# The Contribution of Zn Uptake and Grain Zn Loading Intensity to Achieving Zn Biofortification Breeding Targets in Rice

Mbolatantely Rakotondramanana, James Stangoulis, Cécile Grenier, Matthias Wissuwa

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## ► To cite this version:

Mbolatantely Rakotondramanana, James Stangoulis, Cécile Grenier, Matthias Wissuwa. The Contribution of Zn Uptake and Grain Zn Loading Intensity to Achieving Zn Biofortification Breeding Targets in Rice. Journal of Plant Nutrition and Soil Science, 2025, 188 (4), pp.639-649. 10.1002/jpln.12013 .
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RESEARCH ARTICLE

OPEN ACCESS

# The Contribution of Zn Uptake and Grain Zn Loading Intensity to Achieving Zn Biofortification Breeding Targets in Rice

Mbolatantely Rakotondramanana¹,²

James Stangoulis³

Cécile Grenier⁴,⁵,⁶

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Matthias Wissuwa⁷,⁸

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¹Rice Research Department, The National Center for Applied Research on Rural Development (FOFIFA), Antananarivo, Madagascar | ²University of Antananarivo, Antananarivo, Madagascar | ³College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia | ⁴CIRAD, UMR AGAP Institute, Montpellier, France | ⁵UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institute Agro, Montpellier, France | ⁶Alliance Bioversity-CIAT, A.A.6713, Km 17 Recta Palmira Cali, Cali, Colombia | ⁷Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences, Tsukuba, Ibaraki, Japan | ⁸PhenoRob Cluster & Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany

**Correspondence:** Matthias Wissuwa (mwissuwa@uni-bonn.de)

**Academic Editor:** Geilfus Christoph-Martin.

**Funding:** Field experiments in Madagascar were supported by HarvestPlus, part of the CGIAR Research Program Agriculture for Nutrition and Health (A4NH) (Grant 2021H6468.CIR). The grain Zn analysis was supported by HarvestPlus funding to J.S. M.R. and M.W. have been partly funded by a small grant from the fiat panis foundation and M.W. has been partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy—EXC 2070–390732324.

**Keywords:** gene-bank accessions | genotypic variation | grain zinc | zinc concentrations | zinc translocation

## ABSTRACT

**Background:** Increasing Zinc (Zn) concentrations in rice grains is targeted in Zn-biofortification breeding but most rice varieties have grain Zn concentrations far below breeding targets.

**Aims:** High-Zn donors have been identified and our aim is to investigate whether higher grain Zn concentrations in these donors were the result of superior Zn uptake during the reproductive stage, of higher Zn loading intensities or caused by an extended Zn loading period.

**Methods:** Field experiments comparing two high-Zn donors to two low-Zn modern varieties were conducted over two seasons in Madagascar and tissue Zn concentrations and content determined from heading to maturity.

**Results:** Zn uptake between heading and maturity was not limiting grain Zn concentrations. Neither did studied genotypes differ significantly in the duration of Zn loading, which peaked 8–12 days after flowering and strongly decreased thereafter, even in high-Zn donors. Instead, the Zn loading intensity during grain filling differed with peak loading rates exceeding 60 ng Zn day⁻¹ in high-Zn donors compared to 13–37 ng Zn day⁻¹ in low-Zn varieties. This high Zn loading intensity was further evident when Zn and biomass grain loading rates were compared. Donor IRIS-9368 loaded up to 40 ng Zn mg⁻¹ grain biomass compared to 12–21 ng Zn mg⁻¹ in low-Zn varieties. This high loading intensity resulted in a zinc harvest index of 47.7% in IRIS-9368 compared to 26.1% for local variety X265 despite similar reproductive-stage Zn uptake.

**Conclusions:** We hypothesize that xylem-to-phloem transfer of Zn at upper nodes is more efficient in high-Zn donor IRIS-9368.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly
cited.

© 2025 The Author(s). *Journal of Plant Nutrition and Soil Science* published by Wiley-VCH GmbH.
Zinc (Zn) is an essential micronutrient for plants and humans alike, because Zn is a component of numerous enzymes and a key regulator of gene expression and protein synthesis (Broadley et al. 2007; Galetti 2018). Zn deficiency, together with other micronutrient and vitamin deficiencies, has been termed “hidden hunger” (Kennedy et al. 2003). Malnutrition, therefore, is not simply a matter of quantity but may equally arise if food consumed contains insufficient essential nutrients such as iron (Fe), zinc (Zn), or vitamins. More than a billion people suffer from Zn deficiency globally (WHO 2017) and infants are most at risk, as insufficient Zn intake impairs immune system function, leads to delayed development and stunting, and in the worst case, may cause infant mortality (Roohani et al. 2013; Galetti 2018). Alleviation of such micronutrient and vitamin deficiencies is considered 1 of the top 10 priorities of humankind as defined in the Sustainable Development Goals (Goal 2.2: End all forms of malnutrition).

Rice is the most important food source globally, but Zn concentrations in polished rice are typically around 15–18 ppm and too low to supply a sufficiently high proportion of the daily required intake of Zn (Bouis and Welch 2010). Thus, where rice is the main staple and households cannot afford to diversify their meal by adding mineral-rich fruits, vegetables, and meat, Zn deficiency is prevalent (https://www.harvestplus.org/knowledge-market/BPI). Breeding rice varieties with an increased concentration of Zn in their grains, a process termed biofortification with Zn, has therefore been proposed as a global target in rice breeding (Graham et al. 1999; Bouis and Saltzman 2017). Biofortification of crops represents an alternative to fortification of food during processing, and crop biofortification is considered a very efficient and low-cost tool to reach rural communities that are largely food self-sufficient (Virk et al. 2021). To overcome Zn malnutrition, grain Zn concentration in rice need to be increased by 50% or more, with a long-term breeding target set at 28 ppm Zn in white rice, which would correspond to about 34 ppm Zn in brown rice (Bouis and Welch 2010).

