UNIVERSITY OF HELSINKI

FACULTY OF AGRICULTURE AND FORESTRY

DEPARTMENT OF AGRICULTURAL SCIENCES

Doctoral Programme in Sustainable Use of Renewable Natural Resources

GROWTH AND NUTRITIONAL QUALITY OF CASSAVA UNDER

DEFICIT IRRIGATION AND POTASSIUM FERTIGATION

DOCTORAL THESIS

Daniel Omondi Wasonga

ACADEMIC DISSERTATION

To be presented, with the permission of the Faculty of Agriculture and Forestry of the University of Helsinki, for public examination in Auditorium Ls B3,

Latokartanonkaari 7, Viikki, on 19th May 2021, at 12 o’clock noon.

HELSINKI, FINLAND 2021

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Supervisors: Prof. Dr. Pirjo Mäkelä Department of Agricultural Sciences University of Helsinki, Finland Dr. Docent Jouko Kleemola Boreal Plant Breeding Jokioinen, Finland Prof. Dr. Laura Alakukku Department of Agricultural Sciences University of Helsinki, Finland

Reviewers: Opponent:

Prof. Dr. Timothy Setter School of Integrative Plant Science Cornell University, USA Prof. Dr. Roslyn Gleadow School of Biological Sciences Monash University, Australia Prof. Dr. Antonius Schut Department of Plant Production Systems Wageningen University and Research, the Netherlands

Cover photograph: Daniel Wasonga “Cassava growing in the field”

Dissertationes Schola Doctoralis Scientiae Circumiectalis, Alimentariae, Biologicae PUBLICATION 7/2021 ISBN 978-951-51-7238-9 (Paperback) ISBN 978-951-51-7239-6 (PDF) ISSN 2342-5423 (Print) ISSN 2342-5431 (Online) Electronic publication at: http://ethesis.helsinki.fi Unigrafia Helsinki 2021

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A Time for Everything (*Ecclesiastes 3:1) There is a time for everything,

and a season for every activity under the heavens. *Ecclesiastes 3:1, Holy Bible, New International Version, 2011, Biblica Inc.

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ABSTRACT

Cassava (Manihot esculenta Crantz) is a staple food for millions of people in the tropics, providing high energy and nutritional value. Problems associated with cassava in the arid tropics are low yield, high cyanide concentration, and low mineral and vitamin A availability in leaves and roots. These factors hinder the utilization of cassava greatly and are highly influenced by the plant water status and potassium (K) availability in the growth environment. The productivity of cassava in such environments could be improved using irrigation strategies not based on full crop water requirements, such as deficit irrigation combined with K nutrition to ensure sustainable quantity and quality production. Additionally, employing systems that can allow non-destructive estimation of plant performance, such as the use of plant imaging techniques, could provide early information on plant performance, which would facilitate phenotyping, as well as assist in the mitigation of abiotic challenges in cassava production. This study investigated how irrigation water, K fertigation and their interactions affect the growth responses of biofortified cassava during the early growth phase. It was also investigated whether K fertigation could improve the nutritional and reduce the anti-nutritional qualities of biofortified cassava growing under typical conditions of water deficit. The ability to use red-green-blue (RGB) and multispectral sensors to detect the effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images, were also examined. Pot experiments were conducted at early developmental stages of cassava under controlled greenhouse conditions at the University of Helsinki. Young biofortified cassava plants were established using cuttings. The plants were subjected to three irrigation treatments (30%, 60%, 100% pot capacity) that were split into five K (0.01 mM, 1 mM, 4 mM, 16 mM, 32 mM) application rates beginning 30 days after planting (DAP) and ended at 90 DAP, when plants were harvested.

Irrigation treatments and K application rates significantly affected leaf water potential, leaf osmotic potential, net photosynthesis, stomatal conductance, leaf temperature, leaf chlorophyll, water usage, plant leaf area, plant height, tuber number and whole-plant biomass. The interaction was also statistically significant for these properties. Irrigation treatments and K application rates also affected leaf turgor, but the interaction was not statistically significant. Irrigation at 30% together with 16 mM K lowered the leaf water potential by 69%, leaf osmotic potential by 41%, net photosynthesis by 35%, stomatal conductance by 41%, water usage by 50%, leaf area by 17%, and whole-plant dry mass by 41%, compared with fully irrigated plants. Lowering the K application rate below 16 mM reduced the values further. Most importantly, the growth was decreased least when irrigation was decreased to 60% together with 16 mM K, compared with optimal applications.

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The combined effect of irrigation and K applications was statistically significant for starch, energy, minerals, total carotenoids, and cyanide, but the interaction was not statistically significant for dietary fibre and crude protein in the leaves and roots. In the leaves and roots, water deficit and lower K applications inhibited starch, carotenoids, crude protein, and fibre synthesis. Water deficit interrupted the uptake of minerals by the cassava roots from the soil and ultimately to the leaves. Improved uptake of calcium, phosphorous, magnesium, zinc, iron, sulphur, and chloride to the leaves was recorded with increased K application regardless of water deficit. K application also mitigated the effect of water deficit at 30% irrigation. Crude protein, carotenoids, and minerals were revealed to be more abundant in the leaves than in the roots, while the inverse was the case for starch content and iron. Cyanide was diminished in the leaves and roots of the cassava with increases in both irrigation and K application rates. Full irrigation (100%) with 16 mM K application produced the highest nutritional quality and the least cyanide concentration. Also, 16 mM K significantly improved the nutritional qualities and diminished the cyanide concentration regardless of irrigation treatment. High estimation accuracy (R2 = 0.90) for biomass, chlorophyll, and net photosynthesis were recorded. The estimated leaf area associated strongly (R = 0.98) with the measured leaf area. The estimated biomass also associated strongly with the measured biomass. Starch, energy, total carotenoids, and cyanide were estimated reasonably (R2 > 0.80) and showed strong correlations with the most of spectral indices. The estimation accuracy for all mineral elements was low, and weak relationships existed between the mineral elements and the spectral indices. The regression models identified normalized difference vegetation index, green area index, and simple ratio index as the best estimators of growth and key nutritional traits in cassava. Moreover, irrigation at 30% together with 0.01 mM K reduced all the index values but increased crop senescence index. Regardless of water deficit, increasing K application rate to 16 mM resulted in high values for all the spectral indices but low senescence index. In conclusion, adjusting K fertigation rates in combination with deficit irrigation may improve the growth and dietary quality of young cassava, and reduce cyanide concentration. Findings from this work can be utilized as a foundation to develop agronomic management practices involving K application for cassava growing in water-limited environments. The study also shows that RGB and multispectral sensors can be used to provide indirect measurements of growth and key nutritional traits in cassava, thus representing a tool that breeders may use to facilitate evaluation of cultivars, especially in the developing areas. Information from imaging data may also be used to facilitate corrective measures to avert stress, such as the decision to irrigate or apply fertilizers. Nonetheless, laboratory analysis of plant samples should support sensor estimates, especially under field conditions, when all possible factors affecting plant growth are difficult to forecast. Future studies could place emphasis on field conditions with multiple cassava cultivars and employ different imaging techniques.

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CONTENTS

ABSTRACT ............................................................................................................... 4 CONTRIBUTIONS ................................................................................................... 8 ABBREVIATIONS ................................................................................................... 9 1. INTRODUCTION ............................................................................................... 10

1.1 Origin and importance of cassava .................................................................. 10 1.2 Cyanogenic glycosides ................................................................................... 15 1.3 Potassium nutrition on cassava ...................................................................... 16 1.4 Water deficit on cassava ................................................................................ 19

2. OBJECTIVES OF THE RESEARCH ................................................................. 25 2.1 Research needs ............................................................................................... 25 2.2 Research aims ................................................................................................ 25

3. MATERIALS AND METHODS ......................................................................... 28 3.1 Plant material and experimental design (I-III) ............................................... 28 3.2 Sampling and measurements .......................................................................... 28

3.2.1 Morphological and physiological (I) ....................................................... 28 3.2.2 Plant image acquisition and analysis (III) ............................................... 29

3.3 Statistics ......................................................................................................... 33 4. RESULTS AND DISCUSSION .......................................................................... 34

4.1 Deficit irrigation with K fertigation improves cassava growth ...................... 34 4.2 Deficit irrigation with K fertigation improves the nutritional quality of cassava ............................................................................................................................. 36 4.3 Deficit irrigation with K fertigation reduces the cyanide concentration in cassava ................................................................................................................. 39 4.4 Estimating cassava growth from RGB and multispectral images .................. 40 4.5 Estimating cassava nutritional traits from RGB and multispectral images .... 41

5. CONCLUSIONS .................................................................................................. 43 6. ACKNOWLEDGEMENTS ................................................................................. 45 7. REFERENCES .................................................................................................... 47

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LIST OF ORIGINAL PUBLICATIONS

This thesis is based on the following journal publications, which are referred to by Roman numerals in the text. I. Wasonga DO, Kleemola J, Alakukku L, Mäkelä P. 2020. Growth response of

cassava to deficit irrigation and potassium fertigation during the early growth phase. Agronomy 10: 321-335. DOI:10.3390/agronomy10030321

II. Wasonga DO, Kleemola J, Alakukku L, Mäkelä PSA. 2020. Potassium fertigation with deficit irrigation improves the nutritive quality of cassava. Frontiers in Sustainable Food Systems 4: 575353. DOI: 10.3389/fsufs.2020.575353

III. Wasonga DO, Afrane Y, Kleemola J, Alakukku L, Mäkelä PSA. 2021. Red-green-blue and multispectral imaging as potential tools for estimating growth and nutritional performance of cassava under deficit irrigation and potassium fertigation. Remote Sensing 13: 598. DOI: 10.3390/rs13040598

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CONTRIBUTIONS

The contributions of authors to the original publications on which this thesis is based are as follows:

Publication I

Daniel Wasonga, Jouko Kleemola and Pirjo Mäkelä developed and designed the research plan for this investigation. The experimental work and measurements were performed by Daniel Wasonga with guidance from co-authors. Statistical analysis of data was performed by Daniel Wasonga with guidance of Laura Alakukku and Pirjo Mäkelä. Daniel Wasonga was responsible for the writing of the manuscript and incorporating the inputs of other authors. Publication II

All authors contributed to the plan of this study. The chemical laboratory analyses and measurements were done by Daniel Wasonga with guidance from Pirjo Mäkelä. Daniel Wasonga performed statistical analysis of data with input from co-authors. Daniel Wasonga wrote the manuscript and incorporated the inputs of Jouko Kleemola, Laura Alakukku, and Pirjo Mäkelä.

Publication III

Daniel Wasonga, Jouko Kleemola, Laura Alakukku, and Pirjo Mäkelä came up with the study framework. Daniel Wasonga and Afrane Yaw acquired the plant images and performed data analysis with guidance from co-authors. Daniel Wasonga was responsible for writing the manuscript and incorporating the inputs of other authors.

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ABBREVIATIONS

BCE Before the common era CSI Crop senescence index DAP Days after planting DM Dry matter FAO Food and Agriculture Organization of the United Nations FW Fresh weight GA Green area index GGA Greener area index GNDVI Green normalized difference vegetation index GRVI Green ratio vegetation index HCN Hydrogen cyanide NDVI Normalized difference vegetation index NIR Near infrared PCA Principal component analysis PLSR Partial least squares regression RENDVI Red-Edge normalized difference vegetation index RGB Red-Green-Blue ROS Reactive oxygen species SR Simple ratio index WHO World Health Organization

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1. INTRODUCTION

1.1 Origin and importance of cassava

Cassava (Manihot esculenta Crantz) (Figure 1) is a perennial shrub characterized by its enlarged tuberous roots. Cassava, also known as tapioca, manioc, mandioca, mihogo and yuca, belongs to the family Euphorbiaceae, subfamily Crotonoidea, tribe Manihotae, and genus Manihot (Allem 1994, Olsen and Schaal 2001). Cassava has 36 chromosomes (2n = 36) and is believed to be an amphidiploid or sequential allopolyploid because chromosomes at metaphase one and at anaphase show numerous duplicated nucleolar chromosomes (Kawano 1980, El-Sharkawy 2004). The plant is native to the American tropics, with the Amazon or north-eastern and central regions of Brazil considered to be the centre of origin (Allem 1994, Walting et al. 2018). Cassava was domesticated as early as 6500–5500 BCE (Walting et al. 2018), with the earliest known cultivators and traders being the Arawak and Carib Indian tribes of South America (Henry and Hershey 2002, Nassar 2002). The Portuguese introduced cassava to the west coast of Africa from Brazil in the 16th century, and to the east coast of Africa in the 18th century, from where the plant was taken inland and rapidly established (Carter et al. 1992). Introduction of cassava to Asia occurred in the late 18th and 19th centuries through Goa (India), Java (Indonesia), and Manila galleon (Philippines) by European explorers who had obtained the plant from South America (Onwueme 2002).

