Combining genetic and agronomic fortification is essential to meet human health targets for zinc, iron, and protein concentrations in rice grains: A meta-analysis

Combining genetic and agronomic fortification is essential to meet human health targets for zinc, iron, and protein concentrations in rice grains: A meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Combining genetic and agronomic fortification is essential to meet human health targets for zinc, iron, and protein concentrations in rice grains: A meta-analysis Kalimuthu Senthilkumar, Dominic Mutambu, Gudeta Weldesemayat Sileshi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9267207/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Rice, consumed by half of the world’s population, is inherently low in zinc (Zn), iron (Fe), and protein. We synthesized data from 245 studies across 34 countries to evaluate the impact of genetic, agronomic, and processing interventions on rice grain Zn, Fe, and protein concentrations. Zn-biofortified cultivars had 9.8% higher grain Zn concentrations than non-biofortified cultivars, while Fe-biofortified cultivars did not exhibit a significant improvement in Fe content. The probabilities of achieving the breeding target concentrations of Zn (28 mg kg – 1 ) and Fe (15 mg kg – 1 ) in polished rice were only 4.0% and 10.5%, while Zn and Fe fertilization increased the probability to 41.3% and 67.7% for Zn and Fe, respectively. Milling and polishing of brown rice grains reduced Zn, Fe, and protein concentrations by 21%, 70%, and 6.5%, respectively. Our findings emphasize the need for combined use of genetic and agronomic fortification, and consumption of parboiled rice to attain desired health impacts. Biological sciences/Biochemistry Biological sciences/Biotechnology Biological sciences/Genetics Biological sciences/Plant sciences Agronomic fortification bioavailability genetic biofortification nutrient uptake Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Rice is a staple food for more than half of the world’s population, and the single largest food source for the poor 1 – 4 . It provides 19–21% of the global per capita energy and about 15% of the protein requirement for humans 3 – 6 . Rice is consumed primarily in a polished form which is loaded with easily digestible starch (~ 90% of dry weight) 1 , but low in zinc (Zn), iron (Fe), and protein concentrations compared with other staple cereals such as wheat and maize 7 , 8 . In the past, research and development efforts have focused most on yield improvement, and less on improvement of the nutritional quality. Decades of breeding for higher yields have led to a decline in grain concentrations of Zn and Fe in staple cereals such as rice and wheat 9 , 10 . The grain Zn and Fe concentrations reported in the literature are often lower than the breeding targets proposed for human health 11 . Initially, the CGIAR biofortification challenge program (HarvestPlus) set the breeding target at 15 mg Fe kg – 1 in polished (white) rice grains 11 . However, this breeding target appears to have been abandoned and no longer appears in the recent literature 12 . The breeding target for Zn is 28 mg kg – 1 in polished rice 11 , 12 . These targets are aspirational goals set to achieve a realistic level of the daily requirement of Zn and Fe for human health. However, these targets do not consider the bioavailability of Zn and Fe in the human gut. The bioavailability and uptake of Zn, Fe, calcium (Ca), magnesium (Mg), and manganese (Mn) in the human gut depend on the phytic acid concentrations in the processed grain 13 – 15 . Plants often store excess phosphorus (P) as phytate (the salt form of phytic acid), which accounts for 60–80% of total P in cereal grains 16 . While it has beneficial health effects as an anticarcinogen, antioxidant, and inhibitor of kidney stone formation, phytate is the most abundant anti-nutritional factor in cereal grains with regard to the uptake of bivalent cations 13 – 15 . Earlier work has emphasized the need for improvement of the nutritional value of cereals through reduction of phytate and increased Zn and Fe concentrations in the grain 16 . Generally, whole-grain rice also has one of the lowest protein concentrations among cereals 13 . The protein, nitrogen (N), and amylose concentrations in rice grains also play a key role in the nutritional, cooking, and eating qualities of rice 17 – 19 . Nevertheless, there are no breeding targets for grain protein or phytate concentrations in rice. Recent findings in breeding rice varieties with higher protein content and low glycemic index are opening new opportunities for breeding more nutritious rice 1 . These discoveries highlight the potential to develop rice varieties with improved protein content and better health features, especially for people with diabetes. Our ability to improve grain yield, grain Zn, Fe, and protein concentrations will depend on understanding the complex interactions and correlations between traits in soils and plants. The interaction between two elements in soils may be antagonistic or synergistic, thereby influencing nutrient uptake and use efficiency 20 . For example, P fertilization is expected to increase crop yields due to increased P uptake (Fig. 1 ), which may increase phytate concentrations in grains, but depresses Zn uptake 16 . The plant physiological process of absorption by phytate, decreases the availability of Zn even further. However, information on the entire spectrum of how nutrient uptake and physiological processes would interact with regards to P, Zn, Fe, sulfur (S) (relevant for amino acids in proteins), and proteins, and hence their concentrations in rice grains is lacking. Figure 1 presents a schematic representation of our hypothesized relationships between nutrient uptake, grain yield, and the concentrations of nutrients, protein, and phytate in rice grains. Quantifying the correlations between these variables could inform efforts to simultaneously improve yield and minerals such as Zn and Fe 12,21 . According to Senguttuvel and co-workers 21 , the correlation between grain yield and grain Zn concentrations is a key factor in identifying and releasing Zn-biofortified rice varieties. Genotype-by-environment (G×E) interactions also play a crucial role in identifying stable lines for Zn and Fe concentrations 21 . However, the strength of correlations and the effect of G×E interactions on the traits targeted for rice biofortification are not fully understood. It is also unclear to what degree agronomic and breeding efforts have achieved the initial breeding targets for Zn and Fe in rice. The impact of the production environments (e.g. irrigation vs rainfed) and agronomic interventions (e.g. fertilization) on grain Zn, Fe, and protein concentrations has not been fully quantified. Although several primary studies have measured concentrations of Zn, Fe, and protein in rice grain at the local level, a global synthesis of this information is lacking. Such syntheses are urgently needed for setting target concentrations and identifying management practices to increase the concentration of essential micronutrients and reduce the concentrations of antinutritional factors such as phytic acid in rice grain. Therefore, the overarching aim of this synthesis is to inform global efforts in genetic and agronomic fortification toward achieving the target concentrations 11 of Zn (28 mg kg – 1 ) and Fe (15 mg kg – 1 ) and increasing protein concentration in rice grains. The specific objectives of this synthesis are to: (1) determine the probability of achieving the target concentrations of Zn and Fe in white rice grains; (2) determine trait correlations, the influence of G×E interactions and heritability toward the simultaneous improvement of grain yield, grain Zn, Fe, and protein concentrations; (3) quantify variations in response ratios of grain yield, grain Zn, Fe, and protein concentrations to genotypes, production environments, agronomic practices, and soil and climate variables. The main hypotheses being tested in this analysis are: (1) grain Zn and Fe concentrations in rice cultivars are below the target concentrations for human nutrition; (2) grain Zn and Fe concentrations can be increased with genetic biofortification and Zn and Fe fertilization; (3) the correlations between grain