 Clay % 15.2 28.9 9.9 27.8 Texture Sandy-Loam Sandy-Clay-Loam Loamy-Sand Sandy-Clay-Loam

pH (H2O) 6.51 5.95 6.09 5.87 C % 0.28 0.23 0.35 0.30 Organic matter % 0.48 0.39 0.60 0.51 N % 0.032 0.026 0.036 0.028 C/N 8.8 8.7 9.8 10.6 P available mg kg-1 4.0 1.70 44.0 31.3 K available mg kg-1 79.73 74.97 90.3 116.0 Bulk density g cm-3 1.61 - - - The mean-maximum temperatures (Figure 4.2) during the growing cycle for different sowing dates was the following: 34.8 °C (25-Oct.), 34.6 °C (4-Nov.), 35.0 °C (19-Nov.) and 34.8 °C (8-Dec.). Moreover, at quinoa’s most sensitive phenological phase, flowering (at 40 DAS and lasting for 10 days), the mean-maximum temperatures for each of the sowing dates was: 34.6 °C (25-Oct.), 33.5 °C (4-Nov.), 33.3 °C (19-Nov.) and 34.8 °C (8-Dec.). For the net irrigation requirements (Table 4.2), the amount of water supplied under FI was between 394 mm (8-Dec.) and 457 mm (25-Oct. and 19-Nov.); whereas for PD between 319 mm (8-Dec.) and 328 mm (4-Nov.). On the other side, under DI and EDI, the amount of irrigation applied was between 211 mm (25-Oct.) and 246 mm (4-Nov.).

Figure 4.2. Maximum and minimum temperatures recorded during the two year-experiment (2017- 2018 and 2018-2019)

Table 4.2. Net irrigation under full irrigation (FI), progressive drought (PD), deficit irrigation (DI) and extreme deficit irrigation (EDI) for 2017-2018 (4-Nov. and 8-Dec.) and 2018-2019 (25-Oct. and 19- Nov.).

Irrigation 25-Oct. 4-Nov. 19-Nov. 8-Dec. schedule Amount Events μ Amount Events μ Amount Events μ Amount Events μ FI 457 27 16.9 410 38 10.8 457 26 17.6 394 28 14.1 PD - - - 328 37 8.9 - - - 319 29 11.0 DI - - - 246 34 7.2 - - - 228 26 8.8 EDI 211 29 7.3 - - - 227 27 8.4 - - - Legend: (a) Irrigation: FI (full irrigation); PD (progressive drought); DI (deficit irrigation); EDI (extreme deficit irrigation). Note: Irrigation and rain equal to or more than 1 mm; amount (in mm); Events (in n°); μ (average water supply per irrigation event).

Different sowing dates did not have a major impact on quinoa’s phenological phases (Figure 4.3), as mean-temperatures and photoperiod did not differ much among sowing dates (max ∆T of 0.4 °C and max ∆ photoperiod of 26 min between sowing dates). For the early sowing dates (25-Oct. and 4-Nov.), higher temperatures were recorded at early-vegetative stages while lower at anthesis, and vice-versa for late sowing dates (19-Nov. and 8-Dec.). The observed photoperiodicity at this time of the year, and for this location, was characteristic of tropical zones, with approximately 12 h of daytime and nighttime. The appearance of two-cotyledons was relatively fast, 5 DAS, being faster among experiments sown on 25-Oct. and 19-Nov. This was caused by higher diurnal temperatures (>20 °C) when sowing on the 25-Oct. and 19-Nov, than for the 8-Dec. (<15 °C). Similar growing rates were observed at two and four leaves; with changing patterns at eight leaves, with a lower rate amongst plants sown in 19-Nov. and 8-Dec. Panicle formation was totally reached at 30 DAS for all sowing dates, while flowering at 40 DAS. Nevertheless, the time for reaching flowering was slightly earlier amongst plants sown on the 25-Oct. and 4-Nov, than among those plants sown on the 19-Nov. and 8- Dec. Quinoa milky seeds were observed at 60 DAS, while physiological maturity between 83 and 90 DAS.

For the cumulative growing degree days (CGDD in °C) there was a perfect negative correlation (r) between phenological phases and sowing dates (Table 4.3). In fact, there was a negative relationship between CGDD and sowing dates, with higher accumulation of degrees amongst early-sown plants (25 Oct. and 4 Nov.). In particular, the correlation coefficient was highest (r ≥ 0.79) up to 60 DAS, while decreasing at physiological maturity. This was explained by the fact that harvesting was carried out in different dates, being earlier amongst late-sowing plants (83 DAS).

Figure 4.3. Emergence (E), two leaves (2L), four leaves (4L), eight leaves (8L), panicle formation (PF), flowering (FL) and milky seeds (MG) of quinoa at 5, 15, 18, 24, 30, 40 and 60 days after sowing (DAS), respectively, for different sowing dates (25-Oct., 4-Nov., 19-Nov. and 8-Dec.).

Table 4.3. Cumulative growing degree days (CGDD in °C) for different phenological stages and photoperiodicity (minutes per day) for different sowing dates (25-Oct., 4-Nov., 19-Nov. and 8-Dec.)

25-Nov. 4-Nov. 19-Nov. 8-Dec. Pearson (CGDD Phenological phase vs. sowing date) Emergence (°C) 123.8 123.8 119.0 101.0 -0.93 2 Leaves (°C) 370.3 343.5 329.9 307.0 -0.98 4 Leaves (°C) 438.8 405.5 395.9 366.3 -0.97 8 Leaves (°C) 578.3 542.5 515.7 495.8 -0.97 Panicle formation (°C) 722.8 671.5 637.4 624.5 -0.92 Flowering (°C) 933.7 877.3 831.2 821.5 -0.92 Milky seeds (°C) 1340.7 1293.8 1241.1 1263.0 -0.79 Maturity (°C) 1866.7 1919.8 1785.5 1832.3 -0.55 Harvest (DAS) 86 90 85 83 - Mean photoperiod (min day-1) 692.9 692.6 692.7 696.3 - Min. photoperiod (min day-1) 688.1 688.1 688.1 688.1 - Max. photoperiod (min day-1) 707.3 701.9 704.5 714.3 -

Tables 4.4 and 4.5 report the interaction between irrigation and N-fertilization (independent factors) and its effect on different crop parameters (dependent factors). Even though there was a decreasing trend on the observed parameter-values under drought-stress conditions and N-fertilization reduction, the measured crop parameters were, in many cases, not statistically significant different when N- fertilization and irrigation interacted together (Table 4.4); except for yield (Y) and biomass (AGB) in 2017–2018 under FI and 100 kg N ha−1 (1171 and 3341 kg ha−1).

Furthermore, as observed in Table 4.5, the highest yields and biomass (AGB) were observed amongst plants exposed to PD, with 1012 kg ha−1 and 2436 kg ha−1, respectively, rather than on FI plants. On the contrary, the lowest yields and biomass were observed under DI (663 and 1928 kg ha−1, respectively) and EDI (480 and 1321 kg ha−1, respectively). Statistically significant differences were reported when comparing the yields of PD and EDI. For N-fertilization, the highest yields and biomass were observed when applying 25 kg N ha−1 (1038 and 2426 kg ha-1, respectively); hence, increasing N-fertilization did not result in a higher crop development nor yield. Statistically significant differences were reported for the weight of thousand seeds (TGW), which were lower under EDI and 0 kg N ha−1 (1.37 g). The harvest index (HI) remained constant for all irrigation levels (around 37 % for FI, PD and EDI), except for EDI with a value of 29 %. For the crop water productivity (CWP), the mean value of all irrigation levels (FI, PD, DI, and EDI) was 0.493 kg m−3, having a 2:1 ratio of biomass production per m3 of water applied. Nevertheless, a higher CWP was reported for PD and DI plants, 0.576 and 0.683 kg m−3, respectively. This shows that quinoa was capable of producing the same or more biomass with less water inputs, therefore using water more efficiently under drought- stress conditions. This was corroborated when comparing PD and DI with FI, the latter having a CWP value of 0.373 kg m−3.

Furthermore, deeper roots (RD) were observed among EDI (11.9 cm), with significant differences (p < 0.05) when comparing to PD (6.5 cm) and DI (7.5 cm). The opposite situation was found for the root horizontal length (RHL), being wider among FI plants (19.8 cm), than DI (11.2 cm) and EDI (12.3 cm). The surface water coverage of FI plots was greater, hence roots expanded side-ways. For the branching (B) and stem diameter (ST), plots receiving 25 kg N ha−1 had higher values, with 10 branches per plant and 0.65 cm stem-diameter. Overall, there was a positive relationship between plant height and irrigation but was not stronger enough to be considered as statistically different. The highest plants were observed under FI and PD (40.2 and 40.9 cm), whereas the smallest under EDI (34.5 cm). For N-fertilization, the highest plants were also reported among plots supplied with 25 kg N ha−1 (43.2 cm) and the smallest under 0 kg N ha−1 (36.7 cm).

Table 4.4. Post-hoc Tukey-HSD pairwise comparison test for different crop parameters and interaction between factors of study (irrigation and fertilization), mean-values for each year experimentation (2017-2018 and 2018-2019).

2017-2018 I F PH (cm) B (n°) PS (cm) SD (cm) RD (cm) RHL (cm) Y (kg ha-1) AGB (kg ha-1) TGW (g) HI (%) CWP(kg m-3) 100 100 36.4 ± 4.2a 7.8 ± 4.0a 13.8 ± 1.9a 0.57 ± 0.30a 7.8 ± 1.7a 20.4 ± 14.8ab 430 ± 201a 1527 ± 439a 1.72 ± 0.17a 35 ± 4a 0.258 ± 0.128b 100 50 39.0 ± 5.4a 9.4 ± 4.5a 16.0 ± 3.0a 0.58 ± 0.11a 4.5 ± 2.4a 22.9 ± 9.4a 735 ± 339a 2309 ± 811a 1.76 ± 0.13a 40 ± 2a 0.428 ± 0.097ab 100 25 37.3 ± 6.7a 10.4 ± 2.8a 15.0 ± 1.6a 0.66 ± 0.17a 7.0 ± 4.7a 16.8 ± 7.2ab 727 ± 250a 1743 ± 611a 1.92 ± 0.20a 41 ± 1a 0.368 ± 0.128ab 80 100 32.2 ± 11.1a 6.2 ± 3.7a 12.3 ± 2.4a 0.50 ± 0.12a 4.2 ± 1.1a 9.5 ± 4.4ab 462 ± 175a 1353 ± 530a 1.88 ± 0.23a 35 ± 5a 0.315 ± 0.259b 80 50 44.9 ± 15.9a 8.9 ± 2.5a 16.0 ± 4.8a 0.59 ± 0.13a 8.4 ± 3.9a 16.6 ± 7.0ab 1110 ± 620a 2795 ± 1675a 1.87 ± 0.09a 41 ± 5a 0.744 ± 0.557ab 80 25 45.7 ± 9.9a 11.3 ± 4.3a 16.1 ± 5.5a 0.70 ± 0.17a 7.2 ± 4.0a 19.2 ± 5.4ab 1356 ± 631a 2859 ± 1339a 1.94 ± 0.16a 39 ± 9a 0.670 ± 0.244ab 60 100 30.6 ±10.1a 4.1 ± 3.0a 12.4 ± 3.4a 0.47 ± 0.11a 8.0 ± 4.2a 7.1 ± 4.9b 233 ± 203a 1370 ± 273a 1.89 ± 0.28a 31 ± 5a 0.259 ± 0.093b 60 50 38.9 ± 8.8a 5.6 ± 3.1a 16.2 ± 4.2a 0.65 ± 0.19a 6.5 ± 2.8a 11.5 ± 2.9ab 588 ± 313a 1352 ± 635a 2.04 ± 0.45a 38 ± 10a 0.625 ± 0.257ab 60 25 46.5 ± 14.7a 9.2 ± 4.4a 17.5 ± 5.0a 0.57 ± 0.17a 8.0 ± 4.3a 14.9 ± 3.1ab 1084 ± 972a 2727 ± 1821a 2.16 ± 0.31a 40 ± 9a 1.095 ± 0.773a μ 39.0 ± 9.7 8.1 ± 3.6 15.0 ± 3.5 0.59 ± 0.16 6.8 ± 3.2 15.4 ± 6.6 747 ± 411 2004 ± 904 1.91 ± 0.23 38 ± 6 0.529 ± 0.282 I F 2018-2019 100 100 46.5 ± 8.3a 8.4 ± 1.1a 16.8 ± 4.6a 0.71 ± 0.12a 14.5 ± 2.2a 23.4 ± 4.9a 1171 ± 362a 3341 ± 970a 1.65 ± 0.31a 30 ± 9a 0.468 ± 0.249a 100 0 39.8 ± 7.0a 7.1 ± 0.7ab 14.2 ± 2.9a 0.59 ± 0.10ab 9.3 ± 1.9b 15.4 ± 7.8b 805 ± 114ab 2166 ± 748b 1.54 ± 0.40a 33 ± 10a 0.325 ± 0.122a 50 100 35.4 ± 10.8a 7.4 ± 1.5a 12.4 ± 3.3a 0.59 ± 0.11ab 13.2 ± 3.2ab 13.6 ± 3.1b 441 ± 212b 1293 ± 551b 1.40 ± 0.31a 28 ± 16a 0.357 ± 0.217a 50 0 33.7 ± 8.7a 5.4 ± 1.5b 12.1 ± 3.5a 0.46 ± 0.14b 10.7 ± 4.4ab 11.0 ± 4.2b 519 ± 221b 1077 ± 321b 1.37 ± 0.16a 30 ± 12a 0.326 ± 0.239a μ 38.8 ± 8.7 7.1 ± 1.2 13.9 ± 3.6 0.59 ± 0.12 11.9 ± 2.9 15.9 ± 5.0 678 ± 189 2031 ± 603 1.49 ± 0.30 0.30 ± 0.12 0.37 ± 0.21

Legend: (I) Irrigation: 100 % PET-Full irrigation-FI; 80 % PET-Progressive drought-PD; 60 % PET-deficit irrigation-DI; 50 % PET-extreme deficit irrigation-EDI; (F) Fertilization: 100, 50, 25, 0 kg N ha−1. Note: PH (plant height), B (n° of branches per plant), PS (length of the panicle), SD (stem diameter), RD (root depth), RL (root horizontal length), Y (yield), AGB (aboveground biomass), TGW (thousand-seed weight), HI (harvest index), CWP (crop water productivity). Note: means that do not share a letter are significantly different, p ≤ 0.05 according to the test of Tukey’s HSD. Note: Average value ± standard deviation values are shown, with three repetitions (2017–2018, average 25-Oct and 19-Nov) and four repetitions (2018–2019, average 4-Nov. and 8-Dec.).

Table 4.5. Post-hoc Tukey-HSD pairwise comparison test for different crop parameters and factors of study (irrigation and fertilization), mean of all sowing dates (25-Oct., 4-Nov., 19-Nov. and 8-Dec.).

Irrigation PH B PS SD RD RHL Y AGB TGW HI CWP (cm) (n°) (cm) (cm) (cm) (cm) (kg ha-1) (kg ha-1) (g) (%) (kg m-3)

100 40.2 ± 7.6a 8.5 ± 3.1a 15.2 ± 3.3a 0.63 ± 0.18a 9.0 ± 4.4ab 19.8 ± 9.7a 727 ± 281a 2321 ± 998a 1.71 ± 0.30b 36 ± 8a 0.373 ± 0.176b 80 40.9 ± 14.0a 8.8 ± 4.1a 14.8 ± 4.8a 0.60 ± 0.17a 6.5 ± 3.7b 15.0 ± 7.0b 1012 ± 648a 2436 ± 1468a 1.90 ± 0.17ab 38 ± 7a 0.576 ± 0.425a 60 38.6 ± 13.2a 6.3 ± 4.1b 15.4 ± 4.8a 0.56 ± 0.18a 7.5 ± 3.9b 11.2 ± 4.9b 663 ± 724a 1928 ± 1421ab 2.03 ± 0.37a 37 ± 9a 0.683 ± 0.592a 50 34.5 ± 9.9a 6.4 ± 1.8b 12.2 ± 3.4a 0.53 ± 0.14a 11.9 ± 4.0a 12.3 ± 4.0b 480 ± 220b 1321 ± 397b 1.38 ± 0.25c 29 ± 15a 0.340 ± 0.230b

Fertilization PH B PS SD RD RHL Y AGB TGW HI CWP (cm) (n°) (cm) (cm) (cm) (cm) (kg ha-1) (kg ha-1) (g) (%) (kg m-3)

100 36.8 ± 11.0a 6.9 ± 3.1bc 13.6 ± 3.8a 0.58 ± 0.18b 10.0 ± 4.7a 15.3 ± 9.6ab 483 ± 302b 2032 ± 1116a 1.71 ± 0.32b 32 ± 11b 0.343 ± 0.222b 50 40.9 ± 11.3a 7.9 ± 3.8b 16.1 ± 4.1a 0.60 ± 0.15ab 6.3 ± 3.4b 17.0 ± 8.5a 806 ± 489ab 2141 ± 1310a 1.89 ± 0.30a 40 ± 7a 0.599 ± 0.381ab 25 43.2 ± 11.7a 10.3 ± 4.0a 16.2 ± 4.5a 0.65 ± 0.18a 7.4 ± 4.4ab 17.0 ± 5.8a 1038 ± 733a 2426 ± 1413a 2.01 ± 0.26a 40 ± 8a 0.711 ± 0.560a 0 36.7 ± 8.5a 6.2 ± 1.4c 13.1 ± 3.4a 0.52 ± 0.14b 10.0 ± 3.4a 13.2 ± 6.7b 673 ± 223ab 1789 ± 768a 1.45± 0.32c 32 ± 11b 0.326 ± 0.190b

Legend: Irrigation: 100 % PET-Full irrigation-FI, 80 % PET-Progressive drought-PD, 60 % PET-deficit irrigation-DI, 50 % PET-extreme deficit irrigation-EDI; Fertilization: 100, 50, 25, 0 kg N ha−1. Note: PH (plant height), B (n° of branches per plant), PS (length of the panicle), SD (stem diameter), RD (root depth), RL (root horizontal length), Y (yield), AGB (aboveground biomass), TGW (thousand-seed weight), HI (harvest index), CWP (crop water productivity). Note: Means that do not share a letter are significantly different, p ≤ 0.05 according to the test of Tukey’s HSD. Note: average value ± standard deviation values are shown.

In this study, the canopy cover played an important role on light interception and plant growth, just like on the resulting yield and biomass (Table 4.6). For the canopy cover, statistically significant differences (p < 0.05) were observed between highly irrigated/fertilized treatments (FI-100 kg N ha−1) and less irrigated/fertilized treatments (EDI-0 kg N ha−1), both for 25-Oct. and 19-Nov. For the yield, on the 25-Oct., significant differences (p < 0.05) were observed when comparing FI-100 kg N ha−1 to that of FI-0 kg N ha−1, and the previous with EDI-100 kg N ha−1 and EDI-0 kg N ha−1. For 19-Nov, despite of the decreasing yields with lower N-inputs and water-application, no significant differences were reported between treatments and the interaction amongst them. For both sowing dates (25-Oct. and 19-Nov.), there was a moderate correlation (r ≥ 0.5) between dependent variables (canopy cover, yield, and biomass) and independent variables (irrigation and N-fertilization). Overall, when associating dependent variables (parameter of study) and independent variables (controlled parameter) the Pearson correlation coefficient was higher for irrigation than for N-fertilization. For instance, when correlating biomass and yield with irrigation, the r value was of 0.66 and 0.85 for 25-Oct., and of 0.68 and 0.42 for 19-Nov.

Table 4.6. Relationship between different levels of irrigation and N-fertilization with the maximum canopy cover (CC as a percentage), yield (Y) and aboveground biomass (ABG) (kg ha-1) in the second year of experimentation (25-Oct. and 19-Nov.)

25-October 19-Nov.

I F Max. CC Y ABG Max. CC Y ABG (%) (kg ha-1) (kg ha-1) (%) (kg ha-1) (kg ha-1) 100 100 26.6 ± 8.3a 1380 ± 251a 3522 ± 1231a 44.5 ± 8.9a 752 ± 62a 3205 ± 683a 100 0 13.4 ± 1.5b 875 ± 99b 2005 ± 146b 28.6 ± 9.8b 711 ± 44a 2326 ± 1022ab 50 100 7.7 ± 6.6b 373 ± 185c 1280 ± 702b 26.4 ± 5.6b 508 ± 215a 1311 ± 224b 50 0 4.4 ± 5.3b 322 ± 116c 973 ± 336b 16.5 ± 6.8b 717 ± 80a 1283 ± 137b

Legend: (I) Irrigation: 100 % PET-Full irrigation-FI, 80 % PET-Progressive drought-PD, 60 % PET-deficit irrigation-DI, 50 % PET-extreme deficit irrigation-EDI; (F) Fertilization: 100, 50, 25, 0 kg N ha−1. Note: Means that do not share a letter were significantly different, p ≤ 0.05 according to the test of Tukey’s HSD.

Furthermore, remarkable information was observed when analysing crop’s responses to FI and EDI (100 % and 50 % PET) with different levels of N-fertilization (100 and 0 kg N ha−1) (Figure 4.4). Despite of the greater canopy cover expansion observed in 19-Nov. (maximum CC of 45 %) when compared to 25-Oct. (maximum CC of 27 %), yields were greater in 25-Oct. than in 19-Nov. under FI, and vice versa for EDI (Table 4.6). A greater canopy expansion on 19-Nov. when comparing to 25- Oct. was probably due to the milder temperatures during the vegetative phase. The maximum canopy cover was observed at 50 DAS (25-Oct.) and at 60 DAS (19-Nov.), with a respective soil percentage covered of 27 % and 45 % for FI-100 kg N ha−1, and of 4 % and 17 % for EDI-0 kg N ha−1. In Table 70

4.7, the effect of irrigation on canopy cover was statistically significantly different (p < 0.05) for most of the observations and sowing dates, having a larger canopy cover those plants that were highly irrigated (FI). A Similar statistical difference trend (p < 0.05) was observed between higher and lower levels of N-fertilization; being much greater than the canopy expansion amongst highly fertilized plants. In particular, these differences were depicted from observation N°4 onwards, when N- fertilization had already been assimilated by plants. In Table 4.8, for 25-Oct. observation N°5, the interacting effect of higher irrigation with nitrogen fertilization on the canopy cover was clear, showing statistical differences (p < 0.05) among most groups of means. Despite of the decreasing canopy cover with lower irrigation and N-fertilization, significant differences were only observed for the FI-100 kg N ha−1 when compared to the rest of treatments (FI-0 kg N ha-1, EDI-100 kg N ha-1, and EDI-0 kg N ha-1).

Figure 4.4. Canopy cover during the second year of experimentation, left (25-Oct.) and right (19-Nov.)

Note: FI-100 (Full irrigation-100 % PET and 100 kg N ha-1); FI-0 (Full irrigation-100 % PET and 0 kg N ha-1); EDI-100 (Extreme deficit irrigation-50 % PET and 100 kg N ha-1); EDI-0 (Extreme deficit irrigation-50 % PET and 0 kg N ha-1)

Table 4.7. Post-hoc Tukey-HSD pairwise comparison test for the canopy cover (%) during the second year of experimentation (2018-2019)

25-October Ia N°1 N°2 N°3 N°4 N°5 N°6

100 2.7 ± 1.3a 3.4 ± 3.6a 8.7 ± 5.7a 20.0 ± 9.5a 8.2 ± 3.2a 2.3 ± 1.4a 50 1.3 ± 0.5b 1.2 ± 0.5a 2.1 ± 1.6b 6.1 ± 6.6b 0.7 ± 0.4b 0.5 ± 0.5b Fa 100 2.4 ± 1.1a 3.1 ± 3.5a 7.2 ± 6.6a 17.2 ± 12.9a 5.3 ± 5.2a 1.7 ± 1.8a 0 1.6 ± 1.2a 1.5 ± 1.3a 3.6 ± 3.0a 8.9 ± 6.4b 3.6 ± 3.6a 1.1 ± 0.9a 19-November Ia N°1 N°2 N°3 N°4 N°5 N°6 100 1.2 ± 0.6a 14.2 ± 8.0a 27.3 ± 10.7a 36.6 ± 13.1a 24.9 ± 7.7a 9.3 ± 9.0a 0 0.6 ± 0.5b 8.6 ± 5.4a 17.6 ± 9.3a 21.5 ± 8.0b 16.0 ± 4.7b 2.0 ± 1.3b Fa 100 1.1 ± 0.7a 15.1 ± 8.2a 26.3 ± 12.4a 35.4 ± 12.5a 24.4 ± 8.1a 9.4 ± 8.8a 0 0.7 ± 0.4a 7.7 ± 3.5b 18.5 ± 8.1a 22.6 ± 10.7b 16.6 ± 5.0b 1.9 ± 1.6b Legend: a) Irrigation: 100 % PET-Full irrigation-FI and 50 % PET-extreme deficit irrigation-EDI; b) Fertilization: 100 and 0 kg N ha-1. Note: N°1 to N°6 correspond to measurement dates: 24, 34, 40, 49, 70 and 85 DAS (25-Oct.); and to 24, 34, 40, 60, 75 and 84 DAS (19-Nov.). Note: means that do not share a letter were significantly different, p ≤ 0.05 according to the test of Tukey-HSD

Table 4.8. ANOVA test for the canopy cover and its interaction between irrigation and fertilizer application n°1 n°2 n°3 n°4 n°5 n°6 Irrigation Fertilizer Oct. Nov. Oct. Nov. Oct. Nov. Oct. Nov. Oct. Nov. Oct. Oct. Nov. 100 100 3.3a 1.5a 4.6a 19.5a 11.8a 32.2a 26.6a 44.5a 9.8a 30.1a 3.0a 15.9a 100 0 2.2ab 0.9ab 2.1a 10.8ab 5.6b 22.3ab 13.4b 28.6b 6.5b 19.8b 1.7ab 2.9b 50 100 1.6b 0.8ab 1.6a 8.9b 2.7b 20.4ab 7.7b 26.4b 0.8c 18.7b 0.6b 2.7b 50 0 1.0b 0.5b 0.8a 6.5b 1.5b 14.8b 4.4b 16.5b 0.7c 13.4b 0.3b 1.2b Legend: a) Irrigation: 100 % PET-Full irrigation and 50 % PET-extreme deficit irrigation; b) Fertilization: 100 and 0 kg N ha-1.Note: n°1-6 correspond to measurement dates: 24, 34, 40, 49, 70 and 85 DAS (25-Oct.); and to 24, 34, 40, 60, 75 and 84 DAS (19-Nov.).Note: means that do not share a letter are significantly different, p ≤ 0.05 according to the test of Tukey-HSD

4.4. Discussion Mean-temperatures recorded during the vegetative stage (27.5 °C for 25-Oct. and 25.5 °C for 19-Nov. average of temperature 40 DAS) were close to quinoa’s optimal growth temperatures, 10–25 °C (Garcia et al., 2015). A closer temperature to optimal growth for the sowing on the 19-Nov resulted in greater canopy expansion (47 %), when compared to 25-Oct (27 %). In addition, this research was conducted in a much warmer climate (25–30 °C) to that of its origin (15–20 °C) Bolivian Altiplano (Garcia et al., 2015). This research findings differ to those of Gifford et al., (1984), showing a tight relationship between light interception and production of seeds and biomass. In fact, maximum- temperatures occurring at anthesis were critical for quinoa pollination, resulting in a reduction of pollen production and viability (Jacobsen et al., 2003; Alvar-Beltrán et al., 2019b). Despite of the 72

greater canopy expansion observed in 19-Nov, the yields were lower (752 kg ha-1) to those reported on the 25-Oct (1380 kg ha-1). This research also confirms that quinoa can stand higher temperature thresholds at anthesis (38 °C) than other cereals grown in Burkina Faso; for instance, rice 35 °C, maize 35 °C or sorghum 34 °C (Yoshida, 1981; Crafts-Brandner and Salvucci, 2002; Prasad et al., 2006). Apart from temperature, photoperiodicity plays an important role on the rate of plant growth. In fact, c.v. Titicaca had a higher photoperiod-sensitivity in comparison to other quinoa varieties which were not affected by changes in photoperiodicity (Bertero et al., 2000). For tropical latitudes (11° N, Burkina Faso), the length of the growing cycle of c.v. Titicaca was much shorter, 83–90 days, to that observed in subtropical regions, 169 days and 134 days for maturity, respectively, for Iraq (32° N) and Germany (44° N); but having a similar growing cycle to that of Yemen (14° N), 118 days (Saeed, 2014; Hassan, 2015; Präger et al., 2018). However, in this research, different sowing dates, with slightly different photoperiods (max ∆ photoperiod of 26 min between sowing dates), did not have an impact on the duration of the growing cycle. In respect to the growing degree days (GDD), having 3 °C as a base temperature, very similar values to this research were reported by Präger et al., (2018) for c.v. Titicaca in Germany for the year 2015. Based on their results, the cumulative GDD for c.v. Titicaca was 1874 °C; whereas for Burkina Faso, the mean cumulative GDD for all sowing dates was 1851 °C.

Other abiotic factors, such as irrigation and N-fertilization, were of interest within this study. For irrigation, there was an increase in CWP with decreasing irrigation; except for EDI, that resulted in crop failure due to the excessive water stress. Simulated conditions for quinoa showed CWP values of 0.3 to 0.6 kg m−3 with crop evapotranspiration varying between 200 mm to 400 mm (Geerts and Raes, 2009). These values were in accordance with those reported in Burkina Faso with a CWP value of 0.57 kg m−3 for 325 mm (PD). In fact, in this research, a similar curve was observed to that of Geerts and Raes (2009) with higher CWP between 250–400 mm, while having lower CWP values above or below the previous evapotranspiration-thresholds. The results of this research were in harmony with those observed under field conditions in Bolivia (2005–2006), with CWP values of 0.38 to 0.45 kg m−3 under FI (more than 500 mm) and DI (around 400 mm), respectively (Geerts et al., 2008a). Overall, even if quinoa could stand water-applications as low as 200 mm, the crop sacrificed most of its yield for survival (480 kg ha−1 under EDI compared to 1012 kg ha−1 under PD, average of all sowing dates). So far, in the literature, only one study has examined such water stresses (183 mm during the growing season) to those of this research (211 mm in 85 days growing cycle, equal to 2.48 mm day−1); but with much lower evapotranspiration to that of Burkina Faso (Garcia et al., 2003). In addition, emerging findings corroborate quinoa’s high-water use efficiency under drought-stress conditions and were in harmony with those results reported in the literature (Jacobsen et al., 2003; Geerts et al., 2008a; Hirich et al., 2014c; Azurita-Silva et al., 2015). In fact, rapid stomata closure, sunken stomata, restricted

shoot growth, accelerated leaf-senescence were among quinoa’s physiological responses to drought- stress conditions, while given the plant an optimal adaptability to dry environments. For N- fertilization, little information emerged from this study. Our findings fall within those in the literature acknowledging that N-fertilization plays a role in quinoa growth up to 25 kg N ha−1 (Moreale, 1993). Between 0 to 25 kg N ha−1 significant differences were observed for different crop parameters (plant height, yield, biomass, thousand-seeds weight, among others), but no differences were depicted at higher N-applications (50 and 100 kg N ha−1); therefore N-stabilization was reached at 25 kg N ha−1. On the contrary, many other studies have pointed out that quinoa yields and biomass increase with higher N-fertilization, but stabilize at 80 kg N ha−1 (Kaul et al., 2005; Shams, 2012). Overall, the previous findings on abiotic factors support Atkinson and Porter (1996) insights on faster development among species growing in environments with higher temperatures and water stress conditions.