Figure 1. Cassava plants growing in the field (A), and harvested roots (B) (photographs provided by Oliver Okumu). Cassava is a monoecious species and its pistillate (male) flowers open one to two weeks before the staminate (female) flowers, enhancing outcrossing (Byrne 1984). Flowers are borne in axillary racemes near the ends of branches, and the fruit is a dry, dehiscent capsule containing three seeds. It has palmately lobed leaves, usually divided into 3–9 lobes, and the branching stems are often green, pale or dark grey, or brown. The stems are either erect or spreading types, and the plant can reach four metres in height (Byrne 1984, Alves 2002). Cassava is a C3 plant: It exhibits high photosynthetic CO2 compensation point, and fixes atmospheric CO2 exclusively by the C3 pathway (Edwards et al. 1990, Rosenthal et al. 2012). The elevated activities of C4 enzymes (phosphoenolpyruvate carboxylase (PEP carboxylase), and malic enzymes) in cassava fall within the expected range present in C3 plants (Edwards et

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al. 1990). The most common way to propagate cassava involves the use of mature stem cuttings or stakes measuring 15–30 cm in length, which can be planted horizontally, vertically, or inclined on flat or ridged soils (Keating et al. 1988). Propagation of cassava from true seeds is also possible but is mainly used in breeding programmes to eliminate production constraints, particularly diseases associated with vegetative propagation (Iglesias et al. 1994). The cassava root cannot be used to propagate the plant as it is a true root (Alves 2002). After planting, adventitious roots initiate from the cut-end of the stem, and nodes of the stem cutting within 5–7 days after planting (DAP). Auxiliary buds at the top of the stem cutting sprout and enlarge to form leaves by 10–15 DAP. Sprouting is faster at a soil temperature around 28–30 °C but ceases at temperatures higher than 37 °C or lower than 17 °C (Keating and Evenson 1979). At 30 DAP, the plants form a fibrous root system which replaces the adventitious roots, and the leaves begin to photosynthesize to contribute positively to plant growth. About 60 DAP, some of the fibrous roots (from 3–15 roots per plant) start to expand rapidly, forming storage roots for starch. At 75 DAP the storage roots represent 10–15% of total dry matter (Cock et al. 1979). At about 90 DAP, maximum growth rates of leaves are achieved, and the tuberous roots become the major dry matter sink (Howeler 2002). The storage roots continue to accumulate starch and increase in weight during the growing season until the crop is harvested, 7–24 months after planting, depending on cultivar and growth environment (El-Sharkawy and Cock 1987). Mature cassava root consists of an outer skin or periderm, which may be white, brown or pink in colour; a thin rind or cortex; and a core or parenchyma, which makes up about 85% of the total root weight and is the edible portion, rich in starch, and is often white or yellow (El-Sharkawy 2004).

Cassava is grown over a wide range of environments between 30° N and 30° S at elevations ranging from 0–2000 masl. The plant performs best on light to well drained soils with a pH range of 4.5–7.5, humid-warm climates with annual temperatures of 25–29 °C, and annual rainfall of more than 700 mm (El-Sharkawy 1993). Nevertheless, the plant is commonly cultivated in tropical areas receiving less than 600 mm rainfall per year and dry seasons may extend up to 6 months (Alves and Setter 2000). Cassava can survive during drought with the ability to produce notable yields in low-fertility soils of the arid tropics, where other crops would fail (El-Sharkawy 2004). It is tolerant of low pH, high levels of aluminium (Al) and low phosphorus (P) in the soil (Howeler 2002). Cassava is mainly grown by resource-limited small-scale farmers in infertile soils of the arid tropics, often with minimum inputs (El-Sharkawy and Cock 1987). It is cultivated in monoculture, though intercropping with other food crops such as grain legumes ((cowpea, Vigna unguiculate L.), (mung bean, Vigna radiata L.), (soybean Glycine max L.), (groundnut, Arachis hypogaea L.), (pigeonpea, Cajanus cajan L.)), and cereals ((maize, Zea mays L.), (sorghum, Sorghum bicolor L.)), is common, resulting in improved land equivalent ratios (Amanullah et al. 2007, Delaquis et al. 2018).

The world average cassava root yield is currently 11.4 t ha-1 (Figure 2). In 2018, 291 million tonnes of fresh roots were produced on a cultivated area of about 26.3 million

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hectares globally (FAOSTAT 2020). Cassava production has doubled since 2000, and the output rate has matched that of maize in Africa, and is nearly three times that of rice (Oryza sativa L.) in southern and eastern Asia (FAO 2013). The major drivers for the expanded cassava sector have been rising food demands in Africa (Okudoh et al. 2014) and the increasing industrial uses in Asia and Europe (Nguyen et al. 2007). The highest global production from 2014–2018 was realized in Africa (60%) followed by Asia (30%), and Latin America (10%) (Figure 2). Nigeria is the leading producer of cassava in the world, with an annual production of over 50 million tonnes of fresh root, followed by the Democratic Republic of the Congo with 33 million tonnes (FAOSTAT 2020). Thailand is the world’s leading exporter of dried cassava (starch and chips) with over 60% of world export in 2018 (OEC 2020). In general, Nigeria, Brazil, Thailand, Indonesia, and the Democratic Republic of the Congo produce 70% of cassava globally.

Current cassava yields are still low, particularly in the in the arid tropics where yields are below 10 t ha-1. There is potential to produce an average of 23.2 t ha-1 of cassava roots (FAO 2013), which would translate to over 600 million tonnes annually, based on the current harvested area. Finding statistics on cassava leaf is difficult, but Latif and Müller (2015) determined that cassava leaves have equal yield in terms of fresh weight as the roots. Thus, leaf yields can also surpass the current 10 t ha-1 under good management, and especially if the leaves receive the same attention as the roots. The advances in cassava yield during the past decades were almost entirely due to expanded production areas (FAO 2013). The trend has not changed. Cassava production is currently expanding into more marginal lands and drier environments for subsistence (Fermont et al. 2008). For example, since 2000, the global increase in area (66%) under cassava has continued to surpass the production (55%) increment (Fermont et al. 2008). Though considered drought tolerant, cassava yield is greatly constrained by water deficit due to low precipitation that occurs is the arid tropics (Alves 2002). Potassium (K) deficiency is also a common limiting factor when cassava is grown continuously on the same soils without K fertilization (Howeler 1991), which is a common practice in the arid tropics. Cassava suffers from K deficiency mostly in the arid tropics because the soils (oxisols, ultisols, inceptisols) have low activity clay (Oguntunde 2005). Deficit irrigation strategies combined with improved K nutrition have the capacity to increase cassava leaf and root yields without necessarily expanding production areas, but these options remain poorly explored.

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Figure 2. Global production of fresh cassava roots per unit area land during 2014–2018 (A), and the average global production share of fresh cassava roots during the period 2014–2018 (B) (FAOSTAT 2020).

The nutritional content of cassava leaves and roots is a key factor that determines the use of cassava as food and feed. Cassava is a staple food for over 800 million people worldwide and an important source of dietary energy (Burns et al. 2010). Cassava roots are composed almost exclusively of starch (85% dry matter, DM) mainly in the form of amylose and amylopectin (El-Sharkawy 2004). Moreover, cassava starch is gluten-free and suits gluten-intolerant individuals (Balagopalan 2002). The energy content of cassava roots ranges from 1100 to 1490 kcal kg-1 (Montagnac et al. 2009). The crude protein in roots is approximately 1–3% DM and is low compared with that of cereal grains. The roots have low mineral and vitamin contents. However, the mineral contents still compare favourably with those of most legumes (Adewusi and Bradbury 1993). The minerals are mostly concentrated in the root peel but are lower in root parenchyma, which is consumed (Montagnac et al. 2009).

Cassava leaves are quite nutritious. They have high crude protein, reaching up to 38% DM, with a well‑balanced amino acid profile (Ravindran 1988, Latif and Müller 2015). The dietary fibre content in leaves is high, reaching up to 20% DM and exceeds that of roots (Latif and Müller 2015). The energy of the leaves can range up to 910 kcal kg-1 (Montagnac et al. 2009). Cassava leaves are also a good source of most minerals, carotenoids, and vitamins B1, B2, and C, comparing reasonably with most leafy legumes and green vegetables (Adewusi and Bradbury 1993). The starch content in leaves varies from 7–18% DM and is lower compared with that in roots (Montagnac et al. 2009). Therefore, cassava leaves and roots can provide balanced nutrition when consumed in the diet.

The mineral and vitamin content can be enhanced in crop cultivars through biofortification processes involving conventional plant breeding, modern biotechnology, and improved agronomic practices, to improve the density of these essential micronutrients (White and Broadley 2005). Accordingly, biofortified (yellow-rooted) cassava cultivars have higher content of zinc, iron and provitamin A carotenoids than white-rooted cultivars (Chávez et al. 2005, Sayre et al. 2011) and

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have gained prominence as being able to address vitamin A deficiency and curb malnutrition that is prevalent among the populations of the tropics (Sayre et al. 2011, Buyene et al. 2018). Vitamin A deficiency mainly affects children and women, and causes impaired vision, stunting, weakens the immune system, and can lead to mortality in severe scenarios (WHO 2009). Despite the benefits of cassava, malnutrition and vitamin A deficiency are still common even in cassava growing areas (Gegios et al. 2010, Talsma et al. 2013).

Cassava offers a flexible harvesting date, allowing farmers to keep roots in the ground until needed, and the leaves are available throughout the year, thus acting as a reserve food source (Iglesias et al. 1997). The use of cassava leaves as a vegetable has gained popularity in various countries in Africa such as DR Congo, Mozambique, Cameroun, Sierra Leone, and Burundi (Achidi et al. 2005). Cassava leaves are normally boiled and mixed with other vegetables and served as sauce to accompany other starchy dishes like rice, yams (Dioscorea rotundata Poir) and maize. The roots are consumed when boiled, roasted, or eaten as chips when fried (Figure 3), while the flour can be baked to make bread, scones or fermented to make traditional food products like "fufu", and "gari" through a series of processes involving grating, dewatering, fermentation, and roasting (Latif and Müller 2015). Cassava leaves and roots are also used as animal feed and the root starch has various industrial uses, such as manufacture of coating paper and textiles, as well as alcohol, adhesives, and pharmaceutical products (Ravindra 1993, Balagopalan 2002, Tonukari et al. 2015).

Figure 3. Selected cassava products: (A) boiled roots, (B) roasted roots, (C) starch, (D) crisps (photographs provided by Hillary Amolo and Calvince Ouma).