yield, grain Zn, Fe, and protein concentrations are positive across rice genotypes and fertilization treatments; and (4) Zn and Fe fertilization significantly increase grain Zn, Fe, and protein concentrations across climates, soils, and rice growing environments. Results and discussion Distributions of grain Zn, Fe, protein, and phytate concentrations By consolidating data from 245 publications in studies conducted across 34 countries, and using over 3,600 rice genotypes, this analysis has established benchmark concentrations of Zn, Fe, and protein in rice grain on dry matter basis (Table 1 ). So far, such baselines have not been available, except for Zn and Fe, which were based on limited data generated in the early 2010s. Grain Zn, Fe, and protein concentrations were available in 191, 94, and 89 studies, respectively (Table 1 ), and these were used to establish their empirical distributions (Figure S1 ). Table 1 The mean, median, 25th percentile (Q1), and 75th percentile (Q3) concentrations and the coefficients of variation (CV) of grain zinc, iron, nitrogen, phosphorus, sulfur, protein, and phytate concentrations in rice across both brown and white rice samples Elements (unit) No. studies and observations† Concentration on dry matter basis Mean Median Q1 Q3 CV (%) Zinc (mg kg – 1 )‡ [191; 9801] 25.1 24.8 20.0 29.4 36.9 Iron (mg kg – 1 ) [94; 7104] 14.9 11.6 9.7 13.9 103.6 Nitrogen (%) [89; 1652] 1.4 1.4 1.2 1.6 29.6 Phosphorus (%) [48; 5282] 0.34 0.35 0.31 0.38 25.8 Sulfur (%) [21; 3888] 0.128 0.127 0.112 0.143 20.4 Protein (%) [89; 1652] 8.5 8.1 7.2 9.4 29.6 Phytate (%) [27; 813] 0.94 0.72 0.41 1.20 73.7 CV, coefficient of variation; Q1, lower quantile; Q3, upper quantile. † Figures in brackets represent the number of studies (n) and total sample size (s) available for analysis ‡ 1 mg kg –1 = 1 µg g – 1 . Using data from a total of 38 and 21 studies reporting measurements in polished rice on Zn and Fe concentrations, we estimated the median grain concentrations in white rice at 17.5 mg Zn kg – 1 (confidence interval [CI]: 17.0–18.0) and 3.6 mg Fe kg – 1 (CI: 3.4–3.8). These values are much lower than the breeding targets (28 mg Zn kg – 1 and 15 mg Fe kg – 1 ) 11 . We estimated the probability of exceeding the targets of Zn and Fe in white rice using their cumulative frequency distributions. This was 4.0% in white rice for grain Zn concentration, and 10.5% for grain Fe in the absence of Zn and/or Fe fertilization. These low figures indicate that the progress toward the targets through genetic biofortification has been slow. Where Zn and/or Fe fertilizers were applied, the probabilities of achieving the targets were 41.3% for Zn and 67.7% for Fe. On the basis of these findings, we argue the necessity of complementary efforts in breeding and agronomic interventions to create synergistic effects. The median phytate concentration in rice grain was 0.72% across the 27 studies. The median phytate concentration in white rice (0.38%) was significantly lower than concentrations in brown rice (0.92%) (Table S2 ). According to Kumar and colleagues 22 , high Zn bioavailability is associated with rice cultivars with low grain phytate concentrations (< 0.82%), while those with 2.62% or more phytate concentrations have low Zn and Fe bioavailability. Our analysis reveals that the processing of rice also impacts phytate concentrations, and consequently the bioavailability of Zn and Fe. Trait correlations, G×E interactions, and heritability Figure 1 , Table 2 , and Supplementary Table S3 provide an overview of the relationships and correlations between grain yield and nutritional qualities. The analysis of data aggregated across studies on germplasm and fertilization revealed significantly positive correlations between grain yield and grain Zn concentrations ( r = 0.091; P < 0.001), grain yield and Fe concentrations ( r = 0.198; P < 0.001), and grain Zn and Fe concentrations ( r = 0.418; P < 0.001), but not between grain yield and protein or phytate concentrations (Table 2 ). Significant positive correlations were also observed between grain yield and grain Zn uptake ( r = 0.741; P < 0.001), grain yield and grain Fe uptake ( r = 0.494; P < 0.001), grain yield and grain P uptake ( r = 0.941; P < 0.001), grain Zn uptake and Fe uptake ( r = 0.688; P < 0.001), grain Zn uptake and N uptake ( r = 0.714; P < 0.001), grain Zn uptake and P uptake ( r = 0.643; P < 0.001), grain yield and grain S uptake ( r = 0.671; P < 0.001), and grain Fe uptake and Zn uptake ( r = 0.688; P < 0.001) (Table 2 ). Table S3 a provides the 95% confidence intervals and information on the robustness of the trait correlations. Table 2 Pearson correlation coefficients of the associations between grain yield, grain Zn, Fe, protein, and phytate concentrations (conc), grain Zn uptake, Fe uptake, N uptake, P uptake, and S uptake in rice grains (for details of tests of statistical significance please refer to Table S3 a) Variable Grain yield Zn conc Fe conc Protein conc Zn uptake Fe uptake P uptake Zn conc 0.091*** Fe conc 0.198*** 0.418*** Protein conc 0.002ns 0.179*** 0.097* Phytate conc –0.067ns –0.069* 0.371*** 0.268** –0.555*** 0.026ns P conc 0.346*** –0.058ns –0.119ns –0.125ns 0.192** 0.009ns 0.642*** S conc 0.142* –0.213** 0.423*** –0.164* –0.051ns 0.751*** 0.434*** Zn uptake 0.741*** 0.729*** 0.468*** 0.207*** 0.688*** Fe uptake 0.494*** 0.555*** 0.944*** –0.266*** 0.688** N uptake 0.797*** 0.364*** –0.192** 0.603*** 0.714*** 0.301** 0.580*** P uptake 0.941*** 0.140ns 0.077ns –0.073ns 0.643*** 0.396*** S uptake 0.671*** –0.149* –0.030ns –0.035ns 0.340*** 0.631*** 0.745*** Statistical significance: * α = 0.05; ** α = 0.001; *** α = 0.0001; ns = not significant The findings above generally confirm the relationships depicted in Fig. 1 . We were unable to establish the correlations between concentrations of grain phytate and P uptake or S uptake due to lack of matching data on phytate concentrations. Given the correlations between traits, we posit that improvement of nutritional quality is possible through complementary use of agronomic and genetic fortification of rice. Genetic fortification relies on the inherent genetic potential of crop varieties to accumulate nutrients in the grains. However, the effectiveness of this process depends on nutrient availability, which must be ensured through agronomic fortification via soil and/or foliar application, as their uptake and subsequent accumulation in grains are influenced by soil properties and prevailing weather conditions. In the individual studies that evaluated rice germplasm, the correlations between grain yield, grain Zn, Fe, and protein concentrations were significantly positive in 25–33% of the studies and non-significant in 35–75% of the studies. On the other hand, the correlation between grain Zn and Fe was significantly positive in 59% of the studies. In the meta-analysis, the majority (88%) of Fisher r-to-z transformed correlation coefficients between grain Zn and Fe were positive and the random-effects model estimate (0.368) was significantly different from zero. However, between-study heterogeneity was significant (Table S3 b). From the individual studies on fertilization effects, the correlations between grain yield, grain Zn concentrations, Fe concentrations, and protein concentrations were all significant. The fail-safe number, a tests of publication bias, indicate that the pooled effect size estimates are robust. However, significant between-study heterogeneity was evident in all estimates (Table S3 b). Taken together these positive correlations suggest that grain yield, grain Zn, Fe, and protein concentrations can be simultaneously improved through Zn and Fe fertilization, and (but to a lesser extent) by breeding. We found 7, 5, and 4 studies on G×E interactions for grain yield, grain Zn, and grain Fe concentrations, respectively. The review of these studies suggests that the environment and the G×E interactions account for a significant percentage of the variance in grain yield, grain Zn, and grain Fe concentrations than the genotype effect (Table S4a). In almost all cases, the environment and G×E interaction effects on Zn and Fe concentrations were statistically significant (Table S4a). This indicates a strong influence of the environment on the expression of grain Zn and Fe concentrations. Our review