As for other breeding targets, a prerequisite is the presence of sufficient genotypic variation for the target trait within the breeding population. Studies investigating variation for gain Zn concentrations concluded that 2–3 fold variation was present within the rice gene pool (Graham et al. 1999; Norton et al. 2014; Swamy et al. 2018; Rakotondramanana et al. 2022). But these studies also indicated that modern varieties and breeding lines typically remained well below breeding targets and that only few potential donors had grain Zn concentrations in excess of 30 ppm Zn.

Low grain Zn concentrations may be caused by a combination of insufficient Zn uptake, low root to shoot translocation, poor Zn remobilization from vegetative tissue, or low Zn loading into grains during grain filling (Impa, Gramlich et al. 2013; Jiang et al. 2007; Johnson-Beebout et al. 2016; Stomph et al. 2014; Wu et al. 2010). Experimental evidence is inconclusive regarding the most rate-limiting step. Comparisons of rice to other cereals like wheat showed grain Zn concentrations to be lowest in rice, and that was accompanied by a low Zn harvest index (Zn-HI). This suggests rice has a lower ability to remobilize Zn during the reproductive

stage (Prasad and Shivay 2018). Instead, Zn in rice grains was shown to predominantly originate from Zn taken up during the reproductive stage (Impa, Gramlich et al. 2013; Jiang et al. 2007; Stomph et al. 2014).

However, there appear to be deviations from this general observation depending on which genotype was investigated and whether it could be considered high or low with regards to grain Zn concentrations. A genotype studied frequently is the rice megavariety IR64 (low to medium grain Zn), and it was found to maintain Zn uptake during the reproductive stage like other genotypes, but a high proportion of its Zn was retained in the root and in consequence IR64 relied in part on re-translocation of Zn from vegetative tissues to grains (Wu et al. 2010). Higher contributions of re-translocation compared to Zn uptake (from the soil) during grain filling was also seen in high-Zn genotypes IR68144 (Johnson-Beebout et al. 2016; Wu et al. 2010) and SWHOO (Impa, Morete et al. 2013), whereas other high-Zn genotypes like IR69428 relied predominantly on soil derived Zn uptake during the grain-filling stage (Impa, Gramlich et al. 2013). Where Zn translocations from root to shoot was not limiting, a barrier between stem and panicle was identified as the major bottleneck in achieving high Zn loading into grains (Stomph et al. 2014). Thus, it appears that genotypes with low grain Zn concentrations may be deficient in translocation of Zn either from root to shoot, from shoot to panicles, or in re-translocation of Zn previously stored in vegetative tissue. High-Zn genotypes on the other hand either efficiently translocate Zn taken up during the grain-filling stage to panicles or show high rates of Zn re-translocation.

From the breeding point of view, this may allow for the pyramiding of distinct and possibly non-exclusive mechanisms to elevate grain Zn concentrations. To achieve that in a targeted way, it will be necessary to first identify which step in the most limiting factor in the breeding material to be improved, followed by the identification of donor material that would complement this deficiency. Detailed studies as the ones discussed above typically conduct Zn uptake and translocation studies in artificial media spiked with traceable Zn isotopes. This poses the danger of limited transferability of results to field conditions and may not be feasible in a breeding context where data should be collected with the target germplasm in the target environment.

We have previously evaluated effects of genotype and environment on grain Zn concentrations in Madagascar, where recent surveys estimated that 80% of the population in the central highlands consume inadequate amounts of Zn (Shiratori and Nishide 2018). In rural areas, 50% of children suffer from stunting and are underweight (Stewart et al. 2020), one of the highest rates in the world (UNICEF 2019). Rice is essential in Malagasy diets; it is eaten three times a day and represents 50% of the daily caloric intake, with per capita consumption being above 120 kg annually. Consequently, Madagascar has the 3rd highest Biofortification Prioritization Index (BPI) for Zn in rice for Africa and the 13th highest globally (https://www.harvestplus.org/knowledge-market/BPI).

Recent studies conducted across sites and years showed that grain Zn concentrations of the recommended rice variety (X265) for the
central highlands of Madagascar was consistently below 20 ppm Zn in brown rice, whereas potential donors consistently were at or above 30 ppm (Rakotondramanana et al. 2024). Objectives of this study were to determine (1) to what extent Zn concentrations in grains were limited by the quantity of Zn taken up during the reproductive stage, especially in local low-Zn variety X265; (2) to establish a time-course of Zn and biomass loading during the reproductive stage; and (3) to investigate whether higher grain Zn concentrations in high-Zn genotypes were due to a higher intensity of Zn loading or an extended duration of the loading period. Experiments were conducted in the field and compared X265 to two high-Zn donors and the international check variety IR64 considered to have low to medium grain Zn.

## 2 | Materials and Methods

### 2.1 | Experimental Materials

Trials were conducted with four genotypes, which included the recommended variety for the highlands of Madagascar, X265; the international check variety IR64 (imported from IRRI), and two high-Zn gene-bank accessions from IRRI (IRIS_313-9368 and IRIS_313-10114) that had been identified in previous experiments in Madagascar (Rakotondramanana et al. 2022, 2024). IRIS_313-9368 (hereafter referred to as IRIS-9368) is a landrace from Bangladesh belonging to the *aus* subspecies of rice, whereas IRIS_313-10114 (hereafter referred to as IRIS-10114) is an *indica* accession from China.