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1.2 Cyanogenic glycosides

Cyanogenic glycosides are amino acid-derived phytotoxins that exist in over 2000 plant species (Vetter 2000). They are considered to be the principal anti-nutrient factors in cassava due to their capacity to release toxic cyanide (cyanogenesis) following their enzymatic breakdown (White et al. 1998). The biosynthetic precursors of the cyanogenic glycosides are L-amino acids, which are hydroxylated to N-hydroxylamino acids. The N-hydroxylamino acids are converted to aldoximes, and these are converted into nitriles and hydroxylated to α-hydroxynitriles, and then glycosylated to cyanogenic glycosides (Vetter 2000, Gleadow and Møller 2014). Cyanogenic glycosides present in plants are considered to offer chemical protection to plants against pest attack through the release of toxic cyanide (Møller 2010), with the bitter taste of cyanogenic glycosides functioning as a feeding deterrent (McKey et al. 2010). With the exception of seeds, all cassava parts contain cyanogenic glycosides, linamarin (95%) and lotaustralin (5%) (Conn 1994). Linamarin is localized in the plant cell vacuole, and the enzyme linamarase (β-glycosidase) is located in the cell wall (Mkpong et al. 1990). Synthesis of linamarin occurs in the leaves from where it is transported to the roots via the phloem (Jørgensen et al. 2005). Cyanogenesis in cassava is initiated when the plant tissues are damaged (e.g. by a predator). The disruption of cells releases linamarin from the vacuole, which is hydrolysed by linamarase to yield glucose and acetone cyanohydrin. The cyanohydrin is unstable above pH 5 and rapidly dissociates into a ketone and toxic hydrogen cyanide (HCN) when hydrolysed by the enzyme hydroxynitrile lyase (Cardoso et al. 2005, Bradbury and Denton 2011). The compartmentalization of linamarin in the vacuole, and linamarase and hydroxynitrile lyase in the cell wall prevent the formation of HCN in undamaged cells (White et al. 1998).

Cassava roots vary widely in their cyanogenic glycoside content and cultivars are often classified depending on the taste of the root parenchyma. "Sweet" cultivars contain less than 100 mg kg−1 FW of HCN, and "bitter" cultivars contain more than 100 mg kg−1 of HCN FW (Alves 2002). Cyanogenic glycoside content also differs depending on maturity status of plants – young plants have lower cyanogen contents than older plants (Wobeto et al. 2007). Consuming cassava leaves and roots containing more than 50 mg kg-1 of HCN within 24 hours can cause acute cyanide intoxication, with symptoms of headache, nausea, vertigo, respiratory distress, vomiting, diarrhoea, convulsions, and death (Nhassico et al. 2008). Once consumed, the cyanide ion is readily absorbed into the blood, where it binds strongly with cytochrome oxidase, thereby blocking mitochondrial cellular respiration, oxygen starvation and eventual death (Way 1984, Rosling 1994). Prolonged consumption of cassava leaves and roots containing low levels of cyanide can lead to chronic cyanide intoxication if the diet is deficient in sulphur-containing amino acids (Rosling 1994). The human body normally detoxifies the small quantities of ingested cyanide through a reaction that consumes essential sulphur-containing amino acids methionine and cysteine, to form relatively harmless thiocyanate that is excreted in the urine (Tylleskär et al. 1992, Salkowski and Penney 1994, Bradbury and Denton 2011). The protein content in cassava leaves is high but limited in sulphur-containing amino acids, and like other low protein diets, it slows the detoxification of cyanide when

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consumed (Lancaster and Brooks 1983, Ngudi et al. 2003). Chronic cyanide intoxication can exacerbate goitre and cretinism among individuals consuming iodine-deficient diets (Delange et al. 1994). It may also result in severe diseases such as tropical ataxic neuropathy, neurological disorders such as symmetrical hyperreflexia of the upper limbs, and symmetrical spastic paraparesis of the lower limbs (Osuntokun 1994, Oluwole et al. 2003), and cause konzo, which is characterised by irreversible paralysis of the legs particularly in children and young women (Howlett 1994). Otherwise, the HCN concentration in cassava when consumed should not exceed the safe limit of 10 mg kg-1 for cassava flour, and 50 mg kg-1 for fresh roots as set by FAO/WHO (Codex Alimentarius Commission 2013).

Processing techniques, such as boiling, pounding, drying and fermentation can reduce the cyanogenic glycoside content in cassava leaves and roots by more than half (Essers 1993) but are inefficient in eliminating all the cyanide (Ngudi et al. 2003). Moreover, these processing methods result in concomitant loss of proteins, vitamins, and minerals (Lancaster and Brooks 1983, Achidi et al. 2005), which significantly reduce the nutritional value of this important crop, leading to dietary deficiencies among the rural households. For instance, boiling cassava leaves for 10–30 minutes can reduce the protein content by 58%, methionine content by 71%, and vitamin C content by 60% (Lancaster and Brooks 1983, Bradbury and Denton 2011). Losses in vitamin A (β-carotene) and carbohydrate contents from leaves and roots are far greater when cassava is processed (Oliveira et al. 2010). During growth, the cyanogenic glycoside content of cassava can increase if the plant experiences water deficit (El-Sharkawy 1993), or low K nutrition (El-Sharkawy and Cadavid 2000, Imakumbili et al. 2019). K nutrition can reduce cyanogenic glycoside content in cassava (El-Sharkawy and Cadavid 2000) and alleviate water deficit effects (Cakmak 2005), to result in low cyanogen levels that are easily removed during preparation. 1.3 Potassium nutrition on cassava

Potassium deficiency is a key nutritional problem constraining cassava growth, yield, and nutritional quality in the arid tropics (Cakmak 2010), with early visible symptoms of K deficiency being chlorosis at the tip of the oldest leaves, wilting, as well as limited lignification of cell walls (Zörb et al. 2014). Cassava is efficient in extracting nutrients from low-nutrient soils, and its expanded production in tropical soils without nutrient addition continues to deplete the soil nutrients, leaving the soils poorer than before (Gruhn et al. 2000, Howeler 2002). Cassava has higher demand for K than most crops, with uptake ranges of 15.6–17.0 K kg ha-1 to produce 1000 kg of dry root yield (Howeler 2002, Byju et al. 2012). In addition, the K+ uptake by cassava is greater than that for all other minerals elements, including nitrogen (N) and P. The rapid uptake of K+ usually results in K deficient soils, which produce cassava with low yields, high cyanogenic glycoside content, and as a result the human population in such areas often shows konzo symptoms (Imakumbili et al. 2019). Ezui et al. (2016) established K to be the most limiting nutrient in order to realize high storage root yields in cassava. The yield and quality decline in cassava

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due to K deficiency can be negated by K applications because K plays a significant role in productivity of cassava.

K is responsible for regulating water potential in plants. Water potential is the main indicator of plant water status, with the important components being osmotic potential, and pressure (turgor) potential. The osmotic potential of the cell is largely determined by vacuolar K+ (together with accompanying anions: NO , Cl , malate−), which function to provide turgor, and whose concentrations vary, whereas the concentration of K+ in the cytoplasm is maintained relatively constant at around 50–150 mM (Zörb et al. 2014). Thus, most cellular K+ is generally found in the vacuole, where its concentration can reach over 500 mM, depending on K supply status of the plant (Ahmad and Maathuis 2014). Plants experiencing water deficit require a further reduction of their osmotic potential by increasing the osmolyte/solute concentration in the cells to maintain turgidity for water uptake, a process termed osmotic adjustment. Osmotic adjustment is a process by which cells can accumulate solutes and decrease the water potential without an accompanying decrease in turgor pressure (Hsiao et al. 1976, Munns 1988). Plants can continue to absorb water as long as their water potential is lower (more negative) than that of the soil water. Supplying K at low soil water potentials can increase the cellular K+ concentration, resulting in increased osmotic pressure and greater turgidity of the cells, which in turn results in water uptake through the apoplast (Ahmad and Maathuis 2014). High K+ also supports osmotic adjustment and sustains cell expansion (Sharp et al. 1990, Grzebisz et al. 2013), which can result in plant growth, and yield increments under water deficit. K fertilization increases turgor pressure and decreases water and osmotic potential in a number of crops, such as common bean (Phaseolus vulgaris L.) (Mengel and Arneke 1982), sunflower (Helianthus annuus L.) (Lindhauer 1985), cotton (Gossypium hirsutum L.) (Pervez et al. 2004), and melon (Cucumis melo L.) (Tuna et al. 2010).

Adequate K supply is required to maintain photosynthetic CO2 fixation in plants. K+ plays a key role in regulating stomatal aperture, which controls the flow of CO2 into and the flow of water vapour out of the intercellular spaces, thus affecting the level of CO2 available at the reaction site for photosynthesis (Oosterhuis et al. 2014). It is known that optimum K supply provides the osmotic pressure that can drive water flow into the vacuole of the guard cell, resulting in increased stomatal conductance, and enhanced CO2 assimilation (Peiter 2011). Ample K supply has been shown to favour photosynthesis in cassava (Imas and John 2013). The synthesis and activity of most enzymes involved in carbon assimilation, such as ribulose bisphosphate carboxylase (Rubisco), depends on K+ availability (Zörb et al. 2014) because reduced Rubisco activity has been reported in K-deficient plants (Zhao et al. 2001). Adequate K+ supply is critical for the synthesis and optimal activity of ATPase enzymes, which pump protons across the stroma into cytosol, resulting in a proton gradient necessary for conversion of light energy into chemical energy (Shingles and McCarty 1994). Reduced phosphorylation seen in K-deficient plants has been linked to reduced ATPase activity (Berkowitz and Peters 1993). Nevertheless, Cakmak and Engels (1999) attribute the reduced net photosynthesis often observed in K-deficient plants to the decrease in stomatal conductance, increased mesophyll resistance and lowered

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Rubisco activity. A number of enzymes involved in glycolysis metabolic pathway are regulated by K+, the most important being pyruvate kinase. Glycolysis occurs in the cytosol during plant cellular respiration. In the process, glucose produced from photosynthesis is converted into pyruvate with the release of ATP (adenosine triphosphate) and NADH (nicotinamide adenine dinucleotide) molecules, which the plant uses for growth and reproduction. Pyruvate kinase regulates the conversion of phosphoenolpyruvate to pyruvate (Oosterhuis et al. 2014) and is highly affected by K availability because low K+ inhibits the activity of pyruvate kinase, resulting in plant metabolic disorders (Armengaud et al. 2009). However, abundant K supply can regulate the balance between respiration and photosynthesis in cassava in a way that improves net photosynthesis (Imas and John 2013).

Activation and synthesis of a range of enzymes of primary metabolism require K+ (Bhandal and Malik 1988). The stimulative effect of K+ on the activity of starch synthase is essential for carbohydrate synthesis in plants (Hawker et al. 1974). Previous authors working on cassava (Howeler 2002, Imas and John 2013, Sogbedji et al. 2015) established that the starch content and tuber root quality improve with K fertilization. The other key role of K+ is translocation of carbohydrate from above-ground parts to roots (Oosterhuis et al. 2014). Generally, K+ improves assimilate translocation by increasing the osmotic pressure in the sieve tubes, leading to increased phloem loading, and thus improved partitioning of photosynthates into sink organs (Cakmak et al. 1994, Marschner et al. 1996). Initiation of tuberous roots in cassava during the early stages of growth depends on the carbohydrate supply from the shoots, while root bulking is driven by starch accumulation (Hunt et al. 1977). Lower sucrose and starch content have been recorded in roots of K-deficient plants, with leaves accumulating more sucrose than K-replete counterparts (Cakmak et al. 1994, Hermans et al. 2006). This is attributed to reduced sucrose and starch export to the roots, hence poor energy supply of roots, indicating the requirement of K in phloem loading (Zhao et al. 2001, Deeken et al. 2002). The role of K+ in carbohydrate translocation has also been demonstrated in cassava, where high root starch and yield were recorded in plants fertilized with K compared with the unfertilized plants (Susan John et al. 2010, Imas and John 2013).

K+ also plays an integral role in protein synthesis by transporting the key ingredient in protein, N, to the sites of protein synthesis, and regulating the activity and synthesis of nitrate reductase (Ali et al. 1991). The uptake and transport of NO , within the plant is more efficient with K+, which serves as the accompanying counter cation (Hafsi et al. 2014). Thus, K+ deficiency inhibits nitrogen assimilation. Low nitrate reductase activity and low protein content have been reported for K-deficient plants such as maize (Qu et al. 2011) and cotton (Wang et al. 2012). In cassava, K supply increases the uptake of other minerals such as N, Ca, and Mg (Howeler 2014). K fertilization can also result in reduced HCN concentration in cassava (El-Sharkawy and Cadavid 2000, Susan John et al. 2010).