Regarding physiological parameters, the 1000-seed weight (kernel) observed in this research (2.03 g under PD and 1.38 g for EDI) was lower to that reported in other field studies in Turkey (2.1–3.2 g) and Bolivia (3 to 6 g) (Geerts et al., 2008b; Yazar et al., 2015). Low seed weight in Burkina Faso was the result of high temperatures and longer photoperiods. For cereals, a 3.1 day shortening of the seed filling phase has been fixed per degree Celsius increase in mean daily temperatures, as well as a decrease of 2.8 mg kernel per degree Celsius increase (Wiegand and Cuellar, 1980). This could explain, to some extent, the differences in observed kernel weight between Burkina Faso and elsewhere. Moreover, the observed shallow root system (average 8.7 cm) was the result of high bulk density (1.61 g cm−3). In this case, soil compaction was a limiting factor for the root-system to uptake water and nitrogen from deeper zones. This was corroborated by Daddow and Warrington (1983), pointing out that bulk densities higher than 1.59 g cm-3 for sandy-loam soils was a restrictive factor for root penetration. Poor root colonization in Burkina Faso contrasts to that observed in Bolivia and Chile, where quinoa roots have exceeded 50 cm depth (Alvarez-Flores, 2012).

Finally, observed c.v. Titicaca yields under FI-100 kg N ha−1 (1380 kg ha−1 for 25 Oct.) and PD-25 kg N ha−1 (1356 kg ha−1, average of 2017–2018 experiment) were higher to those observed in Egypt (1034 kg ha−1 under 90 kg N ha−1), in line with those reported in Iraq (1270 kg ha−1), and lower to those observed in Germany (1980 kg ha−1) and Yemen (2100 kg ha−1) (Shams, 2012; Hassan, 2015; Saeed, 2015; Präger et al., 2018).

4.5. Conclusions The length of the growing cycle for c.v. Titicaca in Burkina Faso has shown a photoperiod-sensitivity when compared to other regions in the world. Growing quinoa in Burkina Faso when temperatures are closer to the optimal temperatures for growth can increase the biomass but does not necessarily determine the final yield. In fact, the yield is tightly dependent on the heat stress and water vapor pressure deficits occurring at flowering; if temperatures break a given temperature threshold—around 38 °C—the plants can become sterile. The yields have been stabilized under full irrigation and progressive drought, meaning that further water stressors—DI and EDI—will result in considerable yield losses. Under field conditions, emerging findings have shown that plant’s development and growth was affected, to some extent, by N-fertilization; nevertheless, in general, quinoa N- requirements were low (25 kg N ha−1). Moreover, high temperatures and water vapor pressure deficits during anthesis, above-optimal temperatures for growth, compacted sandy–loam soils under field conditions, and a short growing cycle are among the abiotic factors that reduce quinoa’s yields in the Sahel. However, agricultural management strategies can be tailored for enhancing quinoa yields according to the needs and means of this region. For instance, a two-pass mechanical tillage incorporating organic fertilizer (as it has a longer mineralization than inorganic fertilizers) prior to the sowing is recommended. Frequent irrigation of the soil, but in small quantities in order to reduce high water percolation typically of sandy–loam soils is highly suggested for avoiding soil compaction and creating favorable conditions for the plant root system. Planning of agricultural activities, particularly through a well sowing calendar, is crucial for quinoa in the Sahel. Temperatures during the vegetative stage and heat stress at flowering have to be as close as possible to the mean optimal temperatures for growth. For this reason, this research proposes the following sowing quinoa dates for Burkina Faso: mid-November in the Soudanian agro-climatic zone, late-November in the Soudano-Sahelian zone, and early-December in the Sahelian zone.

Finally, agronomic guidelines for growing quinoa in the country need to be prepared and come in hand with an appropriate scientific knowledge-transfer towards local communities, which will then take over and be in charge of scaling-up the crop throughout the country. Further research must be tailored according to the abiotic stresses found within this region. For this reason, heat- and drought-resistant quinoa, wind-tolerant, and short-cycle varieties should be within the scope of genetic breeders.

CHAPTER 5: HEAT-STRESS EFFECT ON QUINOA (CHENOPODIUM QUINOA WILLD.) UNDER CONTROLLED CLIMATIC CONDITIONS: THE POTENTIAL OF QUINOA IN BURKINA FASO

Abstract Quinoa’s adaptation to multiple-environments is well known and it is largely due to its resilience to abiotic-stresses (salinity, drought and freezing/heat-stress tolerance). Despite of its wide genetic variability, recent introduction to new environmental conditions has revealed new adaptation challenges to be addressed. Crop pollination is highly affected by heat-stress conditions atypical in its ecosystem of origin, while making the plant non-fertile. This research reveals the effect of temperature on plant’s most sensitive phenological phases, flowering and seed-germination. To fulfil the gap in literature we perform thermo-tolerance tests under controlled climatic conditions. The selected variety in study, c.v. Titicaca, relates to the most widespread variety along Sahel and Middle East and North African (MENA) region. Emerging findings are extrapolated and then compared with field observations from Burkina Faso, for then determining the crop’s suitability in different agro-climatic zones. This research concludes that temperatures above 38 °C are a limiting factor for successful quinoa pollination, just like seed-germination rates. Beyond this temperature threshold, yield losses can attain 30 % while seed-germination rates can fall below 50 %. Key words: Africa; abiotic stresses; thermo-tolerant species; super food crops; climate change

5.1 Introduction Abiotic stresses (extreme temperature, drought, salinity, among others) can lead to a range of morphological, physiological, biochemical and molecular changes that negatively affect the plant´s development and productivity (Wang et al., 2000). Salinity and drought-stress conditions are often associated with oxidative disturbances in the balance between the production of reactive oxygen and antioxidant defences, causing the denaturation of functional and structural proteins in plants (Smirnoff, 1998; Betteridge, 2000). Temperatures play a crucial role on seed-germination, plant growth, drooping leaves, leaf senescence, fruit decolouration, yield, parthernocarpy and pollen viability (Paupière et al., 2014). The effect of high-temperature-stresses (HTS) during the plant’s most sensitive phenological phase, reproduction, is well known, with plenty of research examining pollen’s viability under heat- stress conditions. At higher temperatures, vapour pressure deficits are higher, resulting in pollen desiccation and low pollen viability. Under these circumstances, gametophytes dry out, resulting in the non-delivering of gametes to the embryo sac (Hatfield and Prueger, 2015).

For widespread crops along the Soudanian agro-climatic zone, HTS at flowering is fixed at 35 °C, i.e. maize (Zea mays) (Dupuis and Dumas, 1990). For sorghum (Sorghum spp.), HTS at flowering can trigger oxidative damages in leaves as well as on pollen, therefore diminishing the plant´s

photosynthetic activity and affecting seed-formation (Prasad and Djanaguiraman, 2011). For the fruit- set of tomato (Solanum lycopersicum), there is a strong negative relationship between pollen production and pollen viability at temperatures beyond 34 ºC (Sato et al., 2000). For quinoa (Chenopodium quinoa Willd.), the effect of environmental stresses on plant´s morphological and physiological development is thoroughly understood, having the plant halophyte, drought-tolerant, thermic-resistant and pH versatile characteristics (Bertero et al., 1999; Jacobsen et al., 2003; Razzaghi et al., 2011; Shabala et al., 2012; Adolf et al., 2013; Hirich et al., 2014a). However, up to now, there is scarce information on quinoa’s responses to heat-stress conditions. This is because most of the existing research examines freezing conditions usually found in its ecosystem of origin, at 3000 m.a.s.l in the Andean Altiplano (Vacher, 1998; Bertero, 2001, Bois et al., 2006).

Since 2013, the number of countries growing quinoa has doubled, particularly with a rapid expansion in the Middle East and North African region (MENA), just like in the Sahel region, where HTS is frequent (Adolf et al., 2013; Hirich et al., 2014b; Ceccato et al., 2015; Yang et al., 2016; Lesjak and Calderini, 2017). Meanwhile, some studies are looking at the effect of HTS on plant´s most sensitive phenological phases, flowering and seed-germination. A study in Chile affirms that HTS (34 ºC) at flowering can result in considerable yield losses (Lesjak and Calderini, 2017). Others, Yang et al., (2016), find that higher temperatures (20-25 ºC) favour the growth of quinoa when compared to lower temperatures (8-18 ºC). Whereas Hinojosa et al., (2019) have not depict any changes in yields under different temperature thresholds. By studying two genotypes of quinoa (QQ74 and 17GR) under optimal growing temperatures (16-25 °C between night/day-time) and treatments under HTS conditions (24-40 °C between night/day-time), concluding that there are no statistical differences between treatments.

However, there are no trials examining the genotype Titicaca, widespread along the warmest regions (MENA and Sahel region) (Coulibaly and Martinez, 2015; Bazile et al., 2016; Choukr-Allah et al., 2016; Dao et al., 2016; Gacemi, 2016; Habsatou, 2016; Mosseddaq et al., 2016). The aforementioned genotype is now being cultivated in Algeria, Morocco, Lebanon, Mauritania, Yemen, Iraq, Niger, Mali and Burkina Faso, as part of the Technical Cooperation Programs launched by the FAO. In technical reports, researchers affirm that heat-stress (above 35 °C) at flowering has a negative impact on yield, but the extent of effect is not yet known (Breidy, 2015; Djamal, 2015; Hassan 2015; Saeed, 2015; Alvar-Beltrán et al., 2019a; Alvar- Beltrán et al., 2019b). Outside the Sahel and MENA region, in Greece, studies have reported that high temperatures, long days and low relative humidity are limiting factors for pollen availability (Noulas, 2015). Whereas in Italy, genotype Titicaca reacts negatively to HTS, with lower yields when sown in May rather than April (1.5 t ha-1 and 3 t ha-1, respectively) (Pulvento, 2015).

There are several studies examining the effect of temperature, water and salinity on seed-germination of quinoa, but discrepancies upsurge amongst them (Bois et al., 2006; Adolf et al., 2013; Hirich et al., 2014b; Ceccato et al., 2015). Some investigations affirm that quinoa´s optimal temperature for maximum seed-germination is between 18 °C to 23 ºC, while differing according to the genotype of study (Boero et al., 2000; Bois et al., 2006). In contrast, Jacobsen and Bach (1998) recognize that highest germination rates occur at temperatures around 30 ºC to 35 ºC. Mamedi et al. (2017) suggest that for Titicaca genotype, germination rates are highest between 0 °C to 35 ºC, though then declining at temperatures above 40 ºC. Seed-germination rates are reduced when exposed to large day/night- time thermal variations (Boero et al., 2000). The thermal time required for the germination of quinoa seeds is 30 degree-days, but low temperatures during germination can also result in embryo death (Jacobsen and Bach, 1998; Rosa et al., 2004). In addition, quinoa seeds are characterised for having a thick seed coat that hindrances the way out of dormancy (Ceccato et al., 2015). Some studies suggest that temperatures occurring 30 days prior to harvesting can have a positive correlation with germination (Dorne, 1981), though others suggest that low temperatures and long photoperiods during seed formation on mother plants can increase seed dormancy, and vice-versa (Ceccato et al., 2011). For seed-storage temperatures, studies show that quinoa seeds tend to leave dormancy faster at higher temperatures, 25 ºC, rather than lower temperatures, 5 ºC (Ceccato et al., 2011). While ambient humidity and high temperatures at storage conditions in tropical zones can reduce germination rates (Bhargava, 2015).

The present research builds on existing studies and provides further information on the genotype Titicaca, widespread among earth’s hottest regions (Sahel, MENA and Mediterranean region). We evaluate under controlled climatic conditions the effect of HTS on plant’s most sensitive phenological phases, seed-germination and flowering. We then compare our results with field observations from Burkina Faso to determine the crop’s spatiotemporal suitability for finally proposing a crop-calendar.

5.2. Materials and Methods The quinoa genotype used in this research is Titicaca, developed by the University of Copenhagen and selected under Danish climatic conditions. This genotype comes from a hybridization of seeds, by crossing material from southern Chile and Peru. The seeds used in this research were provided by the FAO TCP/SFW/3404 project for the promotion of quinoa in Burkina Faso. They were stored in a fridge seed bank at Institut de l’Environnement et de Recherches Agricoles (INERA), Burkina Faso, at temperatures below 11 °C, relative humidity of 48 % and with seed-germination rates of 93 %.

The experiment was organized in a randomized split-plot design with five HTS levels (30, 34, 38, 42 and 46 ºC), each with three replicates. During the growing season, except flowering, plants were kept in a climatic chamber (Walk-In chamber, internal dimensions 2.5 x 2.0 x 2.5 m equipped with 32 Osram Fluora 36 w lights, equivalent to 230 w m-2) at 25 ºC during night-time (10 hours) and 30 ºC during day-time (14 hours). Once plants reached flowering, they were heat-stressed (30, 34, 38, 42 and 46 °C) in a separated climatic chamber (HPP 750 life chamber) during 6 h day-1 for 10 days (length of the flowering period). Plants were fully irrigated during the complete growing cycle and the evapotranspiration was calculated using the crop coefficients (Kc) provided by Garcia et al. (2003): 0.52 at emergence, 1.0 at maximum canopy cover and 0.70 at physiological maturity. Daily evapotranspiration (Eq. 1) was estimated using the following formula (Hargreaves and Samani, 1985): 0.5 ETo = 0.0023 (T mean + 17.78) Ro (T max – T min) (Eq. 1) -1 Where: Ro = solar radiation was calculated by transforming Watt-hour (W h ) into Mega Joules (MJ day-1) (Allen et al., 1998), then multiplying MJ by the photoperiod (14 h day-1); T mean = mean daily temperature (27.9 ºC); T max = daily maximum temperature (30 ºC); T min = daily minimum temperature (25 ºC).

Seeds were sown in 25 litre plastic pots (20 x 20 cm), each containing two quinoa plants (in total 6 plants per treatment) spaced 10 cm. The pots were filled with 1 kg of clay pebbles, overlying by 10 kg of loamy soil (Table 5.1). Nitrogen fertilisation, in the form of ammonium nitrate (NH4NO3, 26 % N), was applied 35 days after sowing (DAS) at a rate of 25 kg N ha-1.

The measured crop parameters, after drying the plants for 48 h at 80 ºC, were dry-yield and standing- biomass (including the organic mass contained in leaves, stems and seeds, except roots). The potential yield per hectare was calculated by multiplying the average yield per treatment by 200,000 plants ha-1 (standard sowing rate of 10 cm between plants and 50 cm spaced rows). This allowed us to compare results from controlled conditions with field experiments from Burkina Faso.

Seed-germination tests were conducted in a sterilized plastic Petri dish, humidified daily with distilled water. Each trial contained three replicates and 100 seeds, and was heat-stressed (6 h day-1 for 5 days) under night-time conditions in a thermostatic laboratory digital chamber at 30, 34, 38, 42, 46 ºC, as well as at room temperatures between 15-20 ºC. The effects of different temperature thresholds on seed-germination and flowering were analysed using a one-way analysis of variance (ANOVA) on Minitab-18 software. Multiple means were compared using the post-hoc Tukey test (p ≤ 0.05).

Based on the findings from the climatic-chambers, 12 maps of Burkina Faso were prepared, one per month, using the maximum-mean-monthly-temperatures (MMMT) for each of the agro-climatic zones

(Sahel, Soudano-Sahelian and Soudanian zone). The red-tones were giving to those regions with MMMT’s above 36 °C, whereas blue-tones for those regions with MMMT’s below 34 °C. These maps were elaborated in ArcGIS 10.2.1 using the daily meteorological data provided by the National Oceanic Atmospheric Administration from 1973 until 2017 (NOAA, 2018). The location of each of the three meteorological stations was the following: Sahel (Dori; 14º01´N 0º01´W; 280 m.a.s.l); Soudano-Sahelian (Ouagadougou airport; 12º21´N 1º30´W; 310 m.a.s.l); and Soudanian climate (Bobo Dioulasso airport; 11º09´ 4º19´W; 450 m.a.s.l).

5.3. Results 5.3.1. Soil analyses The soil used in this experimentation is characterised for having a loam texture, with slightly acidic properties (Table 5.1). The content of organic carbon is relatively high, just like the nitrogen content in the soil, 1.1 g kg-1. During the experiment, the plants have been fully irrigated and crop water requirements (ETc) have been entirely satisfied. The observed evapotranspiration during the growing cycle has been between 350-390 mm, being slightly higher among HTS-treatments (46 °C) (Table 5.2).

Table 5.1. Main soil physic-chemical characteristics before sowing Parameter Units Soil layer (0-20 cm) Sand % 45.0 Silt % 36.9 Clay % 18.1 Texture Loam pH (H2O) 7.92 C organic % 9.8

N g kg-1 1.1 Electrical conductivity mS cm-1 1.11

Table 5.2. Evapotranspiration and water supply (mm season-1), yield (g plant-1), standing biomass (g plant-1) harvest index (%) and seed germination rates (%)

Temperature threshold (°C) Crop parameter 15 - 20 30 34 38 42 46 Evapotranspiration (mm) - 358 366 373 380 386 Seed yield (Y) (g plant-1) - 5.35±0.11a 4.64±0.14a 3.77±0.22b 3.56±0.04b 2.60±0.40c Potential yield (kg ha-1) 1 - 1068 926 754 712 520 Standing biomass (g plant-1) - 8.59±0.41ab 9.09±0.68a 7.88±1.10ab 7.69±0.86ab 6.23±1.18b Potential biomass (kg ha-1) 1 - 1717 1819 1576 1539 1245 Harvest index (%) - 62±4.3a 51±2.4ab 48±3.9ab 47±4.4b 42±5.8b Seed germination rate (%) 75.0±5.6a 64.7±0.6ab 52.3±4.2b 26.0±17.0c 2.3±2.1d 2.0±2.6d Legend: means that do not share a letter are significantly different (p ≤ 0.05) 1Potential yield at a density rate of 200,000 plants per hectare. 81

5.3.2. Effect of HTS on yield and seed germination This research results show that the genotype Titicaca is highly affected by HTS at flowering. A strong negative correlation has been observed between the seed yield and HTS at flowering, with a twofold decrease between 30 °C and 46 °C HTS-treatments (p = 0.00), from 5.35 g plant-1 (1068 kg ha-1) to 2.60 g plant-1 (520 kg ha-1), respectively (Table 5.2; Figure 5.1, Figure 5.3). The effect of HTS on seed yield is strongest between 34 °C and 38 °C. The greatest yield loss is observed from 34 °C to 38 ºC HTS-treatments (p = 0.02) and from 42 °C to 46 ºC HTS-treatments (p = 0.01), with a yield loss of 19 % and 27 %, respectively. No differences (p = 0.87) are observed when comparing the yields at 38 ºC and 42 °C, respectively with 3.77 g plant-1 (754 kg ha-1) and 3.56 g plant-1 (712 kg ha-1). The lack of differences, between 38 °C and 42 °C HTS-treatments shows a stabilization of yield losses before continue to drop at higher HTS, 46 °C. Mean temperatures for all treatments during the growing cycle are above 28 °C, hence exceeding uninterruptedly optimum growing temperatures for quinoa (20-25 °C). The low standard deviation (SD) observed in all HTS-treatments indicates that seed yield values are generally close to the mean.

The highest above-ground-biomass is observed among 30 ºC and 34 ºC HTS-treatments, with 8.6 g plant-1 (1717 kg ha-1) and 9.1 g plant-1 (1819 kg ha-1), respectively. The general pattern is a decrease of above-ground-biomass with increasing temperatures, besides of having statistical significant differences between 34 ºC and 46 ºC HTS-treatments (p = 0.03). In general, no differences are observed on the biomass coming from stems and branches. This is because most of the biomass is built before (during the vegetative stage) the plant’s experiences HTS at flowering. The harvest index (HI, as a %) is calculated as the ratio between yield (g plant-1) and above-ground-biomass (g plant-1). HI (Table 5.2 and Figure 5.2) is higher amongst plants that are less exposed to HTS (62 % under 30 ºC HTS-treatments), when compared to those at higher HTS (42 % under 46 ºC HTS-treatments). Statistical evidences are depicted when comparing 30 °C and 46 °C HTS-treatments (p = 0.00).

This research results have shown a negative relationship between germination rates and HTS- treatments (Figure 5.2). The greatest differences (p < 0.01) are observed between 34 ºC and 38 ºC HTS-treatments, with germination rates falling by 50 % between the two treatments (52 % and 26 %, respectively). The tipping point, between low and high germination rates, is detected at 38 °C HTS- treatment, with a high SD between replicates (SD 17.0). For the 42 °C and 46 °C HTS-treatments, the germination rates drop down to 0 %.

Figure 5.1. Relationship between seed yield (g) and high temperature stress (°C) during flowering

Note: means that do not share a letter are significantly different Note: plants have been heat-stressed for 10 days, 6 hours a day during flowering.

Figure 5.2. Quinoa germination rates (%) under different heat-stress thresholds and room temperatures (°C)

Note: means that do not share a letter are significantly different Note: seeds have been heat-stressed for 5 days, 6 hours a day

Figure 5.3. Mean comparison under different heat-stress thresholds for the following crop parameters: a) seed yield per plant; b) seed germination rates; c) harvest index; d) total above-ground biomass

Legend: X-axis, high-temperature-stress (HTS) threshold interaction Legend: Y-axis, if an interval does not contain zero, the corresponding means are significantly different Legend: lines defines the width of the confidence interval (CI) and is determined by the observed variability in the sample. Legend: points estimate the difference between a pair of means. The CI is centered on this value

5.3.3. Spatio-temporal suitability of quinoa in Burkina Faso The mapping of mean-maximum monthly temperatures (MMMT) during the year and throughout the country is essential for identifying the suitable timing and place for growing quinoa in Burkina Faso. Figure 5.4 displays with dark-red colours those regions where MMMT’s often exceed 38 °C (HTS threshold considered the tipping point for pollination). For avoiding HTS at flowering and at seed- germination, the following growing periods (from north to south) have been identified. For the Sahelian zone, a first growing window is established between July-September, when MMMT’s oscillate around 34-38 °C, while another longer and colder one between November-February. Particularly between December-January, when MMMT’s are closest (30-32 °C) to optimal growing temperatures (10-25 °C). For the Soudano-Sahelian zone, the growing window for limiting the effects of HTS at flowering extends from June-February. During this period the lowest MMMT’s are recorded between July-September (MMMT’s between 30-34 °C) and from December-January (MMMT’s between 32-34 °C). For the Soudanian agro-climatic zone, MMMT’s throughout the year are in all cases lower than 38 °C, but are close in March and April (MMMT’s of 37 °C). However, the coldest periods are certainly during the rainy season, from July to September (MMMT’s below 32 °C) and from December to January (MMMT’s 30-32°C). 84

Based on the aforementioned crop-calendars and by extrapolating results emerging from climatic- chambers to field conditions (Table 5.2), the potential yields (from north to south) according to HTS distribution along the country (Figure 5.4) are the following. For the Sahelian zone, around 754-929 kg ha-1 when grown between July-September and November-February. For the Soudano-Sahelian zone, around 754-1068 kg ha-1 when grown between June-February, and most likely higher when grown between July-September. For the Soudanian zone, around 754-1068 kg ha-1 along the whole year, being lower when flowering occurs between November-February, while being highest if flowering occurs between July-September and December-January.

Figure 5.4. Maximum mean monthly temperatures (MMMT’s) in the country’s different agro-climatic zones (Sahel-north, Soudano-Sahelian-central, and Soudanian-southernmost parts) from 1973-2017.

5.4. Discussion Our results show a strong negative relationship between HTS and seed yield of c.v. Titicaca, seed germination rates, harvest index and aboveground biomass; in all cases with lower values at higher temperatures. These findings differ to those of Hinojosa et al., (2019), that use different genotypes of quinoa (17GR and QQ74). In their study, although heat-stress reduces pollen viability by 30-70 %, no statistical differences have been reported between HTS-treatments (40/24 °C) and non-HTS-treatments (25/16 °C) on seed yield per plant (around 9 g plant-1).

For the seed yield, observed values in the climatic-chambers (4.63 g plant-1, yield potential of 926 kg ha-) are in harmony with those observed in field conditions in Burkina Faso (4.57 g plant-1, yield potential of 914 kg ha-1), using the same genotype just like similar HTS conditions at flowering (at MMMT’s of 34 °C) (Alvar-Beltrán et al., 2019a). Another country within the MENA region, Iraq, has shown similar values (1270 kg ha-1, HTS at flowering 30 °C) to those of this research (Hassan, 2015). A negative relationship between higher temperatures and lower yields are also observed in Yemen, with yields as high as 2100 kg ha-1 (at MMMT’s 25-30 °C) in the highlands, but no yields reported in coastal zones (at MMMT’s 40-45 °C) (Saeed, 2015). On the contrary, a positive relationship has been observed in countries growing quinoa at lower temperatures. For instance, at MMMT’s lower than 30 °C in Lebanon, Morocco and Italy the following yields have been attained: 3000 kg ha-1, 1535 kg ha-1 and 2700 kg ha-1, respectively (Pulvento, 2012; Hirich, 2012; Breidy, 2015).

Seed-germination rates at 42 ºC and 46 ºC HTS thresholds are lower to those observed by Boero et al. (2000) using similar temperature thresholds to those of this research, but different length of HTS and genotypes of quinoa (cv. Kamiri, Robura, Sajama and Samaranti). In their study, highest germination rates (70-90 %) have been obtained at 20-25 ºC (depending on the variety), though decreasing to less than 40 % at 45 ºC HTS. Similar critical temperature ranges (20-30 ºC) for maximum germination rates have been reported by Mamedi et al., (2017). However, differences between studies have been depicted; having their research germination rates as high as 88 % under 45 °C HTS, whereas in this study germination-rates have been close to 0 % under 42 °C.

Regarding storage conditions, first-generation seeds, coming from Denmark and cultivated in Burkina Faso, had higher germination rates (93 %) to those observed in this research (75.0 %). The comparison between different generation seeds, production and storage methods, was in accordance with Dorne´s (1981) statement; with a positive relationship between germination rates (for Chenopodium bonus- henricus) and temperatures occurring 30 days prior to harvest. While seeds exposed to longer photoperiods and lower temperatures could have resulted in higher seed dormancy, and vice-versa (Ceccato et al., 2011).

Drawing upon the spatio-temporal distribution of HTS in Burkina Faso, this research confirms that lowest MMMT’s are recorded during the rainy season (July-September) and during the winter of the dry-season (December-January). A depletion of Titicaca yields are observed in Burkina Faso, with a yield reduction from 914 kg ha-1 (sowing early-November) to 444 kg ha-1 (sowing early-December), when MMMT’s at flowering are highest (Alvar-Beltrán et al., 2019a). This in line with yields observed in Sudan for c.v. Titicaca, with values depleting from 1790 kg ha-1 down to 1090 kg ha-1, when comparing mid-November and mid-January sowing (Maarouf and Nagat, 2016). In addition, Dao et al., (2016) report considerable damages on genotype Titicaca during the rainy season in Burkina Faso, being the result of waterlogging and strong winds. Overall, as display in Figure 5.4, the MMMT’s in all agro-climatic regions of Burkina Faso are higher to that consider for optimal quinoa growth, 10-25 °C (Garcia et al., 2015)

5.5 Conclusions This study shows the effect of extreme temperatures on quinoa´s most susceptible phenological phases, seed-germination and flowering. Quinoa plants display an outstanding resilience to HTS with non-negligible yields (520 kg ha-1) at 46 °C. However, most of the yield losses (25 % reduction) occur between 34 ºC and 38 ºC HTS thresholds. For this reason, 38 ºC has to be considered the maximum temperature exposure threshold at flowering; except if farmers are willing (or forced) to assume higher yield losses. Same pattern occurs for seed-germination, with rates being highest between 15-30 °C and dropping below 50 % at temperatures higher than 34 °C.

To conclude, under changing climatic conditions and with increasing temperatures, particularly in the Sahel and MENA region, the search of new thermo-tolerant species is becoming imperative. Now that the quinoa genome is described, breeding approaches must target thermic-resistant quinoa genotypes that can stand/adapt to forthcoming critical growing temperatures. In addition, by combining controlled climatic conditions with field observations, just like avoiding HTS periods, this research contributes to a better understanding of a suitable crop-calendar for growing quinoa,. In general, quinoa genotype Titicaca has demonstrated a great potential within hot-spot regions to climate change and therefore can be considered an alternative crop for minimising undernourishment rates in the Sahel.