### 2.2 | Field Experiments

Experiments 1 and 2 were conducted at Anjiro village (elevation 980 m) in Madagascar (18°89'99"S, 47°97'46"E) during the rainy seasons of 2019–20 (Experiment 1) and 2020–21 (Experiment 2) with sowing in November and harvesting in April–May. NPK fertilizer (11:22:16) was applied during field preparations prior to transplanting at a rate of 300 kg ha⁻¹. The investigation of the time-course of Zn loading into grains was done during the 2019–20 season at the same Anjiro field site used in Experiment 1 and at a parallel field trial conducted at a 2nd field site at Behenjy village (elevation 1418 m; 19°20'74"S, 47°48'20"E). A zero-input treatment was included in addition to the NPK fertilizer treatment in these time-course experiments.

All experiments were conducted in farmers' fields under lowland conditions: a 3–8 cm layer of standing water was maintained in bunded fields throughout the crop cycle through surface irrigation with water drawn from small local creeks and canals. Thirty-day old seedlings were transplanted as a single seedling per hill with 20 cm spacing between and within rows. Each genotype was sown into a plot consisting of five 2-m long rows arranged in an RCB with three replications in Experiments 1 and 2. The time-course experiment used a split-plot design with three replications and fertilizer application as the main plot. Weeding was done manually 3 and 8 weeks after transplanting, whereas pest control was not necessary.

### 2.3 | Plant Sampling

In Experiment 1, plants were sampled during the heading, flowering, and maturity stages. Per plot, three entire plants were sampled. First, panicles were cut above the panicle neck node and immediately stored in paper bags to avoid contamination. The remaining shoot was divided into two parts during sampling: the portion above the irrigation water line (≥2 cm above) and the submerged portion of the shoot, excluding roots but including the segment below the water line. Weights of these two portions were combined to estimate the total shoot biomass after separate oven-drying at 65°C for 3 days, whereas only the portion above the irrigation water was used for Zn concentration analysis. This was done to avoid inflated shoot Zn concentrations due to possible contamination of the stem parts in contact with soil.

In Experiment 2, sampling was only done during the heading and maturity stages but otherwise following same procedures as in Year 1. In order to estimate the Zn taken up between heading and maturity, six plants of same size and tiller number were selected per plot at heading, and three of these were tagged for later sampling at maturity.

In the time-course experiment, panicles were sampled every 4 days starting at 4 days after flowering (DAF). At each time point, four panicles were selected for sampling, and an additional four that were at the same developmental stage were tagged for sampling at the following time point. Sampled panicles were immediately placed in a paper bag, brought to the lab, and oven-dried at 50°C for 3 days.

### 2.4 | Determination of Grain and Shoot Zn Concentrations

After oven-dying, shoot samples were ground in a Retsch mill. Seeds were hulled manually avoiding metal contamination and this provided a husk and a grain (unpolished brown rice) sample. For Experiment 1, hulled grain and ground shoot was sent to Flinders University, Australia, for determination of Zn concentrations. For the time-course experiment, whole panicles were sent. Samples from Experiment 2 were sent to JIRCAS/Japan instead, and Zn concentrations in husks were analyzed in addition to grains and shoot.

For the analysis of tissue Zn concentrations, 0.3 g hulled, brown, unbroken rice seed or ground shoot samples were oven dried at 80°C for 4 h to remove remaining moisture, the exact weight and grain number was recorded, followed by acid-digestion in a closed tube as described in Wheal et al. (2011). Elemental concentrations of samples were measured using inductively coupled plasma mass spectrometry (ICP-MS 8900; Agilent, Santa Clara, CA), according to the method of Palmer et al. (2014). A blank and a certified reference material (CRM; NIST 1568b rice flour) were analyzed every 30 samples for quality assurance. Samples with aluminum (Al) present at >5 µg g⁻¹ were considered to have unacceptable levels of purported soil contamination (Yasmin et al. 2014), which meant we omitted 7 samples from the Anjiro and 16 from the Behenjy datasets. Zn concentrations are given in ppm (µg g⁻¹) on a dry weight basis.
<table><caption>**TABLE 1** | Shoot and grain Zn concentrations in four genotypes and their average (in bold script) harvested from a lowland rice field at Anjiro village, Madagascar, between November 2019 and April 2020.</caption><thead><tr><th rowspan="2"></th><th colspan="8">Shoot Zn concentration (µg g<sup>-1</sup>)</th><th colspan="4">Grain Zn concentration (µg g<sup>-1</sup>)</th></tr><tr><th colspan="2">Heading</th><th colspan="2">Flowering</th><th colspan="2">Maturity</th><th colspan="2">Early filling</th><th colspan="2">Maturity</th></tr></thead><tbody><tr><td>X265</td><td>27.0</td><td>(4.1)</td><td>c</td><td>36.2</td><td>(12.8)</td><td>a</td><td>62.6</td><td>(8.0)</td><td>a</td><td>22.7</td><td>(1.4)</td><td>b</td><td>16.4</td><td>(2.4)</td><td>d</td></tr><tr><td>IR64</td><td>33.5</td><td>(13.1)</td><td>abc</td><td>37.9</td><td>(4.9)</td><td>a</td><td>47.1</td><td>(15.5)</td><td>a</td><td>26.6</td><td>(2.5)</td><td>b</td><td>19.2</td><td>(1.3)</td><td>c</td></tr><tr><td>IRIS-9368</td><td>45.1</td><td>(9.7)</td><td>a</td><td>42.3</td><td>(9.5)</td><td>a</td><td>66.9</td><td>(2.8)</td><td>a</td><td>44.2</td><td>(6.3)</td><td>a</td><td>33.9</td><td>(3.9)</td><td>a</td></tr><tr><td>IRIS-10114</td><td>40.0</td><td>(6.1)</td><td>ab</td><td>51.2</td><td>(14.8)</td><td>a</td><td>68.1</td><td>(3.4)</td><td>a</td><td>39.6</td><td>(3.9)</td><td>a</td><td>25.7</td><td>(1.1)</td><td>b</td></tr><tr><td>AVG</td><td><strong>36.4</strong></td><td><strong>(10.0)</strong></td><td><strong>B</strong></td><td><strong>41.6</strong></td><td><strong>(10.1)</strong></td><td><strong>B</strong></td><td><strong>61.9</strong></td><td><strong>(8.7)</strong></td><td><strong>A</strong></td><td><strong>33.0</strong></td><td><strong>(7.4)</strong></td><td><strong>A</strong></td><td><strong>23.8</strong></td><td><strong>(5.7)</strong></td><td><strong>B</strong></td></tr></tbody></table>