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1.4 Water deficit on cassava

Water deficit (drought) is the most important abiotic stress that limits the growth and productivity of crops worldwide, particularly in the arid tropics (Boyer 1982, Wang et al. 2003). Low soil moisture due to low rainfall, and high temperatures, are the common factors that induce water deficit in the arid tropics. The water deficit experienced by plants growing in saline conditions and cold spells, even when sufficient water is available in the soil, is termed physiological drought (Arbona et al. 2013). Water deficit disturbs plant water relations and affects the relative water content, water potential, osmotic potential, and turgor, which are the sound indicators of plant water status (Farooq et al. 2009). Reductions in water potential, osmotic potential, relative water content and turgor are characteristic signs of plants experiencing water deficit (Anjum et al. 2011). Turgor reduction leads to reduced cell expansion and the inhibition of cell growth because water deficit impairs mitosis and cell elongation (Passioura and Fry 1992, Hussain et al. 2008). Poor water flow from the xylem to the nearby cells due to water deficit also impairs cell elongation (Nonami 1998). Plant growth and establishment become constrained under water deficit due to loss of turgor, and the inhibition of cell division and enlargement (Hsiao et al. 1976). Cassava cultivars subjected to water deficit have low cell volume (Alves and Setter 2004), low leaf water potential and osmotic potential (Ike and Thurtell 1981), and low relative water content (Zhu et al. 2020).

Water deficit limits net photosynthesis through stomatal and non-stomatal restrictions (Anjum et al. 2011). The closure of stomata in response to water deficit results in reduced stomatal conductance as a consequence of restricted CO diffusion into the leaf. The internal CO concentration is ultimately lowered, resulting in reduced CO fixation (Cornic and Massacci 1996). Limited availability of CO2 due to water deficit favours excessive production of reactive oxygen species (ROS), which cause oxidative damage and ultimately, cell death (Sharma et al. 2012). Closure of stomata increases leaf temperature, which accumulates to high levels and decreases the maximal quantum efficiency (Fv/Fm) of photosystem II (PSII) (Yanhui et al. 2020). Leaf mesophyll cells are dehydrated under water deficit, leading to impaired mesophyll conductance and eventual inhibition of photosynthesis (Urban et al. 2017). Lowered water potential due to water deficit reduces the activities of photosynthetic enzymes such as Rubisco, PEP carboxylase, nicotinamide adenine dinucleotide phosphate, fructose-1, 6-bisphosphatase, and pyruvate orthophosphate dikinase (Bota et al. 2004), which further limits the rate of photosynthesis. Chlorophyll content is reduced under water deficit, resulting in lowered capacity for light harvesting for photosynthesis (Anjum et al. 2011). Zhu et al. (2020) reported reductions in chlorophyll content in four cassava cultivars subjected to water deficit. Limited stomatal conductance and reduced photosynthesis rates have been reported in cassava subjected to water deficit (Calatayud et al. 2000, El-Sharkawy 2012, Ngugi et al. 2013). Limitation of photosynthesis due to water deficit limits the amount of photosynthates exported from the leaves. Translocation is indirectly affected as phloem transport relies on turgor, and decreased water potential in the phloem inhibits the movement of assimilates (Urban et al. 2017).

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Leaf expansion, and total leaf numbers are reduced under water deficit conditions due to reduced turgor, and low assimilate supply (Rucker et al. 1995). Reduced rate of leaf formation (Connor et al 1981, Okogbenin et al. 2013), decreased leaf expansion (Alves and Setter 2004) and fewer leaf numbers (Alves and Setter 2000, El-Sharkawy 2004) have been observed in cassava experiencing water shortage. Cassava growth and yield are substantially reduced under water deficit (Vandegeer et al. 2012, Okogbenin et al. 2013) despite cassava being regarded as drought tolerant. Cassava shoot and root biomass yield can diminish by 70% (Alves 2002, El-Sharkawy 2004), and the yield reductions can be severe if water deficit occurs 1–5 months after planting (Pardales and Esquibel 1996), a period corresponding to the stages of leaf area expansion, root initiation, and bulking. Yield reductions in water-limited environments are mostly attributed to decreased turgor, reduced leaf areas, reduced light interception, low photosynthesis rates, and disturbed assimilate partitioning (Flexas et al. 2004, Farooq et al. 2009). However, water deficit affects various plant parts differently. In cassava, reductions in plant height (33–47%), shoot yield (37–55%), fresh root yield (38–87%), root dry matter content (26%), and harvest index (31%) have been reported by various authors (Aina et al. 2007, Okogbenin et al. 2013, Adjebeng-Danquah et al. 2016, Oliveira et al. 2017).

Water deficit often alters the nutritional quality of crops (Farooq et al. 2009). Water deficit lowers starch biosynthesis by inhibiting the activity of starch synthase enzymes (Lu et al. 2019). Water deficit reduces the starch granule size and distribution in cassava (Santisopasri et al. 2001) and reduces overall starch content and quality (Oliveira et al. 2015). In maize, water deficit decreased the starch granule size, crystallinity, and peak intensities (Lu et al. 2015). The protein content also decreases under water deficit due to suppressed synthesis (Anjum et al. 2011). This happens because the production of ROS leads to oxidative stress that causes protein oxidation and lowers enzyme activity under water deficit (Berlett and Stadtman 1997). Water deficit also reduces carotenoid content in several plants, including African eggplant (Solanum macrocarpon L.) (Mibei et al. 2016), and sunflower (Kiani et al. 2008). Water deficit reduces plant nutrient uptake by the roots due to decreased rate of mineral diffusion from the soil matrix to the absorbing root surface (Hu et al. 2007). The uptake of key elements such as N, P, K, sulphur (S), calcium (Ca), magnesium (Mg), iron (Fe), and zinc (Zn) are constrained, resulting in their low content in plants, and consequently low bioavailability when plants are consumed. Cassava grown under water deficit possesses lower mineral contents than when well-watered (Helal et al. 2013). Moreover, cassava growing under water deficit conditions contains higher cyanogenic glycoside content than well-watered counterparts (Bokanga et al. 1994).

Plants experiencing water deficit exhibit an enhanced requirement for K compared with those growing under well-watered conditions (Cakmak and Engels 1999, Römheld and Kirkby 2010). This is associated to the requirement for K for photosynthetic CO assimilation (Cakmak 2005, Römheld and Kirkby 2010). The light reaction of photosynthesis is dependent on K+ to provide charge balance, and the inhibition of photosynthesis by both water deficit and K deficiency results in a misallocation of electrons to O2, thereby producing ROS that cause oxidative stress

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(Cakmak 2005). Similarly, prolonged K+ starvation often leads to enhanced production of ROS, which cause photooxidative damage to the chloroplasts (Hernandez et al. 2012). Rapid accumulation of ROS in the roots at 24 h following K+ starvation is often detected by the epidermal cells in the root tip, which sense cellular K+ status of the plant, and initiate signal cascades that lead to the activation of K+ uptake to avert the oxidative damage (Hernandez et al. 2012, Demidchik 2014). Hafsi et al. (2014) presented more details on how plants respond to K+ deficiency. Higher water retention plays a key role in securing the soil productivity in water-limited areas. Ample K+ availability in soil solution increases the soil micro-shear resistance, resulting in enhanced soil water-holding capacity (Holthusen et al. 2010). The translocation of more photoassimilates to the roots under water-limited conditions by the action of ample K+ also proves beneficial in improving root growth, such as deep root systems that can extract water from deeper soil layers and help the plants avoid water deficit stress (Egilla et al. 2001). In contrast, plants growing in water-limited environments with low K+ supply show impeded root growth, and the smaller root system leads to a further reduction in K+ uptake (Hu and Schmidhalter 2005). The poorer K+ supply to cassava growing in water-limited environments renders plants less drought-tolerant, which impairs growth further, again reducing K uptake. This vicious cycle could be overcome in cassava growing in water-limited and low K conditions by optimizing irrigation and K nutrition to achieve the best possible yields and quality.

Irrigation can increase cassava productivity markedly under water-limited conditions, while fertilization can do the same in low-nutrient soils (Howeler 2002). In conditions of both water deficit and low K nutrition, the productivity of cassava may be improved using fertigation – a combination of fertilization and irrigation. It has been shown that fertigation can shorten cassava growth period and increase growth and root yield (Omondi et al. 2018). Fertigation of cassava can also result in high leaf nutrient content, greater root yield, and higher nutrient use efficiency in comparison with soil fertilizer application, when the same amount of fertilizer is used (Xie et al. 2020). Moreover, fertigation can be implemented by automatic controls, reducing the labour time and nutrient quantity in contrast to conventional fertilization. Potassium chloride (KCl) based fertilizers can be used in such fertigation systems rather than potassium sulphate (K SO ) and potassium nitrate (KNO ) fertilizers, which are more expensive compared with KCl, and are mostly suitable for speciality crops sensitive to chloride, unlike cassava.

1.5 Plant imaging

The present methods for assessing plant phenotypic traits in most breeding programmes are largely destructive, laborious, and do not allow repeated measurements on the same plant (Walter et al. 2015). For instance, the conventional method for plant biomass determination involves harvesting plants, transporting to the laboratory, oven‐drying and finally weighing the samples to obtain biomass. In this context, non-destructive optical sensors that allow evaluation of cultivars in controlled and field environments are proposed as effective means for estimating plant performance, and for monitoring the growth and health status of the plants.

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Early pre-visual detection of abiotic/biotic stresses in plants, especially at early developmental phases of the plant, may accelerate cultivar selection in breeding programmes. In addition, early identification of the stresses would enable timely interventions, through prompt crop management decisions to mitigate the problems before critical stress periods are reached, thereby minimizing productivity losses. Plant imaging entails measuring a phenotype quantitatively through the interaction between light and plants, such as measuring absorbed photons, reflected photons, or transmitted photons (Li et al. 2014). The spectral absorbance, reflectance, and transmittance properties of plant leaves are determined by the biophysical and biochemical characteristics of leaves, which are detected at the ultraviolet (100–400 nm), visible (400–700 nm), and near infrared (NIR, 700–1200 nm) wavelengths (Gates et al. 1965). The major pigments in the leaves are chlorophyll, carotenoids, and xanthophylls, and these pigments have unique absorption spectra. For example, chlorophyll absorbs photons primarily in the blue and red regions of visible light, whereas carotenoids and xanthophylls have their absorption features in the blue-green and violet regions of visible light (Kim et al. 2011, Li et al. 2014). These pigments play a central role in photosynthesis. Chlorophylls a and b function as light-harvesting pigments, and in addition, chlorophyll a converts light energy to chemical energy. Carotenoids also capture light energy for photosynthesis, but they play a crucial role in absorbing excess energy and dissipate it as heat. Therefore, changes in the content of these plant pigments affect their spectral properties, which in turn disturb assimilation rate, and ultimately affect the performance and yield of the crop. Lack of chlorophyll pigmentation has been shown to reduce the absorption of visible light by a leaf (Gates et al. 1965). Moreover, water deficit reduces the chlorophyll and carotenoid contents in plants and causes biochemical changes in the plant (Farooq et al. 2009). Plants experiencing stress such as water deficit absorb more light in the NIR spectrum and reflect less, whereas in the visible spectrum the stressed plants reflect more light compared with healthy plants (Knipling 1970). Thus, changes in the spectral properties of the leaves may be used as a method to detect early pre-visual effects of abiotic stresses on plants, such as water deficit and/or K deficiency.

The most direct approach for obtaining the spectral properties (absorbance, reflectance, and transmittance) of plants entails the use of spectroradiometers. A spectroradiometer is a combination of a radiometer and a spectrometer to form an instrument capable of measuring spectral reflectance at a higher spatial resolution from a single sampling unit (Milton et al. 2009, Mac Arthur et al. 2012). Laboratory and field spectroradiometers that are currently available can perform measurements in wavelength ranges from 300–1100 nm and some from 400–2500 nm (Mac Arthur et al. 2012). Examples of popular spectroradiometers include: Leaf Spectrometer (SpectraVue CI-710s; CID Bio-Science, Camas, WA, USA), which can quantify plant chemical concentrations and photochemical reactions such as photosynthesis; GreenSeeker handheld crop sensor (Trimble, Sunnyvale, CA, USA), which can measure the nitrogen status, health, and vigour of a crop based on the red and infrared reflectances.