CHAPTER 6: IRRIGATION SCHEDULING OF DROUGHT-RESISTANT QUINOA IN BURKINA FASO WITH AQUACROP

Abstract Quinoa’s resilience to drought stress conditions makes the crop suitable for the Sahel, and as an alternative for alleviating food insecurity. The modelling of quinoa in new environments, beyond its origin, is required given its rapid worldwide expansion. Crop water models are of increasing interest as pressure on water resources is rising and irrigation scheduling is seen as the best option for water optimisation. The AquaCrop model has been used to simulate crop development and to derive optimal frequencies and net applications of irrigation. Due to limited water resources in the region different irrigation schedules (i.e. full irrigation (FI), progressive drought (PD), deficit irrigation (DI) and extreme deficit irrigation (EDI)) have been selected for studying yield and biomass responses to water stress. In this research, quinoa yields have been stabilised under PD, thereby prioritising maximum water productivity rather than maximum yields. As compared with FI, PD resulted in a 13% lower yield (0.971 Mg ha-1 for FI vs. 0.850 Mg ha-1 for PD) with 25% less irrigation (415 mm for FI vs. 310 mm for PD). Water optimisation is reached by watering less (310 mm) but with more frequent irrigation events (28 rather than 20 times). The model's calibration accuracy, expressed in terms of normalised-root-mean-square-error (NRMSE), was 13.1 % for biomass yield and 13.6 % for seed yield. Overall, farmers exposed to great rainfall variability and growing quinoa in loam-sandy soils must embrace water preservation strategies when planning irrigation. Key words: Water management; deficit irrigation; climate resilient crops; Sahel

6.1. Introduction The African continent, as a whole, is one of the world´s most vulnerable regions to climate change due to its low adaptive capacity (Niang et al., 2014). In particular, the Sahel region, consisting of countries within the southernmost part of the Sahara Desert, is considered a hotspot of climate change, with unprecedented climates expected to happen (Mora et al. 2013). In regard to precipitation, trends over West Africa show an inter-annual variability increase of up to 40 % by the end of the century (Yabi and Afouda, 2012; Niang et al., 2014). For Burkina Faso specifically, regional climate models estimate a significant rainfall decline for the period 2021-2050 (Ibrahim et al., 2014). Changes in the onset/offset of the rainy season have also been observed, particularly with a delay in the onset, thereby shortening the growing season for rainfed crops (Biasutti and Sobel, 2009).

Moreover, traditional water harvesting practices (zaï, half-moons and stone bunds, among others) have been widely used in Burkina Faso to cope with the high rainfall variability (Barbier et al., 2009; Sawadogo et al., 2008). Nevertheless, these techniques remain insufficient for coping with changing climatic threats. In addition, the country’s percentage of cultivated area with irrigation is as low as 0.9

%, with most of the area based on surface irrigation systems (FAO, 2011). Additionally, the lack of governance and the proliferation of uncontrolled pumping, particularly from small reservoirs and groundwater, have widely augmented the pressure on water resources (de Fraiture, 2014). For this reason, appropriate water management is vital for stabilising crop yields, besides satisfying increasing water needs.

Modelling studies have been conducted, using Hydrus and Cropwat programs, with different irrigation schedules and crops in Burkina Faso. For instance, Mermoud et al. (2005) affirm that for onion in Kamboinse, less frequent weekly irrigation increased the root zone water storage as compared to farmer’s practices of daily irrigation that lead to high evaporation rates of the soil surface. While Wang et al. (2009) have estimated crop water demands for different rainfed and vegetable crops for two sites, Ouagadougou and Banfora, and demonstrated that irrigation schedules must be tailored for satisfying crop water demands during critical growth stages.

The AquaCrop is a crop water productivity model developed by the Food and Agriculture Organisation (FAO) that simulates biomass and yield responses to water for multiple crop types and different environmental conditions. It allows to optimise water resources in regions where water is a limiting factor for crop production (Raes et al., 2009; Steduto et al., 2009; FAO, 2019). Little use of the AquaCrop model has been made in sub-Saharan Africa, with most of the research focusing on the modelling of vegetable crops (Karunaratne et al., 2011; Sam-Amoah et al., 2013; Darko et al., 2016). Some validations of the model have been done for arable crops, e.g. in Nigeria with different levels of nitrogen fertilisation for rainfed maize (Akumaga et al., 2017). For the crop of interest, quinoa (Chenopodium quinoa), calibration and validation of the model has been done for its environment of origin, the Bolivian Altiplano (Geerts et al., 2009a; Geerts et al., 2010). These studies have identified actual crop yields under different irrigation schedules, and estimated acceptable economic losses under deficit irrigation.

The main objective of this study has been to calibrate and validate the AquaCrop model for different irrigation schedules of quinoa during the dry season in Burkina Faso. We have aim to improve the management of water resources, by advancing on the timing and frequency of quinoa irrigation within this region. The overall hypothesis of the project is that improved irrigation scheduling is crucial for saving farmers expenses, enhancing yields and preserving water resources. This research also seeks to estimate to what extent yield losses can be assumed by farmers’ deficit irrigation strategies.

6.2. Materials and Methods 6.2.1. Site description We simulated quinoa yields and biomass with the AquaCrop model (version 6.0, 2017) for one location, Institut de l’Environnement et de Recherches Agricoles (INERA), Farako-Ba research station, Bobo Dioulasso (11° 05’ N, 4° 19’ W, 421 m.a.s.l). This research’s location is within the Soudanian agro-climatic zone, with warm mean temperatures and a well-defined rainy season (May- October). The AquaCrop calibration was done for the following sowing dates: 25/10/2018 and 19/11/2018; whereas the validation for the 4/11/2017 and 8/12/2017 (Table 6.1). Adequate water for crop growth (full irrigation (FI), 100 % potential crop evapotranspiration-PETc) was applied in T1 and

T3; whereas T5, T6 and T8 were subjected to progressive drought (progressive deficit-PD, 70-90 %

PETc). T4, T7 and T9 were exposed to half of crop water requirements (deficit irrigation-DI, 50%

PETc); whereas T2 and T10 to extreme water deficits (extreme deficit irrigation-EDI, ≤ 40 % PETc). To accept that different sowing dates did not have an impact on the resulting yields and biomass, a set of trial simulations were conducted with net irrigation requirements and displayed in the first part of the results section.

The experimental field used for the calibration of the AquaCrop model was organised on three irrigation schedules (FI, DI, EDI, corresponding to treatments T1 to T4) and eight repetitions conducted between 2018-2019, whereas the validation was made up of three irrigation schedules (PD, DI and

EDI, corresponding to treatments T5 to T10) and nine repetitions conducted between 2017-2018. For the calibration, the size of each experimental unit was 12.5 m2, and 7.5 m2 for the validation, with 50 cm and 10 cm spaced rows and distance between plants on each row, respectively. The quinoa variety used throughout the whole experiment was c.v. Titicaca, characterised for having a short growing cycle of approximately 90 days. As the transplanting took place 18 DAS, the simulations on the AquaCrop model started on 12/11/2018 (for the 25/10/2018 sowing), 22/11/2017 (for the 4/11/2017 sowing), 7/12/2018 (for the 19/11/2018 sowing) and 26/12/2017 (for the 8/12/2017 sowing).

Table 6.1. Different irrigation schedules, full irrigation (FI), progressive drought (PD), deficit irrigation (DI) and extreme deficit irritation (EDI), and sowing dates used for the calibration and validation of AquaCrop

Calibrated values Validated values Experimental year 2018 2017 Sowing date 25/10 25/10 19/11 19/11 4/11 4/11 4/11 8/12 8/12 8/12 Transplanting date 12/11 12/11 7/12 7/12 22/11 22/11 22/11 26/12 26/12 26/12 Irrigation schedule FI EDI FI DI PD PD DI PD DI EDI

Treatments T1 T2 T3 T4 T5 T6 T7 T8 T9 T10

6.2.2 Sampling and measurement The heat units used for the calibration of the AquaCrop model were both, growing degree days (GDD) and calendar days (DAT). However, as plant development tightly depends on temperature and requires a specific amount of heat to develop from one point in their lifecycle to another, GDD units are considered the most appropriate for comparing with other regions around the world. For describing crop development the following parameters were measured: plant emergence, flowering, senescence, maturity and duration of flowering (using 100 samples per plot); seed yield per plant (using 12 samples per plot); biomass at 24, 40 and 60 days after sowing-DAS (using 3 samples per plot); canopy cover at 24, 34, 40, 49, 70 and 85 DAS (using 10 samples per plot); root depth (using 1 sample per plot). The canopy cover was calculated using the Canopeo app. developed by the Oklahoma University in 2015 (Patrignani and Ochsner 2015). Observations were made at a distance of 60 cm from the top of the canopy and 75x50 cm image cover. Six soil samples were collected at 0-20 and 20- 40 cm depth to determine the soil physical and chemical characteristics.

6.2.3 Soil and crop water aspects The experiment’s soil, loamy-sand, was amended with compost (50.2 % organic matter) at a rate of 5 t -1 -1 ha , as well as with phosphate (26.7 % P2O5) at a rate of 400 kg ha the week prior to sowing. The amount of water applied was calculated using a water counter placed at the entry of each block. The timing of the irrigation was during the afternoon to avoid losses from direct evaporation, while the frequency was 2 to 3 times a week depending on the phenological phase and irrigation schedule. The drip irrigation system had a flow rate of 1.05 l hour-1 per emitter with 30 cm distance between emitters. The climatic data (maximum and minimum temperature, mean relative humidity, evapotranspiration and rainfall) was recorded daily for both 2017-18 and 2018-19 growing seasons and used for determining crop water requirements (Figure 6.1). The daily reference potential evapotranspiration (ET) was calculated using Hargreaves and Samani equation (1985) that requires less parameters, which can easily be obtained (latitude, T max, T min and T mean), than Penman Monteith’s (crop height, albedo, canopy resistance and evaporation from soil) equation: 0.5 ETo = 0.0023 (T mean + 17.78) Ro (T max – T min) (Eq. 1) -2 -1 Where: Ro = solar radiation (MJ m day ) at a given month and latitude (Allen et al. 1998); T mean = mean daily temperature (°C); T max = daily maximum temperature (°C); T min = daily minimum temperature (°C). The unit conversion from MJ m-2 day-2 to mm was the following: 1 mm day-1 equals 2.45 MJ m-2 day-1.

Potential (standard) crop evapotranspiration (PETc) was calculated by multiplying quinoa’s crop coefficient (Kc) by the reference evapotranspiration (ETo) and for different phenological phases (Garcia et al. 2003): 0.52 at emergence, 1.0 at maximum canopy cover and 0.70 at physiological 92

maturity. The previous Kc values were more suitable, in terms of latitude and energy exchange, for this research than those recorded by Razzaghi et al. (2012) in Denmark (Kc 0.20, 1.20 and 0.40 for initial, mid and late stages); besides being the most accepted value at the FAO Irrigation and Drainage Paper N°66 (Steduto et al. 2012).

6.2.4 AquaCrop model The main strength of AquaCrop is the good balance between accuracy, simplicity and robustness. It requires a small number of parameters and inputs variables to predict crop production under different water management conditions (including rainfed, deficit and full irrigation), just like weather and climate change scenarios. AquaCrop system considers the crop-soil interactions, where crop water productivity is the main component together with a set of additional elements: soil (water balances), crop (physiological processes) and atmosphere (temperature, rainfall, evaporative demand and CO2 concentrations). Field management factors (irrigation, soil surface management and soil fertility) complement the previous components as they play an important role on and can affect water balance, crop development and therefore, final yield. Crop simulations with AquaCrop are made in four steps: crop development, crop transpiration, biomass production and yield formation.

6.2.5 Statistical analysis Different statistical indices related to model accuracy were used to evaluate the performance of the AquaCrop model. The normalised-RMSE (NRMSE) gives further information on the average of the measured data ranges (Eq. 2); whereas the root mean square error (RMSE) identifies the differences between estimated and observed values (Eq. 3); whereas. The mean absolute percentage error (MAPE) expressed the differences between simulated and observed values as a percentage (Eq. 4). For NRMSE and MAPE, less than 10 % difference between observed and simulated values was considered as a high performance of the model, whereas 10 % to 20 % as a good performance. Wilmott’s index of agreement (d) was used to evaluate how well the simulated values fitted the observed data (Eq. 5), and Pearson’s correlation coefficient (r) to examine the relationship between different irrigation schedules and different crop parameters (biomass and canopy cover). Finally, the coefficient of determination (r2) was used to find out the variance in the dependent variable that is predictable from the independent one.

NRMSE = (Eq. 2)

RMSE = (Eq. 3)

MAPE = (Eq. 4) 93

d = (Eq. 5)

Where: O’i = [Oi – P], P’i = [Pi – P], Oi is the observed value, Pi is the simulated value, is the simulated mean.

6.3. Results To accept that different sowing dates (25/10/2018, 04/11/2017, 19/11/2018 and 08/12/2017) does not affect the resulting yield and biomass, a set of simulation trials (using net irrigation requirements) have been run. Emerging findings have shown that regardless the sowing date, very little differences are observed in terms of biomass (3003-3014 kg ha-1 min/max value) and yield (1051-1055 kg ha-1 min/max value). Even though a drip irrigation system has been used, small differences (less than 10 %) between net irrigation requirements (modelled in AquaCrop) and full irrigation (calculated under field conditions) have been observed. This is due to the fact that calculated values underestimate and do not consider losses in the form of direct evaporation, surface runoff and percolation.

6.3.1. Field observations The experiment was performed in a loamy-sandy and slightly acidic soil at 0-20 cm (Table 6.2). The percentage of sand in the soil was considerably high (75 %), rendering the soil highly permeable. Despite of the organic amendment used to improve the soil structure, the total amount of organic matter in the soil remained low (0.6 %), resulting in a low water holding capacity. The nitrogen content in the soil was 0.04 %, therefore with a low nutrient availability for the plant. The soil water at permanent wilting point and saturation under 5 t ha-1 of organic carbon was lower than that of sandy- clay-loam soils, but higher when compared to sandy soils (Leu et al. 2010).

The highest accumulated potential (standard) crop evapotranspiration (∑ PETc) was measured for the sowing on 19/11/2018 (416 mm growing season-1), largely due to the consecutive maximum temperatures occurring during crop’s highest water requirement phases (Table 6.3 and Figure 6.1). Plants sown on 8/12/2017 were exposed to heat stress at flowering, particularly during 4 days when temperatures were close to 36 °C. Moreover, irrigation scheduling was crucial for obtaining higher yields. In this case, a close relationship was observed between the sum of irrigation events and final yield (r = 0.72 for the calibration). For instance, in early November, under PD (T6, 272 mm water applied) and DI (T7, 198 mm water applied) the number of irrigation events attained 28, with yields -1 reaching 1.0 Mg ha . On the contrary, irrigation treatments T2, T3, T4, T8, T9 and T10, with just 18 to -1 23 irrigation events, had lower yields (0.556 Mg ha ) when compared to T6 and T7. Overall, if quinoa was less watered but more frequently irrigated, around three times a week from transplanting, the final yield will considerably increase.

Table 6.2. Soil physic-chemical characteristics

Soil layer (cm) Parameter Units 0-20 20-40 Sand % 75.3 59.4 Silt % 14.8 12.8 Clay % 9.9 27.8 Texture (USDA) Loamy Sand Sandy Clay Loam pH (H2O) 6.09 5.87 C % 0.35 0.30 Organic matter % 0.60 0.51 N % 0.036 0.028 C/N 10 11 P available mg kg-1 44.0 31.3 K available mg kg-1 90.3 115.9 Bulk density g cm-3 1.61 -

Figure 6.1. Observed mean absolute maximum and minimum temperatures, and rainfall for the 2017- 2018 experiment (top) and for the 2018-2019 experiment (bottom) at INERA-Farako-Bâ research station.

Table 6.3. Crop irrigation scheduling and crop potential evapotranspiration

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 Irrigation + Rain (mm) 421 154 409 191 350 272 198 310 202 98 ∑ irrigation events 20 22 21 23 30 29 28 20 21 18 ∑ ETc (mm) 1 394 394 416 416 387 387 387 388 388 388 PET2 107 39 98 46 90 70 51 80 52 31 Total water savings (%) 3 - 62.9 - 54.0 15.7 34.5 52.3 25.3 51.3 76.4 Avg. irrigation (1-3 weeks) (mm) 13.8 7.6 19.3 5.1 7.8 6.6 5.7 18.2 7.7 6.0 Avg. irrigation (4-7 weeks) (mm) 28.0 6.9 18.7 10.0 13.3 11.4 7.7 16.0 12.7 4.7 Avg. irrigation (8-10 weeks) (mm) 24.7 6.5 21.3 10.9 17.5 11.3 9.3 11.2 5.9 6.4 Mean T flowering (ºC) 34.6 34.6 33.2 33.2 33.5 33.5 33.5 34.8 34.8 34.8 Max T flowering (ºC) 35.5 35.5 35.0 35.0 36.5 36.5 36.5 35.5 35.5 35.5 ∑ Irrigation flowering (mm) 73.7 15.9 51.5 36.1 46.3 28.4 18.7 35.4 24.5 19.2 ∑ ETc flowering (mm) 4 63.5 63.5 61.5 61.5 49.0 49.0 49.0 53.9 53.9 53.9

1Penman-Monteith equation (FAO, 1990) 2Potential evapotranspiration (PET) 3 Total water savings when compared to FI treatments (T1 and T3, with an average irrigation of 415 mm) 4Hargreaves and Samani equation (1985) using Allen et al. (1998) solar radiation values

6.3.2. Calibration and validation of AquaCrop Quinoa has a wide genetic variability resulting in a great number of cultivars, thereby making the

AquaCrop calibration highly complex. The treatments used for the calibration were from T1 to T4, whereas for the validation from T5 to T10. For this reason, the calibration was based on a high number of field experimentations, four, while the validation on six. The calibrated crop variables (Table 6.4) were mainly related to the crop phenological phases, whereas the agro-meteorological variables were related to soil inputs and weather data (Table 6.2 and Figure 6.1). Calibrated values were compared to the AquaCrop’s default values, showing considerable differences on the quinoa cycle duration. Overall, the duration of different phenological phases and length of the growing cycle was halved between calibrated and default values. High temperatures and short photoperiodicity in Burkina Faso were the main factors responsible for shortening the growing period of quinoa. For the calibration of heat stress effect at flowering, this research was based on field data from Alvar-Beltrán et al. (2019b), where temperatures above 38 °C were considered critical for pollination. In addition, c.v. Titicaca was not very responsive to N-fertilisation under field conditions in Burkina Faso, therefore it was not considered during the calibration process (Alvar-Beltrán et al. 2019b).

Figure 6.2. Cumulative water supply under different irrigation schedules

Note: T1 and T3: full irrigation-FI (100 % PETc); T5, T6 and T8: progressive deficit-PD (70-90 % PETc); T4, T7 and T9: deficit irrigation-DI (50 % PETc); T2 and T10: extreme deficit irrigation-EDI (<40 % PETc)

6.3.3. Simulation of eco-physiological parameters The AquaCrop’s simulations of quinoa yields (Mg ha-1) and dry-above-ground biomass (Mg ha-1) for two years of experimentation (2017-18 and 2018-19) were represented in Table 6.5 and Figure 6.3. -1 For FI treatments (T1 and T3) the average simulated biomass and yields were 2.590 Mg ha and 0.971 Mg ha-1, respectively. Slight decrease in both average simulated biomass and yield was identified -1 -1 under PD treatments (T5, T6 and T8), with 2.315 Mg ha and 0.850 Mg ha , respectively. Under DI -1 -1 (T4, T7 and T9) average simulated biomass and yield were of 2.065 Mg ha and 0.751 Mg ha , respectively; whereas for EDI (T2 and T10) average simulated biomass and yield dropped down to 1.225 Mg ha-1 and 0.302 Mg ha-1, respectively. The AquaCrop model was also capable of bringing to light water stresses hampering canopy expansion, stomata closure and thereby directly affect crop transpiration. Canopy expansion was reduced by ± 42 % (T2 and T10) and stomata closure by 37 % (T2) and 52 % (T10) in EDI treatments.

Table 6.4. Parameters used for the calibration of AquaCrop (Burkina Faso) and default values (Bolivia) Inputs Units Calibrated value Default value Climate Maximum temperature °C Daily data - Minimum temperature °C Daily data - Potential evapotranspiration mm day-1 Daily data Daily data Rainfall mm day-1 Daily data Daily data Mean relative humidity % Daily data - Crop Development: Plant density plants ha-1 200,000 200,000 Type of planting method - Transplanting Direct sowing Transplanting Days 18 - Recovered Days 0 - Initial canopy cover % 1.80 1.30 Canopy size seedling cm2 plant-1 16.0 6.5 Canopy expansion % day-1 12.4 10.0 Canopy decline % day-1 10.7 10.0 Max. canopy cover DAT / GDD 40 / 790 73 / 1314 Senescence DAT / GDD 48 / 950 160 / 2880 Maturity DAT / GDD 73 / 1461 180 / 3240 Max. Canopy cover % 36 75 Canopy decline Days 29 28 Flowering DAT / GDD 25 / 495 70 / 1260 Duration of the flowering Days / GDD 12 / 234 20 / 360 Length building up harvest index Days / GDD 48 / 864 90 / 1620 Root deepening cm 30 100 Crop Production: Crop water productivity g m-2 10.5 10.5 Harvest index % 39 50 Response to stresses: Air temperature stresses: pollination °C Max (36) / Min (8) Not Management considered Irrigation - Method - Drip irrigation Irrigation events According to irrigation schedule - Field - - Soil fertility - Non limiting Mulches - None - Weed management - Perfect - Soil inputs - Texture USDA Loamy-Sandy Permanent wilting point* % v/v 14.8 - Field capacity* % v/v 25.9 - Saturation* % v/v 47.1 - Hydraulic conductivity* mm day-1 557 - Thickness cm 120 - Groundwater table - Depth m 2.00 Salinity dS m-1 2.0 - -

Legend: DAT (Days after transplanting), GDD (Growing degree days) and DAS (Days after sowing) Legend: default values for calibrating AquaCrop in the Bolivian Altiplano using genotypes Santa Maria and Real Blanca (Geerts et al., 2009; FAO, 2019). Crop default use DAS instead of DAT *Soil values provided by Leu et al. (2010) for similar types of soil and same organic amendment

Table 6.5. Observed and simulated biomass and yield for different sowing dates and irrigation schedules (FI, PD, DI and EDI)

Calibrated values Validated values FI EDI FI DI PD PD DI PD DI EDI (T1) (T2) (T3) (T4) (T5) (T6) (T7) (T8) (T9) (T10) Crop outputs Obs. biomass (Mg ha-1) 2655 1338 2765 1300 2545 2420 2540 1285 2174 1071 Sim. biomass (Mg ha-1) 2605 1329 2575 1654 2495 2541 2425 1910 2118 1122 Obs. biomass σ (Mg ha-1) 1195 555 1041 218 534 1597 1693 173 1642 427 Obs. yield (Mg ha-1) 967 348 727 612 871 1005 943 597 748 303 Sim. yield (Mg ha-1) 966 301 976 589 921 941 893 687 771 304 Obs. yield σ (Mg ha-1) 173 172 62 211 304 838 910 89 518 153 Obs. harvest index (%) 36 33 38 42 39 37 37 46 34 28 Sim. harvest index (%) 37 23 38 36 37 37 37 36 36 27 Avg. crop cycle stress * Canopy expansion (%) 1 41 3 35 1 1 4 9 10 43 Stomatal closure (%) 1 37 3 31 2 1 4 14 7 52

Note: σ corresponds to the standard deviation of observed biomass and yield values *Average effect of water-stresses on canopy expansion and stomatal closure during the growing cycle

Figure 6.3. Differences between observed and simulated biomass and yield (kg ha-1)

6.3.4. Statistical analysis of yield, biomass and canopy cover

For the calibration (T1, T2, T3 and T4), different statistical indicators were used to test the degree of correlation between observed and simulated data (Table 6.6). For the aboveground biomass and canopy expansion, the Pearson correlation coefficient remained high in all treatments (r ≥ 0.94), but with relative high RMSE values as a result of a high internal variability within treatments. More in

detailed, for the canopy cover, the largest RMSE differences were observed in T2 (11.6 %), and to a lesser extent in T4, T1 and T3 (7.5, 5.3 and 4.7 %, respectively). The Wilmott index of agreement (d) was in line with Pearson’s correlation coefficient, with biomass values higher than 0.92, implying an agreement between predicted and observed data. Nevertheless, both indices (d and r) strive to depict the internal variability within a treatment, whereas RMSE did not.

For the validation and calibration, a set of statistical indices (NRMSE, RMSE, MAPE and r2) were used to evaluate the performance of the AquaCrop model for simulated seed yield and biomass (Table 6.7). The average calibrated and validated NRMSE values both for biomass and yield were of 13.1 % and 13.6 % (good performance of AquaCrop). In addition, a higher performance was observed when using MAPE. In this case, the average calibrated and validated NRMSE values both for biomass and yield were of 10.1 % and 9.5 % (good and high performance of AquaCrop). Additionally, r2 showed a robust fit between observed and simulated values both in the calibration and validation, with r2 values between 0.84 and 0.95.

Table 6.6. Performance of AquaCrop calibration when comparing observed and simulated aboveground biomass and canopy cover

Above-ground biomass T1 T2 T3 T4 Pearson correlation coefficient 1.00 0.99 0.98 1.00 -1 Root mean square error (kg ha ) 200 200 300 200 Wilmott´s index of agreement 0.98 0.93 0.97 0.92

Canopy cover Pearson correlation coefficient 0.99 0.99 0.98 0.94

Root mean square error (%) 5.3 11.6 4.7 7.5 Wilmott´s index of agreement 0.96 0.67 0.96 0.87

Table 6.7. Performance of AquaCrop in biomass and yield simulation, average of all treatments RMSE NRMSE MAPE r2 (kg ha-1) (%) (%)

Calibrated crop outputs Biomass 297.7 11.4 9.2 0.94 Yield 127.2 18.0 12.9 0.84 Validated crop outputs Biomass 301.1 14.8 11.1 0.84 Yield 63.9 9.2 6.0 0.95 Note: RMSE (root mean square error); NRMSE (normalised root mean square error); MAPE (mean absolute percentage error); R2 (coefficient of determination)

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6.4. Discussion The AquaCrop model was capable of producing accurate results when simulating quinoa canopy cover, biomass and seed yield in Burkina Faso. Quinoa’s high genetic variability and coping capacity to multiple environments was probably the main reason for having large differences between calibrated and model’s crop parameters (Table 6.4), particularly among those parameters related with plant phenology (Ceccato et al., 2015). For instance, flowering and maturity in Burkina Faso was reached after 25 and 73 DAT, while in Bolivia after 75 and 180 DAT (Geerts et al., 2009a). In fact, the AquaCrop’s default calibration and validation was done in four different locations of the Bolivian Altiplano, at 3600 to 4000 m.a.s.l. and at 17 to 21 °S latitude (Geerts et al., 2009a); which were very different to the environment of this research, 421 m.a.s.l. and 11 °N latitude. Up to know, most of the scientific research has been conducted on the effect of temperature and photoperiodicity on quinoa’s crop growth, acknowledging longer cycles at lower temperatures and longer photoperiods (Bertero et al. 2000; Bois et al. 2006; Hirich et al. 2014a). This aspect was corroborated by comparing Mejillones in the Bolivian Altiplano (mean photoperiod of 770 minutes day-1, mean temperature of 12 °C and sowing date September) with Bobo Dioulasso in Burkina Faso (mean photoperiod of 694 minutes day- 1, mean temperature of 26 °C and sowing November), with a resulting time for harvesting of 200 days in Bolivia (Geerts et al., 2009c) and 86 days in Burkina Faso. A similar analysis was made by Karunaratne et al. (2011), considering that the agro-ecological adaptation of hot-pepper landraces and its internal variability was the main reason for the deviation between the AquaCrop’s simulation and field observations.

Even though, the effect of heat stress on quinoa has been frequently reported (Breidy, 2015; CNRADA, 2015; Djamal, 2015; Hassan, 2015; Saeed, 2015; Alvar-Beltrán et al., 2019a); during the calibration we assumed that the plant was not affected by heat stress occurring at temperatures above 40 °C. However, the combination of heat stress and water deficits during flowering could have resulted in lower yields due to pollen dehydration, in particular among plants sown on the 8/12/2017 (Hatfield and Prueger, 2015). Nevertheless, the extent of effect of heat stress on quinoa pollination for this particular cultivar (c.v. Titicaca) was already examined by Alvar-Beltrán et al. (2019a; 2019b). They affirmed that considerable yield loss, up to 30 %, occurred at temperatures higher than 38 °C and not 40 ºC, fixed value in the AquaCrop model. Hence, observed and simulated yield reduction in December could have been the result of high temperatures that increased vapour pressure deficits and result in pollen desiccation and low pollen viability.

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Moreover, this research findings have elucidated two important aspects regarding drip irrigated quinoa. The first one was that under PD (T6) and DI (T7) quinoa was highly performing (around 1.0 Mg ha-1) if irrigated with small and frequent doses, 28 times from transplanting to maturity (10 weeks). As a result, PD and DI were considered as water optimisation strategies. In addition, yield -1 losses between FI (T1 and T3 with 0.971 Mg ha and 415 mm, averages of yield and water supply) and -1 PD (T5, T6 and T8 with 0.850 Mg ha and 310 mm, averages of yield and water supply) were of 13 %, but water savings as much as 25 %. At this point, under PD yields were, to some extent, stabilised and maximum water productivity was obtained rather than maximum yields. Geerts et al. (2008) made similar observations, affirming that quinoa yields could be stabilised under DI in the Bolivian Altiplano (yield under DI of 1.2 to 2 Mg ha-1).

6.5. Conclusions This research showed that quinoa is a drought tolerant crop that has low water requirements besides having extraordinary abilities of adapting to arid conditions (40 % PETc). In addition, this study demonstrates that irrigation scheduling and drip irrigation systems are crucial for water optimisation. Farmers could incur acceptable yield losses if water is a limiting factor. When sown in early

November under PD (T6, with 272 mm water supply) and DI (T7, with 198 mm water supply), quinoa is highly performing (close to 1.0 Mg ha-1) if irrigated frequently, 28 times in the 10 weeks following transplanting. If water is not a limiting factor, farmers could apply FI less frequently (T1 and T3, with 415 mm water supply) and attained higher yields. However, this option is not supported in this research because rainfall decrease and increasing inter and intra annual (between and within years) rainfall variability have augmented pressure on water resources, even leading to conflict in the Sahel.

The AquaCrop model is well calibrated for a new agro-climatic environment, Soudanian, with a good/high performance between observed and simulated quinoa yield and biomass data (NMRSE and MAPE of 11.4 % and 9.2 % for the biomass, while 18.0 % and 12.9 % for the seed yield). In addition, the calibration of the AquaCrop model shows great differences between this research’s crop parameters and the default calibrated values that are taken from research in the Bolivian Altiplano. Crop cycle duration is tightly linked to the thermal time and photoperiodicity for growth; this is the reason why the growing season was shorter than 90 days in Burkina Faso and as long as 200 days in the Bolivian Altiplano. In addition, our simulations have proven that quinoa is extremely tolerant to water stress in terms of canopy expansion and leaf senescence functions.

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Water conservation strategies, through drip irrigation systems, are among the most efficient methods of irrigation and have demonstrated to reduce water losses when compared to flooding and sprinkler irrigation. If appropriate irrigation scheduling is followed, PD instead of FI, reduction of economic losses from fuel for water pumping, benefits from alike seed yields (just 13 % reduction from FI to PD), and water preservation could be strong advantages for farmers that could save up to 25 % water between FI and PD. Because of the loamy-sandy texture, farmers would have to invest more time in irrigation with smaller amounts of water. Nevertheless, farmers could obtain a yield reduction as the overall individual and community cost benefits from water preservation can be considered as positive.