Note: Different letters following means indicate significant differences within a sampling time and between sampling time averages (capital letters) at *p* < 0.05 according to Tukey's HSD test. Values in brackets are standard deviations.

<table><caption>**TABLE 2** | Average Zn concentration, biomass, and Zn content at heading (shoots only) and maturity (in shoots and grains) measured across four genotypes grown in a field at Anjiro village, Madagascar, between November 2020 and April 2021.</caption><thead><tr><th rowspan="2"></th><th colspan="5">Shoot</th><th colspan="2">Grain</th></tr><tr><th>Heading</th><th>Maturity</th><th>Stage (S)</th><th>Genotype (G)</th><th>S × G</th><th>Maturity</th><th>Genotype (G)</th></tr></thead><tbody><tr><td>Zn concentration (µg g<sup>-1</sup>)</td><td>33.1 (6.7)</td><td>47.4 (14.0)</td><td>***</td><td>***</td><td>***</td><td>27.6 (12.6)</td><td>***</td></tr><tr><td>Biomass (g plant<sup>-1</sup>)</td><td>19.0 (6.3)</td><td>19.5 (6.8)</td><td>ns</td><td>***</td><td>ns</td><td>15.0 (4.3)</td><td>***</td></tr><tr><td>Zn content (µg plant<sup>-1</sup>)</td><td>617.2 (222.4)</td><td>892.3 (416.1)</td><td>***</td><td>***</td><td>**</td><td>398.7 (130.6)</td><td>***</td></tr></tbody></table>

Note: Values in brackets are standard deviations.

**, and *** indicate significance of effects at *p* < 0.05, *p* < 0.01, and *p* < 0.001, respectively.

## 2.5 | Statistical Analysis

Analysis of variance (ANOVA) was performed using R [R version 2.11.0 (2010-04-22)]. Further post-hoc analyses were employed to detect significant differences between genotypes within time points and sites on the basis of Tukey's HSD test at *p* < 0.05, using the STATISTIX 10 software. We considered analyzing the data from the time-course experiment as a Repeated Measured Design ANOVA but refrained from doing so because it cannot handle missing data well. With one missing value in a time series, the entire series would have been omitted.

## 3 | Results

In Experiment 1 conducted at Anjiro village, the first objective was to test whether plant Zn status as indicated by shoot Zn concentrations affected grain Zn concentrations. Shoot samples taken at heading had an average Zn concentration of 36.4 µg g⁻¹ (Table 1), which is far above the threshold of 20 µg g⁻¹ considered indicative of Zn deficiency (Dobermann and Fairhurst 2000). Shoot Zn concentrations increased further to 41.6 µg g⁻¹ at early grain filling and 61.9 µg g⁻¹ at maturity, demonstrating that all plants were well-supplied with Zn. Grain Zn concentrations were lower than shoot Zn concentrations and decreased from 33.0 µg g⁻¹ at early grain filling to 23.8 µg g⁻¹ in seeds harvested at maturity.

Genotypic differences were more pronounced for Zn concentrations in grain than in shoot samples (Table 1). At maturity, gene

bank accession IRIS-9368 had the highest grain Zn concentration that was twice as high compared to local variety X265. Accession IRIS-10114 was intermediate, and the same ranking was already detected in grains during the early grain filling stage. Shoot Zn concentrations were significantly different at the heading stage, with IRIS-9368 being highest and X265 lowest. During later stages, differences in shoots were not significant and more than doubled in X265.

## 3.1 | Zn Uptake and Distribution During the Reproductive Stage

In Experiment 2 conducted in Anjiro during the 2020–2021 season, a further objective was to estimate Zn uptake during the reproductive phase. To this end, plants of similar size within a plot were tagged at the booting stage and harvested at booting and maturity to quantify the increase in aboveground Zn content during the reproductive stage. Across all genotypes, shoot biomass increased marginally from 19.0 g plant⁻¹ at booting to 19.5 g plant⁻¹ at maturity (Table 2). This contrasted with a highly significant increase in shoot Zn concentrations from 33.1 to 47.4 µg g⁻¹ between heading and maturity and an increase in shoot Zn content from 617 to 892 µg plant⁻¹. Grain Zn concentrations averaged 27.6 µg g⁻¹ and total grain Zn content (TGZn) 399 µg plant⁻¹ (Table 2). Differences between genotypes were highly significant for all traits.