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Although the latest equipment has relatively low prices, they are still costly for average institutions (Reynolds et al. 2019), especially those in the tropics where cassava is grown. Alternatively, the plant spectral properties can be obtained remotely using imaging techniques such as such as red, green, and blue (RGB) imaging, multispectral imaging, hyperspectral imaging, fluorescence imaging, and thermal infrared imaging. The spectral data from the captured plant images are then combined to compute spectral indices. Spectral indices are formulations based on the reflectance in the red and NIR regions (Bannari et al. 1995). Computing the indices amplifies the spectral differences, for example between red and NIR regions and provides additional details for stress detection (Kim et al. 2011). Spectral indices are generally used to estimate parameters related to canopy greenness, which are normally connected to the morphology, physiology, as well as nutrient content of the plant. The terms spectral index, vegetation index, spectral reflectance index, and reflectance index, are often used interchangeably in most publications. The term spectral index/indices is used in this study.

Several authors have assessed the applicability of spectral indices (for example Aparicio et al. 2002, Royo et al. 2003, Prasad et al. 2007). Common indices include the normalized difference vegetation index [NDVI = (NIR – Red)/(NIR + Red)], simple ratio index [SR = (NIR/Red)], green normalized difference vegetation index [(GNDVI = (NIR – Green) /(NIR + Green)], green-ratio vegetation index [GRVI = (NIR/Green)], and red-edge normalized difference vegetation index [RENDVI = (NIR – RedEdge)/(NIR + RedEdge)] (Bannari et al. 1995, Peñuelas and Filella 1998). NDVI is sensitive to biomass, plant leaf area, chlorophyll, and nitrogen (N) content, while SR can indicate the canopy structure, light absorption, and photosynthetic capacity. GNDVI can estimate leaf chlorophyll, net photosynthesis, and plant-water uptake. GRVI can indirectly measure photosynthetic rates based on the changes in leaf pigments whereas RENDVI is related to leaf chlorophyll and plant health (Bannari et al. 1995, Li et al. 2014). The chlorophyll and carotenoid contents are also useful indicators of plant stress as well as the plant’s ability to endure the stresses (Strzalka et al. 2003). They can be estimated indirectly using the spectral indices (Peñuelas and Filella 1998) because chlorophyll and carotenoids both absorb light in the blue, but only chlorophyll absorbs in the red.

Most phenotyping systems are currently equipped with RGB, multispectral, thermal, fluorescent, and hypespectral sensors. RGB sensors can measure aspects of plant architecture such as growth rates, leaf area, and biomass (Li et al. 2014). Multispectral sensors can discriminate plant performance and can be used to monitor different stresses (Prey et al. 2018). Thermal infrared sensors can assess changes in plant temperature under several stresses, such as water deficit (Vello et al. 2015). Chlorophyll fluorescence imaging can measure photosynthesis (photosystem II) activity of a plant at cellular, leaf and whole plant levels and is a commonly used technique for disease detection (Murchie and Lawson 2013). Hyperspectral sensors are used to assess different plant components such as water and pigment content. High-throughput phenotyping (HTPP) systems are currently available that allow automated measurements of several cultivars and can support in-depth analysis of various plant traits (Fiorani and Schurr 2013). However, HTPP systems are costly to

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establish in the field with all sensors, especially in tropical areas where the population is resource limited. In addition, most indoor phenotyping systems can only support small rosette plants (Li et al. 2014) and not large plants like cassava. Therefore, imaging methods that are cost-efficient and easy-to-use and allow robust evaluation of cultivars in both field and greenhouse conditions would be preferred.

RGB (conventional digital) and multispectral cameras can be convenient methods, as they are portable, affordable, and easy-to-use (Casadesús and Villegas 2014). RGB cameras measure wavelengths from three (red, green, blue) spectral bands, whereas multispectral cameras measure wavelengths mostly from five (blue, green, red, red-edge, and NIR) spectral bands. Pixels derived from a combination of the three colour channels in RGB images are used to calculate indices such as green area index (GA), and greener area index (GGA) which indicate the green biomass (Casadesús et al. 2007). The multispectral bands can be used to compute various indices. RGB images have been used to quantify plant leaf area, vigour, growth performance, and biomass yield (Campillo et al. 2008, Armoniené et al. 2018, Fernandez-Gallego et al. 2019), as well as leaf senescence (Ide and Oguma 2010). Multispectral images have been used to monitor seed viability (Olesen et al. 2015), plant vigour (Prey et al. 2018), and plant growth status and fertilization strategy (Cardim et al. 2020).

The simplest approach for monitoring growth and forecasting crop yields involves the use of regression models, where spectral indices form the input variables (Moran et al. 1997). Multivariate modelling techniques such as partial least-square regression (PLSR) can also be developed from spectral data to estimate growth parameters and plant-nutrient contents effectively (Zhai et al. 2013, Boshkovski et al. 2020). Spectral data have been used in models to predict yields in various crops, including wheat (Das et al. 1993) and maize (Ge at al. 2016). Initial imaging in a controlled environment would be important because the fields where cassava are grown are usually heterogeneous – the crop is mostly grown as an intercrop, and this can affect the spectral indices due to mixed pixels or pixel contamination from other crops. Thus, testing the applicability of the new imaging methods in controlled environments may provide first-hand information on usability of the methods to estimate growth and nutritional contents. It may also represent a baseline for acquisition of vital parameters under field scenarios.

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2. OBJECTIVES OF THE RESEARCH 2.1 Research needs

The possibility of increasing cassava production per unit land area by combining deficit irrigation and K fertigation strategies has been underexploited, despite past research showing that irrigation (Odubanjo et al. 2011) and K applications (Byju et al. 2012, Ezui et al. 2016) can separately improve cassava yields in the arid tropics. The existing K nutrition studies on cassava have mostly been performed with granular applications (Imas and John, 2013, Ezui et al. 2016) with little emphasis on fertigation. This has happened in spite of evidence showing that the fertilizer application chosen for crop production usually influences the yield more than the irrigation (Gaskell and Hartz 2011).

Furthermore, the ability to improve the nutritional quality and reduce the anti-nutritional quality of cassava during growth remains underexplored, particularly for cassava plants growing under water deficit and low K nutrition. Past research in cassava shows that water deficit exacerbates cyanogenic glucoside content (El-Sharkawy 1993) and limits plant mineral content (Helal et al. 2013), whereas K nutrition can reduce the cyanogenic glucoside content (El-Sharkawy and Cadavid 2000) and improve the plant mineral content (Howeler 2014). Despite these findings, effective strategies aimed at managing water deficit and low K nutrition have not been simultaneously combined to result in improved nutritional quality and reduced cyanogenic glycoside contents that represent greater safety for human consumption of cassava. Thus, malnutrition and vitamin A deficiency are still widespread in the tropics (Bouis and Saltzman 2017), even in cassava growing areas where biofortified cultivars are grown (Gegios et al. 2010, Talsma et al. 2013).

Lately, cassava performance and final root yield under water deficit have been estimated using regression models developed from actual physiological and agronomic data (Santos et al. 2019, Vitor et al. 2019). Imaging data obtained with multispectral sensors have also been used recently to construct spectral indices from which regression models were developed to predict canopy traits and root yield of cassava (Selvaraj et al. 2020). However, it has not been determined whether RGB and multispectral images can show the effect of water deficit and low K nutrition in cassava, and whether the growth and nutritional quality responses due to low moisture and low K can be inferred from the images. 2.2 Research aims

This study was conducted to assess how the interaction effect between water deficit and K nutrition affects the growth of biofortified cassava during the early developmental phase. Understanding how young cassava responds to the interrelated challenges of water deficit and low K nutrition is crucial for the enhancement of agronomic management to ensure improvements in growth and harvestable yield in water-limited environments. The nutritive quality of cassava was also evaluated to determine if irrigation strategies based on deficit irrigation and K nutrition improve

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the nutritive and reduce anti-nutritive quality of the biofortified cassava at an early growth phase. Furthermore, the potential use of RGB and multispectral sensors was examined to see whether these imaging sensors could be utilized as a non-destructive method to detect the plant growth and nutritional quality responses caused by water deficit and low K in young cassava plants. This phenotyping method based on image analysis can be a useful alternative technique for evaluating young cassava, given the robust and efficient nature of these imaging techniques in assessing large sets of cultivars and breeding populations under greenhouse and field conditions. The ability to detect water and K deficiency symptoms from the images may guide the development of appropriate phone applications which could be utilized by smallholder farmers for the pre-visual recognition of stress at the early developmental phase of the crop. Information extracted from imaging data may also provide timely crop management decisions to avert the abiotic stresses. Findings from this research, together with relevant literature, are discussed with reference to the view that the growth and nutritional quality of cassava can be improved with irrigation strategies not based on full crop water requirements when combined with K application (Figure 4). It was also envisaged that the growth and nutritional changes of the plants caused by deficit irrigation and K fertigation could be detected from the captured plant images under controlled conditions.

The main working hypotheses were: 1. The interactive effect between deficit irrigation and K fertigation affects the

growth of young cassava plants. 2. Potassium fertigation can improve the nutritional and reduce the anti-nutritional

quality of young cassava plants growing under deficit irrigation. 3. RGB and multispectral sensors can provide an accurate estimation of the growth

and nutritional performance of young cassava plants experiencing water deficit and low K nutrition.

4. The image-derived parameters do not differ from the actual measured parameters.

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Figure 4. Schematic presentation of the connections between the three publications on the growth, nutritional quality, and imaging of cassava. NDVI = normalized difference vegetation index, SR = simple ratio, GA = green area, GGA = greener area, GRVI = green ratio vegetation index, GNDVI = green normalized difference vegetation index, RENDVI = red-edge normalized difference vegetation index, CSI = crop senescence index.

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3. MATERIALS AND METHODS

A general outline of the experiments and analyses conducted is described here. More details are presented in the original publications (I–III). 3.1 Plant material and experimental design (I-III)

Pot experiments were conducted under controlled greenhouse conditions at the University of Helsinki, Finland, from 2017 to 2019. The plant material used in all experiments was cassava cultivar `Mutura´, sourced from Kenya Agricultural and Livestock Research Organization (KALRO), Nairobi, Kenya. The cultivar was introduced to KALRO in 2004 as a provitamin A biofortified clone from the International Institute of Tropical Agriculture (IITA). Farmers have widely adopted the cultivar as the best performer with reference to several attributes, including sweet taste, yellow flesh, high yield, early maturity (10–14 months after planting) and being versatile in different soil types.

There were four pot experiments, which were laid out in a randomised complete block design with four (EXP. I, in total 36 pots) to eight (EXP. II, III, IV, in total 72 pots each) replicates. Single-stem cuttings (25 cm) of cassava cv. `Mutura´ were planted in 5L pots containing 1.7 kg of pre-fertilized potting mix (pH 5.5, N–P–K: 17–4–25, organic matter: 25–40%, Kekkilä Karkea ruukutusseos, W R8014; Kekkilä Oy, Vantaa, Finland). The potting mix was saturated with water and allowed to drain overnight, and the maximum soil water holding capacity (1600 g pot−1) was determined as the difference between water applied and water drained. Throughout the experiments, the photoperiod was set at 12 h light, with photosynthetic photon flux density (PPFD) of 600 μmol photosynthetically active radiation (PAR) m−2 s−1 at the top of the canopy. The temperature was maintained at 28 °C during the day and 20 °C during the night and 55% ± 5% relative humidity. The plants were irrigated for 30 days on every second day on the soil surface until drainage, and the side shoots were clipped to maintain single-stemmed plants. Irrigation and K treatments were initiated 30 days after planting (DAP) and lasted 60 days. There were three irrigation treatments (30%, 60%, 100% pot capacity) in all four experiments that were split into five K (potassium chloride, KCl); Sigma-Aldrich Chemie GmbH, Munich, Germany) application rates of 0.01 mM, 1 mM, 4 mM, 16 mM, and 32 mM of irrigation water. Plants were watered every second day with full-strength Hoagland solution (Hoagland and Arnon 1950) in which K concentration was adjusted to reflect the treatments. 3.2 Sampling and measurements

3.2.1 Morphological and physiological (I)

Morpho-physiological measurements were conducted at 30, 45, 60, 75 and 90 DAP, from 11:00 to 13:00 (I). The uppermost fully expanded leaves were used for physiological measurements that included leaf temperature, leaf chlorophyll content, net photosynthesis, stomatal conductance, leaf water potential and osmolality (Table

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1). The osmotic potential (Ψs) was derived from the osmolality values as π (osmotic pressure, MPa) = cRT, where RT (R is the gas constant (J mol–1 K–1), T is absolute temperature (K)) is 2.48 and c the osmolality (osmol kg–1). Leaf turgor (Ψp) was calculated as the difference between Ψw and Ψs. The contribution of each K solute to the osmotic potential was computed as COP/ Ψs × 100%, in which the calculated osmotic potential (COP) = moles of solute × RK, where R = 0.008314 and K = 298 °K. Growth measurements consisted of plant height, whole plant leaf area, tuber number, and whole plant dry mass (I).

