Next Article in Journal
Investigating Mid-Latitude Lower Ionospheric Responses to Energetic Electron Precipitation: A Case Study
Previous Article in Journal
From Raw GPS to GTFS: A Real-World Open Dataset for Bus Travel Time Prediction
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Data
Volume 10
Issue 8
10.3390/data10080120
data-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Data Descriptor

Time Series Dataset of Phenology, Biomass, and Chemical Composition of Cassava (Manihot esculenta Crantz) as Affected by Time of Planting and Variety Interactions in Field Trials at Koronivia, Fiji

by
Poasa Nauluvula
1,2,
Bruce L. Webber
3,4,
Roslyn M. Gleadow
5,6,
William Aalbersberg
1,
John N. G. Hargreaves
7,
Bianca T. Das
8,
Diogenes L. Antille
7,9,* and
Steven J. Crimp
6,10
1
Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala Campus, Suva VC2X+Q2, Fiji
2
Palladium, Australia Pacific Climate Partnership, Suva VC5X+CW, Fiji
3
CSIRO Health and Biosecurity, Floreat, WA 6014, Australia
4
School of Biological Sciences, University of Western Australia, Crawley, WA 6009, Australia
5
School of Biological Sciences, Monash University, Clayton, Melbourne, VIC 3800, Australia
6
Institute for Climate, Energy and Disaster Solutions, Australian National University, Canberra, ACT 2601, Australia
7
CSIRO Agriculture and Food, Canberra, ACT 2601, Australia
8
Tasmanian Institute of Agriculture, The University of Tasmania, Newnham, TAS 7248, Australia
9
College of Agriculture, Fisheries and Forestry, Fiji National University, Koronivia Campus, Nausori WGXJ+R2X, Fiji
10
Australian Centre for International Agricultural Research, Canberra, ACT 2617, Australia
*
Author to whom correspondence should be addressed.
Data 2025, 10(8), 120; https://doi.org/10.3390/data10080120
Submission received: 30 May 2025 / Revised: 16 July 2025 / Accepted: 21 July 2025 / Published: 23 July 2025
Versions Notes

Abstract

Cassava is the sixth most important food crop and is cultivated in more than 100 countries. The crop tolerates low soil fertility and drought, enabling it to play a role in climate adaptation strategies. Cassava generally requires careful preparation to remove toxic hydrogen cyanide (HCN) before its consumption, but HCN concentrations can vary considerably between varieties. Climate change and low inputs, particularly carbon and nutrients, affect agriculture in Pacific Island countries where cassava is commonly grown alongside traditional crops (e.g., taro). Despite increasing popularity in this region, there is limited experimental data about cassava crop management for different local varieties, their relative toxicity and nutritional value for human consumption, and their interaction with changing climate conditions. To help address this knowledge gap, three field experiments were conducted at the Koronivia Research Station of the Fiji Ministry of Agriculture. Two varieties of cassava with contrasting HCN content were planted at three different times coinciding with the start of the wet (September-October) or dry (April) seasons. A time series of measurements was conducted during the full 18-month or differing 6-month durations of each crop, based on destructive harvests and phenological observations. The former included determination of total biomass, HCN potential, carbon isotopes (δ13C), and elemental composition. Yield and nutritional value were significantly affected by variety and time of planting, and there were interactions between the two factors. Findings from this work will improve cassava management locally and will provide a valuable dataset for agronomic and biophysical model testing.
Dataset: The dataset was submitted and will be published as a supplement to this article in the journal Data. The dataset is available electronically as Supplementary Material.
Dataset licence: Licence under which the dataset is made available (CC-BY)