At the heading stage, varieties IR64 and X265 had the lowest shoot Zn concentrations (<30 µg g⁻¹), whereas the two gene bank accessions exceeded 38 µg g⁻¹ (Figure 1). At maturity shoot
<figure><img src="image_6.png" /><figcaption>**FIGURE 1** | Zinc concentrations in the shoot biomass during the heading and maturity stages compared to grain Zn concentrations at maturity. Plants were grown in an irrigated field at Anjiro village, Madagascar, between November 2020 and April 2021. Error bars represent standard deviations, and different letters indicate significant differences between genotypes within variables according to Tukey's HSD test at *p* < 0.05.</figcaption></figure>

Zn concentrations increased significantly in three of the four accessions, reaching more than 50 µg g⁻¹ in the gene bank accessions and more than 40 µg g⁻¹ in X265. None of the genotypes increased their shoot biomass significantly during this period (Figure S1). That shoot Zn concentrations increased stronger than shoot biomass indicated that an influx of Zn to the shoot during this period was the driving factor (Table 2).

Grain Zn concentrations varied strongly between genotypes from as low as 16.5 µg g⁻¹ in X265 to a high of 47.7 µg g⁻¹ in IRIS-9368 (Figure 1). This extremely high grain Zn concentration may have partly been due to the low grain and shoot biomass of IRIS-9368 in this experiment, which was only half compared to the other three genotypes, which did not differ for shoot biomass and showed only minor differences in grain biomass, with X265 having highest (18.0 g) total grain weight per plant (Figure S1).

Zn content in shoot tissue (at the vegetative and maturity stage), husks, and hulled grains is shown in Figure 2. All genotypes had a higher shoot (straw) Zn content at maturity compared to heading, but this increase was only significant for genotypes X265 and IRIS-10114 (Figure 2a). Interestingly, X265 had the lowest grain Zn content despite this significant increase in shoot Zn. IRIS-10114 on the other hand, matched the strong increase in shoot Zn with highest grain Zn content. The second high-Zn gene-bank accession IRIS-9368 had accumulated almost as much Zn in grains as IRIS-10114, but its shoot Zn content was the lowest (Figure 2a). The Zn content in husks was calculated but husk-Zn contributed only a small portion of between 1.6% and 5.5% to the total above-ground plant Zn content, and differences were not significant.

On the basis of the increase in aboveground Zn content between heading and maturity, the reproductive stage Zn uptake was

calculated. It was lowest in IR64 with 419 µg plant⁻¹ and highest in IRIS-10114 with 1088 µg plant⁻¹ (Figure 2b). This reproductive stage Zn uptake exceeded TGZn (including husks) for all genotypes, ranging from 2.5% in IR64 to >100% in X265 and IRIS-10114.

The distribution of biomass and Zn between grains and vegetative tissue was investigated using the harvest index for biomass (HI) and Zn (Zn-HI). All genotypes had a lower Zn-HI (34.6% on average) compared to the biomass HI (48.8% on average). Interestingly, the contrasting genotypes for grain Zn concentrations, X265 and IRIS-9368, had the highest biomass HI, exceeding 50% (Figure 3). However, for the Zn-HI, they remained at opposite ends of the genotype range, with a low Zn-HI of 26.1% in X265 compared to 47.7% in IRIS-9368.

## 3.2 | Zn Loading During the Grain Filling Period

An additional experiment investigated genotypic differences in Zn loading into grains at different time points throughout the grain filling period. Samples were taken from the field experiments conducted in 2019–2020 at the Anjiro and Behenjy sites.

The global ANOVA indicated that grain Zn concentrations were most strongly affected by the factor genotype, with factor site becoming increasingly influential toward maturity (Table S1). The factor fertilizer application was not significant from 8 DAF onwards, and neither were 2-factor interactions with the factor fertilizer application (Table S1). In contrast to Zn, the concentration of P in grains was very strongly affected by NPK application, which was expected given that soils at experimental sites were highly deficient for the nutrient P (Rakotondramanana et al. 2024).

To study the development of grain Zn in more detail, further ANOVAs were run separately for each time point for the combined data of both fertilizer treatments, as this factor did not affect grain Zn concentrations. Grain Zn concentrations were most strongly and at all time points affected by the factor genotype (G), with site (S) and S × G interaction effects playing a lesser role (Table S2). For TGZn, site and S × G interactions increased in importance in addition to the dominant genotype effect, and further data analyses will therefore be presented separately for each site.

Considering all the genotypes and sites together, highest average grain Zn concentrations of up to 37.7 µg g⁻¹ were observed at the very early grain filling stage 4 DAF (Figure 4a). They decreased to 28.1 µg g⁻¹ at 12 DAF and toward maturity differences between locations became significant, with an average over genotypes of 26.8 µg g⁻¹ in Behenjy at the final sample at maturity (28 DAF) compared to 31.3 µg g⁻¹ in Anjiro.