3.2.2 Nutritional (II)

Plants were harvested at 90 DAP by detaching the leaves from the plants, and the roots were washed carefully with water to remove soil. The FW of leaves (leaf blade and petioles), stems, and roots (fine and thickened-starchy roots) were recorded. Leaves were divided into green and senescent. The green leaves and the roots were split into two subsamples. One subsample of each was snap-frozen in liquid N2 and stored at −20 °C and was used to analyse total carotenoids and total cyanide concentration (II). The other subsample was dried in an oven at 70 °C for 72 h, weighed, and then ground into fine powder using a centrifugal mill (ZM200, Retsch, Haan, Germany) and stored at room temperature. The oven-dried leaf and root samples were used for starch, energy, crude protein, total dietary fibre, and elemental (Ca, Mg, K, P, S, Na, Cu, Mn, Fe, and Zn) analyses (II) (Table 1). The energy content contributions from starch, crude proteins, and total dietary fibre were obtained by multiplying the amount of starch and crude protein (in grams) by 17 kJ, and the amount of dietary fibre (in grams) by 8 kJ. 3.2.2 Plant image acquisition and analysis (III)

Plants were photographed at 30, 45, 60, 75 and 90 DAP around solar noon (± 2 h) on a white background to increase the accuracy of separating background features from plant pixels (III). A 24.2-megapixel Canon digital camera (EOS 760D; Canon Inc., Tokyo, Japan), with no flash and the aperture set at automatic, was used to take the RGB images. Three images were taken of every plant: one top view, and two side view images at 180° horizontal rotation. For every timepoint, the plants were photographed by placing a 1 m ruler beside the plants to adjust for any changes in the camera distance. The images were saved in JPEG format with a resolution size of 4608 × 3072 pixels for processing. RGB images were analysed using ImageJ software (Rueden et al. 2017). The plant images were first converted to the Hue, Saturation, and Intensity (HIS) colour space to increase the contrast between the plant region and background region. Thereafter, a threshold was applied in the hue colour space to separate plant pixels from the background. The plant pixels were selected and the background region was removed, resulting in a binary image. To extract spectral indices, the binary images were analysed using the Breedpix software integrated as a plugin within ImageJ (https://github.com/George-haddad/CIMMYT) as described by Casadesús et al. (2007) and Kefauver et al. (2017). The extracted spectral indices consisted of relative green area (GA) and greener area (GGA). Crop senescence index (CSI) was calculated as:

30

( − ) ) × 100⁄ (Eq.1)

RGB images from the side view were further analysed with the Easy Leaf Area software (https://github.com/heaslon/Easy-Leaf-Area) to derive total plant leaf area as described by Easlon and Bloom (2014). A red area with known dimensions was marked on a sheet, photographed, and used as the reference calibration area for the plant images. A threshold was performed on the imported images to ensure colour uniformity, and subsequently the images were batch processed. The total leaf area excluding overlapping and nonvisible leaf parts was calculated as:

Total leaf area = × Calibration area (Eq.2)

A Micasense camera (RedEdge, MicaSense Inc., Seattle, WA, USA) with 1.2-megapixel resolution was used to acquire multispectral images. The camera was mounted in a zenithal position at 1.6 m above the plants to obtain top view images. A fixed pot position was marked on the table to ensure that the plants were photographed in the same position. The images were stored in a computer in TIF format for processing at a resolution of 1280 × 960 pixels. Multispectral images were opened as a single stacked image in ImageJ software. The scale-invariant feature transform (SIFT) algorithm plugin in ImageJ was used to perform a linear stack alignment to correct the overlap in the images. The pot labels were used as known reference points in the corresponding images. The aligned images were then unstacked to obtain individual images, and a colour threshold was used to mark out the area covered by the plant pixels. The image background was then removed by selecting the region of interest (ROI). The image calculator tool in ImageJ was used to compute the NDVI, GRVI, GNDVI, SR, and RENDVI indices. Detailed methods and calculations are presented in publication III.

31

Tab

le 1

. Sum

mar

y of

mea

sure

men

ts, i

nstru

men

ts a

nd m

etho

ds u

sed

in th

e ex

perim

ents

(pub

licat

ions

I, II

, and

III)

Mea

sure

men

t V

aria

ble

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rum

ent

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eren

ce

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icat

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re

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mom

eter

(Flu

ke 5

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sis

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eter

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400;

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inco

ln,

NE,

USA

)

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e Eq

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orp;

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olet

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SA)

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kel (

1992

) I

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ity

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eter

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300

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ents

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ood,

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, USA

) M

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(199

8)

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32

Mea

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K, P

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plu

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age

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age

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ulat

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ageJ

use

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pute

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s://g

ithub

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7)

III

33

3.3 Statistics

Data sets of different variables for the four experiments were combined and analysed as one experiment after subjection to contrast analysis for experimental differences (I–III). A two-way ANOVA was performed to show the effects of irrigation treatment, K application rate, and their interactions as fixed effects on growth and physiological traits (I), nutritive traits (II), and on image derived parameters (III). Differences were considered significant when P-values were < 0.05, and ANOVA means were separated using Tukey’s HSD test. Pearson correlations were calculated to measure the patterns of relationship among the growth and physiological traits (I), and between the image-derived and the actual measured traits (III). Principal component analysis (PCA) was conducted to identify the elemental and nutritional contents accounting for most of the variation (II). Regression analysis was performed using stepwise and partial least squares regression (PLSR) to identify the spectral indices that were essential in model prediction and to evaluate the performance of the spectral indices in estimating the actual measured traits. In both models, the actual measured traits were set as dependent variables and the spectral indices were set as independent variables. All statistical analyses were conducted using R software (versions 3.5.1 and 4.0.2; R Development Core Team, Vienna, Austria).

34

4. RESULTS AND DISCUSSION

4.1 Deficit irrigation with K fertigation improves cassava growth

Irrigation and K treatments affected the water relations and the leaf gas exchange of the cassava plants (I). There was also interaction between irrigation treatments and K application rates, and the differences between the irrigation treatments were more evident with low K rates compared with high K rates (I). The water potential and osmotic potential of the leaves were lowest by 90 DAP in plants that received irrigation at 30% together with 0.01 mM K (I), which is connected to the known effect of water deficit in lowering water potential and osmotic potential in plants (Anjum et al. 2011). However, the water potential and osmotic potential of the leaves were higher when K application rate was increased to 32 mM K (I). The high leaf osmotic potential recorded when K application rate was increased could be due to the contribution effect of K+ in lowering the leaf solute potential (Mengel and Arneke 1982, Grzebisz et al. 2013). The average solute contribution was highest in plants that received 32 mM K (64%), followed by 16 mM K (28%), whereas 1 mM K and 0.01 mM K contributed least (1%) to the osmotic potential (I). The decreased osmotic potential due to the high K+ in turn reduces the water potential and results in the maintenance of turgor (Sharp et al. 1990, Römheld and Kirkby 2010). This was strengthened by turgor, which was five times higher at the end of the experiment with either 16 mM or 32 mM K application compared with 0.01 mM K (I). The decline in net photosynthesis occurring at the end of the experimental period was more noticeable in plants that received irrigation treatment 30% together with 0.01 mM K, but less noticeable in plants that received irrigation at 60% (I). Lowered photosynthetic rate is a common occurrence under water deficit (Boyer 1985, Rucker et al. 1995). Photosynthetic rate is inhibited by stomatal closure when water supply is limited (Boyer 1970), as well as by reduced activities of photosynthetic enzymes (Bota et al. 2004). The decline in photosynthesis in the present study was linked to the reduction in stomatal conductance and leaf water potential: net photosynthesis was strongly and positively correlated with stomatal conductance (R = 0.94) and leaf water potential (R = 0.94) (I). Increased leaf temperature, which occurred following the low irrigation treatments and low K application rates, also limited photosynthesis through reduced stomatal conductance due to stomatal closure (I). Heat stress can result from prolonged stomatal closure, and PSII is more sensitive to heat stress than PSI under water deficit (Berry and Bjorkman 1980). Thus, it is likely that the decline in leaf chlorophyll due to low irrigation treatments and low K application rates additionally inhibited photosynthesis (I). The decline in chlorophyll is a common phenomenon under water deficit and often considered a non-stomatal limiting factor (Farooq et al. 2009). Chlorophyll reduction is also rapid under low K supply because prolonged K+

deficiency enhances the production of ROS, which damage chloroplasts (Hernandez et al. 2012). In contrast, increasing K application rate to 16 mM increased net photosynthesis by increasing leaf turgor, which in turn increased

35

stomatal conductance, resulting in high photosynthetic rate (I). Turgor controls the opening and closure of stomata (Boyer 1970, Boyer et al. 1985), and the extent of stomatal closure in cassava during water deficit parallels the decline in net photosynthesis (Itani et al. 1999). Water usage followed the trend of changes observed with leaf water potential. The strong significant positive association observed between water usage and stomatal conductance suggests that the reduction in water usage was probably induced by stomatal closure (I). Cassava responds to mild water deficit by partial stomatal closure and complete stomatal closure under severe water deficit (Hsiao et al. 1976, Calatayud et al. 2000). The effective use of water was greatest in plants that received irrigation treatment 60% together with 32 mM K: the plants had high dry mass, relative to full-irrigated plants (I). High dry mass production under water deficit is achieved when plants use a large portion of available soil moisture to maintain higher stomatal conductance (Passioura 2006, Blum 2009). The water usage and stomatal conductance data further imply that partial stomatal closure occurred at about 60 DAP when irrigation was 30% or 60% (I). The marked increase in water usage when K application rate was 32 mM, regardless of the irrigation treatments, suggests that high K application rates increased the stomatal conductance, owing to the improved turgor that was recorded (I). Irrigation and K treatments affected the growth parameters, including plant leaf area, plant height, as well as leaf, root, and whole-plant dry mass, and tuber number (I). Interaction effects between irrigation treatments and K application rates were also recorded for plant leaf area, plant height, whole-plant dry mass, and tuber number (I). The differences between the irrigation treatments were more evident at low K application rates compared with high K application rates. Plant leaf area and plant height were greatly reduced in plants that received irrigation treatment 30% together with 0.01 mM K. These reductions could be linked to the observed low turgor, decreased leaf water potential, decreased stomatal conductance, and the decline in net photosynthesis (I). It has been demonstrated that the expansion rate and growth of cells is reduced under water deficit because low turgor limits the extensibility of cells, which ultimately inhibits the growth of tissues and organs (Boyer et al. 1985, Passioura and Fry 1992). The small leaf area and the reduced stem height observed at 90 DAP in plants that received irrigation at 30% and 0.01 mM K (I) were likely due to the effects of low turgor (Aina et al. 2007, Ngugi et al. 2013). In comparison, plant leaf areas and plant height were higher due to increased turgor with irrigation at 60%. These results concur with Alves and Setter (2004), who recorded decreased leaf area expansion in cassava eight days after initiation of water deficit. Reduced leaf area, plant height, and stem diameter were also reported in greenhouse-grown cassava that was exposed to water deficit (Nesreen et al. 2013). Leaf, root, and whole-plant dry mass were equally reduced in plants that received irrigation treatment 30% together with 0.01 mM K (I). These reductions can be largely attributed to the decline in net photosynthesis and reduced leaf areas that were influenced by the low water potential, osmotic