1. Introduction

Cassava (Manihot esculenta Crantz; Euphorbiaceae) is a perennial woody shrub with edible tuberous roots that has inherent tolerance to marginal land and stressful environmental conditions [1,2,3,4]. The crop is cultivated annually or biannually in tropical and subtropical areas of the world by millions of small-scale farmers in more than 100 countries [4,5,6,7]. It is estimated that approximately 750 million people depend on cassava as their staple food crop and it is the sixth most important food crop globally in terms of annual production [4,6,8,9]. Cassava is an important source of starch (mainly in tubers) and protein (mainly in leaves) for both human and livestock consumption [6]. Growing cassava can play an important role in climate change adaptation strategies in the South Pacific. Given its long growing cycle (typically, between 6 and 20 months from planting to harvest), the crop is considered to be more resilient to future climate scenarios compared with other important food crops such as taro [10].
Pacific Island countries are vulnerable to climate change because they rely on agriculture for livelihoods, food security, and commodity export income [11,12]. The vulnerability of Pacific farming systems to climate change is compounded by a declining trend in soil quality observed across the region, which affects the resilience of those systems to environmental disasters such as drought or extreme rainfall [12,13,14,15]. In these countries, cassava is becoming more common relative to traditional crops (e.g., yams, taro, and sweet potato) due to its ease of cultivation, especially in drier and marginal areas with less use of agricultural inputs, particularly nutrients [6,16,17]. Climate change across the Pacific is projected to include increases in mean air temperature and the number of unusually hot days and nights and a decrease in the number of unusually cool days and nights [18]. Changes in rainfall are projected to differ between-Pacific Island countries, while there is a lack of consensus regarding changes in the frequency or strength of El Niño and La Niña events. Cassava yield in the future may be further impacted by waterlogging in soils with impaired drainage, high winds, and changing incidence of pests and diseases [11]. Given its potential resilience and nutritional value, cassava should be prioritised in future crop management strategies aimed at climate adaptation and food security in the region. Specifically, a focus should be on locally adapted agronomy suited to different Pacific Island countries and soil types to enhance food and nutritional security while minimising production or quality losses by factors such as waterlogging.
Food safety is an additional consideration in cassava production under future climates. Cassava contains two cyanogenic glucosides (90% linamarin and 10% lotaustralin), which break down to release toxic hydrogen cyanide (HCN, referred to as ‘cyanide’) when crushed or chewed [19,20]. Chemical compounds, such as cyanogenic glucosides, are known to protect plants from pests and diseases, but utilise limited resources that may otherwise be used for crop growth. The synthesis and recycling of cyanogenic glucosides may also help plants manage their nitrogen and mitigate oxidative stress [21], so their effect on productivity may vary depending on plant age and growing conditions [22]. Cassava germplasm is traditionally grouped based on taste: “bitter” varieties generally have tubers with genetically determined higher concentrations of cyanide (typically >50 ppm, fresh weight [FW]) whereas “sweet” varieties have tubers of lower concentrations of cyanide (typically < 50 ppm FW) [23,24,25]. However, in both varieties, the concentration of linamarin increases in a phenotypic response when plants are drought- or salt-stressed [26,27,28]. Dry conditions are common in the Pacific under current climate and are expected to persist and worsen in the future [29].
The ecology of cassava as a crop, agronomic practices, and trade-offs between yield, nutritional quality and tuber toxicity is generally well known and well documented (e.g., [4,6,7,20,30,31,32], but there have been few studies focused on understanding its chemistry and agronomy in the Pacific. Knowledge gaps exist related to the nutritional value and toxicity of local Pacific varieties, agronomy, and likely impact of climate change on both toxicity and production. Agronomic and biophysical models are useful tools to explore cropping systems management, including under future climate projections, but the few models that can integrate cassava growth and management with historical and projected climates are limited by training and testing data [33]. The dataset described in this article was collected for the purpose of reducing such knowledge gaps and supporting model development. The dataset consists of crop phenology, biomass, and chemical composition measurements of two cassava varieties grown in field conditions with three different sowing times [34]. These experiments were conducted with the aims of: (1) comparing the growth of two varieties of cassava that differed in their cyanogenic potential; (2) providing a dataset to support development of a dedicated agronomic model for cassava; and (3) comparing the cassava genotypes genetically. The data was collected as part of a doctoral programme of research undertaken by the lead author [34] and received financial and operational support from The Australian Centre for International Agricultural Research (ACIAR, Australian Government) [35]. Publications that reference this data include Nauluvula [34] and Nauluvula [36]. Data generated here contributed to building a cassava model in the Agricultural Production Systems Simulator (APSIM, https://www.apsim.info/, accessed on 17 May 2025) [37] by collaborators at CSIRO (https://www.csiro.au/en/, accessed on 17 May 2025) in Australia.

2. Data Description

2.1. Overview of Experiments and Experimental Site

The dataset was collected during three experiments in which cassava was grown from cuttings to maturity in a field setting at the Koronivia Research Station near Suva (Fiji Ministry of Agriculture, latitude: 18.0487° S, longitude: 178.5301° E, elevation: ~9 m above-sea-level) from October 2010 to March 2011. The same two varieties of cassava were grown in each of the experiments; namely, a purportedly bitter variety (Nadelei) and a purportedly sweet variety (Merelesita), both of which are commonly grown in Fiji [38]. The main experiment was grown for 18 months (henceforth the ‘Main’ experiment). The remaining two experiments were grown for 6 months, each using a planting date that was delayed by 6 months from the Main experiment (henceforth the ‘First 6-Month’ experiment) or delayed by 12 months from the Main experiment (henceforth the ‘Second 6-Month’ experiment). The planting times corresponded with the start of the wet (Main and Second 6-Month experiments) and dry (First 6-Month experiment) seasons, which typically commence in mid-September/early October and April, respectively. Initial and periodic characterisation of soil properties were performed, and daily weather data was recorded. The phenology, biomass, and chemical composition of cassava was measured at regular time intervals during the experiment. All data are provided electronically in the Supplementary folder attached to this article.
Rainfall, minimum and maximum temperatures, and solar radiation were recorded daily at the experimental site (Table 1 and ‘Weather’ file, Supplementary Material). Average daily temperatures during the experiment ranged from 20 to 30 °C during the dry season (April to October) to 22 to 32 °C during the wet season (October to April). Monthly rainfall ranged from 85 mm (August 2011) to 663 mm (November 2010) for the same period.
The soil water characteristics, soil bulk density, and initial soil organic carbon (Org-C) and soil mineral nitrogen—that is, the sum of ammonium-N (NH4-N) and nitrate-N (NO3-N)—were measured at the beginning of the experiment (Table 2). Measurements for the soil water characteristics included the water content of the soil when at air dry (AD), permanent wilting point (PWP), drained upper limit (DUL; also known as field capacity), and saturated (SAT) water contents, respectively. The water content of soil that remained after maximum extraction by cassava (that is, the crop lower limit or CLL) was also measured. The laboratory determination of the PWP is referred to as LL15 (lower limit 15 bar) and is obtained after applying a pressure of 15 bar to the soil until it reaches equilibrium (no drainage is observed) [39]. The soil pH (1/soil–water ratio) at the site was between 6.4 and 7.0 throughout the profile.

2.2. Cassava Crop Data

2.2.1. Phenology

Phenological observations of cassava plants were conducted on a weekly basis in order to track development of tagged plants and to measure leaf dynamics (namely: leaf expansion rates, senescence, canopy height) as shown in Table 3. The number of leaves was divided into the number of expanded leaves (i.e., fully expanded mature leaves) and younger expanding leaves (not fully expanded young leaves that were less than 15 days old). ‘Expanding’ leaves were a lighter green, generally smaller and with a soft lamina whereas ‘expanded’ leaves were darker green, of regular size, and with a toughened lamina.