The development of grain biomass and grain Zn content was parallel from 4 to 8 DAF (Figure 4b,c), but during the main grain filling stage between 8 to 12 DAF in Anjiro and 8 to 20 DAF in Behenjy, grain biomass increased faster than Zn loading, resulting in the reduced Zn concentrations seen in Figure 4a. Zn loading continued until maturity at 28 DAF but
<figure><img src="image_7.png" /><figcaption>**FIGURE 2** | Zinc content at the heading (H) and maturity (M) stages in the shoot biomass, husks, and grains (a) and calculated Zn uptake during the reproductive stage based on the difference between plant Zn content between H and M (b). Plants were grown in an irrigated field at Anjiro village, Madagascar, between November 2020 and April 2021. Error bars represent standard deviations, and different letters indicate significant differences between genotypes within time points and tissues according to Tukey's HSD test at *p* < 0.05.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**FIGURE 3** | Harvest index for biomass (HI) and for Zn (Zn-HI). Plants were grown in an irrigated field at Anjiro village, Madagascar, between November 2020 and April 2021. Error bars represent standard deviations, and different letters indicate significant genotypic differences for biomass HI or Zn-HI (Tukey's HSD, *p* < 0.05).</figcaption></figure>

at a reduced rate compared to the first 12 days. Sites differed during the period between 8 to 20 DAF with slower grain filling (biomass) and Zn loading in Behenjy, which is likely caused by the lower temperature in this higher elevation site (1418 m asl; 20.7/27.5°C average/maximum temperature from December to April) compared to Anjiro at 980 m asl and corresponding average/maximum temperatures of 22.2/32.0°C.

Average hulled grain weight increased from 5.2 mg per grain at 4 DAF (both sites) to 21.1 mg at Anjiro and 19.0 mg at Behenjy at maturity. Genotype IRIS-10114 had heaviest grains from DAF 12 onwards, reaching 24.9 and 21.2 mg at maturity at respective sites (Figure 5c,f). Other genotypes had similar mature weight and during the course of grain filling only minor and transient

differences were detected. The total panicle weight per plant ranged from 17.7–24.8 g plant⁻¹ at Anjiro and 17.9–21.6 g plant⁻¹ at Behenjy, and IRIS-10114 was the highest yielder at both sites (Table S3). IRIS-9368 did not differ from IR64 and X265 unlike in Experiment 2 where it grew poorly and only had half the biomass compared to other genotypes (Figure S1).

## 3.3 | Genotypic Differences in Zn Loading

Genotypic rankings for grain Zn concentrations remained largely unchanged over the course of grain filling. At both locations, IRIS-9368 had highest grain Zn concentrations, and this was evident as early as 4 DAF, where it ranged from 56.0 µg g⁻¹ at Anjiro to 64.2 µg g⁻¹ at Behenjy (Figure 5a,d). IR64 and X265 had consistently low grain Zn concentrations, starting at around 30 or 20 µg g⁻¹ at Anjiro and Behenjy, respectively. Grain Zn content per single grain at 4 DAF ranged from 0.08–0.20 µg Zn in Behenjy to 0.15–0.30 µg Zn in Anjiro, and genotypic differences were not significant (Figure 5b,e). Until 12 DAF grain Zn content rapidly increased in the two gene bank accessions, whereas increases were moderate for other genotypes, leading to significant differences between these groups from 8 DAF in Anjiro and 12 DAF at Behenjy.

To further examine grain Zn loading speed and peak period, the loading rates between sampling points were calculated and plotted in Figure 6. Generally, the period of peak grain Zn loading overlapped with the period of peak grain biomass loading between Days 8 and 12 DAF. The main difference between genotypes appears the intensity of loading, which is highest in both gene-bank accessions for loading of Zn (Figure 6a,c). Biomass loading was equally intense in IRIS-10114, but IR64 (Anjiro) and X265 (Behenjy) also showed strong peak biomass loading between 8 and 12 DAS (Figure 6b,d). Accession IRIS-9368 behaved differently at Anjiro, as Zn loading and, to a lesser extent, also biomass loading, peaked between Days 4 and 8 and remained
<figure><img src="image_9.png" /><figcaption>**FIGURE 4** | Evolution of grain Zn concentration (a), biomass per grain (b), and grain Zn content (c) between 4 and 28 days after flowering (DAF) at field sites Anjiro and Behenjy between November 2019 and April 2020. Error bars represent standard deviations, with * and ** indicating significant differences between site means at that sampling point at *p* < 0.05 and *p* < 0.01, respectively.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**FIGURE 5** | Comparison of four rice genotypes for the evolution of grain Zn concentration (a and d), grain Zn content (b and e), and grain weight per hulled grain (c and f) between 4 and 28 days after flowering (DAF) at field sites Anjiro and Behenjy between November 2019 and April 2020. Error bars represent standard deviations, and different letters indicate significant genotypic differences within time points (Tukey's HSD, *p* < 0.05).</figcaption></figure>

slightly elevated even after biomass loading had ceased. However, this pattern was not seen in Behenjy.

## 4 | Discussion

Large genotypic differences in grain Zn concentrations were observed, with local variety X265 being consistently below 20 ppm Zn, as reported in previous studies (Rakotondramanana et al. 2022, 2024). International check variety IR64 had been included

for comparison to studies conducted elsewhere. With grain Zn concentrations in brown rice varying between 19.2 and 22.0 ppm in IR64, our data is similar to a multi-location study from India (Naik et al. 2020) and to field trials conducted at IRRI, Philippines, and CIAT, Colombia, both fertilized with Zn (Trijatmiko et al. 2016), and slightly higher than reported by Swamy et al. (2018) from unfertilized field trials conducted at IRRI. We therefore assume that our field trials were representative with regards to Zn status of the field and not deficient for Zn. That shoot Zn concentrations increased from 36.4 ppm at heading to 61.9 ppm
<figure><img src="image_11.png" /><figcaption>**FIGURE 6** | Comparison of grain Zn (a and c) and grain biomass loading rates (b and d) per day in four rice genotypes between 4 and 28 days after flowering (DAF) at field sites Anjiro and Behenjy between November 2019 and April 2020. Error bars represent standard deviations.</figcaption></figure>

at maturity further supports the assumption that plants in our experiment did not experience a deficiency of Zn, considering the threshold indicating Zn deficiency being 20 ppm (Dobermann and Fairhurst 2000).