36

potential, and turgor (I). Duque and Setter (2013) observed a 78% loss in total plant dry mass in cassava after 31 days of water deficit treatment. However, increasing K application rate to 16 mM improved the growth of the cassava plants, regardless of the irrigation treatments. This effect can be related to high K, which improved turgor (I) and presumably allowed turgor-dependent processes, such as photosynthesis, and stomatal conductance, to continue progressively under lower leaf water potential (Zörb et al. 2014), resulting in growth increment. Mengel and Arneke (1982) also found improved water status and high dry mass in cassava that received high K in comparison with low K. Plants that received irrigation treatment 30% together with 0.01 mM K had no tubers, whereas increasing K application rate to 16 mM resulted in four tubers (I). This observation is linked to the role of K in assimilate translocation to the storage roots (Vreugdenhil et al. 1985). Increased assimilate accumulation in the roots triggers tuberous root growth and root expansion in cassava (Alves 2002). Thus, improved cassava root yield can be attained in inherently low K soils through K applications (Fernandes et al. 2017) as shown also in (I). Overall, the growth and yield of cassava diminished when K application rate was increased from 16 to 32 mM, regardless of the irrigation treatment (I). This indicates that the application rate of K is important and needs to be suitable for cassava (Boateng and Boadi 2010). 4.2 Deficit irrigation with K fertigation improves the nutritional quality of cassava

Starch content in leaves and roots was affected by the irrigation, K treatments, and their interaction (II). Large variations were recorded with low K application rates across the irrigation treatments compared with high K application rates. Starch content in leaves and roots was lowest with irrigation at 30% and 0.01 mM K (II). This could be connected to reduced net photosynthesis (I). According to Kaur et al. (2017), the decline in net photosynthesis due to water deficit reduces the activity of ADP-glucose pyrophosphorylase, which is a key regulatory enzyme in starch synthesis. Thus, inhibition of starch synthesis in leaves in turn lowers the root starch content (Orzechowski 2008, Streb and Zeeman 2012). In agreement, Santisopasri et al. (2001) also reported reduction in root starch content in cassava plants that were grown under water deficit compared with well-watered plants. Starch content in leaves and roots increased following 16 mM K application and was linked to the increased K supply (II). The increased K supply possibly improved the activity of ADP-glucose pyrophosphorylase and stimulated starch synthase, which is involved in starch synthesis (Oosterhuis et al. 2014). It is also likely that K enhanced carbohydrate translocation from photosynthesizing leaves to storage roots (Armengaud et al. 2009). Cassava storage roots normally contain about 80% starch on a DW basis at maturity (Alves and Setter 2004). Therefore, the low starch content of roots obtained in (II) can be attributed to the plants having been harvested at an early stage (90 DAP).

37

The energy content followed the trend of changes observed with starch content (II). The contribution to the energy content in leaves was mainly by crude proteins (89%), followed by total dietary fibre (11%) and starch (<1%). The contribution to energy content in roots was similar to that in leaves (II). This observation suggests that proteins could be contributing most of the energy reserve in young cassava plants. The low energy from starch in the roots is attributable to the low starch content in the roots (II). According to Figueiredo et al. (2015), differentiation of a fibrous root system into tuberous roots that store carbohydrates (tuberization) continues until 120 DAP. Hence, it means that most of the plant energy reserves were still focused on shoot growth and root development and less on starch accumulation when the present experiments ended (90 DAP) (II). In essence, more energy could be obtained by consuming the young cassava leaves during early growth of cassava. Notably, a high K application of 16 mM resulted in the highest energy content, which is connected to the effects of high K on the starch, crude protein, and dietary fibre content (II).

Crude protein content was affected by the irrigation and K treatments (II). Irrigation treatment 30% resulted in the lowest crude protein content in both leaves and roots. It is possible that water deficit could have lowered the amount and reduced activity of photosynthesis-related enzymes in the leaves responsible for protein synthesis, such as nitrate reductase (Bota et al. 2004). Dhindsa and Cleland (1975) showed that water deficit inhibited the incorporation of amino acids into proteins in oat plants (Avena sativa L.) and caused a greater decrease in protein synthesis, resulting in decreased protein content of the plants. Crude protein content of leaves and roots increased with 16 mM K (II). This may be associated with the role of K in enzyme activation and thus could have increased nitrate reductase activity, which catalyses the formation of proteins (Khanna-Chopra et al. 1980). The crude protein content of leaves was two-fold higher than that of roots (II). This concurs with earlier reports (Yeoh and Truong 1996, Montagnac et al. 2009) that leaves are higher in protein than roots. Therefore, the young cassava leaves obtained in (II) would represent a perfect protein source when consumed. Irrigation, K treatments, and their interactions affected total carotenoid contents (II). Carotenoids were lowest in leaves and roots of plants that received irrigation treatment 30% together with 0.01 mM K (II). Carotenoids are key photosynthetic pigments responsible for light harvesting (Nisar et al. 2015). Thus, reductions in net photosynthesis due to water deficit limits the synthesis of carotenoids and in turn, the translocation of carotenoids to the roots is inhibited (II). Moreover, low K restricts the availability of geranyl-geranyl pyrophosphate, a precursor to carotenoids (Taber et al. 2008). Leaf and root carotenoid contents increased with 16 mM K (II). This can be because K is an important cofactor in the isoprenoid pathway of carotenoid synthesis and is usually needed by the enzymes pyruvic kinase and acetic thiokinase (Trudel and Ozbun 1970). Carotenoid contents were higher in leaves than in roots, indicating that leaves are richer in carotenoids (II). Regardless of the irrigation treatments, the carotenoid contents obtained in (II) with 16 mM or

38

32 mM K would be a rich source of vitamin A when consumed because the contents were above the average carotene requirement set by WHO (2009). The total dietary fibre content was affected by the irrigation and K treatments (II). Dietary fibre consists of polysaccharides whose synthesis depends on photosynthetic activity (Porter 1962). Thus, the reduced photosynthesis due to water deficit and K deficiency (I) possibly affected the dietary fibre content in leaves and roots: total dietary fibre content was lowest in plants that received irrigation treatment 30% and 0.01 mM K, but high in plants that received 16 mM K (II). Importantly, the total dietary fibre content obtained in (II) would provide an adequate daily fibre intake of 25 g for women and 38 g for men recommended by the panel on dietary reference intakes for macronutrients (Slavin 2005). The mineral contents were affected variously by the irrigation and K treatments (II). Irrigation at 30% reduced the Fe and Cl content in leaves, Cu, P, S and Mg in roots, and Ca, K, Zn, and Mn in leaves and roots, but the mineral contents were highest at full irrigation. This can be attributed to the limited water supply that possibly limited the uptake of the minerals from the rhizosphere (Hu et al. 2007, Helal et al. 2013). Reduced stomatal conductance due to water deficit induces a decrease in the transpiration rate, which in turn limits nutrient transport from the roots to the shoots (Boyer 1985). Regardless of the irrigation treatments, increasing K application rate increased the K, Ca, P, Mg, Zn, Fe, and S contents in both leaves and roots, and Cl in leaves. An increased K supply to plants lowers the solute potential, which in turn causes a decrease in total water potential and creates a solute potential gradient that stimulates the uptake of nutrients and water (Marschner et al. 1996). In contrast to the other minerals, the N content in leaves and roots was high in plants that received irrigation treatments 30% and 60%, compared with fully irrigated plants (irrigation at 100%). This observation strengthens the view that high leaf N in drought-exposed plants is an acclimation to water deficit (Weih et al. 2011). Increasing K application rates tended to increase the N content in which the highest N content in leaves and roots was with 16 mM K (I). However, the N contents in leaves and roots were reduced with 32 mM K, suggesting that the N uptake was depressed. According to Thummanatsakun and Yampracha (2018), synergistic effects exist between N and K because high K applications demand high N applications for the efficient uptake of N. The N applications were maintained constant, whereas the K applications varied from low to high, which is noteworthy (I-III). The Ca, S and Mg contents in leaves and roots were also highest with 16 mM K but were then reduced with 32 mM K (II). This indicates that 32 mM K application was excessive and likely resulted in nutrient imbalance, thereby limiting the uptake of other cations (Fernandes et al. 2017, Howeler 2014). Generally, mineral content was higher in the leaves than in the roots. Therefore, consuming the leaves would provide essential minerals that could improve human health in the tropics, especially the K, Ca, Fe, Zn, S and Mg contents, which were within the normal range (II).

39

4.3 Deficit irrigation with K fertigation reduces the cyanide concentration in cassava

The cyanide concentration was affected by the irrigation, K treatments, and their interactions (II). Variations in cyanide concentration were larger with irrigation treatments at low K application rates compared with high K application rates. The leaf and root cyanide concentrations were highest with irrigation treatment 30% together with 0.01 mM K. This can be associated with the increased N content that occurred when irrigation was decreased (I). Thus, changes in the N content likely impacted the cyanogenic glucoside production, given that N is a major component of cyanide (Gleadow and Møller 2014). It is also possible that water deficit could have caused an increase in the synthesis of cyanogenic glucosides in leaves, given that the cyanide concentration was minimal with irrigation at 60% together with 0.01 mM K and was lowest in fully irrigated plants. In agreement, El-Sharkawy (1993) reported a 40% increase in cyanide in four cassava cultivars subjected to water deficit from three to six months after planting. Bokanga et al. (1994), Santisopasri et al. (2001), Vandegeer et al. (2012), and Imakumbili et al. (2019) also reported increased cyanide concentration due to water deficit in cassava. Cyanide concentration in leaves and roots was reduced substantially with 16 mM K over the irrigation treatments, compared with lower K application rates. Given that K+ regulates the activity of more than 50 enzymes (Bhandal and Malik 1988), it could also function to regulate the synthesis of linamarase in cassava and result in reduced cyanide concentration. The reduced cyanide concentration in the leaves and roots of cassava can also be associated with the increased dry matter and starch content following K application (II), which might have created a concentration effect, i.e. reduced cyanide per unit mass (Burns et al. 2010). This finding concurs with those of El-Sharkawy and Cadavid (2000), who also reported reduced root cyanide in cassava plants that were fertilized with K under rainfed conditions. Cyanide concentration increased when K supply was increased from 16 mM to 32 mM at irrigation treatments 30% and 60%. It has been established that adequate uptake of Ca, Mg, Zn and S is beneficial for reducing cyanogenic glucosides in cassava, similarly to K (Susan John et al. 2016). Therefore, the reduced Ca, Mg and S contents, whose uptake was constrained by 32 mM K, may partly explain the increased cyanide concentration (II). Moreover, the cyanide concentration was twice as high in leaves as in roots (II). This observation can partly be attributed to linamarin levels, which are usually 20 times higher in leaves than in roots (Cardoso et al. 2005, Jørgensen et al. 2005). Importantly, the cassava leaves and roots obtained in (II) would be safe for consumption because the cyanide concentrations did not surpass the maximum recommended level of 50 mg kg-1 HCN in FW (Codex Alimentarius Commission 2013).

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4.4 Estimating cassava growth from RGB and multispectral images The irrigation and K treatments indirectly affected the spectral indices computed from the images (III) by affecting the plant growth (I) and changing the spectral signatures of the plants. Water deficit (30%, 60%) and low K (0.01 mM, 1 mM, 4 mM) applications resulted in low spectral values, whereas full irrigation and high K (16 mM and 32 mM) applications resulted in high spectral values (III). Spectral reflectance indices often follow the changes in canopy structure (Aparicio 2002). Thus, the low spectral values that were recorded with NDVI, SR GRVI, GNDVI, and RENDVI following water deficit and low K applications, suggest that the treatments probably induced stress in the plants (III). Therefore, the stress likely changed the plant reflectance properties (Gates et al. 1965). Similar results were found in wheat, where the spectral indices were greater under irrigated compared with non-irrigated conditions (Aparicio et al. 2000). The CSI that was highest in plants that received irrigation treatment 30% together with 0.01 mM K, further suggesting that the plants were experiencing stress because the CSI was lowest in fully irrigated plants together with 16 mM or 32 mM K (III). Merzlyak et al. (1999) reported that an increase in CSI depicts an increase in plant stress and can signify the onset of canopy senescence.