2.2.2. Biomass

The biomass of cassava plants was measured in the Main experiment with 10 destructive harvests conducted at 1, 2, 3, 4, 6, 8, 10, 12, 15, and 18 months after planting, respectively. The harvest intervals were chosen because they corresponded to either periods of high growth or significant changes in resource allocation trajectories. At each harvest, individual plants were separated into their different organs (Table 4). The fresh and dry weights of each organ were recorded; in addition, the area of leaves and diameter of plant canopy were also measured. The biomass of cassava plants in the First 6-Month and Second 6-Month experiments was measured in only one destructive harvest, at six months after planting (referred to as ‘Harvest 1’ for these Experiments) as shown later in Table 5.

2.2.3. Chemical Composition

The concentration of cyanide glycoside (mg g−1), 13C (δ13C, ‰) and total elements (N, P, K, S, Ca, Mg, and Na expressed in % by weight, and Cu, Zn, Mn, Fe, B, Mo, and Si expressed in ppm) in tubers and leaves of cassava was measured in the Main Experiment when plants were 6, 12, and 18 months old, and in the First 6-Month and Second 6-Month Experiments when plants were 6 months old. Fewer samples were analysed for HCN and nutrients in plants from the Second 6-Month Experiment due to cost constraints.

3. Methods

3.1. Field Experiments

3.1.1. Weather

Rainfall, minimum and maximum temperatures, and radiation were measured daily at the weather station available at the Koronivia Research Station (Fiji Ministry of Agriculture).

3.1.2. Soil

Data on soil parameters were collected before starting the trial from a single pit adjacent to the trial site excavated to a depth of approximately 2 m. The soils were sampled at seven depths over 1.80 m (0.15 m per increment) to determine the water-holding capacity (DUL and LL15), soil bulk density, soil pH, and soil organic carbon content. Soil bulk density (SBD) was determined from replicated intact cores of the known volume collected from each depth interval. The replicates were weighed before and after oven drying (105 °C, 72 h) to calculate the dry weight. The SBD was then calculated as the weight of dry soil divided by the soil volume. The soil water-retention properties were calculated using additional soil samples from each depth increment. The CLL was estimated as the water content in each layer of the soil profile at which a crop is no longer able to extract water through its root system [40]. The DUL of the soil profile was estimated as the highest water contents occurring during the wet season after allowing for gravitational drainage, typically ~48 h after the soil has reached saturation [41].

3.1.3. Experiment Layout

The experiments were set up in a block design layout with non-random allocation of destructive harvests within each block. The two cassava varieties were planted in plots of 4 m × 7 m with 4 (Main Experiment) or 5 (First 6-Month and Second 6-Month Experiments) replicates. Harvests were blocked to actively avoid having the latter harvests next to an ‘unoccupied’ (i.e., harvested earlier) row. Later harvest rows had ‘spare’ plants acting as filler rows from 6 months onwards to avoid the issue of plant canopies expanding out into newly opened-up space once an adjacent row had been harvested. Plants were spaced at 1 m between-rows and 0.5 m between-plants in the intra-row (this arrangement resulted in 75 plants per plot equivalent to 20,000 plants per ha). For all observed variables, measurements were replicated four times (n = 4). The field trial layout is included in the Supplementary Material (file name: ‘Field trial design’).

3.1.4. Crop Management

The soil was mechanically cultivated with two disc-ploughing operations before planting, and a single pass of a rotovator used for seedbed refinement. These operations were followed by soil ridging, which was performed on the planting day. Cutting stock of 20–30 mm diameter and 250–300 mm length was sourced from 10- to 12-month-old plants. Cuttings were planted at a 45° angle at 100–150 mm into the soil at the three planting times (Table 5) and pruned to a single stem 30 days later. Fertiliser was incorporated prior to planting during soil cultivation for all experiments and applied at a rate of 200 kg ha−1 of NPK (13:13:21). An additional application of urea at the rate of 10 g plant−1 was performed to the plants in the Main experiment 10 weeks after planting. Weeds were controlled with weekly manual weeding and herbicide (Paraquat, applied on 1st April and 16th June 2011, respectively). White peach scale (Pseudaulacaspis pentagona) was controlled with Confidor® (Imidacloprid) on 4th August 2011. No irrigation was applied.
Table 5. Planting and series of destructive harvest dates (the First 6-Month and Second 6-Month experiments were only harvested once at 6 months after planting). Dates are shown as DD/MM/YYYY.
Table 5. Planting and series of destructive harvest dates (the First 6-Month and Second 6-Month experiments were only harvested once at 6 months after planting). Dates are shown as DD/MM/YYYY.
Field OperationMain
Experiment		First 6-Month
Experiment		Second 6-Month
Experiment
Planting24/10/201011/4/201122/9/2011
Harvest 1 24/11/201011/10/2011 *23/3/2012 *
Harvest 2 20/12/2010
Harvest 3 17/1/2011
Harvest 4 15/2/2011
Harvest 5 11/4/2011 *
Harvest 6 6/6/2011
Harvest 7 1/8/2011
Harvest 8 26/9/2011 *
Harvest 9 13/12/2011
Harvest 1019/3/2012 *
* Harvests from which plant parts were subsampled for chemical composition analyses.

3.2. Crop Measurements

3.2.1. Phenology Observations

Phenology observations (Table 3) were conducted on a weekly basis from planting to final harvest based on the same three tagged plants per plot. Leaning plants were not straightened during measurements. ‘Standard’ leaf area measurements were also performed for 10 consecutive expanded leaves on the same branch throughout the trial on a weekly basis. Here, a proxy for leaf area was measured by counting the number of lobes (‘fingers’) and measuring the lengths of the left (shortest) and central (longest) lobes. This process was repeated for up to 10 consecutive expanded leaves on the same branch each week. Precise leaf area measurements were made on 4–7 leaves from 20 plants per variety at six months after planting (i.e., at harvest 5 in the Main experiment, and at the final harvest of the First 6-Month and Second 6-Month experiments) using ImageJ software Version 1.47h of 2012 [42].