of 20 studies on heritability indicated low to high broad-sense heritability (H 2 ) for grain yield (median 80%; range: 12–95%), grain Zn concentrations (median 86.5%; range: 7–99.5%), and grain Fe concentrations (median 72.8%; range: 7–99.5%) (Table S4b). Low heritability (H 2 < 40%) was found in less than 20% of the observations (Table S4b). In the majority of cases, heritability appears to be high enough for genetic biofortification of Zn and Fe through conventional breeding techniques. Variations in grain yield, grain Zn, Fe, and protein concentrations with varietal release year Grain yields of newly released rice varieties significantly increased between 1960 and 2024 (Figure S2 a), while grain Zn concentrations significantly declined over the same period (Figure S2 b). Grain Fe concentrations were significantly higher in varieties released after 2000 than earlier years (Figure S2 c) but with a very wide confidence interval reflecting greater uncertainty in varieties released during the 2001–2024 period. Grain protein concentrations did not vary significantly with the year of release of varieties (Figure S2 d). Fan and colleagues 23 report a similar decline in micronutrient concentrations of Zn, Fe, copper (Cu), and Mg in wheat over the years. Ecotypic differences Only one study reported measurements of nutrient concentrations in African rice ( Oryza glaberrima ). Therefore, the rest of this analysis focuses on comparing ecotypes and admixtures of Asian rice ( O. sativa ) in terms of grain yield and nutrient concentrations in brown rice. Data on white rice were excluded because of the small sample size available for the different ecotypes. Violin plots and Kruskal–Wallis test revealed significant differences between sample medians for all variables used in the comparison of rice ecotypes (Table S5). The highest median grain yield was recorded in Japonica cultivars (8.3 t ha – 1 ) followed by admixtures (6.1 t ha – 1 ), which were significantly higher than yield recorded in AUS (3.2 t ha – 1 ) and Indica cultivars (3.3 t ha – 1 ) (Table S5). This may be attributed to the fact that significant increases in harvest index (HI) have been achieved in Japonica cultivars (Table S5) contributing to the improvements in grain yield 24 . Grain Zn concentrations were significantly higher in AUS (27.7 mg kg – 1 ) and Japonica cultivars (27.5 mg kg – 1 ) than in Indica cultivars (23.9 mg kg – 1 ) (Table S5) across all treatments. Indica cultivars had significantly lower Fe concentrations than the other ecotypes (Table S5). Grain protein concentrations did not differ significantly between the different ecotypes of rice (Table S5). Differences between genetically biofortified and regular cultivars A total of 43 cultivars have been reported to be genetically biofortified with Zn and one cultivar (NSIC Rc172 [MS 13]) biofortified with Fe. Data on Zn and Fe concentrations in brown rice were available for only 11 cultivars from 13 field studies comparing biofortified with regular cultivars side by side. Comparisons of grain yields were reported in 7 studies, grain Zn concentrations in 13 studies, and grain Fe concentrations in 4 studies that trialed biofortified and regular cultivars side by side. All of the studies report grain yields and grain Zn and Fe concentrations in brown rice but not in white rice. Our analysis did not reveal significant differences in grain yield (Table 3 ; Figure S3 g), and a marginal difference in Fe concentrations between the genetically biofortified and regular cultivars (Table 3 ). On the other hand, the median grain Zn concentration of genetically biofortified cultivars (24.8 mg kg – 1 ; CI: 24.3–25.7 mg kg – 1 ) was significantly higher than that in regular cultivars (22.6 mg kg – 1 ; CI: 22.2–23.0 mg kg – 1 ), but the difference was only 9.7 (Table 3 ; Figure S3 h). Table 3 Differences between genetically biofortified and regular cultivars in median grain yield, grain Zn and Fe concentrations of brown rice for studies that tested biofortified and regular cultivars side by side in the same trial. Variable Biofortified cultivars (95% CI) † Regular cultivars (95% CI) † % change P value (M-W