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CHAPTER 7: CAN GLOBAL WARMING HINDER THE POTENTIAL OF QUINOA IN THE SAHEL?

Abstract Crop systems can be critically affected by climate shifts, particularly in hot-spot regions of climate change. C3 crops, within tropical and subtropical zones, are an opportunity for agricultural adaptation to changing climates in vulnerable regions. Enhancement of C3 crops’ photosynthetic rate by doubling

CO2 concentrations and increasing temperatures can have a positive impact on crop yield and biomass production. Using observed baseline information for Burkina Faso (1973-2017), near surface air temperatures have been projected until the end of the century, bringing to light mean-temperature rises of 1.67 °C (RCP 4.5) and 4.90 °C (RCP 8.5). These research findings have also shed light on the understanding of C3 crops’ responses and their adaptability to increasing greenhouse gas emissions. Further climate disruptions can have both a positive and negative impact on quinoa (Chenopodium quinoa Willd.) that depend on the challenges emerging from spatio-temporal decision-making made by farmers. Crop modelling has allowed to select the most suitable transplanting dates according to different locations and time-horizons in Burkina Faso. Finally, the results have shown that irrigated quinoa yields are likely to increase by 14 % to 20 % in 2075 both in the Sahel and Soudanian agro- climatic zones under RCP 4.5, and by 24 % to 33 % under RCP 8.5 when transplanting between November and January. Key words: Quinoa; irrigation; climate change adaptation; AquaCrop; GIS; Burkina Faso

7.1 Introduction During the 21st century, the African continent will continue to experience a higher temperature rise than the global average (Sanderson et al., 2011; James and Washington, 2013). Within the continent, the Western African countries are expected to face unprecedented climate shifts that will occur one or two decades earlier than expected for the rest of the planet (Niang et al., 2014). The timing of climate departure, i.e. when the coldest year in the future will be warmer than the hottest year of the past, using 1860-2005 as baseline for this region has been placed between 2030 and 2040 (Mora et al., 2013). For this reason, the Sahel and tropical Western Africa are now widely recognised as hot-spot regions of climate change (Diffenbaugh and Giorgi, 2012; Mora et al., 2013).

Overall, there are very few studies examining the likely behaviours of forthcoming climates within this region. This is often attributed to the difficulties related with poor signal to noise, or the inability of climate models to include future changes in land cover as well as periodic shifts in wind and sea surface temperatures (ENSO) (Hulme et al., 2001). Moreover, uncertainties surrounding temperature projections have also been observed between General and Regional Climate Models (GCMs and RCMs), simulating consistent warming but with different magnitude and spatial patterns (Diallo et al., 105

2012; Mariotti et al., 2011). Nevertheless, the construction of climate scenarios maps, by Hulme et al. (2001), on future annual warming and precipitation across the African continent has been a tipping point for the climate modelling literature within this region. In their research, temperature changes in Burkina Faso, under the SRES A2 high-emission-scenario, are expected to be of approximately 2.2 °C by 2020, 4.2 °C by 2050 and 6.5 °C by 2080 with respects to 1961-1990. Whereas under IPCC SRES B1 low-emission-scenario, temperature rise has been estimated at 1.0 °C by 2020, 1.5 °C by 2050 and 2.0 °C by 2080 with respects to 1961-1990. A similar study using 24 climate models provided by the Coupled Model Inter-comparison Project Phase 3 (CMIP-3) has shown a greater temperature rise in Western and South Africa than in other parts of the continent (James and Washington, 2013). More in detail, expected anomalies in mean annual temperatures are larger than 4 °C in both SRES A2 and A1B scenarios by the end of the century; besides of been higher during the boreal summer (Diallo et al., 2012; James and Washington, 2013).

In general, crop modelling efforts have been focused on major food crops, with very little attention on crops grown in the African continent (Challinor et al., 2007). There is even less research concerning crop modelling in hot-spot regions of climate change, due to the lack of field-based reliable data. Some of the research on crop modelling (using DSSAT) under different climate scenarios (increase of 1 °C by 2020, 1.5 °C by 2050 and 3 °C by 2080, with respects to 1961-1990) has been conducted in the Soudano-Sahelian (Kaya, Koudougou and Ouagadougou) and Sahelian (Ouahigouya and Dori) agro-climatic zones of Burkina Faso (Salack, 2006). For the Sahelian zone, millet (c.v. zatib) yield is expected to deplete by 12 %, 17 % and 30 % respectively by 2020, 2050 and 2080, while in the Soudano-Sahelian zone by 15 %, 23 % and 42 %, using the same time-horizons. For sorghum (c.v. motta maradi) yield is expected to decrease by 6 %, 10 % and 15 % respectively by 2020, 2050 and 2080 in the Sahelian zone; while by 5 %, 8 % and 17 % in the Soudano-Sahelian zone. Moreover, Schlenker and Lobell (2010) have also predicted a yield depletion of maize, sorghum, millet, groundnuts and cassava for the period 2046-2065 in sub-Saharan Africa. Based on their results, even if rainfall remains constant, mean yield losses for the previous crops are estimated at 16 % by the mid- century, while losses being greater for maize (22 %) and sorghum (17 %), and lower for cassava (8 %). In fact, it is acknowledged that temperature changes will have stronger impacts on yields than precipitation because of a much larger standard deviation of temperatures.

Sorghum and millet have been modelled with SARRA-H with a large ensemble of future climate projections (CMIP-5 RCPs 4.5, 6.0 and 8.5) for the periods 2031-2050 and 2071-2090, using 1961- 1990 as baseline (Sultan et al., 2013). Despite of the uncertainty regarding rainfall simulations, temperature increase within Soudanian and Soudano-Sahelian agro-climatic zones is estimated to cause a 10 % yield reduction for 50 % and 80 % of the climate simulations carried out for 2031-2050

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and 2071-2090. Thornton et al., (2011) have acknowledged similar yield reductions to the previous, higher than 10 % in West Africa. Nevertheless, higher yield losses have been observed in the Soudanian zone because of greater crop sensitivity to temperature changes; whereas in the Sahel region, crops are more susceptible to rainfall changes. Whether or not rainfall variability increases within this region, a warming climate in Western Africa is going to elevate the pressure on water resources besides of increasing water demand of plants (higher evapotranspiration). In top of all that, warming temperatures can also be damaging to crop production because of plant’s sensitivity to heat- stress during flowering (Hatfield et al., 2011). For this reason, the adaptability of the so-called climate resilient crops that can cope with higher CO2 concentrations, heat-stress and water stress conditions, among other factors, has been of increasing scientific interest.

The intent of this paper is to provide the scientific literature with the outlooks and challenges that agricultural adaptation, through crop-selection, may face in hot-spot regions of climate change. The impacts of rising temperatures and CO2 concentrations are evaluated using recently introduced climate resilient crops, quinoa (Chenopodium quinoa Willd.), often grown within the region during the dry season and having high nutritional properties (Repo-Carrasco et al., 2003; Adolf et al., 2013; Alvar- Beltrán et al., 2019a). The adaptability of this crop is evaluated under different climate change scenarios and time horizons, agro-climatic zones and diverse soil textures found within Burkina Faso. These reasons make this research unique and expand the knowledge on this specific climate resilient crop and its phenological and physiological responses to climate change in the Sahel. The emerging findings have allowed to identify the spatiotemporal suitability of quinoa within the country.

7.2 Materials and methods This study has been carried out in Burkina Faso, where three agro-climatic zones have been selected: Sahelian, Soudano-Sahelian and Soudanian zones. For each agro-climatic zone, three soil textures have been identified: Sahel (Sand, Sandy-Clay-Loam, and Sandy-Loam), Soudano-Sahelian (Sandy- Clay-Loam, Sandy-Loam, and Loam) and Soudanian (Sandy-Clay-Loam, Sandy-Loam, and Loam). All these simulations have been conducted for two climate scenarios (RCP 4.5 and RCP 8.5) and four time horizons (2020, 2025, 2050 and 2075). The baseline for determining the time horizons was: 2020 (from 2006 to 2036), 2025 (from 2010 to 2040), 2050 (from 2035 to 2065) and 2075 (from 2060 to 2090).

7.2.1. Crop modelling with AquaCrop and ArcGIS AquaCrop is a crop water productivity model developed by the Food and Agricultural Organization (FAO) that can improve crop planning by optimizing water resources in regions where water can be a limiting factor for crop production (Raes et al., 2009; Steduto et al., 2009; FAO, 2019b). It also takes

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into account the effect of different CO2 concentrations on the crop’s photosynthesis, and therefore can be a useful tool for simulating crops responses to different atmospheric conditions (Gobin et al., 2017; Garofalo et al., 2019). In this research, the calibrated crop parameters for quinoa are those provided by Alvar-Beltrán et al., (2019c; 2019d) for the Soudanian climate of Burkina Faso, in which they establish 73 days for maturity from transplanting and 17 days for transplanting from sowing. Further calibration of AquaCrop was achieved using the critical temperature thresholds at flowering asserted by Alvar-Beltrán et al., (2019c; 2019d), which is between 36 °C and 41°C.

7.2.2. Generation of future climates The Representative Concentration Pathways (RCP) are long-term scenarios of different range spans of atmospheric CO2eq concentration levels. The RCP’s selected in this research, both for modelling with

AquaCrop and for different climate scenarios, refer to RCP 4.5 (between 530 and 580 ppm CO2eq atmospheric concentration by the end of the century) and RCP 8.5 (more than 1000 ppm CO2eq atmospheric concentration by the end of the century) (IPCC, 2014a). In total, 43 GCMs, part of the Coupled Model Inter-comparison Project Phase 5 (CMIP-5, Table 7.1), have been used to simulate forthcoming temperature trends in the country’s different agro-climatic zones during the dry season (October to March). For the modelling of future scenarios past climatic data has been downloaded from the National Oceanic and Atmospheric Administration (NOAA) for the period 1973-2017 (NOAA, 2019). For each agro-climatic zone one weather station has been selected: Bobo Dioulasso (Soudanian climate; latitude 11°09’38”N; longitude 4°19’36”W; altitude 460 m.a.s.l.), Ouagadougou (Soudano-Sahelian climate; latitude 12°21’12”N; longitude 1°30’45”W; altitude 316 m.a.s.l.) and Dori (Sahelian climate; latitude 14°02’00”N; longitude 0°02’00”W; altitude 277 m.a.s.l.). The daily solar radiation for a given latitude has been calculated using the template provided by Dingman (2002).

7.2.3. Soil grids The soil data was obtained from the International Soil Reference and Information Centre (ISRIC, 2019). The soil raster grid, 0.25 x 0.25 km, for each type of soil texture (sand, silt and clay) has been downloaded for post treatment in ArcMap 10.2.1. Using the Soil Texture Triangle (USDA, 2019) and raster calculator tool from ArcMap, different soil texture classifications were identified for Burkina Faso. Other soil inputs, such as soil moisture at permanent wilting point, field capacity and saturation, were acquired from a study conducted on water holding capacity of different types of soil in Burkina Faso (Leu et al., 2010). All of these soil profile characteristics in the three different agro-climatic zone were introduced in AquaCrop for afterwards modelling.

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Table 7.1. List of the ensemble of GCMs used in this study

CMIP5 Atmospheric Ocean CMIP5 Atmospheric Ocean Model ID resolution resolution Model ID resolution resolution (°lat. x °long.) (°lat. x °long.) (°lat. x °long.) (°lat. x °long.) ACCESS1.0 1.0 x 1.0 1.9 x 1.2 GISS-E2-H** 2.5 x 2.0 2.5 x 2.0 ACCESS1.3 1.0 x 1.0 1.9 x 1.2 GISS-E2-H-CC 1.0 x 1.0 1.0 x 1.0 BCC-CSM1.1 1.0 x 1.0 2.8 x 2.8 GISS-E2-R** 2.5 x 2.5 2.5 x 2.0 BCC-CSM1.1(m) 1.0 x 1.0 1.1 x 1.1 GISS-E2-R-CC 1.0 x 1.0 1.0 x 1.0 BNU-ESM 0.9 x 1.0 2.8 x 2.8 HadGEM2-AO 1.0 x 1.0 1.9 x 1.2 CanESM2 1.4 x 0.9 2.8 x 2.8 HadGEM2-CC 1.0 x 1.0 1.9 x 1.2 CCSM4 1.1 x 0.6 1.2 x 0.9 HadGEM2-ES 1.0 x 1.0 1.9 x 1.2 CESM1(BGC) 1.1 x 0.6 1.2 x 0.9 INM-CM4 0.8 x 0.4 2.0 x 1.5 CESM1(CAM5) 1.1 x 0.6 1.2 x 0.9 IPSL-CM5A-LR 2.0 x 1.9 3.7 x 1.9 CMCC-CM 2.0 x 1.9 0.7 x 0.7 IPSL-CM5A-MR 1.6 x 1.4 2.5 x 1.3 CMCC-CMS 2.0 x 2.0 1.9 x 1.9 IPSL-CM5B-LR 2.0 x 1.9 3.7 x 1.9 CNRM-CM5 1.0 x 0.8 1.4 x 1.4 MIROC-ESM 1.4 x 0.9 2.8 x 2.8 CSIRO-Mk3.6.0 1.9 x 0.9 1.9 x 1.9 MIROC-ESM-CHEM 1.4 x 0.9 2.8 x 2.8 EC-EARTH 1.0 x 0.8 1.1 x 1.1 MIROC5 1.6 x 1.4 1.4 x 1.4 FIO-ESM 1.1 x 0.6 2.8 x 2.8 MPI-ESM-LR 1.5 x 1.5 1.9 x 1.9 FGOALS-g2 2.8 x 2.8 1.0 x 1.0 MPI-ESM-MR 0.4 x 0.4 1.9 x 1.9 GFDL-CM3 1.0 x 1.0 2.5 x 2.0 MRI-CGCM3 1.0 x 0.5 1.1 x 1.1 GFDL-ESM2G 1.0 x 1.0 2.5 x 2.0 NorESM1-M 1.1 x 0.6 2.5 x 1.9 GFDL-ESM2M 1.0 x 1.0 2.5 x 2.0 NorESM1-ME 1.1 x 0.6 2.5 x 1.9 **Ensemble of three models

7.3. Results The topsoil, 0 cm depth, across Burkina Faso is characterised by different types of soil texture: 72.5 % sandy-loam, 15.8 % sandy-clay-loam, 9.3 % loam, while sand and clay-loam both represent 1.2 % (Figure 7.1). These textures play an important role in the water holding capacity of the soil; and, hence on the water availability for the plant, being highest among soils with higher clay-loam content and lower for sandy-loam soils, which cover a large area in Burkina Faso.

Mean temperature trends during the dry season (Figure 7.2), for the period 1973-2017, showed a warmer increase (1.62 °C) in southernmost parts of the country, doubling those observed within the central (0.77 °C) and northern parts (1.08 °C). A warming climate is very likely to occur under RCP 4.5 and RCP 8.5 by the end of the century, while the rate of increase is expected to be similar in all agro-climatic zones (0.20 °C per decade for RCP 4.5 and 0.58 °C per decade for RCP 8.5). This means that temperatures are expected to rise by 1.7 °C for RCP 4.5 and 4.9 °C for RCP 8.5 by the end of the century. For the Sahel (Dori), the mean-maximum temperatures are now exceeding the 40 °C threshold in October and March, and will continue to do so in November under RCP 8.5 in the second half of the century. For the Soudano-Sahelian zone (Ouagadougou), the 40 °C value is now being 109

exceeded in March and will become normal in November and February from 2050 onwards under RCP 8.5. For the mildest parts of the country, Soudanian zone-Bobo Dioulasso, mean-maximum temperatures will only exceed 40 °C in October and March under the worst case scenario from 2070 onwards.

Figure 7.1. Spatial variation of soil texture in Burkina Faso at soil-surface (0 cm depth)

Figures 7.3 and 7.5 show the spatiotemporal variability of quinoa yields within the country’s different agro-climatic zones under RCP 4.5, with an emission increase up to 530 and 580 ppm CO2eq by the end of the century. The simulated quinoa yields showed a constant increase (between 14 % to 19 % depending on the region and time-period) when transplanted in November/December, with yields exceeding 1000 kg ha-1 from 2050 onwards. January transplantation seemed suitable in all agro- climatic zones with similar growing-yield-trends to that of November/December. Nevertheless, October transplantation in the Sahel in 2050 can be considered a tipping point, with an abrupt yield reduction of 60.7 %, from 461 kg ha-1 to 181 kg ha-1 in 2050 when compared to 2020. Overall, for the Soudanian (Bobo Dioulasso) and Soudano-Sahelian (Ouagadougou) agro-climatic zones, the simulated temperature increase (1.1 ºC when comparing 2075 with 2020) was not expected to have such a negative impact on the yield. For the Sahel (Dori), with larger annual temperature fluctuations due to greater distance from the sea, the transplantation of quinoa will be narrowed down to November and December.

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Figure 7.2. Mean monthly temperature projection (October-March) until the end of the century using the ensemble of GCMs from CMIP5, with 1973-2017 as a baseline: (A) Dori (Sahel); (B) Ouagadougou (Soudano-Sahelian); and, (C) Bobo Dioulasso (Soudanian)

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Furthermore, Figure 7.4 and Table 7.2 display the expected quinoa yield trends under RCP 8.5 (CO2eq emissions higher than 1000 ppm by the end of the century). Up to 2050, yields are likely to increase between 10 % and 18 % in all agro-climatic regions when transplanted between November and January. In fact, yields could attain more than 1200 kg ha-1 if transplanted in December by 2075, with an increase of more than 30 % (Sahel and Soudanian zones) when compared to 2020. On the contrary, October transplantation in the central and southernmost parts of the country is suitable up to 2050, but yield losses were estimated to attain 50 % by 2075. A similar situation is expected to occur in the Sahel with literally no yield production from 2025 onwards. Overall, simulated values show that quinoa has a long-term potential in all regions when transplanted between November and January, but not in October. In addition, different types of soil texture did not have a significant impact (low standard deviation amongst soil types) on yield production, with maximum yield differences between soil textures lower than 10-15 %. Indeed, simulated values show slightly larger yields in sandy-loam and loam soils than in sandy-clay-loam soils.

Yield losses can be explained by heat-stress effects occurring at flowering, increasing water vapour pressure deficits leading to pollen desiccation; hence, hampering pollination and the production of seeds. In fact, a strong negative correlation between maximum-mean temperatures occurring the month after transplanting and the resulting yield has been observed. More in detail, the Pearson correlation coefficient between temperatures occurring a month after transplanting and the resulting yield for the different agro-climatic regions has been: Sahel (r: -0.77 for RCP 4.5 and r: -0.85 for RCP 8.5), Soudano-Sahelian (r: -0.90 for RCP 4.5 and r: -0.81 for RCP 8.5) and Soudanian (r: -0.91 for RCP 4.5 and r: -0.71 for RCP 8.5).

Moreover, Table 7.3 displays an increasing biomass production trend both in RCP 4.5 and RCP 8.5, with biomass exceeding 3000 kg ha-1 in all agro-climatic zones and diverse types of soil texture from 2050 onwards. However, it is likely that under RCP 8.5 biomass increase will fold that of RCP 4.5, with a rise of 32 % and 16.5 %, respectively by 2075 when comparing to baseline. The rate of biomass increase will be larger in RCP 4.5 between 2025 and 2050; and in RCP 8.5 between 2050 and 2075. Similar trends are observed in all agro-climatic regions and no major differences were depicted between different types of soil texture, but being slightly higher among sandy-loam and loam soils than for sandy-clay-loam soils. It can be asserted that both CO2 and temperature increase in all agro- climatic zones do not exceed the plant’s physiological optimum thresholds hence having a positive impact on the biomass production. Overall, the weight of the harvested product as a percentage of the total plant weight of a crop (harvest index) is expected to decrease, as biomass is expected to increase at a faster rate than yield. However, the harvest index will vary depending on the climatic scenario, agro-climatic region and time-period.

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Figure 7.3. Spatio-temporal distribution of average quinoa seed yield (kg ha-1) in Burkina Faso’s agro- climatic zones, by combining main soil textures for different planting-periods under RCP 4.5

Note: Values within maps correspond to average seed yield (kg ha-1) and standard deviation (SD)

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Figure 7.4. Spatio-temporal distribution of average quinoa seed yield (kg ha-1) in Burkina Faso’s agro- climatic zones, by combining main soil textures for different planting-periods under RCP 8.5.

Note: Values within maps correspond to average seed yield (kg ha-1) and standard deviation (SD)

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Table 7.2. Average quinoa yield changes (%) and standard deviation (SD) under RCP 4.5 and RCP 8.5 in Burkina Faso’s different agro-climatic zones for different time-periods in comparison with 2020. RCP 4.5 RCP 8.5 2025 2050 2075 2025 2050 2075 (%) SD (%) SD (%) SD (%) SD (%) SD (%) SD Sahel October -7.6 0.2 -60.7 0.1 -86.4 0.1 -23.3 0.1 -97.3 0.1 -100.0 0.0 November 5.9 4.3 12.3 0.1 19.2 4.2 4.3 0.2 16.4 0.0 31.2 0.3 December 5.4 3.6 12.0 0.4 18.6 4.0 4.2 0.1 16.5 0.1 32.2 0.1 January 6.5 3.9 11.1 0.6 20.0 4.8 4.5 0.2 16.0 0.1 32.5 0.3 Soudano-Sahelian October 3.7 0.1 13.4 0.1 2.4 0.0 4.6 0.1 18.1 0.3 -68.8 0.1 November 2.9 1.5 8.7 3.8 15.0 1.5 4.1 0.1 17.0 0.8 -53.3 1.8 December 2.2 1.7 6.6 5.8 14.2 2.0 3.5 0.4 15.4 1.6 18.9 6.2 January 1.4 2.6 6.0 4.1 -20.9 3.9 0.9 5.5 10.0 8.2 -72.8 2.4 Soudanian October 3.3 0.2 12.3 0.2 16.8 1.7 4.8 0.1 16.8 0.6 -100.0 0.0 November 1.6 2.9 10.3 3.3 14.0 3.9 4.1 0.7 17.0 0.3 27.4 4.5 December 1.9 2.5 10.6 2.8 14.5 3.5 4.8 0.2 17.1 0.1 31.0 1.9 January 0.8 4.6 9.8 4.8 15.2 6.2 5.1 0.4 17.3 0.8 24.4 6.0

Table 7.3. Average biomass changes (%) and standard deviation (SD) under RCP 4.5 and RCP 8.5 in Burkina Faso’s different agro-climatic zones for different time-periods in comparison with 2020

RCP 4.5 RCP 8.5 2025 2050 2075 2025 2050 2075 (%) SD (%) SD (%) SD (%) SD (%) SD (%) SD Sahel October 3.6 0.2 12.7 0.2 16.8 0.4 4.4 0.2 16.7 0.1 33.5 0.8 November 5.8 4.3 12.3 0.0 19.1 4.2 4.3 0.2 16.4 0.0 32.6 0.3 December 5.3 3.5 12.1 0.3 18.5 3.9 4.2 0.0 16.5 0.1 32.2 0.1 January 6.4 3.8 11.2 0.5 19.8 4.7 4.5 0.2 16.0 0.1 32.4 0.2 Soudano-Sahelian October 3.7 0.1 13.3 0.1 16.8 0.0 4.6 0.1 18.0 0.2 34.6 0.4 November 2.8 1.4 8.7 3.7 15.0 1.4 4.2 0.0 17.0 0.8 36.2 5.3 December 2.3 1.6 6.8 5.7 14.3 2.0 3.6 0.4 15.4 1.6 35.3 7.0 January 1.5 2.4 6.4 4.0 15.5 5.3 4.0 0.0 17.7 3.6 36.0 6.3 Soudanian October 3.3 0.3 12.2 0.2 16.7 1.5 4.7 0.0 16.8 0.6 26.1 3.5 November 1.6 2.9 10.3 3.3 14.0 3.9 4.6 0.3 16.9 0.3 28.9 4.5 December 1.9 2.5 10.7 2.8 14.5 3.5 4.8 0.1 17.1 0.1 31.1 1.9 January 0.8 4.4 9.8 4.7 15.1 6.0 5.1 0.3 17.3 0.7 28.2 3.0

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7.4. Discussion Regarding mean-temperature trends, this study emerging results have shown that temperatures are likely to rise by 0.66 °C and 1.95 °C by 2050, and by 1.67 °C and 4.90 °C by 2100, respectively under RCP 4.5 and RCP 8.5 and using 1973-2018 as baseline information. These values are in harmony with the expected increase of global mean surface temperature by the end of the 21st century, of 1.1 °C to 2.6 °C under RCP 4.5 (IPCC, 2014b). However, for the RCP 8.5, simulated values for Burkina Faso are over the expected global mean surface air temperature rise, of 2.6 °C to 4.8 °C by the end of the century (IPCC, 2014b). In addition, simulated values fall within the mean-temperature threshold values, 1.5 °C by 2050, simulated by Salack (2006) for the Soudano-Sahelian and Sahelian agro- climatic zones of Burkina Faso. Simulated values are also in harmony with those highlighted in the IPCC report for Western Africa (Niang et al., 2014), where temperatures are expected to rise by 2 °C and 4 °C under the worst-case scenario (RCP 8.5), respectively by the mid-century and end-century. Moreover, the simulated rate of mean-temperature rise is similar within all agro-climatic regions. These values differ to those highlighted by Roudier et al., (2011), acknowledging a greater regional warming towards the Sahel than for the Guinean zone (agro-climatic zone located in the south of the Soudanian zone). In fact, observed values for Burkina Faso, period 1973-2017, have brought to light a greater temperature increase in southernmost parts of the country than in northernmost regions, with a rate of increase of 0.36 °C and of 0.24 °C per decade in the Soudanian and Sahelian zone, respectively.

It is widely accepted that climate change will result in a negative impact on African cereal yields, among other crops (Challinor et al., 2007). For instance, under A1F1 scenario cereal yields are expected to decrease by 20 % by 2080, and by 30 % for maize (Parry et al., 2004). In Burkina Faso, yield reduction is estimated to be close to 17 % for maize (Jones and Thornton, 2003), between 17 % to 23 % for millet and 8 % to 10 % for sorghum by 2050 (Salack, 2006). This research results with quinoa, using the average of all simulated and most common transplanting dates within the region

(October to January), shows the following effect of rising CO2 and temperatures on yield by 2075: Sahel (-7 % to -1 %), Soudano-Sahelian (+2.7 % to -44 %), Soudanian (+15 % to -4.3 %), respectively under RCP 4.5 and RCP 8.5. Nevertheless, yield projection can be considerably higher if the appropriate transplanting dates are selected. For instance, if transplanting between November and January in the Sahel and Soudanian agro-climatic zones yields enhancement can exceed 30 % by 2075 both in RCP 4.5 and 8.5 when comparing to 2020. This brings to light quinoa’s resilience to abiotic stresses and its capability to take advantage of increasing temperatures and CO2 concentrations for yield enhancement and biomass production.

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Furthermore, the simulated biomass increase is explained by rising temperatures and CO2 concentration which directly and positively affect the photosynthetic rate. An increasing CO2 concentration in the air will enhance the rate at which carbon is incorporated into carbohydrates in the so-called light reaction, and will continue to do so until there is another limiting factor (Poorter, 1993). While an increase in temperature will accelerate the reactions catalysed by enzymes, thus increasing the photosynthetic rate (Long, 1991). Nonetheless, if optimum temperatures for ideal photosynthetic rate is exceeded enzymes will denature; therefore the photosynthetic rate will decrease until it stops, resulting in crop failure (Bowes, 1991). In fact, some authors have acknowledged that doubling CO2 concentrations will increase by one third the yield in many crops; in particular among crops having a C3 photosynthetic pathway (Kimball, 1983; Bowes, 1991; Poorter, 1993). This is because the rubisco enzymes are not yet denaturalised under current CO2 concentrations and have not yet reached the optimal atmospheric concentrations; hence leaving space for improving their photosynthetic rate, just like yield and biomass production (Kimball, 1983; Ceccarelli et al., 2010). Furthermore, 2020 biomass simulated values for the Soudanian zone are, to some extent, in accordance with those observed under field conditions in Burkina Faso in 2018 (Alvar-Beltrán et al., 2019a). Under full irrigation the observed yields have been of 800 kg ha-1 while the simulated yields of approximately 900 kg ha-1, for the sowing and transplantation in November. Similar trends between field observations and this research’s simulated biomass can be highlighted, with 2230 kg ha-1 and 2626 kg ha-1 respectively (Alvar-Beltrán et al., 2019a).

Moreover, critical heat-stress thresholds at flowering have resulted in yield failure in some of the simulations. For instance, in the Sahel (October transplanting both in RCP 4.5 and 8.5 for all time- horizons), Soudano-Sahelian (October, November and January transplanting in RCP 8.5 by 2075) or in the Soudanian agro-climatic zones (October transplanting under RCP 8.5 by 2075). The effect of temperature at flowering has been widely studied, with increasing water vapour pressure deficits under higher temperatures (Sato et al., 2000; Young et al., 2004; Prasad and Djanaguiraman, 2011; Hatfield and Prueger, 2015). In fact, it is known that there is a strong negative relationship between pollen production and pollen viability at higher temperatures. In this research, for quinoa, the critical temperature threshold at flowering has been established at 38 °C resulting in strong yield reduction (Alvar-Beltrán et al., 2019c). This research results are in accordance with the previous, with strong yield depletions on the month after transplanting. In fact, the temperature stress pollination value used on AquaCrop, and set-up at 36-41 °C in the Sahel and at 40-45 °C in the Bolivian Altiplano, shows that the Ks (heat stress coefficient for pollination) value equals to 0.5 at 38.5 °C and 42.5 °C, respectively (Geerts et al., 2009b; Alvar-Beltrán et al., 2019c). This means that yields will deplete by half once this temperature threshold (38.5 °C) is exceeded repeatedly during flowering. In the present

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it already occurs in October, November and March in the Sahel, and in February and March in the Soudano-Sahelian zone, but it is likely to extent in time and space both under RCP 4.5 and 8.5.

Finally, slight yield differences have been observed among soils, having sand and sandy-loam soils higher yields than sandy-clay-loam soils. These values are, to some extent, in harmony with those observed by Razzaghi et al. (2012), acknowledging that sandy-loam and sandy-clay-loam soils are the most suitable for growing quinoa. This is because nitrogen-uptake in sandy-loam and sandy-clay- loams is higher than in sandy soils; hence, having an impact on the interception of photosynthetic active radiation (Razzaghi et al., 2012).