3.2.2. Biomass Measurements and Subsampling for Chemical Analyses

Five plants of each variety were selected per replicate in destructive harvests (Table 5). At the time of harvest, each plant was carefully dug up and it was verified that no leaf or root tissue had been lost. The plants were then partitioned into separate organs (Table 4) and fresh weights were obtained. The fresh plant materials were placed into labelled paper bags for oven drying at 60 °C for 72 h to determine dry weights. Subsamples of expanded leaves and inner tuber flesh were taken from freshly harvested and partitioned plants in selected harvests (Table 5) for additional chemical composition analyses (Section 3.2.3, Section 3.2.4 and Section 3.2.5). Leaves that were freeze-dried were the same ones used for leaf area determination in order to determine specific leaf weight. The middle of the stem was taken for the subsample. Other tissue types were sampled randomly from the harvested material. The subsamples were measured for fresh weight, then frozen in liquid nitrogen, transported back to the laboratory and stored at −70 °C until they could be freeze dried. Freeze-dried tissue was finely ground in a water-cooled IKA Labortechnic A10 Analytical Mill (Janke and Kunkel GmbH Co., Stanfen, Germany, https://www.ika.com/en/Impressum-imp.html, accessed on 7 May 2025), placed in screw-topped falcon tubes and stored at −20 °C in sealed bags containing silica gel to ensure all plant material remained dry.

3.2.3. Analysis for Cyanogenic Glucoside Concentrations in Leaf and Tuber

Leaf and tuber subsamples (Section 3.2.2) were tested for total cyanogenic glycoside concentration using the evolved cyanide method to provide what is referred to as the cyanogenic potential (HCNp), or the total amount of cyanide released from all sources in the tissue [43]. Approximately 20 mg of finely ground freeze-dried tissue was placed into a sealed vial with 270 μL of 0.1 M phosphate buffer pH 6.8 and a separate 0.2 mL tube containing 200 μL of 1 M NaOH, which was placed, unsealed, into the larger cyanide assay vial. To ensure complete conversion of the cyanogenic glycoside to cyanide, 30 μL of latex-buffer solution (obtained by mixing latex collected from the stem of cassava plants raised in greenhouses at Monash University, Melbourne, Australia) with phosphate buffer 1:5 (v/v) was added to the reaction complex following Gleadow [28]. The vials were frozen at −20 °C and thawed at room temperature twice, then incubated for approximately 19 h at 37 °C. The freeze–thaw process helps break down cells in the plant tissue to allow the cyanogenic glycosides to be released and mixed with the catabolic enzymes in the latex. By incubating this mixture, it is possible to completely hydrolyse the cyanogenic glycosides through the catabolic enzymes, causing HCN to be released. The HCN reacts with the NaOH in the smaller tube to produce sodium cyanide (NaCN), which is then quantified via a colorimetric assay [43]. For accurate quantification of the cyanogenic glycosides, standards were made using NaCN of known concentrations. The colour reactions were performed in triplicate in titre plates following the method of Brinker and Seigler [44] as modified by Woodrow [45]. Added to each well of the titre plate was 45 μL of distilled water, 5 μL of 50 mM sample (containing the trapped NaCN), 50 μL of 0.5 M acetic acid, and 125 μL of succinimide reagent plus 50 μL of pyridine reagent. The plates were left to stand for 15 min and then absorbance was read at 585 nm using a FLUOstar Galaxy spectrophotometric microplate reader (BMG LABTECH, Melbourne, Australia; https://www.bmglabtech.com; accessed on 7 May 2025).

3.2.4. Nutrient Analyses in Leaf and Tuber

Leaf and tuber subsamples (Section 3.2.2) were sent to the Environmental Analysis Laboratory at Southern Cross University (New South Wales, Australia) for determination of elemental composition. Tissue samples were microwave-digested before being analysed by inductively coupled plasma mass spectrometry (ICP-MS) for micro- and macronutrients. Carbon and nitrogen were measured using a LECO CNS2000 analyser [46]. Crude Protein concentrations were estimated by multiplying the percentage N concentration found in the analyses by a factor of 6.25 [47].

3.2.5. 13C Analysis in Leaf and Tuber

The relative amount of 13C was determined as an indication of the degree of stomatal opening and hence an estimate of water use efficiency [48]. The isotopic composition of the subsample of finely ground leaf and tuber tissue (Section 3.2.2) was determined using a Fisons Isochrom continuous-flow isotope ratio mass spectrometer, after combustion at 1050 °C in a Carlo Erba 1110 CHN-S elemental analyser at the Australian National University (Canberra, Australia), as described in Burns [49].

4. User Notes

The dataset is available electronically by reference to the Supplementary Data attached to this article. Future uses of the dataset may include: (1) parameterization of cassava models within APSIM (https://www.apsim.info/; accessed on 17 May 2025) and other cropping systems models that incorporate a cassava module, and (2) contribution toward a broader exploration of variation across cassava germplasms (>20 varieties) used in Fiji and South Pacific Island countries.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/data10080120/s1, Supplemental Data: Biomass; Delta 13C; Field Trial Design; HCN (Hydrogen Cyanide); Nutrients; Phenology; Weather.

Author Contributions

Conceptualization: S.J.C. and B.L.W.; methodology: B.L.W. and S.J.C.; validation: P.N., B.L.W., and R.M.G.; investigation, formal analysis, review and editing: B.T.D., J.N.G.H.; investigation: P.N., R.M.G., and B.L.W.; resources: P.N., W.A., and S.J.C.; data curation: P.N., R.M.G., and B.L.W.; writing—original draft preparation: P.N.; writing—review and editing: R.M.G., B.L.W., W.A., J.N.G.H., S.J.C., and D.L.A.; supervision: R.M.G., B.L.W., S.J.C., and D.L.A.; project administration: S.J.C.; funding acquisition: S.J.C. and D.L.A. All authors have read and agreed to the published version of the manuscript.