Zn uptake during the reproductive stage was larger than grain Zn content in every genotype, indicating that theoretically all grain Zn could have been derived from direct uptake during grain filling. Marked differences existed to what extent late Zn uptake exceeded grain Zn content, with relatively more Zn remaining in shoot tissue in X265 compared to Zn loaded into grains. Very low grain Zn concentrations of X265 are therefore not due to insufficient Zn uptake or root to shoot translocation but very likely to some stronger barrier reducing the transfer of Zn from vegetative to reproductive tissues (Stomph et al. 2014). The other extreme was represented by IR64, for which reproductive stage total Zn uptake barely surpassed grain Zn content. That IR64 has lower influx of Zn into shoots during the reproductive stage had been reported earlier and was attributed to poor Zn translocation from root to shoot (Impa, Morete et al. 2013; Wu et al. 2010).

Impa, Morete et al. (2013) also showed that IR64 is capable of re-translocation from leaves and stems to grains of Zn taken up pre-grain filling. Although the absence of isotope data does not allow for quantification of re-translocation in this field study, the comparatively high Zn-HI of IR64 and the absence of an increase in shoot Zn concentrations (Figure 1) may indicate that re-translocation could have played a role in IR64. An even higher Zn-HI was detected in high-Zn donor IRIS-9368. To what extent

this is due to effective re-translocation or direct Zn uptake cannot be ascertained with certainty, but the latter is more likely because the significant increase in shoot Zn concentrations during that period would not support a strong role of re-translocation. It is interesting and unexpected that the two genotypes with Zn-HI below 30%, X265 and IRIS-10114, differed almost 2-fold in grain Zn concentrations (16.4 vs. 31.1 ppm, respectively). IRIS-10114 had by far the largest Zn uptake between heading and maturity and while the proportion of Zn retained in the shoot was similar to X265, the barrier for Zn translocation from vegetative to reproductive tissue presumed to exist (Stomph et al. 2014) must have been much more limiting in X265 compared to IRIS-10114.

## 4.1 | Time-Course of Biomass and Zn Loading

Grain samples taken 4 DAF showed highest grain Zn concentration, and this was particularly pronounced in IRIS-9368, with concentrations of 55.6 ppm in Anjiro and 63.2 ppm in Behenjy. Genotypic differences remained unchanged throughout the grain filling period, with IRIS-9368 being twice as high or more compared to X265. At Behenjy grain, Zn loading was generally parallel to grain biomass loading in all genotypes, with a clear peak for both between 8 and 12 DAF. Therefore, no indication existed that Zn loading was independent of carbohydrate loading in any of the genotypes. In Anjiro, IRIS-9368 behaved differently from other genotypes with a peak in Zn loading between 4 and 8 DAF, which was accompanied by an equally early peak in biomass loading. Biomass and Zn loading are therefore not uncoupled in IRIS-9368 but simply peak slightly earlier. The reason for this is
<table><caption>**TABLE 3** | Ratio of grain Zn and biomass accumulation in ng Zn mg⁻¹ biomass calculated for each interval from data shown in Figure 6a,d.</caption><thead><tr><th rowspan="2"></th><th colspan="4">Days after flowering</th></tr><tr><th>4-8</th><th>8-12</th><th>12-20</th><th>20-28</th></tr></thead><tbody><tr><td><strong>Anjiro</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>X265</td><td>12.7</td><td>12.9</td><td>10.1</td><td>NA</td></tr><tr><td>IR64</td><td>24.4</td><td>20.4</td><td>10.6</td><td>NA</td></tr><tr><td>IRIS-10114</td><td>24.0</td><td>24.8</td><td>19.5</td><td>NA</td></tr><tr><td>IRIS-9368</td><td>43.4</td><td>39.1</td><td>NA<sup>a</sup></td><td>NA</td></tr><tr><td><strong>HSD<sup>b</sup></strong></td><td><strong>26.1</strong></td><td><strong>25.6</strong></td><td><strong>ns</strong></td><td></td></tr><tr><td><strong>Behenjy</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>X265</td><td>12.6</td><td>13.4</td><td>12.6</td><td>NA</td></tr><tr><td>IR64</td><td>15.0</td><td>21.4</td><td>8.9</td><td>NA</td></tr><tr><td>IRIS-10114</td><td>30.6</td><td>23.2</td><td>29.8</td><td>NA</td></tr><tr><td>IRIS-9368</td><td>25.8</td><td>42.9</td><td>8.6</td><td>NA</td></tr><tr><td><strong>HSD<sup>b</sup></strong></td><td><strong>ns</strong></td><td><strong>17.6</strong></td><td><strong>ns</strong></td><td></td></tr></tbody></table>

unknown. IRIS-9368 is maturing earlier than other genotypes and may have developed slightly faster than other genotypes at the warmer Anjiro site but not at the cooler high-elevation Behenjy.

One objective was to clarify whether higher grain Zn concentrations can be attributed to a longer duration of the Zn loading period. In the studied genotypes, there was no evidence that late Zn loading contributed to higher grain Zn concentrations, as little extra Zn entered grains after 12 DAF (Figure 6a,b). Instead, the onset and intensity during early grain filling between 4–8 and 8–12 days differed and was mainly responsible for higher grain Zn content and concentrations. To further investigate whether Zn and biomass loading rates were related for each interval, we calculated the ratio of Zn per biomass accumulated. The calculated rates for X265 were constant around 13 ng Zn mg⁻¹ biomass at 4–8 and 8–12 DAF (Table 3). In comparison, IRIS-9368 peaked at 43.4 ng Zn mg⁻¹ biomass between 4 and 8 DAF in Anjiro and at 42.9 ng Zn mg⁻¹ biomass during peak loading between 8 and 12 DAF in Behenjy. Though differences were not significant because of the large variation associated with derived traits, IRIS-10114 also loaded roughly twice the amount of Zn mg⁻¹ compared to X265.