7.5. Conclusions The CMIP-5 global climate model ensemble simulation for Burkina Faso has confirmed that the Sahel, and in particular Burkina Faso, is a hot-spot region of climate change, with unprecedented climates expected to happen in the nearby future. Mean-temperature trends for the dry season (October to March) are likely to be higher than those evaluated for mean global surface temperatures under RCP 8.5. The Sahel, among the most vulnerable regions in the world, besides of being largely dependent on weather and having highly sensitive crop systems, must adapt to cope with changing climatic trends. For this reason, reinforcing science, through climate and crop modelling, by using climate-resilient crops is fundamental. This study has helped to reduce the uncertainties emerging from likely climate scenarios and its spatiotemporal impacts in Burkina Faso. Evaluating the adaptability and performance of highly nutritional crops is important since they could, to some extent, alleviate undernourishment rates within the region. This research has also given evidence of the potential of certain drought- tolerant crops during the dry season, when farming activities and revenues are at their lowest and undernourishment rates are blooming. In addition, quinoa can be a suitable alternative for this region, as C4 crop’s yield are expected to deplete under changing climatic conditions. Finally, further research is required to assess the impacts of changing climate during the rainy season and its impacts on rainfed crops.

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CHAPTER 8: GENERAL CONCLUSIONS

8.1. Past climatic trends, farmer’s awareness and agricultural adaptation The main finding of this research is that for the globe two-thirds of the warming has occurred since 1975, at a rate of 0.15-0.20 °C decade-1; whereas for Burkina Faso, the rate of increase has been of 0.24 decade-1 for Dori (Sahelian climate), 0.17 °C decade-1 for Ouagadougou (Soudano-Sahelian climate) and 0.36 °C decade-1 for Bobo Dioulasso (Soudanian climate). For this reason, the Sahel region is a hot-spot region of climate change, where temperatures are increasing at a faster rate than global average, in some cases doubling global temperature rise, i.e. Soudanian zone. This is also typical of the Middle-East/North Africa (MENA) region, where typically daytime maximum temperatures are increasing faster than night-time minimum temperatures (Lelieveld et al., 2016).

The extent of farmer’s awareness on temperature trends is critical for carrying out agricultural adaptation. Between 89-95 % of all farmers in all agro-climatic regions are aware of increasing temperatures within the last decade. Around 50 % of the farmers in the Soudanian and Soudano- Sahelian zone have acknowledged using better performing or climate-resilient seeds; however, this value drops down to less than 10 % in the Sahelian zone. The latter region, with a non-market oriented economy, often faces chronic food insecurity and farmers have poor access to better performing seeds. In fact, a recent study has shown that the seeds distributed by the 23 major companies in Western Africa are too old for current erratic weather conditions and cannot assure the seed production during adverse years (The Guardian, 2019). Our research has revealed that plant-breeding efforts are far behind on crops such as maize that dominate the food consumption in Burkina Faso, therefore limiting the potential of addressing food insecurity.

Regarding precipitation trends, there has been a reduction throughout the country, being greatest in central parts (58 mm decade-1) and lowest in southernmost parts (4 mm decade-1). However, datasets show a precipitation recovery in the last decade, being moderate in central and northern parts of the country, but remaining lower than the 1973-2017 average. The number of rainy days have decreased in all regions: 16 % in Dori, 4 % in Ouagadougou and 13 % in Bobo Dioulasso, when comparing the 1973-1982 to 2003-2012 periods. Of concern is that the inter-annual rainfall variability for the period 2003-2012 has increased, with a coefficient of variation (CV) (as a %) of 21.1, 18.3 and 10.1 for Dori, Ouagadougou and Bobo Dioulasso, respectively, when compared to that of 1973-1982, with a CV of 12.5, 12.7 and 5.6, respectively. This means that the variability of precipitation has nearly doubled within these periods of study. Finally, a higher number of days per year with precipitation greater than 40 mm has been observed in all regions: from 0.6 to 1.6 days year-1 in Dori, from 3.2 to 4.4 days year-1 in Ouagadougou, and from 4.5 to 5.6 days year-1 in Bobo Dioulasso, between the two periods.

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The emerging conclusions from empirical evidence on precipitation parameters are consistent with observed trends. For instance, more than half of the respondents affirmed there was greater precipitation variability in northern and southernmost parts of the country. A similar pattern is observed for the intensification of precipitation, with approximately two-thirds of the farmers affirming so in northern and central parts of the country; however, in the Soudanian zone only one- third of the farmers asserted so. This probably because the number of rainy days with precipitation greater than 40 mm day-1 has not increased as much as in other regions. In addition, the high number of soil and water conservation strategies adopted by farmers has shown that water can be a much more limiting factor than temperature. Where water resources are scarce (Sahel agro-climatic zone) the deployment of traditional agricultural practices (i.e. zaï, stone bunds and half-moons) that reduce surface run-off besides of increasing the fertility of the soil is higher. In the south, where water is abundant during the rainy season, conventional agriculture is widespread with a market-oriented focus.

8.2. Future warming of Burkina Faso This research has focused on both a moderate (RCP 4.5) and a business as usual (BAU) climate scenario which the globe is currently tracking on (RCP 8.5). In total, 43 GCMs, with a 45-year baseline (1973-2017), have been used to project temperatures changes during the dry season (October- March) in Burkina Faso. The main climate modelling results have shown a similar temperature increase in all regions: 0.20 °C decade-1 under RCP 4.5 and 0.58 °C decade-1 under RCP 8.5. The warming in Burkina Faso will attain 1.7 °C and 4.9 °C under RCP 4.5 and RCP 8.5, respectively, by the end of the century. Our modelling results are similar to those predicted by the IPCC (2014b) for the end of the century, with a global warming of 1.9 °C under RCP 4.5, but a degree higher to that projected by the IPCC under RCP 8.5, 4.1 °C. Hence, our climate-modelling for the BAU scenario is showing a pace of increase greater than the global average.

8.3. Further research on climatic trends and agricultural adaptation Topics which already have a considerable amount of research effort must be identified before determining the new areas of research. Without downplaying their importance, as some topics are key, the following have a lower priority: (i) Indigenous knowledge and awareness on extreme weather events, as there is already a consensus among farmers in certain climatic parameters: an increase in temperatures, less frequent but more intense precipitation, shortening of the rainy season, among others. Farmer’s perceptions on extreme weather events has been widely studied in Western Africa (Adesina and Baidu-Forson, 1995; Nyong et al., 2007; Maddison, 2007; Ouédrago et al., 2010; Sanfo et al., 2014; Fonta et al., 2015);

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(ii) A similar situation occurs for traditional agricultural practices adopted by farmers throughout the Sahel (Roncoli et al., 2001; Sawadogo et al. 2010; Zougmoré et al., 2004; Sanfo et al., 2014). We recommend that research goes beyond the listing of the different agricultural practices, and valuation of how effective these strategies are in terms of costs and benefit parameters: economic yield, time-consumption for farmers, human-resources and investment required, improvement of soil-structure, long-term durability and association with other water and soil conservation strategies etc.

(iii) The “re-greening of the Sahel” has concentrated much of the scientific attention in the new millennia, creating a positive narrative among scientists and policymakers considering a wetter region in the nearby future (Hickler et al., 2005; Olsson et al., 2005; Giannini et al., 2008; Dardel et al., 2014; Bichet and Diehiou, 2018).

(iv) National meteorological services (ANAM) efforts should be focused on developing a denser network of land-based weather stations that can provide researchers with reliable climate data for carrying out high resolution modelling. Up to now, there are only 10 synoptic stations (one every 27,000 km2) and 19 agro-meteorological stations throughout the country (ANAM, 2015). There is not yet a radar-reflectivity network that allows spotting convective precipitation, which are likely to be more recurrent and intense under warming climatic conditions.

(v) Furthermore, research improvements should go towards a more realistic improvement of climate models that can better determine the onset and strength of the rainy season; instead of relying in low-resolution models (part of the CMIP 5 project) that overestimate the strength of the ITCZ and continue to simulate consistent temperature-warming but different magnitude and spatial patterns. For this reason, the difficulties related with poor signal to noise, and therefore the resulting uncertainties from simulations, need to be among researchers’ higher priorities.

8.4. Abiotic factors: lessons from quinoa experimentation The two-year trials (2017-2018 and 2018-2019), including four field-experimentations, with quinoa have been decisive for better understanding quinoa’s responses to new environmental conditions, to that of origin, and for creating agronomic-guidelines that support forthcoming research activities, besides of incentivizing farmers for more appropriate crops. From this two-year experimentation, a substantial amount of interesting agronomic information has emerged and is as follows:

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1. Drought: the main emerging finding is the use of four levels of irrigation (FI, PD, DI and EDI) including 10 treatments and numerous repetitions, and that under PD (80 % PET, 323 mm in 90 days) quinoa yields can exceed 1000 kg ha-1 regardless the amount of N-fertilization that is incorporated into the soil. Modelling with AquaCrop has identified the timing and amount of irrigation for maximizing the yields. Twenty eight (28) irrigation events in a period of 10 weeks following transplantation (17 DAS), with a total of 235 mm of water, 8 mm of water every 2 to 3 days, should be enough for attaining yields higher than 1 t ha-1. Our research has shown that by using alternative crops, and more efficient irrigation techniques (drip-irrigation systems) and irrigation schedules, large amounts of water can be saved when compared to other widespread crops with much higher water requirements: maize (± 800 mm), rice (± 700 mm), millet and sorghum (± 550mm) (FAO, 1986). In addition, the encouragement of the use of quinoa during the dry season is desirable when food production of major crops (maize, rice, millet and sorghum) is at its lowest, while food insecurity at its highest.

2. Heat-stress: this is, with drought, the abiotic stress that can affect the most the final yield. During the first-year (2017-2018) and second-year (2018-2019) of experimentation a parallel experimentation was carried out to determine the degree of effect of heat-stress at quinoa’s most sensitive phenological phases, germination and flowering, under controlled climatic conditions. The germination rates, at temperatures below 34 °C remained high, but fell to 0 % at temperatures above 46 °C. However, the effect at flowering was significant, having observed (both in field and climatic chambers) that persistent temperatures above 38 °C could substantially affect the amount of yield. The emerging finding of controlled experimentations is that if temperatures exceed 38 °C for more than 6 hours during the flowering period of the plant (10 days) the yield losses will be very high (around 50 %).

3. N-fertilization: after conducting four experimental trials with a diverse number of N-fertilization levels (100, 50, 25 and 0 kg N ha-1) the main conclusion was that from 0 to 25 kg N ha-1 there are significant statistical differences on important crop parameters such as yield, biomass and quality of the seeds, but higher N-fertilization does not necessary enhance the performance of the previous. For this reason, nitrogen requirements of quinoa remain low (25 kg N ha-1) when compared to other local crops such as maize (200 kg N ha-1) and rice (100-200 kg N ha-1).

4. Photoperiodicity: with different sowing dates (25-Oct, 4-Nov, 19-Nov and 8-Dec) for this latitude (11 °N) photoperiodicity changes did not affect the plant’s growth as only 26 minutes was the maximum difference in photoperiodicity between the four sowing dates. Nevertheless, it is recognized amongst researchers (Bertero et al., 1999; Bertero et al., 2000) that changes in

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photoperiod, for c.v. Titicaca, highly affects the length of the cycle. If grown in northern part of Burkina Faso, where latitudes are up to 15 °N or more, it is likely that the cycle will be lengthen by couple of weeks. For example, in Yemen at 14 °N, the cycle of c.v. Titicaca was of 118 days.

8.5. Quinoa in the Sahel: agronomic guidelines (see annex 2) 1. Sowing date: we recommend the following sowing dates: mid-November in the Soudanian agro- climatic zone, late-November in the Soudano-Sahelian zone, and early-December in the Sahelian zone. This is because the rainy season lasts until end of October, with waterlogging inhibiting growth and development of quinoa. Additionally the occurrence of extreme temperatures occurring in March and April should be avoided.

2. Tillage: a two-pass moderate depth (15-20 cm) mechanical tillage is highly recommended to reduce soil compaction. Sandy-loam soils are widely distributed in Burkina Faso (72.5 % of the surface area) and are likely to have a high bulk density when rains are absent and soil temperatures are elevated (in the first year-experimentation soil temperatures at 5 cm depth were between 30-45 °C). This research shows that bulk density, around 1.60 g cm-3, has been a limiting factor for the root- system to uptake water and nitrogen from deeper zones. The soil crusts typically in dry and warm environments, reducing infiltration and increasing surface run-off with retarded capillary movement. The soil crusts can be removed from the soil when doing carrying out weeding practices.

3. Fertilization: organic matter, in the form of compost, should be applied to the soil when tilling. This will enhance soil microbes which are responsible of recycling nutrients of the soil, besides of

increasing soil water holding capacity. Mineral fertilization, with ammonium nitrate (N2H4O3),

rather than urea CO(NH2)2, are recommended because it has already been mineralized from the organic form to the inorganic form required by plants. This research results have shown that N- requirements of quinoa are low, therefore 25 kg N ha-1 should be enough to satisfy quinoa N- demands.

4. Sowing: the soil should be moderately watered 1 or 2 days before sowing to enhance seed- germination. 10 kg of seeds should be sufficient for a whole hectare and should be sown at a depth of 1-2 cm. The recommended distance between rows is 40-50 cm and a spacing between holes of 10 cm, as the horizontal expansion of the root system of c.v. Titicaca is relatively small.

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5. Transplantation: it is recommended to transplant after the appearance of 4-8 leaves (2 to 3 weeks after sowing), and to carry out this activity at dusk, when evapotranspiration is lower. The soil must be irrigated previously with considerable amounts of water (10-15 mm). The transplant ought not to be conducted after 3 weeks, as there is a high chance of wilting.

6. Irrigation: frequent irrigation (every 2 to 3 days) with small amounts (8 mm irrigation event-1) is highly recommended. Maximum crop-water requirements occur at 5 to 10 weeks after sowing, with higher water demands (more than 10 mm irrigation event-1). During leaf senescence crop-water requirements drop and the amount, and frequency of irrigation can be lowered down.

7. Weeding: two time-weeding during the whole growing cycle is enough to keep quinoa growing successfully. A weeding during the vegetative stage (after 2 to 3 weeks) is highly recommended. This weeding breaks the soil-crust and enhances water infiltration.

8. Pests, insects and diseases: when growing during the dry season the incidence of pest and diseases are less because of the lower relative humidity in the air/soil. However, as it is sown immediately after the rainy season, it is likely that quinoa will be affected by mildew (Peronospora farinosa) and leaf spots (Ascochyta hyalospora). Locusts can cause irreversible damages to the plant in any phenological phase, while ants are likely to collect the seeds and significantly reduce quinoa germination rates. To avoid ants from collecting seeds, they must be treated before sowing with the powder form of insecticides. Prevention from locusts is by immediate insecticide foliar application, or, if possible, coverage of crops with an insect net.

8.5. SWOT (Strengths, Weaknesses, Opportunities and Threats) Strategic planning techniques in agriculture are essential for understanding the potential and limitations that a crop might face in a region where it is currently being introduced. Even though this research does not include a multi-criteria analysis for decision-making and has not examined in depth the environmental, economic and social cost and benefits of introducing quinoa, some analytical tools such as SWOT’s can be used for facilitating the framework and structuring the previous (Figure 8.1). The strengths of introducing quinoa, when compared to local crops, are numerous. For example, it has a lower water-demand than maize, sorghum millet and rice (all above 500 mm), thus being suitable during dry periods. Quinoa’s thermo-tolerance is very high and can potentially be used during most time of the year. It is also complete in amino acids and in some cases exceeding the values recommended by the FAO, therefore can complement and enhance the diet of Burkinabe. Nevertheless, some weaknesses emerge, as quinoa is sensitive to strong winds occurring in January and February (Harmattan), as well as to waterlogging (from May to October) and resulting diseases 124

from high humid periods. The main threats of the crop are related to its acceptance among Burkinabe. In top of that, acceptance does not mean consumption, and its production can be merely focused on economic interests. This is not new, as when quinoa became a superfood crop (around 2014), its consumption dramatically increased in Europe, just like increasing its production in the Andes. As people in the Andes saw a potential in revenues, they diminished their consumption thus impoverishing their diet. If the introduction of the crop in Burkina Faso is supported by a governmental food and climate awareness programs emphasizing benefits to diet and resilience to abiotic stresses, there is a chance that threats become an opportunity and that quinoa is fostered during the dry periods.

Figure 8.1. Strengths, weaknesses, opportunities and threats (SWOT) of introducing quinoa in Burkina Faso

STRENGTHS WEAKNESSES . Good adaptation to new environmental conditions . Low wind-tolerance . Lower water needs (<400mm) than local crops . Susceptible to water logging (rainy season) . Low nitrogen requirements (25 kg N ha-1) . Fungi appearance (during rainy season)

. Short cycle varieties (c.v. Titicaca, 90 days) . Vulnerable to locust, ants and birds . More performing under higher temperatures and . Lack of bonds at the agricultural value chain CO2 concentrations than maize, sorghum, millet . Not known by people . Thermo-tolerant at flowering (up to 38 °C) . Not widespread throughout the country . Higher protein content to those established by FAO . Manual seed-separation is complex and time- . Widely accepted by producers consuming SWOT ANALYSIS 12 OPPORTUNITIES THREATS . Alleviate food insecurity when undernourishment rates are highest (dry season) . Super-food crops can monopolized production . Researchers in plant breeding programs . International markets can backfire efforts for . Cash-crop for farmers tackling food insecurity and undernourishment . Spatio-temporal suitability . Can put into risk local crops (i.e. fonio) . Strong regional agricultural market . Financial requirements for scaling-up the crop . Crop diversification . Conflict and displacement of the population . Can be cooked in the same way as local dishes . Introduction of new plant pathogens . Fodder for livestock

8.6. Further research on quinoa Field experimentations revealed many aspects that need to be addressed among researchers for assuring the medium and long-term viability of quinoa in the Sahel. The recent discovery of quinoa’s genome should help plant-breeders tackle many questions emerging from research here (Jarvis et al.,

2017). Breeding strategies should focus on reducing the physiological cycle of the plant (c.v. Titicaca to less than 90 days), while stabilizing the yields. With this strategy harvesting could be carried out 125

twice a year instead of once. Research on plant resilience to wind is recommended in order to avoid damage to crops during Harmattan winds (January and February). This could be performed by crossing varieties that developed a wider stem than c.v. Titicaca. Some of the breeding approaches for improving crop-tolerance to heat and hence create new thermic-resistant varieties are: QTL (quantitative trait loci), RIL (recombinant inbred lines), SNP (single-nucleotide polymorphism) and AS (anti-sense) (Paupière et al., 2014). Finally, during seed-separation, c.v. Titicaca, segregates considerable amounts of saponins that have been accumulated in the panicle reducing the quality of the seeds, while making the rinsing difficult. For this reason, breeding-research focusing on varieties with a lower saponin content is highly recommended.

8.7. Recommendations for national and supra-national institutions This research has gathered sufficient information and has rendered it publicly available (through scientific publications, press releases and workshops) so that researchers and policymakers can use it for decision-making. This tool is now available to use appropriately for developing strategic planning for agricultural adaptation in the most vulnerable zones of the country. In addition, provisional technical guidelines on how to grow quinoa, based on two-year experimentation trials have facilitated the communication between researchers/policymakers with farmers (see annex 2).

Moreover, as the timing for climate change departure for this region is getting close (2030-2040), policymakers, researchers and the population must prepare to adjust and mitigate its consequences. This must be prioritized. The scaling of funds, which continue to be half (60 million € in 2014) to those promised at the NAPA (133 million € per year) for short, medium and long term adaptation to climate change must be increased. The list of actions for building climate-resilient communities are widely known and accepted, and should be implemented immediately. This requires the tightening of relationships between stakeholders across scales, as well as leadership and commitment of national institutions. The Food and Agricultural Organization (FAO) has to be more active on the funding and monitoring of the Technical Cooperation Programs (TCP/SFW/3404 and TCP/RAF/3602) which are already in place in many Sahelian countries.

In the meantime, buying the quinoa seeds (by FAO) from farmers is appropriate, but is not economically sustainable on-time, and must be accompanied with strong awareness campaigns on the vast benefits for the population of consuming and growing quinoa. During the quinoa-workshops we verified that stakeholders’ across-scales have poor access to quinoa-seeds, just like to the emerging findings of research activities in general. This is a very important aspect to be considered by governmental institutions, as it is often the agro-business sector, agricultural associations and farmers, the ones that make the change by appropriating new crops and commence growing it at a larger scale.

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APPENDICES

Annex 1. Summary of activities During the three-year PhD (2016-2019), the aforementioned PhD student, has completed the compulsory courses on: Deepening of statistics (CFU 7), Programming and data manipulation in R (CFU 3), English Academic Writing (CFU 3), Criteria for the Realisation of a Research Project (CFU 2), Elements of Intellectual Property (CFU 3), The Concept of Principal Investigator (CFU 2), Criteria for Objective Evaluation of Articles and Magazines (CFU 1), Critical Analysis, Writing and Review of a Scientific Articles (CFU 1), Occupational Safety-Chemical Risk (CFU 1), Occupation safety- biological risk (CFU 1). Moreover, free-credits have been awarded for attending to: International Summer School in Agrometeorology and Crop Modelling, Novi Sad University, Serbia (CFU 5), Course on Food Security and Sustainability (crop production), Wageningen University, The Netherlands (CFU 5), and WMO-International Training Course on the Assessment of Climate Change Impacts on Agriculture with Remote Sensing Technology, Nanjing University, China. Regarding field-experimentation in Burkina Faso, the PhD student has conducted six missions (with a total duration of 12 months) throughout the PhD: 11/06/2017 until 05/07/2017 (experimental trial at agricultural association Yelemani Loumbila), 22/09/2017 until 17/12/2017 (first-year of experimentation at INERA Farako-Bâ), 30/01/2017 until 29/04/2017 (continuing first-year of experimentation at INERA Farako-Bâ), 14/10/2018 until 20/12/2018 (second-year of experimentation at INERA Farako-Bâ), 12/01/2019 until 28/01/2019 and 9/02/2019 until 25/02/2019 (continuing of second-year experimentation at INERA Farako-Bâ).

The emerging findings of this research have been shared and disseminated with the scientific community and public through the submission of six manuscripts (one per chapters 2 to 7) to peer- review journals; as well as on workshops, training schools and conferences of which the following deserve highlighting: - Presentations at INERA Farako-Bâ research station, Bobo Dioulasso, on May 2018 and on February 2019, with the participation of researchers, officials and students from Burkina Faso. - Quinoa tasting and result dissemination at the Centre for Economic and Social Studies of Western Africa (CESAO), Bobo Dioulasso, on February 2019, with stakeholders across-scales (agro-business sector, focal points, researchers and media) (see annex 3). - Presentation at Nanjing University, China (meteorologists from different national services) - Presentation at CUCS VI Congress at University of Trento, Italy, on September 2019 (researchers and cooperation members). - Presentation at the Royal Academy of Medicine in Andalusia (RAMAO), Granada, Spain, on October 2019 (researchers and medicine students). - Final dissertation at the University of Florence, Italy, on 2020 (researchers). 141

Finally, between July and September 2018, the PhD student has supported, through a consultancy, the Agro-meteorology Division of the World Meteorological Organization (WMO), on the development of national agro-meteorological and agro-climatological capacities of Rwanda and Senegal meteorological services; including, two field visits to Senegal and one to Rwanda. In particular, the duties were focused on the reinforcement of communication means from national meteorological services to end-users (farmers, livestock producers and fishermen).

Figure A.1. PhD work-plan of activities

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Annex 2. Technical guide for growing quinoa in Burkina Faso

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Annex 3. Results dissemination: quinoa workshop FASO NET Press release link: https://lefaso.net/spip.php?article88127

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Annex 4. Surveys on farmer’s perceptions on climate change

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Annex 5. Field evidences

Photo 1. Broadcast application of compost during the first year-experiment (2017-2018) at a rate of 5 t ha-1

Photo 2. Installing drip-irrigation system (04/11/2017 experiment) (left); Photo 3. Drip-irrigation counter placed at the entry of each irrigation block (top-right); Photo 4. Deployment of drip-irrigation system (04/11/2017 experiment) (bottom-right)

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Photo 5. Quinoa plants (c.v. Titicaca) at eight leaves (08/12/2017 experiment) (top); Photo 6. Quinoa plant (c.v. Titicaca) at flowering (bottom-left); Photo 7. Quinoa plants (c.v. Titicaca) at milky seeds formation (bottom- right)

Photo 8. Quinoa plants (c.v. Negra Collana) at panicle formation under deficit irrigation (04/11/2017 experiment) (bottom-left); Photo 9. Quinoa plants (c.v. Negra Collana) close to physiological maturity under deficit irrigation (04/11/2017 experiment) (bottom-right)150

Photo 10. Sowing of quinoa (08/12/2017 experiment) (top-left); Photo 11. Tool for preparing the sowing lines (top-right); Photo 12. Discussion with Prof. Abdalla Dao about the status of the experimentation (middle-left); Photo 13. Measuring chlorophyll with Spad-502 (07/11/2017 experiment) (bottom-left); Photo 14. Technician measuring plant height (08/12/2017 experiment) (bottom-right).

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Photos 15-17. Quinoa plants (c.v. Titicaca) at physiological maturity (19/12/2018 experiment)

Photo 18. Different treatments of quinoa plants during the drying process (bottom-right); Photo 19. Harvesting of quinoa (bottom-left) 152

Photo 20. Harvested yield (c.v. Titicaca) for the 04/11/2017 experiment (left); Photo 21. Root system (c.v. Titicaca) for the 04/11/2017 experiment (right)

Photo 22. Analysis of soil samples for the 2017-2018 experimentation (left); Photo 23. Machine for measuring the kernel weight (right)

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Photo 24. Agro-meteorological station at INERA Farako-Bâ (left); Photo 25. Weather data-collection (right)

Photo 26. Measuring the stem-diameter of quinoa at the lab (left); Photo 27. Seed-separation process (right)

Photo 28. Measuring the weight of harvested seeds (left); Photo 29. c.v. Negra Collana and c.v. Titicaca harvested seeds (right) 154

Photo 30. Group photo with surveyed farmers at Banfora (March 2018)

Photo 31. Workshop on climate change with farmers surveyed in Loumbila (April 2018)

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Photo 32. Workshop on climate change with farmers surveyed in Loumbila (April 2018) (top); Photo 33. Women farmer being surveyed in the rice fields of Banfora (middle-left); Photo 34. Farmer being surveyed close to Banfora (middle-right); Photo 35. Rice farmer and surveyor close to Banfora (bottom)

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Photos 36-37. Dissemination of research findings with stakeholders across scales at CESAO (February 2019) (top-left and top-right); Photo 38. Women showing how to separate quinoa seeds to participants of CESAO workshop (February 2019) (bottom-left); Photo 39. Quinoa tasting after workshop at CESAO (February 2019) (bottom-right)

Photos 40. Different local dishes prepared together with quinoa. Quinoa tasting after workshop at CESAO

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Italian Journal of Firenze University Press Agrometeorology www.fupress.com/ijam Rivista Italiana di Agrometeorologia

Effect of drought and nitrogen fertilisation on quinoa (Chenopodium quinoa Willd.) under Citation: Jorge Alvar-Beltrán, Couli- baly Saturnin, Abdalla Dao, Anna Dalla field conditions in Burkina Faso Marta, Jacob Sanou, Simone Orlandini (2019) Effect of drought and nitrogen fertilisation on quinoa (Chenopodium Effetto della siccità e della fertilizzazione azotata su quinoa quinoa Willd.) under field conditions in (Chenopodium quinoa Willd.) in Burkina Faso Burkina Faso. Italian Journal of Agro- meteorology (1): 33-43. doi: 10.13128/ ijam-289 1, 2 2 Received: February 01, 2019 Jorge Alvar-Beltrán *, Coulibaly Saturnin , Abdalla Dao , Anna Dalla Marta1, Jacob Sanou2, Simone Orlandini1 Accepted: March 01, 2019 1 Department of Agriculture, Food, Environment and Forestry (DAGRI) - University of Published: June 04, 2019 Florence, Italy 2 Institut de l’Environnement et de Recherches Agricoles (INERA) - Bobo Dioulasso, Bur- Copyright: © 2019 Jorge Alvar- kina Faso Beltrán, Coulibaly Saturnin, Abdalla *Corresponding author e-mail: [email protected] Dao, Anna Dalla Marta, Jacob Sanou, Simone Orlandini. This is an open access, peer-reviewed article published Abstract. Chenopodium quinoa (Willd.) is an herbaceous C3 crop originating in the by Firenze University Press (http:// Andean Altiplano. Quinoa possesses a great deal of genetic variability, can adapt to www.fupress.com/ijam) and distribuited under the terms of the Creative Com- diverse climatic conditions, besides of having seeds with high nutritional properties. mons Attribution License, which per- An experiment conducted in Burkina Faso has determined the response of two qui- mits unrestricted use, distribution, and noa varieties (Titicaca and Negra Collana) to different planting dates (November vs reproduction in any medium, provided December), irrigation levels (Potential evapotranspiration-PET, 100, 80 and 60% PET), the original author and source are and N fertilization rates (100, 50 and 25 kg N ha-1). Main research findings have shown credited. that quinoa can be highly performant under drought stress conditions and low nitrogen inputs, besides of coping with high temperatures typically of the Sahel. The high- Data Availability Statement: All rel- -1 evant data are within the paper and its est yields (1.9 t ha ) were achieved when sown in November at 60 % PET and 25 kg -1 Supporting Information files. N ha . For this location, short cycle varieties, such as Titicaca, were recommended in order to avoid thermic stress conditions occurring prior to the onset of the rainy sea- Competing Interests: The Author(s) son (May-October). declare(s) no conflict of interest. Keywords. Sahel, agro-meteorology, extreme climatic conditions, abiotic stress, water management.