Funding

The research reported in this article was funded by the Australian Centre for International Agricultural Research (Grant Number PC-2012-011) as part of a project awarded to CSIRO Agriculture and Food (Canberra, Australia) and led by Dr Steven J. Crimp. Dr Diogenes L. Antille received funding from The Department of Foreign Affairs and Trade (DFAT, Australian Government, https://www.dfat.gov.au/; accessed on 22 July 2025), as part of their SciTech4Climate Indo-Pacific Climate-Smart Agriculture Initiative (https://research.csiro.au/pcra/; accessed on 22 July 2025), which enabled the data contained herein to be consolidated and reported.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Any data contained in this article can be requested from the corresponding author or downloaded from the Supplementary Data File available from the online platform.

Acknowledgments

The authors are grateful to Elizabeth A. Meier (Meier IT Pty Ltd.) for helping with the preparation of the draft, review and formatting of the manuscript, and consolidation of the data. The assistance of the field harvesting (Livai Vakatikati, Timoci Dakuna, Inoke Baininmoli, Seva Dauciri, Joe Nagonenaka, Aminiasi Vola, and Leone Odro) and freeze-drying (Usaia Dolodolotawake, Fane Gucake, and Jerry) teams was greatly appreciated. Comments and suggestions by anonymous reviewers from CSIRO Agriculture and Food (Australia), and the editors and reviewers of this journal are gratefully acknowledged.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Authors’ Disclaimer

The information reported in this article may include the views or recommendations of third parties and does not necessarily reflect the views of the Department of Foreign Affairs and Trade, The Australian Centre for International Agricultural Research, or The Australian Government, nor it indicates a commitment to a particular course of action.