It was not possible to discriminate from the side and top view images the stress due to water deficit from the stress due to low K (III). A possible explanation is that the plant spectral properties that are also informative of stress are usually computed collectively based on their absorbance or reflectance in the NIR and red regions and disregard the specific stress causes. Kim et al. (2011) observed that identifying specific stress causes from plant images is challenging because plant stress can result from stresses attributable to water, nutrients, pathogens, or their combination. In the present study, there was accurate control and isolation of irrelevant stress-causing factors, leaving deficit irrigation and K applications as the focus factors (III). The GA, GGA, GRVI, GNDVI, NDVI, RENDVI indices were high in plants that received 16 mM or 32 mM K applications, regardless of irrigation treatments, but the CSI values were depressed (III). This suggests that cassava does not show visually apparent water-deficit symptoms until plants experience an extreme stress. K nutrition increased leaf chlorophyll content (I) and likely resulted in high spectral absorption in the blue and red regions of the spectrum, signifying healthy plants (Knipling 1970, Peñuelas and Filella 1998).

Regression models based on the spectral indices estimated biomass, net photosynthesis, and chlorophyll content accurately (R2 > 0.90) (III). Results also indicated a strong positive connection between the estimated growth parameters (biomass, net photosynthesis, and chlorophyll content) and the measured growth parameters. This observation is supported by the measured growth parameters also being associated strongly with each other (I). Working with cassava, Selvaraj et al. (2020) also found strong connections between leaf

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and root biomass, and various spectral indices (NDVI, GNDVI, GRVI) at different phenological stages of the plant. The accurate estimation of the growth parameters illustrates that the spectral indices derived from RGB and multispectral images could provide reliable indirect measurements for cassava growth. The NDVI, GA, GGA, and SR indices, which provided most of the accurate estimates, may be more useful (III). The overlap observed in some of the leaves during imaging can partly explain the reason why the leaf area was moderately estimated (R2 = 0.76) and was lower than the actual measured leaf area (III). Chlorophyll content estimated from spectral indices integrates the chlorophyll content of all the leaves in the canopy (Filella et al. 1995). This can partly explain the observed high estimation accuracy of leaf chlorophyll using indices (III). A strong connection also existed between the NDVI and the actual measured chlorophyll content as shown in previous studies (Aparicio et al. 2002, Royo et al. 2003). The low NDVI values obtained in plants that received irrigation at 30% together with 0.01 mM was an indication of reduced chlorophyll content occasioned by the water deficit and low K nutrition. In effect, the loss in chlorophyll causes the reflectance in the red to increase, while the NIR reflectance remains unchanged, leading to a decrease in NDVI value (Filella et al. 1995). It seems that the SR and NDVI indices may be used as reliable measures to monitor the growth of cassava at an early growth stage – the two indices provided the highest predictive values for the growth parameters of all spectral indices. Aparicio et al. (2000) also established SR and NDVI as better predictors of leaf area and dry mass yield in wheat. 4.5 Estimating cassava nutritional traits from RGB and multispectral images

The regression models presented a wide range of variability in estimating the cassava nutritional traits based on the spectral indices (III). Energy content was accurately estimated (R2 ≈ 0.9), whereas starch, total carotenoids and cyanide concentrations were estimated satisfactorily (R2 ≈ 0.8). These nutritional compounds have spectral absorption properties in the red and NIR regions of the spectrum (Gates et al. 1965). For instance, carotenoids absorb wavelengths at about 400–500 nm (Peñuelas et al. 1994), whereas starch absorbs wavelengths at about 510 nm (McCleary et al. 1994), which allows their quantification. The observed relationship between the measured nutritional traits and the spectral indices could be a result of the strong connection between the nutritional traits and growth parameters (III). Growth parameters such as leaf chlorophyll and leaf area have a close relationship with spectral indices (Peñuelas and Filella 1998, Li et al. 2014). Thus, changes in plant growth status influence nutritional content, and the changes can be established through indices calculated based on plant images. This may be one of the possible reasons why the described nutritional traits were modelled with high accuracy (III). As observed in this study, the computed SR and NDVI indices quantified starch, energy, and total carotenoids accurately (III). In contrast, the crude protein content was modelled with the lowest (R2 ≈ 0.3)

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accuracy of prediction. It could be reasoned that the low wavelengths (200–280 nm), which are typically used for protein quantification (Stoscheck 1990), were probably beyond the reach of the sensors used in this study. Spectrophotometers that are typically used for protein determination have spectral sensors that measure a broad spectrum in the ultraviolet (10–400 nm) and visible light regions (Scopes 1974), which is different from the RGB and multispectral sensors that measure in the visible and NIR regions. Among the indices, SR showed the strongest positive correlation with starch, energy content and crude protein (III). Though estimated accurately, cyanide exhibited negative regression coefficients with all the spectral indices except with CSI (III). This may imply that lower GA, GGA, SR, GRVI and GNDVI values could suggest an increase in cyanide concentration in cassava. Moreover, the estimated cyanide also showed negative associations with all the other estimated nutritional compounds (III).

The mineral elements were modelled with a lower (R2 < 0.5) accuracy of prediction, which indicates that the spectral indices had a low power for estimating the minerals (III). Moreover, all the minerals, apart from S, showed weak correlations with all the spectral indices, implying that there was no connection between the minerals and the spectral indices. The minerals K, Ca, Mg, Na, Fe, Mn, Zn, and Cu are metallic elements that exist primarily as ions in plant tissues, and thus, in ionic form they do not produce active spectral absorption features in the light spectrum (Pandey et al. 2017). The fact that S had a strong correlation with the spectral indices in the present study could be explained by the participation of S in the covalent bonding of carbon compounds, which absorb wavelengths in the electromagnetic spectrum, leading to its quantification (Curran et al. 2001, Pandey et al. 2017). Boshkovski et al. (2020) also reported low estimation accuracy for most mineral elements and a weak relationship between the minerals and the spectral data that they acquired with a hyperspectral sensor in common bean. Thus, it seems that it is less useful to use spectral indices derived from RGB and multispectral images to estimate the mineral contents from young cassava plants (III).

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5. CONCLUSIONS

The growth of cassava in water-limited environments can be improved with deficit irrigation together with K fertigation. This was demonstrated by plants that received an irrigation treatment of 60% together with 16 mM K exhibiting the least reduction in growth in comparison with full irrigation (100%) together with 16 mM or 32 mM K. The irrigation and K treatments also showed significant interactive effects for the physiological and growth measurements, which leads to the conclusion that the first hypothesis was valid. Thus, agricultural areas with limited water resources could utilize deficit irrigation strategies combined with K fertigation as a tool to develop management applications to improve cassava productivity under low moisture conditions. Farmers in the tropics could manipulate irrigation and K application rates depending on the soil moisture levels and inherent soil K content for optimum effects. The nutritional quality of cassava can be improved under water-limited environments through optimizing K application rates with irrigation water. This was demonstrated by water deficit and low K applications restricting starch, energy, carotenoids, crude protein, dietary fibre, and mineral contents in leaves and roots of cassava, but increasing the cyanide concentration. Increasing K application rates to 16 mM and more improved the nutritional and mineral contents in leaves and roots regardless of the irrigation treatment. The cyanide concentrations in the leaves and roots were also diminished with increases in both irrigation and K applications. Importantly, irrigation at 60% together with 16 mM K caused the least reduction in nutritional quality but reduced the cyanide concentration significantly, compared with full irrigation with 16 mM K. This leads to accepting the second working hypothesis. The ability to produce cassava low in cyanide in water-limited environments could result in minimum processing and prevent nutrient loss, and simultaneously alleviate cyanide related diseases common in the tropics where fresh cassava is used for human consumption. RGB and multispectral images can provide valuable indirect estimates for growth and key nutritional traits of cassava. Biomass, chlorophyll, and net photosynthesis, as well as starch, energy, total carotenoids, and cyanide, were modelled with the highest accuracy of prediction based on the spectral indices. However, the estimation accuracy for the mineral elements was low and it was not possible to discriminate the effects caused by water deficit from those caused by low K nutrition. Thus, the third and fourth working hypotheses are partially valid. The NDVI, SR, and GA were the best estimators of growth and key nutritional traits of cassava. The spectral indices values were low with water deficit and low K application rates but increasing K from 0.01 mM to 16 mM over the irrigation treatments resulted in high index values, but low CSI. Overall, the RGB and multispectral imaging techniques may be utilized for rapid evaluation of cultivars in breeding work, and to facilitate crop management decisions such as corrective measures to avert stress during the early phases of plant growth. In particular, the growth and nutritional

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parameters that were accurately estimated in this study could be included as baseline parameters in future work.

There is a need for further tests with several cassava cultivars under different field conditions to calibrate findings in the present study. Various optical techniques that quantify traits in cassava may also be incorporated. This would assist in developing feasible fertigation rates for K, depending on the inherent soil K and moisture levels. Overall, the experimental conditions enabled analytical investigations without abiotic or biotic interference and prevented pixel contamination during imaging that is otherwise common under field conditions. This study broadens the physiological understanding of the putative link between water deficit and K nutrition, and also of the usefulness of low-cost imaging in estimating cassava performance.

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6. ACKNOWLEDGEMENTS

This research would have not materialized without the financial support of CIMO Finland, and the Ella and Georg Ehrnrooth foundation. The University of Helsinki Library is appreciated for covering the publication costs of the articles realized in this research. I am deeply grateful to Professor Pirjo Mäkelä, who took an interest in my admission request, enrolled me as her student, and sourced CIMO funding that brought me to Finland. Professor Mäkelä provided me endless practical guidance, trained me in experimentation, posed hard questions, which pushed me to accept challenging tasks, reviewed my writing, and offered me limitless motivation, and patience, which helped me greatly. Professor Mäkelä’s immense scientific knowledge and supervision over the four years has deepened my understanding of crops, agronomy, and soils. I also wish to thank Dr. Jouko Kleemola for conceiving the rates for the various potassium treatments that were used in this research. Dr. Kleemola also guided me on plant imaging, analyses, and provided critical comments on my work. My sincere thanks also go to Professor Laura Alakukku for the vital suggestions she made in the project, her constructive comments and interpretations of my original writing, and supporting my grant applications. I could not have imagined having better advisors and mentors for my PhD programme. This research would not have been possible without the assistance of other key individuals. Dr. Teppo Mattsson is appreciated for providing some of the sophisticated statistical interpretations we encountered. Dr. Seija Jaakkola shared her valuable knowledge on biomass energy analysis, particularly when we needed to re-calibrate the bomb calorimeter. I would like to thank Markku Tykkyläinen, Afrane Yaw and Juho Kotala for providing immense technical support with the greenhouse experiments and plant imaging. Daniel Richterich and Sanna Peltola are appreciated for availing instruments whenever I needed. Marjo Kilpinen is deeply acknowledged for assisting with the laboratory analyses. I am also grateful to Karen Sims-Huopaniemi and Mia Vehkaoja for their limitless doctoral programme support from the graduate school of Sustainable Use of Renewable Natural Resources. I also acknowledge YEB doctoral school for the travel grants that enabled me to attend important workshops and present findings of this research in key international conferences within and outside Europe. I thank my fellow labmates: Kifflemariam, Mina, Nashmin, Subin, Clara, and Anthony for the many stimulating discussions we had on science and general life, which spiced our office coffee breaks, cake clubs, and evening dinners. I also thank several friends in Helsinki who made my stay more interesting. Pst. Sillah, Becky and all church members in Helsinki are acknowledged for spiritual support. My warmest thanks go to my father Martin and my mother Consolata for their prayers and encouragement. I also thank my family members and all my

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friends who supported me in one way or another when the “unfair winds blew foul” and whom I could not list here for lack of space. In a special way, I would like to thank my spouse Cynthia for taking care of our lovely daughter Alicia in my absence, and for her patience, encouragement, and unwavering support which contributed significantly to the completion of this work. Finally, I thank the Almighty God for health, strength, protection, and providence to this end.

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