Abstract. Chenopodium quinoa (Willd.) è una coltura erbacea C3 originaria dell’Al- tiplano andino. La quinoa, la cui granella è dotata di ottime proprietà nutrizionali, è caratterizzata da un’elevata variabilità genetica e ben si adatta a diverse condizioni climatiche. Lo scopo della ricerca, condotta in un sito sperimentale in Burkina Faso, è stato di valutare la risposta di due varietà di quinoa (Titicaca e Negra Collana) a diverse date di semina (novembre vs dicembre), diversi livelli di irrigazione (evapotraspirazione potenziale - PET, 100, 80 e 60% PET) e diverse dosi di concimazione azotata (100, 50 e 25 kg N ha-1). I risultati hanno dimostrato che la quinoa può essere alta- mente performante anche in condizioni di stress idrico e bassi input di azoto, oltre a riuscire ad adattarsi alle alte temperature tipiche dell’area del Sahel. Le rese più elevate

Italian Journal of Agrometeorology (1): 33-43, 2019 ISSN 2038-5625 (print) | DOI: 10.13128/ijam-289 34 Jorge Alvar-Beltrán, Coulibaly Saturnin, Abdalla Dao, Anna Dalla Marta, Jacob Sanou, Simone Orlandini

(1,9 t ha-1) sono state ottenute per la quinoa seminata a novembre, irrigata al 60% di PET e fertilizzata con 25 kg di N ha-1. In base ai risultati ottenuti, per l’area considerata si raccomanda l’utilizzo di varietà a ciclo breve, come il Titicaca, per evitare condizioni di stress termico che si verificano prima dell’inizio della stagione delle piogge (maggio-ottobre).

Parole chiave. Sahel, agrometeorologia, condizioni climatiche estreme, stress abiotico, gestione irrigua.

1. INTRODUCTION its turgidity with very low water potentials, while optimizing water use through minimum leaf gas exchange Climate change affects agricultural productivity (Jensen et al., 2000; Jacobsen et al., 2003). It can also that needs to adapt to satisfy food demand. Agricultural increase its assimilation efficiency by improving the ratio adaptation becomes crucial in hot-spot regions of cli- of photosynthetic rate over transpiration up to 2 (Vach- mate change, especially affected by drought and water er, 1998; Geerts et al., 2008). Other morphological and scarcity (Morsy et al., 2018). These areas often match anatomical responses are the presence of calcium oxa- with those having highest undernourishment rates and late crystals in leaf vesicles, reducing leaf-transpiration, greatest population growth, low use of external inputs besides of having a thick plant cuticle and sunken sto- such as improved seeds and fertilizers; absence of mech- mata (Azurita-Silva et al., 2015). anization; and poor linkage to markets. This makes agri- Furthermore, the wide geographical distribution culture highly vulnerable to climate change (Eroula et of quinoa has given the plant a great genetic variability, al., 2013). Among scientists, quinoa (Chenopodium qui- besides of increasing its coping-capacity under extreme noa Willd.) is considered a climate resilient and super- climatic conditions (Ceccato et al., 2015). Indeed, tem- food crop, while being promoted in regions vulnerable perature is the environmental factor affecting the most to climate change. It is a highly nutritional and gluten crop´s cycle duration, germination, development and free crop, having a balanced composition of essential seed formation (Hirich et al., 2014b; Bertero, 2015; amino-acids sometimes scarce in legumes and cereals Hassan, 2015). Further research on nitrogen (N) sug- (Repo-Carrasco et al., 2003); as well as for been rich in gests that greater N fertilisation can result in a signifi- Ca, Fe, and Mg, with high content of vitamins A, B2 and cant yield increase, but having no effect on seed size or E (Adolf et al., 2013). weight (Shams, 2012; Benlhabib et al., 2013; Piva et al., Moreover, quinoa is well-known for its resilience to 2015). Soils with higher clay content are the most suit- abiotic stresses being drought-tolerant, halophyte, pH able for growing quinoa, as N-uptake, organic matter, versatile, and resistant to thermic variability. Most of soil´s water holding capacity is highest (Razzaghi et al., the scientific research is focused on its adaptability to 2012). saline levels, being as high as those found in sea water To promote quinoa´s consumption in West Africa, (Jacobsen et al., 2003; Razzaghi et al., 2011; Hirich et al., the Food and Agriculture Organization of the United 2014a; Riccardi et al., 2014; Fghire et al., 2015). In fact, Nations (FAO) has developed Technical Cooperation its salt tolerance is the result of osmotic adjustment, Programs (TCP/SFW/3404 and TCP/RAF/3602) together osmo-protection, sodium exclusion and xylem load- with the Ministries of Agriculture. ing, potassium retention, gas exchange, stomatal con- The aim of this research was to investigate the trol and water use efficiency (Adolf et al., 2013). As a C3 adaptability and performance of two quinoa varieties crop, quinoa´s crop water productivity (CWP), expressed when sown at different dates, under decreasing levels of in kg of biomass produced per m3 of water applied, is irrigation and different N fertilisation rates. generally low, lying between 0.3-0.6 kg m-3 in the Boliv- ian Altiplano while exceeding 1 kg m-3 in Morocco and Italy (Geerts et al., 2009; Hirich et al., 2014a; Riccardi 2. MATERIALS AND METHODS et al., 2014). Indeed, quinoa´s transpiration rate is similar to that of reference evapotranspiration, hence having The experiment was carried out during the dry low water requirements, around 400 mm (Steduto et al., season, from November 2017 to May 2018, at Institut 2012). Moreover, rapid stomata closure, restricting shoot de l’Environnement et Recherches Agricoles (INERA), growth and accelerated leaf senescence makes quinoa Farako-Bâ´s research station (11º05´N; 4º20´W). The area highly adaptable to drought stress conditions (Azurita- of study is within Burkina´s Soudanian agro-climatic Silva et al., 2015). In addition, it’s capable of maintaining belt, with a tropical savanna-wet and hot climate. The Effect of drought and nitrogen fertilisation on quinoa under field conditions in Burkina Faso 35

0.5 onset of the wet season is in May and offset in October, ETo = 0.023 (T mean + 17.78) Ro (T max – T min) with a total amount of rainfall exceeding 900 mm year-1; where mean annual temperatures can attain 28 ºC. Where: Ro = solar radiation at a given month and The experimental field was organized in a ran- latitude (Allen et al., 1998); T mean = mean daily tem- domized split-split block design with a multiple fac- perature; T max = daily maximum temperature; T min tor analysis of variance (ANOVA): 3 levels of irrigation = daily minimum temperature. according to the potential evapotranspiration (PET) Moreover, crop evapotranspiration (ETc = Kc*ET0) (Full irrigation-FI: 100 % PET; Progressive Drought-PD: was calculated using the crop´s coefficient (Kc) for 80 % PET; Deficit Irrigation-DI: 60 % PET), 3 levels of quinoa´s different phenological phases (Garcia et al., N fertilisation (100, 50 and 25 kg N ha-1), two quinoa 2003): 0.52 at emergence, 1.0 at maximum canopy varieties (Titicaca-short cycle-85 days, and Negra Colla- cover and 0.70 at physiological maturity. Net irriga- na-long cycle-150 days), and 3 repetitions. Quinoa seeds tion requirements were estimated using ETc daily data were sown in 54 plots, each of 7.5 m2, in 50 cm row dis- and adjusted according to the level of irrigation: ETc*1.0 tance and 10 cm space between plants (200000 plants (FI); ETc*0.80 (PD) and ETc*0.60 (DI). In fact, ETc was ha-1), at a rate of 10 kg seeds ha-1. The ANOVA was done adapted according to the growing cycle of both quinoa using IBM SPSS software and Tukey´s HSD test with varieties. A water-counter was placed at the entrance Minitab 2018. of each irrigation block to estimate the amount of water Sowing was carried out in two dates: 4/11/2017 applied. The drip irrigation flow rate was of 1.05 l hour-1, (hereinafter November) and 8/12/2017 (hereinafter varying according to the water pressure, maximum 1 bar, December). The harvesting of November´s sowing was and the frequency of water application, between 2 to 4 done at the beginning of February for Titicaca (89 days times a week depending on the growing stage of the plant. after sowing-DAS) and end of March for Negra Collana (139 DAS). Whereas the harvesting for December´s sowing, it was carried out beginning March for Titicaca (82 3. RESULTS DAS) and end of May for Negra Collana (159 DAS). Prior to sowing the soil was amended with com- The experimental field was characterised for hav- post (50.2 % organic matter) at a rate of 5 t ha-1, as well ing a sandy-loam texture in the first soil layer (0-20 cm) as with phosphate (26.7 % P2O5) at a rate of 400 kg P with high infiltration rate, low water holding capac- ha-1. Nitrogen fertilisation, in the form of urea (46.2 % ity and very poor organic matter content, below 0.5 N), was split into two doses and was applied 25 and 40 % (Table 1 and 2). Mineral nitrogen (ammonium and DAS. Weed removal was carried out manually every 3/4 nitrate) was negligible (0.03 %), while soil pH was slight- weeks to avoid weed interference with actual crop water ly acidic (pH 6.5). As a result of low soil carbon content requirements. Seeds were treated with fungicides/insec- and mineral nitrogen, the C/N ratio remained low (8.8 ticides (Permethrin 25 g kg-1 + Thirame 250 g kg-1) at a rate of 25 g per 10 kg of seeds, and through foliar application (Cypermethrin) at a rate of 1 litre ha-1. Prior to sowing and post-harvesting, soil samples Tab. 1. Main soil physic-chemical characteristics before sowing were extracted at 0-20, 20-40 and 40-60 cm for the (average of 5 samples). Tab. 1. Principali caratteristiche fisico-chimiche del suolo prima determination of its main physic-chemical character- della semina (media di 5 campioni). istics. Leaf chlorophyll was recorded at 30 and 68 DAS using a Leaf Chlorophyll Meter SPAD 502 Plus with a Soil layer (cm) total of 25 observations per plot. The canopy cover was Parameter Units measured at 40 DAS (sowing November) and 56 DAS 0-20 20-40 40-60 (sowing December) using the Canopeo app. developed Sand % 67.2 54.6 41.3 by the University of Oklahoma. The rest of the param- Silt % 17.6 16.5 15.7 Clay % 15.2 28.9 43.0 eters, including plants height (10 per plot); biomass and Texture Sandy-Loam Sandy-Clay-Loam Clay seed yield (12 per plot); 1000 seeds weight (3 per plot); pH (H2O) 6.51 5.95 6.05 branching, panicle size, panicle width, stem diameter (5 C % 0.28 0.23 0.23 per plot); root depth and root length (1 per plot), were Organic matter % 0.48 0.39 0.39 done at physiological maturity. N % 0.032 0.026 0.027 Daily evapotranspiration was calculated using the C/N 8.8 8.7 8.4 P available mg/kg 4.0 1.70 1.02 following formula (Hargreaves and Samani, 1985): K available mg/kg 79.73 74.97 58.70 36 Jorge Alvar-Beltrán, Coulibaly Saturnin, Abdalla Dao, Anna Dalla Marta, Jacob Sanou, Simone Orlandini

Tab. 2. Main soil physic-chemical characteristics post-harvesting (average of 3 samples). Tab. 2. Principali caratteristiche fisico-chimiche del suolo dopo la raccolta (media di 3 campioni).

Soil horizon (cm) and Nitrogen fertilisation (kg N ha-1)

Parameter Units 0-20cm 20-40cm 40-60cm

100kgN 50kgN 25kgN 100kgN 50kgN 25kgN 100kgN 50kgN 25kgN Bulk density g/cm3 1.55 1.66 1.63 ------C % 0.43 0.41 0.51 0.40 0.38 0.46 0.38 0.39 0.40 Org. Matter % 0.75 0.71 0.89 0.68 0.66 0.79 0.66 0.66 0.69 N % 0.035 0.038 0.051 0.032 0.034 0.044 0.029 0.033 0.037 C/N 12.5 10.8 10.0 12.4 11.2 10.5 13.4 11.7 10.9 P total mg/kg 95.8 97.6 85.0 83.8 91.6 90.0 93.5 106.6 103.5 K total mg/kg 711.9 879.6 993.4 1101.7 1123.5 1405.3 1535.5 1741.3 1881.7

Fig. 1. Meteorological observations at INERA Farako-Bâ research Fig. 2. Soil temperatures at 5 and 10cm depth during the growing station during the growing period. period. Fig. 1. Dati meteorologici misurati nel sito sperimentale (INERA Fig. 2. Temperatura del suolo a 5 e 10 cm misurata durante il peri- Farako-Bâ) durante il periodo di crescita. odo di crescita.

Fig. 3. Daily evapotranspiration for Titicaca (left) and Negra Collana (right). Fig. 3. Evapotraspirazione giornaliera della vareità Titicaca (sinistra) e Negra Collana (destra). Effect of drought and nitrogen fertilisation on quinoa under field conditions in Burkina Faso 37

Fig. 4. Full Irrigation (FI-100% PET), Progressive Drought (PD-80% PET), and Deficit irrigation (DI-60% PET) for Titicaca (left) and Negra Collana (right) for first sowing date (November). Fig. 4. Full Irrigation (FI-100% PET), Progressive Drought (PD-80% PET), Deficit irrigation (DI-60% PET) per le varietà Titicaca (sinistra) e Negra Collana (destra) alla la prima data di semina (novembre). Note: bars showing Potential Evapotranspiration (PET); lines irrigation applied, and clouds week rainfall; total PET (columns) and irrigation applied (lines) do not always match at the end of the growing period.

Fig. 5. Full Irrigation (FI-100% PET), Progressive Drought (PD-80% PET), and Deficit irrigation (DI-60% PET) for Titicaca (left) and Negra Collana (right) in the second sowing date (December). Fig. 5. Full Irrigation (FI-100% PET), Progressive Drought (PD-80% PET), e Deficit irrigation (DI-60% PET) per le varietà Titicaca (sinistra) e Negra Collana (destra) alla la seconda data di semina (dicembre). Note: bars showing Potential Evapotranspiration (PET); lines irrigation applied, and clouds week rainfall; total PET (columns) and irrigation applied (lines) do not always match at the end of the growing period. 38 Jorge Alvar-Beltrán, Coulibaly Saturnin, Abdalla Dao, Anna Dalla Marta, Jacob Sanou, Simone Orlandini

Tab. 3. WUE (Water Use Efficiency in kg-3 m ); GYP (Grain yield per plant in grams); HI (Harvest Index in yield/biomass); TGW (Thou- sand grain weight in grams); CL1-2 (Chlorophyll content); CC (Canopy Cover in %); PH (Plant Height in cm) of quinoa under the different treatments. Factors: a Variety (V) of Chenopodium quinoa Willd.; b Irrigation level (I) (100% PET; 80% PET; 60% PET); c Fertilisation (F) (100 kg N ha-1; 50 kg N ha-1; 25 kg N ha-1). μ, σ, CV represents mean value, standard deviation and coefficient of variation of three repetitions, respectively. Tab. 3. WUE (Efficienza d’uso dell’acqua in kg-3 m ); GYP (resa per pianta in g); HI (Harvest Index in resa/biomassa); TGW (peso mille semi in g); CL1-2 (contenuto in clorofilla); CC (Canopy Cover in %); PH (altezza della pianta in cm) della quinoa nei diversi trattamenti. Fattori: a Vatrietà (V) di Chenopodium quinoa Willd.; b Livello irriguo (I) (100% PET; 80% PET; 60% PET); c Fertilizzazione (F) (100 kg N ha-1; 50 kg N ha-1; 25 kg N ha-1). μ, σ, CV rappresentano rispettivamente la media, la deviazione standard e il coefficiente di variazione delle tre ripetizioni.

Factors NOVEMBER DECEMBER

a b c V I F WUE GYP HI TGW CL1 CL2 CC PH WUE GYP HI TGW CL1 CL2 CC PH 60 25 1.69 9.56 0.48 2.40 55.3 21.9 6.26 59.4 0.50 1.27 0.32 1.91 47.9 37.7 7.44 33.5 60 50 0.72 3.73 0.46 2.41 53.2 23.2 5.13 44.8 0.53 1.30 0.31 1.68 45.5 32.0 5.55 33.0 60 100 0.21 0.85 0.25 2.01 50.3 21.1 1.39 28.7 0.23 1.29 0.29 1.78 41.6 30.6 6.06 32.4 80 25 0.90 9.04 0.48 2.06 43.2 33.4 5.04 52.7 0.44 2.75 0.30 1.82 44.9 48.4 7.62 38.7 Titicaca 80 50 0.68 5.47 0.45 1.87 45.4 31.4 2.33 45.1 0.81 4.05 0.36 1.87 41.6 54.6 8.66 44.8 80 100 0.08 0.56 0.35 1.92 50.4 29.2 0.87 25.7 0.55 2.70 0.35 1.84 45.4 44.3 6.59 38.6 100 25 0.29 4.29 0.41 2.02 39.1 45.7 1.97 34.7 0.44 2.98 0.43 1.83 46.7 31.8 6.14 39.8 100 50 0.43 5.16 0.38 1.85 44.6 35.5 1.74 44.0 0.42 2.19 0.42 1.67 45.8 35.0 4.68 34.0 100 100 0.17 2.42 0.38 1.75 40.6 25.4 1.36 36.4 0.35 1.42 0.32 1.68 46.1 30.4 6.56 36.3 μ - - 0.57 4.57 0.41 2.03 46.9 29.7 2.90 41.3 0.48 2.22 0.35 1.79 45.1 38.3 6.59 36.8 σ - - 0.57 3.61 0.08 0.28 6.35 9.46 2.58 13.1 0.30 1.71 0.07 0.22 4.75 10.3 3.33 10.1 CV - - 0.32 13.0 0.01 0.08 40.3 89.4 6.67 171.8 0.09 2.91 0.01 0.05 22.6 104.9 11.1 101.0 60 25 1.43 2.32 0.12 0.87 42.6 33.3 2.29 54.2 0.08 0.04 0.02 0.77 37.1 41.9 2.06 26.8 60 50 0.96 0.91 0.07 0.84 41.8 43.6 1.22 47.3 0.02 0.04 0.02 0.77 36.2 45.7 1.96 35.0 60 100 0.24 0.20 0.03 0.88 43.0 42.8 0.35 26.4 0.10 0.05 0.02 0.71 31.6 51.3 2.18 33.4 80 25 0.64 1.46 0.10 1.38 39.5 32.5 1.48 60.5 0.18 0.10 0.02 1.07 38.9 55.4 3.21 49.8 Negra 80 50 0.47 0.69 0.09 1.38 39.9 39.4 0.95 44.0 0.22 0.05 0.01 0.99 44.5 50.7 2.78 49.3 Collana 80 100 0.07 0.08 0.05 1.13 48.1 38.6 0.42 21.9 0.20 0.09 0.01 1.11 43.0 51.3 3.84 49.4 100 25 0.37 1.11 0.05 1.32 30.5 44.9 0.64 54.9 0.24 0.68 0.06 1.02 41.1 47.0 2.42 52.3 100 50 0.22 0.38 0.05 0.98 36.2 44.0 0.86 54.3 0.28 0.48 0.05 1.03 42.9 52.2 2.25 51.6 100 100 0.05 0.13 0.04 0.85 42.8 41.5 0.22 30.8 0.29 0.51 0.05 0.99 41.4 47.0 4.11 48.2 μ - - 0.50 0.81 0.07 1.07 40.5 40.1 0.94 43.8 0.18 0.22 0.03 0.94 39.6 49.2 2.76 44.0 σ - - 0.49 0.82 0.04 0.26 5.78 5.47 0.85 15.7 0.11 0.28 0.02 0.27 4.85 7.75 1.49 12.7 CV - - 0.24 0.67 0.00 0.07 33.4 29.9 0.72 244.9 0.01 0.08 0.00 0.07 23.5 60.1 2.23 161.3 μ - - 0.54 2.69 0.24 1.58 43.7 34.9 1.92 42.5 0.33 1.22 0.19 1.36 42.3 43.7 4.67 40.4 σ - - 0.53 3.21 0.18 0.55 6.83 9.29 2.15 14.4 0.27 1.57 0.17 0.49 5.49 10.5 3.21 11.9 CV - - 0.28 10.3 0.03 0.31 46.6 86.2 4.61 206.1 0.07 2.48 0.03 0.24 30.1 111.1 10.3 141.8

units of C per 1 unit of N), but slightly increased after roots (average depth, 6.5 cm, for both varieties) were organic amendment up to 10-12 C units per 1 N unit at thermic-stressed throughout the whole growing period 0-20 cm depth. As a result of phosphate fertilisation, P (Figure 2). within the first layer had boosted from 4 mg kg-1 prior Estimated ETc (Figure 3) was lowest at plant emer- to sowing, up to 84-106 mg kg-1 after harvesting. Finally, gence and two leaves stage (±3 mm day-1), while steadily bulk densities were of 1.66 g cm-3. increasing at a rate of +0.5 mm week-1 during the vegeta- Mean daily temperature during the growing period tive stage up to 6-7 mm day-1. The plateau phase of max- was 28.6 ºC (Figure 1). The 40 °C threshold was tres- imum water requirements for Titicaca was reached after passed 14 times, especially in March and April. In addi- 6 weeks (ETc = ±6 mm day-1). Once leaf senescence took tion, longer cycle varieties (Negra Collana) were affected place, ETc started to decline, thus depleting during pasty to a larger extent than short cycle varieties (Titicaca) by seed formation and physiological maturity of the plant, maximum temperatures at flowering (> 39 ºC). Finally, 10-13 weeks (ETc = ±5.5 mm day-1). For Negra Collana, soil temperatures, at 5 and 10 cm depth, have shown that with longer cycle, the ETc reached its maximum after Effect of drought and nitrogen fertilisation on quinoa under field conditions in Burkina Faso 39

Tab. 4. Post-hoc Tukey´s pairwise comparison test for different crop parameters and factors of study (variety, irrigation and fertilisation). Tab. 4. Post-hoc Tukey´s pairwise test per I diversi paretri e fattori analizzati (varietà, irrigazione e concimazione).

WUE GYP HI TGW CL1 CL2 CC PH Factor Level N OV. DEC. N OV. DEC. N OV. DEC. N OV. DEC. N OV. DEC. N OV. DEC. N OV. DEC. N OV. DEC. Titicaca 0.57 0.48A 4.57A 2.22A 0.40A 0.35A 2.03A 1.79A 46.9A 45.1A 29.6B 38.3B 2.90A 6.59A 41.3 36.8B Variety Negra 0.50 0.18B 0.81B 0.22B 0.07B 0.03B 1.06B 0.94B 40.5B 39.6B 40.1A 49.2A 0.94B 2.75B 43.8 44.0A 60 0.89A 0.24 2.93 0.67 0.23A 0.16B 1.57 1.27 47.7A 40.0B 31.0B 39.8B 2.78A 4.21 43.5 32.4B Irrigation 80 0.47B 0.40 2.88 1.62 0.25A 0.18B 1.60 1.45 44.4A 43.0AB 34.1AB 50.8A 1.85AB 5.45 41.6 45.1A 100 0.26B 0.34 2.25 1.37 0.22A 0.22A 1.46 1.37 39.0B 44.0A 39.5A 40.6B 1.31B 4.36 42.5 43.7A 25 0.89A 0.31 4.63A 1.30 0.27A 0.19 1.67A 1.40 41.7B 42.8 35.3 43.7 2.95A 4.82 52.7A 40.2 Fertilisation 50 0.58B 0.38 2.72B 1.35 0.25A 0.20 1.55AB 1.33 43.5AB 42.7 36.2 45.0 2.04AB 4.31 46.6A 41.3 100 0.15C 0.29 0.70C 1.01 0.19B 0.17 1.41B 1.35 45.8A 41.5 33.1 42.5 0.77B 4.89 28.3B 39.7

Note: capital letter (significant difference between set of groups); “A” is the group with highest value when compared to the other sets of groups “B” or “C” (in all cases statistically significant different); NOV. corresponds to the sowing in November and DEC. to the sowing in December.

Tab. 5. ANOVA for different crop parameters and interactions between factors (variety, irrigation and fertilisation). Tab. 5. ANOVA per diversi parametri misurati e interazioni tra fattori.

WUE GYP HI TGW CL1 CL2 CC PH Source N OV. DEC. N OV. DEC. N OV. DEC. N OV. DEC. N OV. DEC. N OV. DEC. N OV. DEC. N OV. DEC. V ns *** *** *** *** *** *** *** *** *** *** *** *** *** ns * I *** ns ns ns * ** ns ns *** * *** *** * ns ns ** F *** ns *** ns *** ns ** ns * ns ns ns *** ns *** ns V x I ns ns ns ns ns ns *** ns * * ** * ns ns ns ns V x F ns ns *** ns ** ns ns ns * ns ** ns ns ns ns ns I x F ** ns * ns *** ns ns ns * ns * ns ns ns ns ns V x I x F ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns R2 0.77 0.53 0.82 0.59 0.97 0.95 0.93 0.81 0.77 0.57 0.76 0.65 0.65 0.45 0.71 0.44

Abbreviations: WUE (Water Use Efficiency in kg-3 m ); GYP (Grain yield per plant in grams); HI (Harvest Index in yield/biomass); TGW (Thousand grain weight in grams); CL1-2 (Chlorophyll content); CC (Canopy Cover in %); PH (Plant Height in cm); NOV. (November sowing); DEC. (December sowing). Note: a Variety (V) of Chenopodium quinoa Willd. ; b Irrigation level (I) (100% PET; 80% PET; 60% PET); c Fertilisation (F) (100 kg N ha-1; 50 kg N ha-1; 25 kg N ha-1); *** extremely significant (p<0.001); ** very significant (p<0.01); * significant (p<0.05); ns: not significant (p>0.05); R2 is the proportion of variance in the dependent variable (crop parameter) which can be explained by the independent variables (V, I, F).

10 weeks, just after flowering. It remained on the pla- mm. For deficit irrigation (DI), the amount of water sup- teau phase (ETc = ±6.5 mm day-1) until 18 weeks, then plied was 231 mm and 437 mm to Titicaca and Negra decreased to ±4.5 mm day-1 during pasty seed formation Collana, respectively. and physiological maturity, 19-23 weeks. The statistical analysis has shown that water use Quinoa´s water requirements (Figures 4 and 5) efficiency (WUE, expressed in kg biomass per m3 of under field conditions varied considerably depend- water applied) was higher under PD and DI, meaning ing on: cultivar, phenological phase, evapotranspiration that quinoa was performant under drought-stress con- rate, type of soil texture and efficiency of the irriga- ditions (except for Negra Collana sown in December). tion system. Full irrigation (FI) results have shown that For the grain yield per plant (GYP), there was signifi- Titicaca´s water demand was 403 mm, whereas for Negra cant difference (p<0.001) between the two varieties and Collana 811 mm (average of both sowing dates). Under for both sowing dates, being up to 10 times higher for progressive drought (PD), the amount of water supplied Titicaca than for Negra Collana. Moreover, yields have to Titicaca was 323 mm, whereas for Negra Collana 614 depleted by half between November and December, from 40 Jorge Alvar-Beltrán, Coulibaly Saturnin, Abdalla Dao, Anna Dalla Marta, Jacob Sanou, Simone Orlandini

Fig. 6. Relationship (linear regression) between grain yield per plant (g) and plant height (cm) at harvest for Titicaca and Negra Collana. Fig. 6. Relazione tra resa di granella per pianta (g) e altezza della pianta (cm) alla raccolta per Titicaca e Negra Collana. Note: r shows Pearson correlation coefficient; Negra Collana (sowing date: December) was removed from the graphs due to high seed abortion in plants.

Fig. 7. Relationship (linear regression) between grain yield per plant (g) and canopy cover (%) for Titicaca and Negra Collana. Fig. 7. Relazione tra resa di granella per pianta (g) e canopy cover (%) per Titicaca e Negra Collana. Note: r shows Pearson correlation coefficient; Negra Collana (sowing date: December) was removed from the graphs due to high seed abortion in plants.