References

  1. Brown, A.L.; Cavagnaro, T.R.; Gleadow, R.; Miller, R.E. Interactive effects of temperature and drought on cassava growth and toxicity: Implications for food security? Glob. Chang. Biol. 2016, 22, 3461–3473. [Google Scholar] [CrossRef] [PubMed]
  2. Gleadow, R.M.; Maher, K.; Cliff, J. Cassava. Curr. Biol. 2023, 33, R384–R386. [Google Scholar] [CrossRef] [PubMed]
  3. El-Sharkawy, M.A. Drought-tolerant cassava for Africa, Asia, and Latin America. Bioscience 1993, 43, 441–451. [Google Scholar] [CrossRef]
  4. Burns, A.; Gleadow, R.; Cliff, J.; Zacarias, A.; Cavagnaro, T. Cassava: The drought, war and famine crop in a changing world. Sustainability 2010, 2, 3572–3607. [Google Scholar] [CrossRef]
  5. Hillocks, R.J.; Thresh, J.M.; Bellotti, A.C. Cassava: Biology, Production and Utilization; CABI Publishing: Oxford, UK, 2002; p. 332. ISBN 978-0-85199-524-3. [Google Scholar]
  6. Lebot, V. Tropical Root and Tuber Crops: Cassava, Sweet Potato, Yams and Aroids, 2nd ed.; CABI Publishing: Oxford, UK, 2020; p. 515. ISBN 978-1-78924-336-9. [Google Scholar]
  7. McKey, D.; Cavagnaro, T.R.; Cliff, J.; Gleadow, R.M. Chemical ecology in coupled human and natural systems: People, manioc, multitrophic interactions and global change. Chemoecology 2010, 20, 109–133. [Google Scholar] [CrossRef]
  8. FAO; IFAD. The World Cassava Economy: Facts, Trends and Outlooks; Food and Agriculture Organisation of the United Nations and International Fund for Agricultural Development: Rome, Italy, 2000; ISBN 92-5-104399-X. Available online: https://www.fao.org/4/x4007e/X4007E00.htm (accessed on 17 May 2025).
  9. FAOSTAT. FAOSTAT Database. 2016. Available online: http://www.fao.org/faostat/ (accessed on 6 November 2018).
  10. Jarvis, A.; Ramirez-Villegas, J.; Herrera Campo, B.V.; Navarro-Racines, C. Is cassava the answer to African climate change adaptation? Trop. Plant Biol. 2012, 5, 9–29. [Google Scholar] [CrossRef]
  11. Taylor, M.; McGregor, A.; Dawson, B. Vulnerability of Pacific Island Agriculture and Forestry to Climate Change; Pacific Community (SPC): Suva, Fiji, 2016; Available online: https://www.spc.int/sites/default/files/wordpresscontent/wp-content/uploads/2016/12/Vulnerability-of-Pacific-Island-agriculture-and-forestry-to-climate-change.pdf (accessed on 14 December 2024).
  12. Meier, E.A.; Antille, D.L.; Mahimairaja, S. Priorities for narrowing the yield gap and increasing farming systems resilience in the Fiji sugar industry. Farming Syst. 2023, 1, 100048. [Google Scholar] [CrossRef]
  13. Antille, D.L.; Macdonald, B.C.T.; Uelese, A.; Webb, M.J.; Kelly, J.; Tauati, S.; Stockmann, U.; Palmer, J.; Barringer, J.R.F. Toward soil nutrient security for improved agronomic performance and increased resilience of taro production systems in Samoa. Soil Syst. 2023, 7, 21. [Google Scholar] [CrossRef]
  14. Antille, D.L.; Field, D.J.; Halavatau, S.M.; Iramu, E.T.; Macdonald, B.C.T.; Singh, K.; Webb, M.J. Regional soil priorities creating partnerships with Australia and New Zealand across the Pacific. Geoderma Reg. 2022, 29, e00517. [Google Scholar] [CrossRef]
  15. Susumu, G.; Sharma, A.; Halavatau, S.; Antille, D.L.; Webb, M.J.; Barringer, J.; Kelly, J.; Macdonald, B. Declining soil nutrient status can constrain agricultural productivity and food security in Pacific Island countries: A country-scale assessment. Pac. Sci. 2023, 76, 337–348. [Google Scholar] [CrossRef]
  16. Jansen, A.A.J.; Parkinson, S.; Robertson, A.F.S. (Eds.) Food and Nutrition in Fiji: A Historical Review. In Nutrition-Related Diseases and Their Prevention; The Department of Nutrition and Dietetics, Fiji School of Medicine, and The Institute of Pacific Studies of The University of the South Pacific, School of Medicine and Institute of Pacific Studies: Suva, Fiji, 1991; Volume 2, p. 719. ISBN 9-8202-0061-X. [Google Scholar]
  17. Hone, P. An investigation of determinants of food choice in Fiji: Their role in demand for nutritionally dense food and nutrition security (ADP/1998/095). In Adoption of ACIAR Project Outputs—Studies of Projects Completed in 2003–2004; Pearce, D., Davis, J., Eds.; Australian Centre for International Agricultural Research (Australian Government): Canberra, Australia, 2008; pp. 19–22. [Google Scholar]
  18. CSIRO; Australian Bureau of Meteorology; SPREP. Climate in the Pacific: A summary of new science management tools. In Change Science and Adaptation Planning Program Summary Report; Commonwealth Scientific and Industrial Research Organisation (CSIRO): Melbourne, Australia, 2015; p. 48. [Google Scholar] [CrossRef]
  19. Cardoso, A.P.; Ernesto, M.; Nicala, D.; Mirione, E.; Chavane, L.; N’Zwalo, H.; Chikumba, S.; Cliff, J.; Mabota, A.P.; Haque, M.R.; et al. Combination of cassava flour cyanide and urinary thiocyanate measurements of school children in Mozambique. Int. J. Food Sci. Nutr. 2004, 55, 183–190. [Google Scholar] [CrossRef] [PubMed]
  20. Gleadow, R.M.; Møller, B. Cyanogenic glycosides: Synthesis, physiology, and phenotypic plasticity. Annu. Rev. Plant Biol. 2014, 65, 155–185. [Google Scholar] [CrossRef] [PubMed]
  21. Gleadow, R.M.; McKinley, B.; Blomstedt, C.; Lamb, A.; Møller, B.L.; Mullet, J.E. Regulation of dhurrin pathway gene expression during sorghum development. Planta 2021, 254, 119. [Google Scholar] [CrossRef] [PubMed]
  22. Forbes, S.; Cernusa, L.; Northfield, T.; Gleadow, M.R.; Lambert, S.; Cheeseman, A. Elevated temperature and carbon dioxide alter resource allocation to growth, storage and defence in cassava (Manihot esculenta). Environ. Exp. Bot. 2020, 173, 103997. [Google Scholar] [CrossRef]
  23. King, N.L.; Bradbury, J.H. Bitterness of cassava: Identification of a new apiosyl glucoside and other compounds that affect its bitter taste. J. Sci. Food Agric. 1995, 68, 223–230. [Google Scholar] [CrossRef]
  24. Bolhuis, G.G. The toxicity of cassava roots. Neth. J. Agric. Sci. 1954, 2, 176–185. [Google Scholar] [CrossRef]
  25. FAO; WHO. Codex Alimentarius—International Food Standards. General Standard for Contaminants and Toxins in Food and Feed (Codex Stan 193–1995). 2016. p. 77. Available online: https://www.fao.org/fao-who-codexalimentarius/sh-proxy/fr/?lnk=1&url=https%253A%252F%252Fworkspace.fao.org%252Fsites%252Fcodex%252FStandards%252FCXS%2B193-1995%252FCXS_193e.pdf (accessed on 17 May 2025).
  26. Ernesto, M.; Cardoso, A.P.; Cliff, J.; Bradbury, J.H. Cyanogens in cassava flour and roots and urinary thiocyanate concentration in Mozambique. J. Food Compos. Anal. 2002, 13, 1–12. [Google Scholar] [CrossRef]
  27. Vandegeer, R.; Miller, R.E.; Bain, M.; Gleadow, R.M.; Cavagnaro, T.R. Drought adversely affects tuber development and nutritional quality of the staple crop cassava (Manihot esculenta Crantz). Funct. Plant Biol. 2013, 40, 195–200. [Google Scholar] [CrossRef] [PubMed]
  28. Gleadow, R.; Pegg, A.; Blomstedt, C. Resilience of cassava (Manihot esculenta Crantz) to salinity: Implications for food security in low-lying regions. J. Exp. Bot. 2016, 67, 5403–5413. [Google Scholar] [CrossRef] [PubMed]
  29. Myrans, H.; Xu, S.; Mahani, M.; Crimp, S.; Torres, J.F.; Gleadow, R.M. Irrigation using slightly saline water from emerging desalination technologies as a solution to short-term drought. Plants People Planet 2024. [Google Scholar] [CrossRef]
  30. Gleadow, R.M.; Woodrow, I.E. Constraints on effectiveness of cyanogenic glycosides in herbivore defense. J. Chem. Ecol. 2002, 28, 1301–1313. [Google Scholar] [CrossRef] [PubMed]
  31. Sardos, J.; McKey, D.; Duval, M.-F.; Malapa, R.; Noyer, J.-L.; Lebot, V. Evolution of cassava (Manihot esculenta Crantz) after recent introduction into a South Pacific Island system: The contribution of sex to the diversification of a clonally propagated crop. Genome 2008, 51, 912–921. [Google Scholar] [CrossRef] [PubMed]
  32. Nyaika, J.; Abayomi, L.; Parmar, A.; Coast, O. Cyanide in cassava: Understanding the drivers, impacts of climate variability, and strategies for food security. Food Energy Security 2024, 13, e573. [Google Scholar] [CrossRef]
  33. Moreno-Cadena, P.; Hoogenboom, G.; Cock, J.H.; Ramirez-Villegas, J.; Pypers, P.; Kreye, C.; Tariku, M.; Ezui, K.S.; Lopez-Lavalle, L.A.B.; Asseng, S. Modeling growth, development and yield of cassava: A review. Field Crops Res. 2021, 267, 108140. [Google Scholar] [CrossRef] [PubMed]