test) ‡ P value (K-S test) ‡ Grain yield (t ha − 1 ) 3.5 (3.2; 3.7) 3.7 (3.4; 4.1) -5.4 0.071 0.016 Grain Zn (mg kg − 1 ) 24.8 (24.3; 25.7) 22.6 (22.2; 23.0) 9.7 < 0.001 < 0.001 Grain Fe (mg kg − 1 ) 9.4 (7.8; 11.6) 11.3 (11.1; 11.6) -16.8 0.011 0.007 † Figures in parentheses represent the 95% confidence intervals (CI) of median values estimated using bias-corrected and accelerated boot strapping. Two or more medians are deemed significantly different if their 95% CIs do not overlap and the Mann-Whitney test and the Kolmogorov-Smirnov tests produce P < 0.05. ‡ The P values are for the Mann-Whitney (M-W) test of equality medians and the Kolmogorov–Smirnov (K-S) test of equality of distributions Loss of nutrients with grain processing Using two independent estimation methods, we found that milling and polishing can reduce grain Zn concentrations by 21.2–30.9%, Fe concentrations by 69.3–70.3%, protein concentrations by 6.5–7.1%, and phytate concentrations by 47.4–59.3% in rice grain (Table 4 ). Table 4 Percentage loss of grain concentrations of zinc, iron, phosphorus, protein, and phytate with processing of brown rice to white rice estimated using two different methods. Nutrient [n, s] † Method 1* Method 2* % loss (95% CI) ‡ % loss Zinc [25; 1366] -21.2 (-21.9; -20.4) -30.9 Iron [9; 640] -70.3 (-71.2; -68.3) -69.3 Phosphorus [1; 6] -52.3 (-54.0; -49.0) -63.5 Protein [3; 36] -6.5 (-8.1; -4.2) 7.1 Phytate [8; 146] -47.4 (-51.0; -46.0) -59.3 * Method 1 is based on percentage changes between mean values from brown and white rice reported in the same study, while Method 2 is based on the percentage change between the median values in brown rice and white rice from all studies. † Figures in brackets represent the total number of studies (n) and the total sample size (s) available for the analysis in Method 1. See the sample size for Method 2 in Supplementary Table S2 . ‡ Figures in parentheses represent the 95% confidence intervals (CI) of median values estimated using bias-corrected and accelerated boot strapping. Some studies show that limiting the degree of milling to 5% can reduce losses of Zn and Fe substantially. For example, in an analysis of 30 landraces from Manipur in India, Longvah and co-workers 25 found 12.5% reduction in Zn concentrations with 5% milling but 19.8% reduction with 10% milling. In the case of Fe concentrations, they found 41.8% reduction with 5% milling but 61.3% reduction with 10% milling 25 . There is growing interest in promoting the consumption of whole-grain brown rice 26 , 27 . However, brown rice is often considered as “peasant food,” and only consumed by the elderly among Asians 28 . Meanwhile, parboiled rice is known to have higher nutrient and vitamin B6 concentrations but lower phytic acid concentrations than non-parboiled rice 29 . Parboiling involves soaking of brown rice in hot water, followed by steaming and drying to a moisture content of 12–14%. During the parboiling process, vitamins and micronutrients are infused into the starch endosperm, thereby reducing nutrient losses during milling and making parboiled rice nutritionally superior to non-parboiled white rice 30 . Bioavailability of Zn and Fe The bioavailability of Fe and Zn in rice grain is estimated at 10–25% 11 , which is largely determined by the concentration of chelating molecules such as phytate (phytic acid) in the grain. The phytate-to-Zn and phytate-to-Fe molar ratio is considered the first proxy for bioavailability of Zn, Fe, and P to humans 16 . Phytate-to-Zn molar ratios greater than 15 are associated with low bioavailability, while ratios of 5–15 and below 5 are associated with moderate and high Zn bioavailability 31 . Zn fertilization reduced the phytate-to-Zn molar ratio by 29.4% and phytate-to-Fe molar ratio by 55% in rice grain relative to treatments without Zn fertilization (Table 5 ). This is because Zn fertilization significantly increases grain Zn uptake without significantly affecting grain phytate concentrations (Fig. 3 a). The negative correlation between grain Zn uptake and phytate concentrations (Table 2 ) is also supportive of this finding. Similarly, Fe fertilization reduced the phytate-to-Zn and phytate-to-Fe molar ratios by 60 and 51%, respectively (Table 5 ). The results in Table 5 further indicate significant improvements in the bioavailability of Zn and Fe with Fe fertilization. Table 5 Comparison of phytate-to-zinc and phytate-to-iron molar ratios in rice grain without Zn fertilization (Without Zn) and with Zn fertilization (With Zn) or without Fe fertilization (Without Fe) and with Fe fertilization (With Fe).