2.69 to 1.22 g plant-1 (average of both varieties). In fact, total dry matter) have shown statistical significant dif- extreme temperatures during flowering, higher than 39 ferences (p<0.001) between the two varieties, 0.38 and ºC, have resulted in high seed abortion in plants. For 0.05 HI for Titicaca and Negra Collana (average of both Titicaca sown in November under DI and 25 kg N ha-1 sowing dates), respectively. In addition, statistical signifi- fertilisation was the most performant, with yields of 9.5 cant differences (p<0.001) between quinoa varieties were g plant-1 (equivalent to 1.9 t ha-1). However, for the sow- observed when analysing the weight of thousand grains ing in December, higher yields (4.05 g plant-1, equiva- (TGW) for both sowing dates; having Titicaca seeds lent to 0.8 t ha-1) were observed under PD and 50 kg N doubled the weight of Negra Collana seeds, 1.94 and 1.00 ha-1. Harvest index (HI, as a ratio of harvested grain to g, respectively. Effect of drought and nitrogen fertilisation on quinoa under field conditions in Burkina Faso 41

Chlorophyll content (CL), N in the leaf, has shown was the critical threshold at flowering, if exceeded qui- statistical significant differences (p<0.001) amongst quinoa plants would become sterile (Breidy, 2015; CNRA- noa varieties, with higher N values for Titicaca sown DA, 2015; Djamal, 2015; Hassan, 2015; Saeed, 2015). in November. This was probably the consequence of N Nonetheless, this research has proven that Titicaca can redistribution from leaf to storage organs, hence lead- stand temperatures above 35 °C during flowering and ing to leaf senescence and fostering seed filling. Canopy still be highly performant (up to 1.9 t ha-1). In regards cover (CC) had varied between quinoa varieties, with to Negra Collana, long cycle variety, the effect of tem- 3 times more vegetation coverage for Titicaca than for peratures above 39 °C has resulted in a very low num- Negra Collana. Quinoa sown in December has shown ber of plants with seeds. This is because pollen viability statistical significant differences (p<0.05) among the is a function of pollen moisture content which is strong- heights of the two varieties, 44 and 39 cm for Negra ly dependent on vapour pressure deficit (Hatfield and Collana and Titicaca, respectively (average of both sow- Prueger, 2015). At high temperatures, vapour pressure ing dates). Strong relationships, using Pearson correla- deficits were highest resulting in pollen desiccation and tion coefficient (r), were observed between plant height low pollen viability. In this line, further research would and GYP (Figure 6), with values of 0.88 and of 0.63 for be required to better understand the effect of tempera- Titicaca and Negra Collana, respectively. Figures 6 and ture on plant fertility. 7 show the notable enhancement of GYP (5 g per plant-1, In contrast with other studies, this research did equivalent to 1 t ha-1) once the plant exceeded 50 cm not bring to light any relevant information on yield height. On the other hand, the relationship between enhancement with increasing nitrogen fertilisation (Kaul GYP and CC (Figure 7) was robust, showing a correla- et al., 2005; Shams, 2012). But was in harmony with oth- tion coefficient higher than 0.7 for both varieties and er investigations (Moreale, 1993), showing that N-fertili- sowing dates. In fact, greater canopy was responsible of sation does not play a crucial role on crop growth nor an increase in light interception, enhancing assimilation seed yield, and that quinoa’s N uptake was of 25 kg N and plant growth. ton-1 of seed produced (1:40 ratio). In addition, the combination of high temperatures and soil moisture in sandy-loam soils during fertilisation could have resulted 4. DISCUSSION in urea volatilization (ammonia losses) and hydrolysis. Overall, this investigation has shown that quinoa Despite of the amount of research examining can adapt and be highly performant in poor structured quinoa´s water requirements under water-stress condi- (sandy-loam texture) and low fertility soils (<0.5 % tions, there were no studies displaying such low water organic matter and 0.03 % N), typically of the Sahel. inputs than those observed in this research (231 mm Titicaca and 437 mm Negra Collana, average of both sowing dates under DI). Furthermore, this study´s aver- 5. CONCLUSIONS age WUE results (0.53 kg m-3 Titicaca and 0.34 kg m-3 Negra Collana) were similar to those recorded in Boliv- This research confirms that quinoa is a climate ia (0.21-0.45 kg m-3) (Geerts et al., 2008), but lower to resilient crop that can cope with high temperatures those observed in Italy and Morocco (0.6 and 1.7 kg m-3, and drought-stress conditions. It has a good adaptation respectively) (Hirich et al., 2014a; Hirich et al., 2014c; to slightly acidic, poor structured and low fertile soils, Riccardi et al., 2014). In fact, drought stress conditions besides of having low N-requirements. Moreover, Titi- at key phenological stages (pre-flowering, flowering and caca yields could attain 900 kg ha-1 if sown in Novem- pasty grain formation) have had a negative effect both ber (average of all treatments), and could be exceeded on grain yield per plant and WUE (Geerts et al., 2008). if appropriate agronomic practices are followed. For the GYP results were in harmony with those modelled in time being, it will be important to prioritize the use of AquaCrop showing that quinoa can be highly perfor- short-cycle varieties (Titicaca, 85 days), rather than long mant under DI (Geerts et al., 2009; Cusicanqui et al., cycle varieties (Negra Collana, 150 days). By sowing 2013). Titicaca´s harvest index (HI) results (0.38, average short cycle varieties in November, the effect of extreme of both sowing dates) were lower to those observed in temperatures occurring in mid-February until the onset Morocco (0.57-0.67), but higher than those of Iraq (0.28) of the rainy season will be diminished. In fact, quinoa´s (Hirich et al., 2014c; Hassan, 2015). sowing could be advanced by several weeks towards Moreover, recent research in Algeria, Lebanon, northern parts of the country. Moreover, organic Mauritania, Yemen and Iraq have suggested that 35 °C amendment is highly recommended at the rate of 1 t ha-1 42 Jorge Alvar-Beltrán, Coulibaly Saturnin, Abdalla Dao, Anna Dalla Marta, Jacob Sanou, Simone Orlandini or higher prior to sowing; besides of a two-time mineral Benlhabib, O.; Jacobsen, S. E.; Jellen, E. N.; Maughan, fertilisation in the form of ammonium nitrate, rather P. J.; & Choukr-Allah, R. 2015. In State of the Art than urea, at the rate of 50 kg N ha-1. Mechanised tilling, Report of Quinoa in the World in 2013. Chapter at 10-20 cm depth, would be advised, and if irrigated 6.1.5: Status of quinoa production and research in frequent soil aeration would be recommended to avoid Morocco, pp. 478-491. Rome: FAO and CIRAD soil adhesion that allows effective root development. Bertero, D. 2015. In State of the Art Report of Quinoa in For that, sowing in furrows would also be supported. the World in 2013. Chapter 2.1: Environmental con- Furthermore, research on plant-breeding should target trol of development, pp. 120-130. Rome: FAO and higher-temperature and wind tolerant varieties capable CIRAD of standing the warmest months and winds occurring Breidy, J. 2015. In Final Report on quinoa Evaluation Tri- during the Harmattan. This could potentially broad- als in Lebanon. Rome: FAO en the spatial distribution and sowing time across the Ceccato, D.; Delatorre-Herrera, J.; Burrieza, H.; Berte- country, as well as to other hot-spot regions to climate ro, D.; Martínez, E.; Delfino, I.; Moncada, S.; Bazile, change. Overall, this research has allowed settling a pro- D.; Castellión, M. 2015. In State of the Art Report visional quinoa crop-calendar, besides of describing ide- of Quinoa in the World in 2013. Chapter 2.2: Seed otype cultivars and suitable agro-meteorological zones physiology and response to germination conditions, for quinoa production in Burkina Faso. For that, qui- pp. 131-142. Rome: FAO and CIRAD noa regional programmes implemented by FAO, TCP/ Centre National de Recherche Agronomique et de Dével- SFW/3404 and TCP/RAF/3602 (Burkina Faso, Camer- oppement Agricole (CNRADA). 2015. In Mauritania oun, Niger, Senegal, Chad, Togo and Ghana), need to be Final Evaluation Report on quinoa. Rome: FAO further supported and its production scaled-up. Cusicanqui, J.; Dillen, K.; Garcia, M.; Geerts, S.; Raes, D.; & Mathijs, E. 2013. Economic assessment at farm level of the implementation of deficit irrigation for 6. ACKNOWLEDGEMENTS quinoa production in the Southern Bolivian Alti- plano. Spanish Journal of Agricultural Research, 11 We would like to thank the Food and Agricultural (4). 894-907. Organisation of the United Nations (FAO), through Djamal, S. 2015. In Technical assistance for the introduc- the Institut de l’Environnement et de Recherches Agri- tion of quinoa and appropriation / institutionaliza- coles (INERA), for quinoa seed provision to carry out tion of its production in Algeria-Second Evaluation this investigation. Special recognitions to the workforce Report. Rome: FAO of Farako-Bâ, for been so committed, interest and sup- Eruola, A.; Bello, N.; Ufeogbune, G., & Makinde, A. 2013. portive when carrying out agronomic practices in such Effect of climate variability and climate change on difficult environmental conditions; just like the women, crop production in tropical wet-and dry climate. Ital- for their end-less patience and humour during the seed ian Journal of agrometeorology, 17(1), 17-22. separation process and sowing. Fghire, R.; Wahbi. S.; Anaya, F.; Issa Ali, O.; Benlhabib, O.; & Ragab. R. 2015. Response of quinoa to different water management strategies: field experiments and 7. REFERENCES SALTMED model application results. Irrigation and Drainage, 64 (1), 29-40. Adolf, V. I.; Jacobsen, S. E.; & Shabala, S. 2013. Salt toler- Garcia, M.; Raes, D.; & Jacobsen, S. E. 2003. Evapotran- ance mechanisms in quinoa (Chenopodium quinoa spiration analysis and irrigation requirements of Willd.). 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water availability in the Bolivian Altiplano. Agricul- quinoa Willd.) under salinity and soil drying. Journal tural Water Management, 96 (11), 1652-1658. of Agronomy and Crop Science, 197 (5), 348-360 Hargreaves, G. H.; & Samani, Z. A. 1985. Reference crop Razzaghi, F.; Plauborg, F.; Jacobsen, S. E; Jensen, C. R.; & evapotranspiration from temperature. Applied engi- Andersen, M. N. 2012. Effect of nitrogen and water neering in agriculture, 1(2), 96-99 availability of three soil types on yield, radiation use Hassan, L. 2015. Iraq Final Evaluation Report on quinoa. efficiency and evapotranspiration in field-grown qui- Rome: FAO noa. Agricultural water management, 109, 20-29. Hatfield, J. L., & Prueger, J. H. 2015. Temperature Repo-Carrasco, R.; Espinoza, C.; & Jacobsen, S. E. 2003. extremes: Effect on plant growth and develop- Nutritional value and use of the Andean crops qui- ment. Weather and climate extremes, 10, 4-10. noa (Chenopodium quinoa) and kañiwa (Cheno- Hirich, A.; Jelloul. A.; Choukr‐Allah, R.; & Jacobsen, S. podium pallidicaule). Food reviews international, 19 E. 2014a. Saline water irrigation of quinoa and chick- (1-2), 179-189 pea: seedling rate, stomatal conductance and yield Riccardi, M.; Pulvento, C.; Lavini, A.; d’Andria, R.; & responses. Journal of Agronomy and Crop Science, Jacobsen, S. E. 2014. Growth and ionic content of 200 (5), 378-389 quinoa under saline irrigation. Journal of agronomy Hirich, A.; Choukr‐Allah, R.; & Jacobsen, S. E. 2014b. and crop science, 200 (4), 246-260 Quinoa in Morocco–effect of sowing dates on devel- Saeed, A.L. 2015. In Yemen Progress Report on quinoa. opment and yield. Journal of Agronomy and Crop Rome: FAO Science, 200 (5), 371-377 Shams. A. S. 2012. Response of quinoa to nitrogen ferti- Hirich, A.; Choukr‐Allah, R.; & Jacobsen, S. E. 2014c. lizer rates under sandy soil conditions. Proceedings Deficit irrigation and organic compost improve 13th International Conference Agronomy. Faculty of growth and yield of quinoa and pea. Journal of Agriculture, Benha University, Egypt, 9-10 Agronomy and Crop Science, 200 (5), 390-398 Steduto, P.; Hsiao, T. C.; Fereres, E.; & Raes, D. 2012. In Jacobsen, S. E.; Mujica, A.; & Jensen. C. R. 2003. The Crop yield response to water. Chapter 3.4: Herba- resistance of quinoa (Chenopodium quinoa Willd.) ceous crops: quinoa. pp. 230. Rome: FAO. to adverse abiotic factors. Food Reviews Internation- Vacher, J. 1998. Responses of two main Andean crops, al, 19 (1-2), 99-109 quinoa (Chenopodium quinoa Willd) and papa Jensen, C. R.; Jacobsen, S. E.; Andersen, M. N.; Nunez, amarga (Solanum juzepczukii Buk.) to drought on N.; Andersen, S. D.; Rasmussen, L.; & Mogensen, the Bolivian Altiplano: Significance of local adapta- V. O. 2000. Leaf gas exchange and water relation tion. Agriculture, ecosystems & environment, 68 (1), characteristics of field quinoa (Chenopodium qui- 99-108. noa Willd.) during soil drying. European Journal of Agronomy, 13 (1), 11-25. Kaul, H. P.; Kruse, M.; & Aufhammer, W. 2005. Yield and nitrogen utilization efficiency of the pseudocereals amaranth, quinoa, and buckwheat under differing nitrogen fertilization. European Journal of Agrono- my, 22 (1), 95-100. Moreale, A. 1993. In The quinoa project: Wageningen University. [Accessed on 27th August 2018] Available at: http://edepot.wur.nl/354101 Morsy, M.; El Afandi1, G.; Naif, E. 2018. CropSyst Simu- lation and Response of Some Wheat Cultivars to Late Season Drought. Italian Journal of Agrometeorology 1, 15-24. DOI:10.19199/2018.1.2038-5625.015 Piva, G.; Brasse, C.; & Mehinagic, E. 2015. In State of the Art Report of Quinoa in the World in 2013. Chap- ter 6.1.2: Quinoa D’Anjou: The beginning of a French quinoa sector. pp. 447-453. Rome: FAO and CIRAD Razzaghi, F.; Ahmadi. S. H.; Adolf. V. I.; Jensen. C. R.; Jacobsen. S. E.; & Andersen. M. N. 2011. Water relations and transpiration of quinoa (Chenopodium

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agronomy

Article Eﬀect of Drought, Nitrogen Fertilization, Temperature and Photoperiodicity on Quinoa Plant Growth and Development in the Sahel

Jorge Alvar-Beltrán 1,* , Abdalla Dao 2, Anna Dalla Marta 1 , Coulibaly Saturnin 2, Paolo Casini 1, Jacob Sanou 2 and Simone Orlandini 1

1 Department of Agriculture, Food, Environment and Forestry (DAGRI)-University of Florence, 50144 Florence, Italy; anna.dallamarta@unifi.it (A.D.M.); paolo.casini@unifi.it (P.C.); simone.orlandini@unifi.it (S.O.) 2 Institut de l’Environnement et de Recherches Agricoles (INERA), Bobo Dioulasso BP910, Burkina Faso; [email protected] (A.D.); [email protected] (C.S.); [email protected] (J.S.) * Correspondence: jorge.alvar@unifi.it; Tel.: +39-275-5741

Received: 13 September 2019; Accepted: 30 September 2019; Published: 2 October 2019

Abstract: Drought, heat stress, and unfavorable soil conditions are key abiotic factors affecting quinoa’s growth and development. The aim of this research was to examine the effect of progressive drought and N-fertilization reduction on short-cycle varieties of quinoa (c.v. Titicaca) for different sowing dates during the dry season (from October to December). A two-year experimentation (2017–2018 and 2018–2019) was carried out in Burkina Faso with four levels of irrigation (full irrigation—FI, progressive drought—PD, deficit irrigation—DI and extreme deficit irrigation—EDI) 1 and four levels of N-fertilization (100, 50, 25, and 0 kg N ha− ). Plant phenology and development, just like crop outputs in the form of yield, biomass, and quality of the seeds were evaluated for different sowing dates having different temperature ranges and photoperiodicity. Crop water productivity (CWP) function was used for examining plant’s water use efficiency under drought stress conditions. 3 Emerging findings have shown that CWP was highest under DI and PD (0.683 and 0.576 kg m− , respectively), while highest yields were observed in 2017–2018 under PD and its interaction with 25 1 1 to 50 kg N ha− (1356 and 1110 kg ha− , respectively). Mean temperatures close to 25 ◦C were suitable for optimal plant growth, while extreme temperatures at anthesis limited the production of grains. Small changes in photoperiodicity from different sowing dates were not critical for plant growth.

Keywords: Burkina Faso; Chenopodium quinoa Willd.; heat stress; irrigation; crop water productivity

1. Introduction Sustainable irrigation strategies that can increase crop water productivity are of growing interest in arid and semi-arid regions [1]. This is the case of the Sahel, with a rapid growing population and an agricultural system extremely dependent on weather; 98% of its agriculture is rainfed, in addition to experiencing larger inter/intra annual rainfall variability [2,3]. In Burkina Faso, one-third of the country’s surface area is degraded, with an estimated annual expansion of degraded land of 3600 km2 [4]. In this region, changes in weather patterns are expected to have a negative impact on the yields of major cereals [5,6]. Some studies conducted in Burkina Faso estimate a yield reduction of 10% for sorghum, close to 17% for maize, and up to 23% for millet by 2050, and is expected to worsen with changing climatic conditions [7–9]; all of the previous crops having a C4 photosynthetic pathway. Even though C4 crops have the most eﬃcient form of photosynthesis [10], crop’s with C3 photosynthetic pathways can beneﬁt more from increasing CO2 concentrations [11,12]. This is because

Agronomy 2019, 9, 607; doi:10.3390/agronomy9100607 www.mdpi.com/journal/agronomy Agronomy 2019, 9, 607 2 of 16

optimal CO2 atmospheric concentrations for enhancing the photosynthetic rate of C3 crops have not yet been reached [11,12]. Quinoa is an herbaceous crop belonging to the C3 group of plants, besides of having a high nutritional value [13,14]. In the last decades, there has been an intensification of scientific research, and in 2013, the International Year of Quinoa was declared—all of which, doubling the number of countries growing quinoa since then [15]. In the Sahel region, several countries (Senegal, Mauritania, Mali, Burkina Faso, Niger, Chad, and Sudan) are now growing quinoa for research purposes, as well as seeking to promote quinoa for local consumption [15]. The latest scientific efforts have focused on quinoa’s adaptability under adverse environmental conditions. Its resilience to abiotic stresses is the 1 result of a wide genetic variability [16], giving the plant an excellent salt-tolerance, up to 40 dS m− , and drought-resistance, with water requirements as low as 230 mm for c.v. Titicaca [17–20]. The crop water productivity (CWP; amount of biomass in kg per volume of water supply in m3) of quinoa is low, and can be increased: if water losses from evaporation are reduced, if the negative effect of drought stresses at specific phenological phases are avoided, and if unfavorable conditions during crop growth, (i.e., pests and diseases) are diminished [21]. In addition, quinoa can adapt to most types of soils, but differences in the literature arise when assessing crop nitrogen-uptake. Some studies argue that 1 maximum yields are reached at 80 kg N ha− , but yields can differ according to the soil type, being highest among sandy–clay–loam soils [22,23]. On the contrary, some authors have acknowledged that increasing N-fertilization does not determine quinoa growth nor yield [24]. In this paper, the effect of drought and nitrogen fertilization on plant’s phenology and physiological responses of short-cycle varieties is evaluated. A crop’s performance during the dry season in the Soudanian belt and its adaptability to unfavorable soil and climatic conditions was monitored. Moreover, different sowing dates were tested and some agricultural planning practices for farmers were proposed. Finally, as a climate resilient and C3 crop, this research aims to look beyond the traditional grown C4 cereals (e.g., maize, sorghum, and millet) and has selected quinoa for enhancing food security and nutrition in Burkina Faso under changing climatic conditions.

2. Materials and Methods

2.1. Experimental Setup This experiment was carried out between 2017 and 2019 at Institut de l’Environnement et Recherches Agricoles (INERA) Farako-Bâ research station (11◦050 N; 4◦200 W), Burkina Faso. The study area was characterized for having a tropical savannah-wet and hot climate, with a well-defined dry season (from November to April). In the first year, 2017–2018, the experimental design was organized in a split-block design with a multiple factor analysis of variance (ANOVA), with three levels of irrigation according to the potential evapotranspiration (PET) (FI 100% PET, progressive drought (PD) 80% PET, and deficit irrigation (DI) 60% PET) and three levels of N-fertilization (100, 50, and 1 25 kg N ha− ), while each treatment having three replicates (Figure1). For the second year, 2018–2019, the experimental set-up was as follows: two levels of irrigation (FI 100% PET and extreme deficit 1 irrigation (EDI) 50% PET), two levels of N-fertilization (100 and 0 kg N ha− ), with each treatment having four replicates (Figure1). Between 2017–2018 and 2018–2019 slightly di fferent irrigation levels and N-fertilization levels were used in order to identify and minimized the gaps between inputs (crop N-fertilization and irrigation requirements) and outputs (losses in the form of volatilization, leaching and water-surface runoff). Throughout the two years of experimentation, 2017–2018 and 2018–2019, maize was the crop grown during the rainy season whereas quinoa during the dry season. Moreover, for the first year of experimentation, quinoa (c.v. Titicaca) was sown on the 4th November (hereinafter, 4-Nov.) and 8th December (hereinafter, 8-Dec.); whereas for the second year, the sowing date was the 25th October (hereinafter, 25-Oct.) and 19th November (hereinafter, 19-Nov.). The sowing rate was 10 kg of seeds per hectare, with 50 cm distance between rows and 10 cm between plants, with a total density of Agronomy 2019, 9, 607 3 of 16

1 1 200,000 plants ha− . Prior to the sowing of quinoa, the ﬁeld was amended with 5 t ha− of compost 1 (50.2% organic matter) as well as phosphate (26.7% P2O5) at a rate of 400 kg P ha− . N-fertilization was applied twice, 2 to 3 weeks and 4 to 5 weeks after sowing, at a same concentration rate.

Figure 1. Experimental field design with different levels of irrigation (full irrigation (FI), progressive drought (PD), deficit irrigation (DI), extreme deficit irrigation (EDI)), and N-fertilization (100, 50, 25 1 and 0 kg N ha− ) for the two year-experimentation (2017–2018 and 2018–2019).

2.2. Sampling and Measurement Maximum and minimum air temperatures, maximum and minimum relative humidity, and daily rainfall values were obtained from an agro-meteorological station placed nearby to the experimental ﬁeld. Irrigation was measured using a water-counter placed at the entry of each irrigation block 1 and was drip-irrigated at a rate of 1.05 L h− . Crop’s daily potential evapotranspiration (PETc) was calculated using the following formula [25]:

0.5 ETo = 0.023 (T mean + 17.78) Ro (T max T min) (1) − 2 1 where: Ro = solar radiation (MJ m− day− ) at a given month and latitude [26]; T mean = mean daily temperature (◦C); T max = daily maximum temperature (◦C); T min = daily minimum temperature (◦C). The different phenological phases were measured and analyzed separately according to the different sowing dates, being the following: emergence (E), two leaves (2L), four leaves (4L), eight leaves (8L), panicle formation (PF), flowering (FL), and milky grains (MG) at 5, 15, 18, 24, 30, 40, and 60 days after sowing (DAS), respectively with a total of 100 samples per plot. The phenological phase was determined once 50% of the plants had reached a given stage. Regarding plant’s morphology, the plants’ height (10 measurements per plot), number of branches (10 per plot), panicle length (10 per plot), stem diameter (5 per plot), root depth and horizontal length (1 per plot) were all measured at physiological maturity. In addition, the plants’ performance was measured for the following crop parameters: grain yield per plant (12 measurements per plot) and thousand grain weight (3 per plot). The crop water productivity (CWP) was defined as the ratio between biomass production and water Agronomy 2019, 9, 607 4 of 16

3 applied (kg m− ). The harvest index (HI) was calculated as the ratio between yield and biomass, as a percentage. For the second year, the canopy cover was measured throughout the growing cycle using a mobile-application, Canopeo, developed by the Oklahoma University [27]. Measurements were taken at 60 cm distance from the top of the canopy with an image cover of 75 50 cm. During × the 2018–2019 experimentation, the canopy cover was measured six times: at 24, 34, 40, 49, 70, and 85 DAS for the sowing on the 25-Oct., and at 24, 34, 40, 60, 75, and 84 DAS for the sowing on the 19-Nov. The calculation of the cumulative growing degree-days (CGDD in ◦C) was done using the following formula, and where Tbase was equal to 3 [28]:

(Tmax + Tmin) GDD = Tbase (2) 2 − Finally, prior to sowing, ﬁve soil samples were extracted from each experimental ﬁeld at 0–20 cm and 20–40 cm depth. The main physic-chemical properties of the soil, texture, pH, N, P, K, C, and organic matter were analyzed.

2.3. Statistical Analysis Since no major differences were observed between different sowing dates on the plants’ physiology and phenology, the sowing dates were combined together in a two-year experimentation (2017–2018 and 2018–2019). For this reason, sowing-date and year were not considered as independent factors, whereas irrigation and N-fertilization were the only independent factors of statistical interest. The analysis of variance (ANOVA) and Tukey’s HSD post-hoc test were used to estimate the mean variation among and within groups for different crop parameters, as well as to examine the effect of irrigation and N-fertilization and its interaction on a wide-range of crop parameters. The ANOVA and Tukey’s HSD post-hoc test was done using Minitab 18 and IBM SPSS software. Finally, the Pearson correlation coefficient (r) was used to test the association or correlation between two variables.

3. Results The two-year experimental field was characterized for being sandy–loam in the first layer (0–20 cm) and sandy–clay–loam in the second layer (20–40 cm), besides having slightly acidic properties (Table1). Mineral nitrogen (in the form of ammonium-NH4 and nitrate-NO3) in the soil was very low with N-content lower than 0.036% at all depths (0–20 cm and 20–40 cm) and for the two years. The organic matter found in the second year of experimentation had higher values (0.60%) when compared to that of the first year (0.48%). The ratio of C/N was higher for the second year of experimentation (9.8) than for the first year (8.8). The P-availability within the first soil layer was considerably higher for 1 1 the second year-experiment (44 mg kg− ) when comparing to that of the first year (4 mg kg− ); while 1 similar K-availability (between 79 and 90 mg kg− ) was found for both years. The mean-maximum temperatures (Figure2) during the growing cycle for di fferent sowing dates were the following: 34.8 ◦C (25-Oct.), 34.6 ◦C (4-Nov.), 35.0 ◦C (19-Nov.), and 34.8 ◦C (8-Dec.). Moreover, at quinoa’s most sensitive phenological phase, flowering (at 40 DAS and lasting for 10 days), the mean-maximum temperatures for each of the sowing dates was: 34.6 ◦C (25-Oct.), 33.5 ◦C (4-Nov.), 33.3 ◦C (19-Nov.), and 34.8 ◦C (8-Dec.). For the net irrigation requirements (Table2), the amount of water supplied under FI was between 394 mm (8-Dec.) and 457 mm (25-Oct. and 19-Nov.); whereas for PD between 319 mm (8-Dec.) and 328 mm (4-Nov.). On the other side, under DI and EDI, the amount of irrigation applied was between 211 mm (25-Oct.) and 246 mm (4-Nov.). Agronomy 2019, 9, 607 5 of 16

Table 1. Soil main physico-chemical characteristics of the two-year experimentation (2017–2018 and 2018–2019) (average of 5 samples).

2017–2018 2018–2019 Parameter Depth/Unit 0–20 cm 20–40 cm 0–20 cm 20–40 cm Sand % 67.2 54.6 75.3 59.5 Silt % 17.6 16.5 14.8 12.7 Clay % 15.2 28.9 9.9 27.8 Texture Sandy–Loam Sandy–Clay–Loam Loamy–Sand Sandy–Clay–Loam pH (H2O) 6.51 5.95 6.09 5.87 C % 0.28 0.23 0.35 0.30 Organic matter % 0.48 0.39 0.60 0.51 N % 0.032 0.026 0.036 0.028 C/N 8.8 8.7 9.8 10.6 1 P available mg kg− 4.0 1.70 44.0 31.3 1 K available mg kg− 79.73 74.97 90.3 116.0 3 Bulk density g cm− 1.61 - - -

Figure 2. Maximum and minimum temperatures recorded during the two year-experimentation (2017–2018 and 2018–2019).

Table 2. Net irrigation (in mm) under full irrigation (FI), progressive drought (PD), deﬁcit irrigation (DI), and extreme deﬁcit irrigation (EDI) for 2017–2018 (4-Nov. and 8-Dec.) and 2018–2019 (25-Oct. and 19-Nov.).

Irrigation 25-Oct. 4-Nov. 19-Nov. 8-Dec. Schedule Amount Events µ Amount Events µ Amount Events µ Amount Events µ FI 457 27 16.9 410 38 10.8 457 26 17.6 394 28 14.1 PD - - - 328 37 8.9 - - - 319 29 11.0 DI - - - 246 34 7.2 - - - 228 26 8.8 EDI 211 29 7.3 - - - 227 27 8.4 - - - Legend: (a) Irrigation: FI (full irrigation); PD (progressive drought); DI (deﬁcit irrigation); EDI (extreme deﬁcit irrigation). Note: Irrigation and rain equal to or more than 1 mm; amount (in mm); Events (in n◦); µ (average water supply per irrigation event).

Different sowing dates did not have a major impact on quinoa’s phenological phases (Figure3), as mean-temperatures and photoperiod did not differ much among sowing dates (max ∆T of 0.4 ◦C and max ∆ photoperiod of 26 min between sowing dates). For the early sowing dates (25-Oct. and 4-Nov.), higher temperatures were recorded at early-vegetative stages while lower at anthesis, and vice-versa for late sowing dates (19-Nov. and 8-Dec.). The observed photoperiodicity at this time of Agronomy 2019, 9, 607 6 of 16 the year, and for this location, was characteristic of tropical zones, with approximately 12 h of daytime and nighttime. The appearance of two-cotyledons was relatively fast, 5 DAS, being faster among experiments sown on 25-Oct. and 19-Nov. This was caused by higher diurnal temperatures (>20 ◦C) when sowing on the 25-Oct. and 19-Nov, than for the 8-Dec. (<15 ◦C). Similar growing rates were observed at two and four leaves; with changing patterns at eight leaves, with a lower rate amongst plants sown in 19-Nov. and 8-Dec. Panicle formation was totally reached at 30 DAS for all sowing dates, while flowering at 40 DAS. Nevertheless, the time for reaching flowering was slightly earlier amongst plants sown on the 25-Oct. and 4-Nov, than among those plants sown on the 19-Nov. and 8-Dec. Quinoa milky grains were observed at 60 DAS, while physiological maturity between 83 and 90 DAS.

Figure 3. Emergence (E), two leaves (2L), four leaves (4L), eight leaves (8L), panicle formation (PF), ﬂowering (FL), and milky grains (MG) of quinoa at 5, 15, 18, 24, 30, 40, and 60 days after sowing (DAS), respectively, for diﬀerent sowing dates (25-Oct., 4-Nov., 19-Nov., and 8-Dec.).

For the cumulative growing degree days (CGDD in ◦C) there was a perfect negative correlation (r) between phenological phases and sowing dates (Table3). In fact, there was a negative relationship between CGDD and sowing dates, with higher accumulation of degrees amongst early-sown plants (25 Oct. and 4 Nov.). In particular, the correlation coefficient was highest (r 0.79) up to 60 DAS, while ≥ decreasing at physiological maturity. This was explained by the fact that harvesting was carried out in different dates, being earlier amongst late-sowing plants (83 DAS). Tables4 and5 report the interaction between irrigation and N-fertilization (independent factors) and its effect on different crop parameters (dependent factors). Even though there was a decreasing trend on the observed parameter-values under drought-stress conditions and N-fertilization reduction, the measured crop parameters were, in many cases, not statistically significant different when N-fertilization and irrigation interacted together (Table4); except for yield (Y) and biomass (AGB) in 1 1 2017–2018 under FI and 100 kg N ha− (1171 and 3341 kg ha− ). Furthermore, as observed in Table5, the highest yields and biomass (AGB) were observed amongst 1 1 plants exposed to PD, with 1012 kg ha− and 2436 kg ha− , respectively, rather than on FI plants. On the 1 contrary, the lowest yields and biomass were observed under DI (663 and 1928 kg ha− , respectively) 1 and EDI (480 and 1321 kg ha− , respectively). Statistically significant differences were reported when comparing the yields of PD and EDI. For N-fertilization, the highest yields and biomass were observed 1 1 when applying 25 kg N ha− (1038 and 2426 kg ha− , respectively); hence, increasing N-fertilization did not result in a higher crop development nor yield. Statistically significant differences were reported Agronomy 2019, 9, 607 7 of 16

1 for the weight of thousand grains (TGW), which were lower under EDI and 0 kg N ha− (1.37 g). The harvest index (HI) remained constant for all irrigation levels (around 37% for FI, PD and EDI), except for EDI with a value of 29%. For the crop water productivity (CWP), the mean value of all 3 irrigation levels (FI, PD, DI, and EDI) was 0.493 kg m− , having a 2:1 ratio of biomass production per m3 of water applied. Nevertheless, a higher CWP was reported for PD and DI plants, 0.576 and 3 0.683 kg m− , respectively. This shows that quinoa was capable of producing the same or more biomass with less water inputs, therefore using water more efficiently. This was corroborated when comparing 3 PD and DI with FI, the latter having a CWP value of 0.373 kg m− . Furthermore, deeper roots (RD) were observed among EDI (11.9 cm), with significant differences (p < 0.05) when comparing to PD (6.5 cm) and DI (7.5 cm). The opposite situation was found for the root horizontal length (RHL), being wider among FI plants (19.8 cm), than DI (11.2 cm) and EDI (12.3 cm). The surface water coverage of FI plots was greater, hence roots expanded side-ways. For the 1 branching (B) and stem diameter (ST), plots receiving 25 kg N ha− had higher values, with 10 branches per plant and 0.65 cm stem-diameter. Overall, there was a positive relationship between plant height and irrigation but was not stronger enough to be considered as statistically different. The highest plants were observed under FI and PD (40.2 and 40.9 cm), whereas the smallest under EDI (34.5 cm). 1 For N-fertilization, the highest plants were also reported among plots supplied with 25 kg N ha− 1 (43.2 cm) and the smallest under 0 kg N ha− (36.7 cm).