  34. Nauluvula, P. Growth, Physiology and Nutritional Value of Cassava in Fiji for Adaptation Solutions to Changes in Climate. Ph.D Thesis, The University of the South Pacific, Suva, Fiji, 2019; p. 215. [Google Scholar]
  35. Crimp, S.; Lisson, S.; Gleadow, R.M.; Hargreaves, J.; Gabriel, E.; Meier, E.; Nishi, M.; Nauluvula, P.; Melteras, M.; Leo, P. Understanding the Response of Taro and Cassava to Climate Change; ACIAR Project Number: PC-2012-011. Final Report; Australian Centre for International Agricultural Research: Canberra, Australia, 2017; p. 140. Available online: https://www.aciar.gov.au/sites/default/files/project-page-docs/final_report_hort.2012.011.pdf (accessed on 1 May 2025).
  36. Nauluvula, P.; Webber, B.; Crimp, S.; Gleadow, R.M. Growth, physiology and nutritional value of cassava in Fiji for adaptation solutions to changes in climate. Cyanide Cassava Dis. Netw. (CCDN) News 2023, 39, 9–10. [Google Scholar]
  37. Holzworth, D.P.; Huth, N.I.; deVoil, P.G.; Zurcher, E.J.; Herrmann, N.I.; McLean, G.; Chenu, K.; van Oosterom, E.J.; Snow, V.; Murphy, C.; et al. APSIM—Evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 2014, 62, 327–350. [Google Scholar] [CrossRef]
  38. Bredeson, J.V.; Lyons, J.B.; Prochnik, S.E.; Wu, G.A.; Ha, C.M.; Edsinger, E.; Grimwood, J.; Schmutz, J.; Rabbi, I.Y.; Egesi, C.; et al. Sequencing wild and cultivated cassava and related species reveals extensive interspecific hybridization and genetic diversity. Nat. Biotechnol. 2016, 34, 562–570. [Google Scholar] [CrossRef] [PubMed]
  39. Paydar, Z.; Cresswell, H.P. Water retention in Australian soils. II. Prediction using particle size, bulk density, and other properties. Aust. J. Soil Res. 1996, 34, 679–693. [Google Scholar] [CrossRef]
  40. Ngo-Cong, D.; Antille, D.L.; van Genuchten, M.T.; Nguyen, H.Q.; Tekeste, M.Z.; Baillie, C.P.; Godwin, R.J. A modeling framework to quantify the effects of compaction on soil water retention and infiltration. Soil Sci. Soc. Am. J. 2021, 85, 1931–1945. [Google Scholar] [CrossRef]
  41. Burk, L.; Dalgliesh, N. Estimating Plant Available Water Capacity; Grains Research and Development Corporation: Canberra, Australia, 2013; p. 24. Available online: https://grdc.com.au/__data/assets/pdf_file/0029/208379/grdc-plant-available-water-capacity.pdf.pdf?utm_source=website&utm_medium=download_button&utm_campaign=pdf_download&utm_term=National;%20North;%20South;%20West&utm_content=Estimating%20Plant%20Available%20Water%20Capacity (accessed on 21 May 2025).
  42. Schneider, C.A.; Rasband, W.S.; Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 2012, 9, 671–675. [Google Scholar] [CrossRef] [PubMed]
  43. Gleadow, R.M.; Bjarnholt, N.; Jørgensen, K.; Fox, J.; Miller, R.E. Chapter 12: Detection, identification and quantitative measurement of cyanogenic glycosides. In Research Methods in Plant Science, Vol. 1: Soil Allelochemicals; Narwal, S.S., Szajdak, L., Sampietro, D.A., Eds.; International Allelopathy Foundation: Haryana, India; Studium Press: New York, NY, USA, 2012; pp. 283–310. ISBN 1-933699-64-7/1-933699-65-5. [Google Scholar]
  44. Brinker, A.M.; Seigler, D.S. Methods for the detection and quantitative determination of cyanide in plant materials. Phytochem. Bull. 1989, 21, 24–31. [Google Scholar]
  45. Woodrow, I.E.; Slocum, D.J.; Gleadow, R.M. Influence of water stress on cyanogenic capacity in Eucalyptus cladocalyx. Funct. Plant Biol. 2002, 29, 103–110. [Google Scholar] [CrossRef] [PubMed]
  46. Kowalenko, C.G. Assessment of LECO CNS-2000 analyzer for simultaneously measuring total carbon, nitrogen, and sulphur in soil. Commun. Soil Sci. Plant Anal. 2001, 32, 2065–2078. [Google Scholar] [CrossRef]
  47. Krul, E.S. Calculation of nitrogen-to-protein conversion factors: A review with a focus on soy protein. J. Am. Oil Chem. Soc. 2019, 96, 339–364. [Google Scholar] [CrossRef]
  48. Farquhar, G.D.; Richards, R.A. Isotopic composition of plant carbon correlates with water-use efficiency of wheat genotypes. Funct. Plant Biol. 1984, 11, 539–552. [Google Scholar] [CrossRef]
  49. Burns, A.E.; Gleadow, R.M.; Zacarias, A.M.; Cuambe, C.E.; Miller, R.E.; Cavagnaro, T.R. Variations in the chemical composition of cassava (Manihot esculenta Crantz) leaves and roots as affected by genotypic and environmental variation. J. Agric. Food Chem. 2012, 60, 4946–4956. [Google Scholar] [CrossRef] [PubMed]
Table 1. Daily weather data recorded for the experimental site (Koronivia Research Station, Ministry of Agriculture, Suva, Fiji).
Table 1. Daily weather data recorded for the experimental site (Koronivia Research Station, Ministry of Agriculture, Suva, Fiji).
DataDescriptionUnits
RainfallCumulative rainfallmm
TemperatureMinimum and maximum temperatures occurring in the 24 h period °C
Solar radiationCumulative solar radiationMJ m−2
Table 2. Soil properties recorded at the experimental site (Koronivia Research Station, Fiji Ministry of Agriculture): soil bulk density (SBD), soil organic carbon (Org-C), ammonium- (NH4-N) and nitrate (NO3-N), and soil water holding properties. Soil water included the content of the soil when at air dry (AD), permanent wilting point or lower limit at 15 bar (LL15), drained upper limit (DUL), and saturated (SAT) water contents, and the soil water content remaining after maximum extraction by cassava (CLL).
Table 2. Soil properties recorded at the experimental site (Koronivia Research Station, Fiji Ministry of Agriculture): soil bulk density (SBD), soil organic carbon (Org-C), ammonium- (NH4-N) and nitrate (NO3-N), and soil water holding properties. Soil water included the content of the soil when at air dry (AD), permanent wilting point or lower limit at 15 bar (LL15), drained upper limit (DUL), and saturated (SAT) water contents, and the soil water content remaining after maximum extraction by cassava (CLL).
Depth IntervalSBDOrg-CNO3-NNH4-NADLL15DULSATCLL
mg cm−3%, w/wkg ha−1kg ha−1mm mm−1mm mm−1mm mm−1mm mm−1mm mm−1
0.00–0.150.921.021.47.00.2170.3620.4620.6240.362
0.15–0.300.810.96.02.00.3400.4540.5540.6630.504
0.30–0.600.770.94.41.00.4440.4440.5440.6780.520
0.60–0.900.760.92.50.50.4510.4510.5510.6840.551
0.90–1.200.790.85.00.80.4700.4700.5700.6700.570
1.20–1.501.030.52.20.30.4120.4120.5120.5820.512
1.50–1.800.980.40.90.10.3770.3770.4770.5980.477
Table 3. Phenological data derived from repeated weekly measurements conducted on tagged cassava plants within the experimental site.
Table 3. Phenological data derived from repeated weekly measurements conducted on tagged cassava plants within the experimental site.
Phenological DataDescriptionUnits
Plant heightVertical distance, soil surface to top of tallest stemcm
Stem lengthLength from basal branching point to shoot tipcm
Expanded leavesFully expanded mature leavesCount, cm2
Expanding leavesNot fully expanded young leaves (less than 15 days old)Count, cm2
Senesced leavesLeaves with ≥50% yellowing or brown leaf areaCount, cm2
Petiole scarsNodesCount
Canopy diameter Horizontal distance, edges of the canopy irrespective of height from groundcm
Table 4. Cassava plant organs measured in destructive harvests during the life of the crop at intervals of between one and three months after planting.
Table 4. Cassava plant organs measured in destructive harvests during the life of the crop at intervals of between one and three months after planting.
Biomass DataDescription
Shoot (above-ground) biomass material above the cutting stock
Reproductive tissueFlowers, fruits
Mature stemsHard, woody stems
Young stemsSoft, green stems
Expanding leavesNot fully expanded young leaves (less than 15 days old)
Fully expanded leavesFully expanded mature leaves
Senesced leavesLeaves with ≥50% yellowing or brown leaf area
Root (below-ground) biomass material below the cutting stock
Cutting stockCutting stock used as planting material
Fine absorbent rootsRoots < 3 mm diameter
Thick rootsRoots ≥ 3 mm diameter
Outer tuberTuber periderm (peel)
Inner tuberTuber flesh
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Nauluvula, P.; Webber, B.L.; Gleadow, R.M.; Aalbersberg, W.; Hargreaves, J.N.G.; Das, B.T.; Antille, D.L.; Crimp, S.J. Time Series Dataset of Phenology, Biomass, and Chemical Composition of Cassava (Manihot esculenta Crantz) as Affected by Time of Planting and Variety Interactions in Field Trials at Koronivia, Fiji. Data 2025, 10, 120. https://doi.org/10.3390/data10080120