Zn that is loaded into grains may originate from Zn taken up during grain filling that is directly translocated from roots to upper nodes in the shoot, from Zn recently taken up and transiently stored in shoot tissue, or from Zn re-translocated from leaves. In the first case, Zn is likely translocated in the xylem to upper nodes, where Zn is transferred to the phloem by the xylem-to-phloem transport system operating at stem nodes (Yoneyama et al. 2015). Zn re-translocated from other vegetative tissue is expected to be transported in the phloem and would also have to pass through the uppermost stem node, where a barrier to further translocation to panicles was

shown to exist (Hu et al. 2023; Nishiyama et al. 2013; Stomph et al. 2014).

Under conditions of this study, Zn uptake during grain filling was sufficiently high in the low-Zn variety X265, as well as in the high-Zn gene-bank accessions. Increasing straw Zn concentrations between heading and maturity in these three contrasting genotypes would suggest that re-translocation may have played a minor role. With clearly enough Zn in the shoot, the limitation, especially for X265, is likely the xylem-to-phloem transfer of Zn at the nodes as it is being absorbed (Nishiyama et al. 2013). A second limiting step may be the actual loading into the endosperm. Kawakami and Bhullar (2018) pointed out that as filial tissues of rice (endosperm and embryo) are symplastically isolated from the parental tissues, loading of Zn into grains requires a pair of Zn transporters that accomplish Zn efflux from the maternal tissue and Zn influx into seed tissue. If this would have been a major limiting step in X265, higher grain Zn concentrations would have been expected during the earliest sampling points, when the endosperm was barely formed. This was not the case, and X265 showed a lower rate of dilution of Zn as grains filled compared with IRIS-9368, pointing toward the xylem-to-phloem transfer at the nodes as the main limiting step.

X265 is an improved variety that is well-accepted by farmers, and attempts to develop locally adapted Zn-biofortified lowland rice may need to employ a recipient parent like X265 in breeding efforts. Our study indicates that of the four possible bottlenecks limiting grain Zn concentrations, X265 is neither deficient for Zn uptake or translocation to shoots, but that the translocation from shoots to panicles is the principle limiting factor. Of the genotypes studied, IRIS-9368 is clearly having an outstanding ability to load Zn into grain, and remarkably, X265 and IRIS-9368 both had a similarly high biomass harvest index, indicating that biomass loading was not impaired in X265. Thus, IRIS-9368 may be a suitable donor to overcome the loading bottleneck in X265 and other breeding lines with a similar limitation. That it had poor yield and biomass in one out of the three field plots used in this study may indicate that it is not adapted to all conditions in Madagascar and that further backcrossing may be needed in breeding with this donor. IRIS-10114 on the other hand would be the preferred donor if uptake of Zn needs to be increased. Data from Figure 2 suggests that may not be a necessity; however, the surplus Zn uptake of X265 was evident because of the low Zn translocation to grains. If it were possible to increase grain Zn concentrations from 18 to 30 ppm in X265, the surplus uptake would no longer be pronounced, and Zn uptake may become limiting. Donors for improved uptake may therefore be a necessity once grain loading has been improved unless the larger sink for Zn in biofortified grains would cause Zn uptake to increase generally.

IRIS-10114 belongs to the *indica* subspecies and was selected as the accession with the highest grain Zn concentration (around 30 ppm) among *indicas* on the basis of field screens combined with a genomic prediction approach (Rakotondramanana et al. 2022). Generally, the *indica* group was characterized by lower average grain Zn concentrations (25.1 ppm) compared to the *japonica* (28.4) and *aus* (33.5 ppm) subspecies (Rakotondramanana et al. 2022) to which IRIS-9368 belongs. This big difference between subspecies and the fact that a superior *indica*

<footnote><sup>a</sup>The ratio of Zn to biomass loading could not be calculated because biomass loading had nearly ceased at this time point.</footnote>

<footnote><sup>b</sup>Significant difference at *p* < 0.05 according to Tukey's HSD test.</footnote>
accession remained below the average of the *aus* group may suggest the presence of distinct mechanism(s) favoring grain Zn loading in the *aus* subspecies. Future studies should aim to identify the physiological and genetic basis underlying this difference, and on the basis of results from the present study with high-Zn IRIS-9368, one may hypothesize that differences in the xylem-to-phloem transfer of Zn may be the target mechanism to focus on.

## Author Contributions

**Matthias Wissuwa:** conceptualization, conducted field experiments, data curation and statistical analyses, writing—original draft. **Cécile GRENIER:** conceptualization, writing—original draft. **James Stangoulis:** conceptualization, did Zn analysis. **Mbolatantely Rakotondramana:** conducted field experiments, data curation and statistical analyses, writing—original draft.

## Acknowledgments

The authors would like to acknowledge the support of *Nicole Ranaivo* for conducting field experiments at FOFIFA and of *Lyndon Palmer* for the analysis of grain samples at Flinders University.

Open access funding enabled and organized by Projekt DEAL.

## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

All data supporting the findings of this study are available within the article and within its Supporting Information published online.

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**Supporting Information**

Additional supporting information can be found online in the Supporting Information section.