Table 3. Cumulative growing degree days (CGDD in ◦C) for diﬀerent phenological stages and photoperiodicity (minutes per day) for diﬀerent sowing dates (25-Oct., 4-Nov., 19-Nov. and 8-Dec.).

Pearson (CGDD versus Phenological Phase 25-Oct. 4-Nov. 19-Nov. 8-Dec. Sowing Date) Emergence ( C) 123.8 123.8 119.0 101.0 0.93 ◦ − 2 Leaves ( C) 370.3 343.5 329.9 307.0 0.98 ◦ − 4 Leaves ( C) 438.8 405.5 395.9 366.3 0.97 ◦ − 8 Leaves ( C) 578.3 542.5 515.7 495.8 0.97 ◦ − Panicle formation ( C) 722.8 671.5 637.4 624.5 0.92 ◦ − Flowering ( C) 933.7 877.3 831.2 821.5 0.92 ◦ − Milky grains ( C) 1340.7 1293.8 1241.1 1263.0 0.79 ◦ − Maturity ( C) 1866.7 1919.8 1785.5 1832.3 0.55 ◦ − Harvest (DAS) 86 90 85 83 - 1 Mean photoperiod (min day− ) 692.9 692.6 692.7 696.3 - 1 Min. photoperiod (min day− ) 688.1 688.1 688.1 688.1 - 1 Max. photoperiod (min day− ) 707.3 701.9 704.5 714.3 - Agronomy 2019, 9, 607 8 of 16

Table 4. Post-hoc Tukey’ HSD pairwise comparison test for diﬀerent crop parameters and interaction among factors of study (irrigation and fertilization), mean-values for each year experimentation (2017–2018 and 2018–2019).

2017–2018 1 1 3 I F PH (cm) B (n◦) PS (cm) SD (cm) RD (cm) RHL (cm) Y (kg ha− ) AGB (kg ha− ) TGW (g) HI (%) CWP (kg m− ) 100 100 36.4 4.2 a 7.8 4.0 a 13.8 1.9 a 0.57 0.30 a 7.8 1.7 a 20.4 14.8 a,b 430 201 a 1527 439 a 1.72 0.17 a 35 4 a 0.258 0.128 b ± ± ± ± ± ± ± ± ± ± ± 100 50 39.0 5.4 a 9.4 4.5 a 16.0 3.0 a 0.58 0.11 a 4.5 2.4 a 22.9 9.4 a 735 339 a 2309 811 a 1.76 0.13 a 40 2 a 0.428 0.097 a,b ± ± ± ± ± ± ± ± ± ± ± 100 25 37.3 6.7 a 10.4 2.8 a 15.0 1.6 a 0.66 0.17 a 7.0 4.7 a 16.8 7.2 a,b 727 250 a 1743 611 a 1.92 0.20 a 41 1 a 0.368 0.128 a,b ± ± ± ± ± ± ± ± ± ± ± 80 100 32.2 11.1 a 6.2 3.7 a 12.3 2.4 a 0.50 0.12 a 4.2 1.1 a 9.5 4.4 a,b 462 175 a 1353 530 a 1.88 0.23 a 35 5 a 0.315 0.259 b ± ± ± ± ± ± ± ± ± ± ± 80 50 44.9 15.9 a 8.9 2.5 a 16.0 4.8 a 0.59 0.13 a 8.4 3.9 a 16.6 7.0 a,b 1110 620 a 2795 1675 a 1.87 0.09 a 41 5 a 0.744 0.557 a,b ± ± ± ± ± ± ± ± ± ± ± 80 25 45.7 9.9 a 11.3 4.3 a 16.1 5.5 a 0.70 0.17 a 7.2 4.0 a 19.2 5.4 a,b 1356 631 a 2859 1339 a 1.94 0.16 a 39 9 a 0.670 0.244 a,b ± ± ± ± ± ± ± ± ± ± ± 60 100 30.6 10.1 a 4.1 3.0 a 12.4 3.4 a 0.47 0.11 a 8.0 4.2 a 7.1 4.9 b 233 203 a 1370 273 a 1.89 0.28 a 31 5 a 0.259 0.093 b ± ± ± ± ± ± ± ± ± ± ± 60 50 38.9 8.8 a 5.6 3.1 a 16.2 4.2 a 0.65 0.19 a 6.5 2.8 a 11.5 2.9 a,b 588 313 a 1352 635 a 2.04 0.45 a 38 10 a 0.625 0.257 a,b ± ± ± ± ± ± ± ± ± ± ± 60 25 46.5 14.7 a 9.2 4.4 a 17.5 5.0 a 0.57 0.17 a 8.0 4.3 a 14.9 3.1 a,b 1084 972 a 2727 1821 a 2.16 0.31 a 40 9 a 1.095 0.773 a ± ± ± ± ± ± ± ± ± ± ± µ 39.0 9.7 8.1 3.6 15.0 3.5 0.59 0.16 6.8 3.2 15.4 6.6 747 411 2004 904 1.91 0.23 38 6 0.529 0.282 ± ± ± ± ± ± ± ± ± ± ± IF 2018–2019 100 100 46.5 8.3 a 8.4 1.1 a 16.8 4.6 a 0.71 0.12 a 14.5 2.2 a 23.4 4.9 a 1171 362 a 3341 970 a 1.65 0.31 a 30 9 a 0.468 0.249 a ± ± ± ± ± ± ± ± ± ± ± 100 0 39.8 7.0 a 7.1 0.7 a,b 14.2 2.9 a 0.59 0.10 a,b 9.3 1.9 b 15.4 7.8 b 805 114 a,b 2166 748 b 1.54 0.40 a 33 10 a 0.325 0.122 a ± ± ± ± ± ± ± ± ± ± ± 50 100 35.4 10.8 a 7.4 1.5 a 12.4 3.3 a 0.59 0.11 a,b 13.2 3.2 a,b 13.6 3.1 b 441 212 b 1293 551 b 1.40 0.31 a 28 16 a 0.357 0.217 a ± ± ± ± ± ± ± ± ± ± ± 50 0 33.7 8.7 a 5.4 1.5 b 12.1 3.5 a 0.46 0.14 b 10.7 4.4 a,b 11.0 4.2 b 519 221 b 1077 321 b 1.37 0.16 a 30 12 a 0.326 0.239 a ± ± ± ± ± ± ± ± ± ± ± µ 38.8 8.7 7.1 1.2 13.9 3.6 0.59 0.12 11.9 2.9 15.9 5.0 678 189 2031 603 1.49 0.30 0.30 0.12 0.37 0.21 ± ± ± ± ± ± ± ± ± ± ± Legend: (I) Irrigation: 100% PET-Full irrigation-FI; 80% PET-Progressive drought-PD; 60% PET-deficit irrigation-DI; 50% PET-extreme deficit irrigation-EDI; (F) Fertilization: 100, 50, 25, 0 1 kg N ha− . Note: PH (plant height), B (n◦ of branches per plant), PS (length of the panicle), SD (stem diameter), RD (root depth), RL (root horizontal length), Y (yield), AGB (aboveground biomass), TGW (thousand-grain weight), HI (harvest index), CWP (crop water productivity). Note: means that do not share a letter are significantly different, p 0.05 according to the test of Tukey’s HSD. Note: Average value standard deviation values are shown, with three repetitions (2017–2018, average 25-Oct and 19-Nov) and four repetitions≤ (2018–2019, average 4-Nov. and 8-Dec.). ± Agronomy 2019, 9, 607 9 of 16

Table 5. Post-hoc Tukey’s HSD pairwise comparison test for diﬀerent crop parameters and factors of study (irrigation and fertilization), mean of all sowing dates (25-Oct., 4-Nov., 19-Nov., and 8-Dec.).

PH B PS SD RD RHL Y AGB TGW HI CWP Irrigation 1 1 3 (cm) (n◦) (cm) (cm) (cm) (cm) (kg ha− ) (kg ha− ) (g) (%) (kg m− ) 100 40.2 7.6 a 8.5 3.1 a 15.2 3.3 a 0.63 0.18 a 9.0 4.4 a,b 19.8 9.7 a 727 281 a 2321 998 a 1.71 0.30 b 36 8 a 0.373 0.176 b ± ± ± ± ± ± ± ± ± ± ± 80 40.9 14.0 a 8.8 4.1 a 14.8 4.8 a 0.60 0.17 a 6.5 3.7 b 15.0 7.0 b 1012 648 a 2436 1468 a 1.90 0.17 a,b 38 7 a 0.576 0.425 a ± ± ± ± ± ± ± ± ± ± ± 60 38.6 13.2 a 6.3 4.1 b 15.4 4.8 a 0.56 0.18 a 7.5 3.9 b 11.2 4.9 b 663 724 a 1928 1421 a,b 2.03 0.37 a 37 9 a 0.683 0.592 a ± ± ± ± ± ± ± ± ± ± ± 50 34.5 9.9 a 6.4 1.8 b 12.2 3.4 a 0.53 0.14 a 11.9 4.0 a 12.3 4.0 b 480 220 b 1321 397 b 1.38 0.25 c 29 15 a 0.340 0.230 b ± ± ± ± ± ± ± ± ± ± ± PH B PS SD RD RHL Y AGB TGW HI CWP Fertilization 1 1 3 (cm) (n◦) (cm) (cm) (cm) (cm) (kg ha− ) (kg ha− ) (g) (%) (kg m− ) 100 36.8 11.0 a 6.9 3.1 b,c 13.6 3.8 a 0.58 0.18 b 10.0 4.7 a 15.3 9.6 a,b 483 302 b 2032 1116 a 1.71 0.32 b 32 11 b 0.343 0.222 b ± ± ± ± ± ± ± ± ± ± ± 50 40.9 11.3 a 7.9 3.8 b 16.1 4.1 a 0.60 0.15 a,b 6.3 3.4 b 17.0 8.5 a 806 489 a,b 2141 1310 a 1.89 0.30 a 40 7 a 0.599 0.381 a,b ± ± ± ± ± ± ± ± ± ± ± 25 43.2 11.7 a 10.3 4.0 a 16.2 4.5 a 0.65 0.18 a 7.4 4.4 a,b 17.0 5.8 a 1038 733 a 2426 1413 a 2.01 0.26 a 40 8 a 0.711 0.560 a ± ± ± ± ± ± ± ± ± ± ± 0 36.7 8.5 a 6.2 1.4 c 13.1 3.4 a 0.52 0.14 b 10.0 3.4 a 13.2 6.7 b 673 223 a,b 1789 768 a 1.45 0.32 c 32 11 b 0.326 0.190 b ± ± ± ± ± ± ± ± ± ± ± Legend: Irrigation: 100% PET-Full irrigation-FI, 80% PET-Progressive drought-PD, 60% PET-deficit irrigation-DI, 50% PET-extreme deficit irrigation-EDI; Fertilization: 100, 50, 25, 1 0 kg N ha− . Note: PH (plant height), B (n◦ of branches per plant), PS (length of the panicle), SD (stem diameter), RD (root depth), RL (root horizontal length), Y (yield), AGB (aboveground biomass), TGW (thousand-grain weight), HI (harvest index), CWP (crop water productivity). Note: Means that do not share a letter are significantly different, p 0.05 according to the test of Tukey’s HSD. Note: average value standard deviation values are shown. ≤ ± Agronomy 2019, 9, 607 10 of 16

In this study, the canopy cover played an important role on light interception and plant growth, just like on the resulting yield and biomass (Table6). For the canopy cover, statistically significant differences (p < 0.05) were observed between highly irrigated/fertilized treatments 1 1 (FI-100 kg N ha− ) and less irrigated/fertilized treatments (EDI-0 kg N ha− ), both for 25-Oct. and 19-Nov. For the yield, on the 25-Oct., significant differences (p < 0.05) were observed when comparing 1 1 1 1 FI-100 kg N ha− to that of FI-0 kg N ha− , and the previous with EDI-100 kg N ha− and EDI-0 kg N ha− . For 19-Nov, despite of the decreasing yields with lower N-inputs and water-application, no significant differences were reported between treatments and the interaction amongst them. For both sowing dates (25-Oct. and 19-Nov.), there was a moderate correlation (r 0.5) between dependent variables (canopy ≥ cover, yield, and biomass) and independent variables (irrigation and N-fertilization). Overall, when associating dependent variables (parameter of study) and independent variables (controlled parameter) the Pearson correlation coefficient was higher for irrigation than for N-fertilization. For instance, when correlating biomass and yield with irrigation, the r value was of 0.66 and 0.85 for 25-Oct., and of 0.68 and 0.42 for 19-Nov.

Table 6. Relationship between diﬀerent levels of irrigation and N-fertilization with the maximum 1 canopy cover (CC as a percentage), yield (Y), and aboveground biomass (ABG) (kg ha− ) in the second year of experimentation (25-Oct. and 19-Nov.).

25-Oct. 19-Nov. Max. CC Y ABG Max. CC Y ABG IF 1 1 1 1 (%) (kg ha− ) (kg ha− ) (%) (kg ha− ) (kg ha− ) 100 100 26.6 8.3 a 1380 251 a 3522 1231 a 44.5 8.9 a 752 62 a 3205 683 a ± ± ± ± ± ± 100 0 13.4 1.5 b 875 99 b 2005 146 b 28.6 9.8 b 711 44 a 2326 1022 a,b ± ± ± ± ± ± 50 100 7.7 6.6 b 373 185 c 1280 702 b 26.4 5.6 b 508 215 a 1311 224 b ± ± ± ± ± ± 50 0 4.4 5.3 b 322 116 c 973 336 b 16.5 6.8 b 717 80 a 1283 137 b ± ± ± ± ± ± Legend: (I) Irrigation: 100% PET-Full irrigation-FI, 80% PET-Progressive drought-PD, 60% PET-deficit irrigation-DI, 1 50% PET-extreme deficit irrigation-EDI; (F) Fertilization: 100, 50, 25, 0 kg N ha− . Note: Means that do not share a letter were significantly different, p 0.05 according to the test of Tukey’s HSD. ≤

Furthermore, remarkable information was observed when analyzing crop’s responses to FI and 1 EDI (100% and 50% PET) with different levels of N-fertilization (100 and 0 kg N ha− ) (Figure4). Despite of the greater canopy cover expansion observed in 19-Nov. (maximum CC of 45%) when compared to 25-Oct. (maximum CC of 27%), yields were greater in 25-Oct. than in 19-Nov. under FI, and vice versa for EDI (Table6). A greater canopy expansion on 19-Nov. when comparing to 25-Oct. was probably due to the milder temperatures during the vegetative phase. The maximum canopy cover was observed at 50 DAS (25-Oct.) and at 60 DAS (19-Nov.), with a respective soil percentage 1 1 covered of 27% and 45% for FI-100 kg N ha− , and of 4% and 17% for EDI-0 kg N ha− . In Table7, the effect of irrigation on canopy cover was statistically significantly different (p < 0.05) for most of the observations and sowing dates, having a larger canopy cover those plants that were highly irrigated (FI). A Similar statistical difference trend (p < 0.05) was observed between higher and lower levels of N-fertilization; being much greater than the canopy expansion amongst highly fertilized plants. In particular, these differences were depicted from observation N◦4 onwards, when N-fertilization had already been assimilated by plants. In Table8, for 25-Oct. observation N ◦5, the interacting effect of higher irrigation with nitrogen fertilization on the canopy cover was clear, showing statistical differences (p < 0.05) among most groups of means. Despite of the decreasing canopy cover with lower 1 irrigation and N-fertilization, significant differences were only observed for the FI-100 kg N ha− when 1 1 1 compared to the rest of treatments (FI-0 kg N ha− , EDI-100 kg N ha− , and EDI-0 kg N ha− ). Agronomy 2019, 9, 607 11 of 16

Figure 4. Canopy cover during the second year of experimentation, left (25-Oct.) and right (19-Nov.). 1 1 Note: FI-100 (full irrigation-FI and 100 kg N ha− ); FI-0 (full irrigation-FI and 0 kg N ha− ); EDI-100 1 1 (extreme deﬁcit irrigation-EDI and 100 kg N ha− ); EDI-0 (extreme deﬁcit irrigation-EDI and 0 kg N ha− ). Table 7. Post-hoc Tukey’s HSD pairwise comparison test for the canopy cover (%) during the second year of experimentation.

25-October

IN◦1 N◦2 N◦3 N◦4 N◦5 N◦6 100 2.7 1.3 a 3.4 3.6 a 8.7 5.7 a 20.0 9.5 a 8.2 3.2 a 2.3 1.4 a ± ± ± ± ± ± 50 1.3 0.5 b 1.2 0.5 a 2.1 1.6 b 6.1 6.6 b 0.7 0.4 b 0.5 0.5 b ± ± ± ± ± ± F 100 2.4 1.1 a 3.1 3.5 a 7.2 6.6 a 17.2 12.9 a 5.3 5.2 a 1.7 1.8 a ± ± ± ± ± ± 0 1.6 1.2 a 1.5 1.3 a 3.6 3.0 a 8.9 6.4 b 3.6 3.6 a 1.1 0.9 a ± ± ± ± ± ± 19-November

IN◦1 N◦2 N◦3 N◦4 N◦5 N◦6 100 1.2 0.6 a 14.2 8.0 a 27.3 10.7 a 36.6 13.1 a 24.9 7.7 a 9.3 9.0 a ± ± ± ± ± ± 0 0.6 0.5 b 8.6 5.4 a 17.6 9.3 a 21.5 8.0 b 16.0 4.7 b 2.0 1.3 b ± ± ± ± ± ± F 100 1.1 0.7 a 15.1 8.2 a 26.3 12.4 a 35.4 12.5 a 24.4 8.1 a 9.4 8.8 a ± ± ± ± ± ± 0 0.7 0.4 a 7.7 3.5 b 18.5 8.1 a 22.6 10.7 b 16.6 5.0 b 1.9 1.6 b ± ± ± ± ± ± Legend: (I) Irrigation: 100% PET-Full irrigation-FI and 50% PET-extreme deficit irrigation-EDI; (F) Fertilization: 100 1 and 0 kg N ha− . Note: N◦1 to N◦6 correspond to measurement dates: 24, 34, 40, 49, 70 and 85 DAS (25-Oct.); and to 24, 34, 40, 60, 75 and 84 DAS (19-Nov.). Note: means that do not share a letter were significantly different, p 0.05 according to the test of Tukey-HSD. ≤

Table 8. ANOVAtest for the canopy cover (%) and its interaction between irrigation and fertilizer application.

25-Oct.

IFN◦1 N◦2 N◦3 N◦4 N◦5 N◦6 100 100 3.3 0.8 a 4.6 4.8 a 11.8 6.6 a 26.6 9.5 a 9.8 2.7 a 3.0 1.5 a ± ± ± ± ± ± 100 0 2.2 1.5 a,b 2.1 1.7 a 5.6 2.7 b 13.4 1.7 b 6.5 2.9 b 1.6 0.8 a,b ± ± ± ± ± ± 50 100 1.6 0.5 b 1.6 0.4 a 2.7 1.7 b 7.7 7.6 b 0.7 0.3 c 0.3 0.1 b ± ± ± ± ± ± 50 0 1.0 0.3 b 0.8 0.3 a 1.5 1.4 b 4.4 6.1 b 0.7 0.5 c 0.6 0.7 b ± ± ± ± ± ± 19-Nov.

IFN◦1 N◦2 N◦3 N◦4 N◦5 N◦6 100 100 1.4 0.6 a 19.4 8.1 a 32.2 11.8 a 44.5 10.3 a 30.1 7.4 a 15.9 8.3 a ± ± ± ± ± ± 100 0 0.9 0.4 a,b 8.9 3.3 b 22.3 7.9 a,b 28.6 11.3 b 19.8 3.5 b 2.9 1.9 b ± ± ± ± ± ± 50 100 0.8 0.6 a,b 10.8 6.5 a,b 20.4 11.2 a,b 26.4 6.5 b 18.7 3.6 b 2.7 1.1 b ± ± ± ± ± ± 50 0 0.5 0.3 b 6.5 3.6 b 14.8 7.4 b 16.5 6.7 b 13.4 4.4 b 1.2 0.9 b ± ± ± ± ± ± Legend: (I) Irrigation: 100% PET-Full irrigation-FI and 50% PET-extreme deficit irrigation-EDI; (F) Fertilization: 100 1 and 0 kg N ha− . Note: N◦1 to N◦6 correspond to measurement dates: 24, 34, 40, 49, 70 and 85 DAS (25-Oct.); and to 24, 34, 40, 60, 75 and 84 DAS (19-Nov.). Note: means that do not share a letter were significantly different, p 0.05 according to the test of Tukey’s HSD. ≤ Agronomy 2019, 9, 607 12 of 16

4. Discussion

Mean-temperatures recorded during the vegetative stage (27.5 ◦C for 25-Oct. and 25.5 ◦C for 19-Nov. average of temperature 40 DAS) were close to quinoa’s optimal growth temperatures, 10–25 ◦C[29]. A closer temperature to optimal growth for the sowing on the 19-Nov resulted in greater canopy expansion (47%), when compared to 25-Oct (27%). In addition, this research was conducted in a much warmer climate (25–30 ◦C) to that of its origin (15–20 ◦C) Bolivian Altiplano [29]. This research findings differ to those of Gifford et al. [30], showing a tight relationship between light interception and production of grains and biomass. In fact, maximum temperatures occurring at anthesis were critical for quinoa pollination, resulting in the reduction of pollen production and viability [14,20]. Therefore, 1 despite the greater canopy expansion observed in 19-Nov, the yields were lower (752 kg ha− ) to 1 those reported on the 25-Oct (1380 kg ha− ). This research also confirms that quinoa can stand higher temperature thresholds at anthesis (38 ◦C) than other cereals grown in Burkina Faso; for instance, rice 35 ◦C, maize 35 ◦C or sorghum 34 ◦C[31–33]. Apart from temperature, photoperiodicity plays an important role on the rate of plant growth. In fact, c.v. Titicaca had a higher photoperiod-sensitivity in comparison to other quinoa varieties which were not affected by changes in photoperiodicity [34]. For tropical latitudes (11◦ N, Burkina Faso), the length of the growing cycle of c.v. Titicaca was much shorter, 83–90 days, to that observed in subtropical regions, 169 days and 134 days for maturity, respectively, for Iraq (32◦ N) and Germany (44◦ N); but having a similar growing cycle to that of Yemen (14◦ N), 118 days [35–37]. However, in this research, different sowing dates, with slightly different photoperiods (max ∆ photoperiod of 26 min between sowing dates), did not have an impact on the duration of the growing cycle. In respect to the growing degree days (GDDs), having 3 ◦C as a base temperature; very similar values to this research were reported by Präger et al. [37] for c.v. Titicaca in Germany for the year 2015. Based on their results, the cumulative GDD for c.v. Titicaca was 1874 ◦C; whereas for Burkina Faso, the mean cumulative GDD for all sowing dates was 1851 ◦C. Other abiotic factors, such as irrigation and N-fertilization, were of interest within this study. For irrigation, there was an increase in CWP with decreasing irrigation; except for EDI, that resulted in crop failure due to the excessive water stress. Simulated conditions for quinoa showed CWP values 3 of 0.3 to 0.6 kg m− with crop evapotranspiration varying between 200 mm to 400 mm [21]. These 3 values were in accordance with those reported in Burkina Faso with a CWP value of 0.57 kg m− for 325 mm (PD). In fact, in this research, a similar curve was observed to that of Geerts and Raes [21], with higher CWP between 250–400 mm, while having lower CWP values above or below the previous evapotranspiration-thresholds. The results of this research were in harmony with those 3 observed under field conditions in Bolivia (2005–2006), with CWP values of 0.38 to 0.45 kg m− under FI (more than 500 mm) and DI (around 400 mm), respectively [38]. Overall, even if quinoa could stand water-applications as low as 200 mm, the crop sacrificed most of its yield for survival 1 1 (480 kg ha− under EDI compared to 1012 kg ha− under PD, average of all sowing dates). So far, in the literature, only one study has examined such water stressors (183 mm during the growing 1 season) to those of this research (211 mm in 85 days growing cycle, equal to 2.48 mm day− ); but with much lower evapotranspiration to that of Burkina Faso [39]. In addition, emerging findings corroborate quinoa’s high-water use efficiency under drought-stress conditions and were in harmony with those results reported in the literature [14,19,38,40]. In fact, rapid stomata closure, sunken stomata, restricted shoot growth, accelerated leaf-senescence were among quinoa’s physiological responses to drought-stress conditions, while given the plant an optimal adaptability to dry environments. For nitrogen fertilization, little information emerged from this study. Our findings fall within those in 1 the literature acknowledging that N-fertilization plays a role in quinoa growth up to 25 kg N ha− [24]. 1 Between 0 to 25 kg N ha− significant differences were observed for different crop parameters (plant height, yield, biomass, thousand-grain weight, among others), but no differences were depicted at 1 1 higher N-applications (50 and 100 kg N ha− ); therefore N-stabilization was reached at 25 kg N ha− . On the contrary, many other studies have pointed out that quinoa yields and biomass increase with 1 higher N-fertilization, but stabilize at 80 kg N ha− [22,41]. Overall, the previous findings on abiotic Agronomy 2019, 9, 607 13 of 16 factors support Atkinson and Porter’s [42] insights on faster development among species growing in environments with higher temperatures and water stress conditions. Regarding physiological parameters, the 1000-seed weight (kernel) observed in this research (2.03 g under PD and 1.38 g for EDI) was lower to that reported in other field studies in Turkey (2.1–3.2 g) and Bolivia (3–6 g) [43,44]. Low seed weight in Burkina Faso was the result of high temperatures and longer photoperiods. For cereals, a 3.1 day shortening of the grain filling phase has been fixed per degree Celsius increase in mean daily temperatures, as well as a decrease of 2.8 mg kernel per degree Celsius increase [45]. This could explain, to some extent, the differences in observed kernel weight between Burkina Faso and elsewhere. Moreover, the observed shallow root system (average 8.7 cm) 3 was the result of high bulk density (1.61 g cm− ). In this case, soil compaction was a limiting factor for the root-system to uptake water and nitrogen from deeper zones. This was corroborated by Daddow 3 and Warrington [46], pointing out that bulk densities higher than 1.59 g cm− for sandy-loam soils was a restrictive factor for root penetration. Poor root colonization in Burkina Faso contrasts to that observed in Bolivia and Chile, where quinoa roots have exceeded 50 cm depth [47]. 1 1 Finally, observed c.v. Titicaca yields under FI-100 kg N ha− (1380 kg ha− for 25 Oct.) and 1 1 PD-25 kg N ha− (1356 kg ha− , average of 2017–2018 experiment) were higher to those observed in 1 1 1 Egypt (1034 kg ha− under 90 kg N ha− ), in line with those reported in Iraq (1270 kg ha− ), and lower 1 1 to those observed in Germany (1980 kg ha− ) and Yemen (2100 kg ha− )[35–37,41].

5. Conclusions The length of the growing cycle for c.v. Titicaca in Burkina Faso has shown a photoperiod-sensitivity when compared to other regions in the world. Growing quinoa in Burkina Faso when temperatures are closer to the optimal temperatures for growth can increase the biomass but does not necessarily determine the final yield. In fact, the yield is tightly dependent on the heat stress and water vapor pressure deficits occurring at flowering; if temperatures break a given temperature threshold—around 38 ◦C—the plants can become sterile. The yields have been stabilized under full irrigation and progressive drought, meaning that further water stressors—DI and EDI—will result in considerable yield losses. Under field conditions, emerging findings have shown that plant’s development and growth was affected, to some extent, by N-fertilization; nevertheless, in general, quinoa N-requirements 1 were low (25 kg N ha− ). Moreover, high temperatures and water vapor pressure deficits during anthesis, above-optimal temperatures for growth, compacted sandy–loam soils under field conditions, and a short growing cycle are among the abiotic factors that reduce quinoa’s yields in the Sahel. However, agricultural management strategies can be tailored for enhancing quinoa yields according to the needs and means of this region. For instance, a two-pass mechanical tillage incorporating organic fertilizer (as it has a longer mineralization than inorganic fertilizers) prior to the sowing is recommended. Frequent irrigation of the soil, but in small quantities in order to reduce high water percolation typically of sandy–loam soils is highly suggested for avoiding soil compaction and creating favorable conditions for the plant root system. Planning of agricultural activities, particularly through a well sowing calendar, is crucial for quinoa in the Sahel. Temperatures during the vegetative stage and heat stress at flowering have to be as close as possible to the mean optimal temperatures for growth. For this reason, this research proposes the following sowing quinoa dates for Burkina Faso: mid-November in the Soudanian agro-climatic zone, late-November in the Soudano-Sahelian zone, and early-December in the Sahelian zone. Finally, agronomic guidelines for growing quinoa in the country need to be prepared and come in hand with an appropriate scientific knowledge-transfer towards local communities, which will then take over and be in charge of scaling-up the crop throughout the country. Further research must be tailored according to the abiotic stresses found within this region. For this reason, heat- and drought-resistant quinoa, wind-tolerant, and short-cycle varieties should be within the scope of genetic breeders. Agronomy 2019, 9, 607 14 of 16

Author Contributions: J.A.-B. (experimental design, data-collection and writing), A.D. (supervision and revision), A.D.M. (supervision and revision), C.S. (data-collection), P.C. (revision), J.S. (supervision) and S.O. (supervision, revision and financial support). Funding: This research did not receive external funding. Acknowledgments: We would like to thank the Food and Agricultural Organization (FAO) for quinoa seed provision under the Technical Cooperation Programs (TCP/SFW/3404 and TCP/RAF/3602). To Institut de l’Environnement et de Recherches Agricoles (INERA) for facilitating an experimental field during the two-year experiment (2017–2019). Special recognitions to the staff of INERA, professors, technicians, student’s, workforce, and women for helping, accepting, appropriating, and taking over this research project for promoting quinoa in Burkina Faso and enhancing food security within the country. Conflicts of Interest: The authors declare no conflict of interest.

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