AMA Style

Nauluvula P, Webber BL, Gleadow RM, Aalbersberg W, Hargreaves JNG, Das BT, Antille DL, Crimp SJ. Time Series Dataset of Phenology, Biomass, and Chemical Composition of Cassava (Manihot esculenta Crantz) as Affected by Time of Planting and Variety Interactions in Field Trials at Koronivia, Fiji. Data. 2025; 10(8):120. https://doi.org/10.3390/data10080120

Chicago/Turabian Style

Nauluvula, Poasa, Bruce L. Webber, Roslyn M. Gleadow, William Aalbersberg, John N. G. Hargreaves, Bianca T. Das, Diogenes L. Antille, and Steven J. Crimp. 2025. "Time Series Dataset of Phenology, Biomass, and Chemical Composition of Cassava (Manihot esculenta Crantz) as Affected by Time of Planting and Variety Interactions in Field Trials at Koronivia, Fiji" Data 10, no. 8: 120. https://doi.org/10.3390/data10080120

APA Style

Nauluvula, P., Webber, B. L., Gleadow, R. M., Aalbersberg, W., Hargreaves, J. N. G., Das, B. T., Antille, D. L., & Crimp, S. J. (2025). Time Series Dataset of Phenology, Biomass, and Chemical Composition of Cassava (Manihot esculenta Crantz) as Affected by Time of Planting and Variety Interactions in Field Trials at Koronivia, Fiji. Data, 10(8), 120. https://doi.org/10.3390/data10080120

Article Metrics

Back to TopTop