† Molar ratio Treatments [n; s]‡ Median (95% CI) Equality of medians§ Equality of distributions§ Phytate : Zn Without Zn [25; 427] 31.3 (28.6; 33.3) < 0.001 < 0.001 With Zn [13; 322] 22.1 (21.0; 23.8) Without Fe [25; 717] 27.1 (26.2; 28.5) < 0.001 < 0.001 With Fe [5; 32] 10.8 (9.7; 17.4) Phytate : Fe Without Zn [18; 321] 36.2 (31.7; 42.1) < 0.001 < 0.001 With Zn [6; 103] 12.2 (11.6; 14.8) Without Fe [18; 380] 34.7 (30.5; 39.8) < 0.001 < 0.001 With Fe [6; 44] 16.9 (14.9; 19.3) † The median value of 31.3 indicates that for each mole of Zn there were 31.3 mols of phytate. The Mann-Whitney test of equality of medians and Kolmogorov–Smirnov test of equality of distributions are shown on the right. ‡ Figures in brackets represent the number of studies (n) and the total sample size (s) available for analysis. § P values estimated with 999 Monte Carlo permutations. Variations in response ratios Variation with nutrient inputs Among the various nutrient inputs tested (Table S6), significantly greater increments in grain yield were achieved with Zn seed treatment (response ratio [RR] = 1.29), followed by a combination of soil and foliar application of Zn (RR = 1.25), and soil application of Fe (RR = 1.24); these gave 29, 25, and 24% increase in grain yield over the recommended NPK fertilizer (Fig. 2 a). The highest increment in grain Zn concentrations (RR = 1.88) was achieved with the combination of soil and foliar application of Zn (Fig. 2 b). This was followed by foliar application of Zn (RR = 1.36), seed treatment with Zn (RR = 1.34), and soil application of Zn (RR = 1.23). The highest increment in grain Fe concentrations (RR = 1.40) was achieved with growth-promoting rhizobacteria/zinc solubilizing bacteria (GPR + ZSB), followed by soil application of Fe (RR = 1.27), and foliar application of Fe (RR = 1.24) (Fig. 2 c). Greater increments in grain protein concentrations were achieved with soil application of Fe (RR = 1.17), foliar application of Zn (RR = 1.13), and foliar application of Fe (RR = 1.12) than the other treatments (Fig. 2 d). Treatments involving other inputs (“Other”) did not significantly increase grain yields over the recommended fertilizer, but slightly increased grain Zn, Fe, and protein concentrations. Significant reduction in grain yield and grain Zn, Fe, and protein concentrations was noted in the absence of external inputs (“Noinput”) (Fig. 2 ). These findings highlight the value of applying Zn, Fe, and other micronutrients in addition to the recommended NPK fertilizer. Application of Zn fertilizer (“WithZn”) together with the recommended NPK fertilizer achieved 17% increment in grain yields (RR = 1.17) relative to the recommended NPK fertilizer alone. But the 2% yield increment (RR = 1.02) achieved with all other treatments without Zn (“WithoutZn”) relative to the recommended NPK fertilizer was negligible (Fig. 3 a). Zn fertilization also increased grain Zn concentrations by 44%, Fe concentrations by 5%, and protein concentrations by 7%, and reduced phytate concentrations by 8% compared with the recommended fertilizer (Fig. 3 a). Between-study heterogeneity was high for all effect sizes with both with and without Zn (Figure S6). The fail-safe numbers from the random effects models indicated that the effect size estimates are all robust (Figure S6). It is likely that Zn fertilization increases grain yields and grain protein concentrations via increased grain Zn uptake, N uptake (see positive correlations in Table 2 ), and better agronomic use efficiency of the applied N fertilizers. For example, grain Zn uptake increased by 68% and N uptake by 29% with Zn fertilization (Fig. 3 a). Zn fertilization also increased the agronomic efficiency of N fertilizers by 4.8 kg per kg of applied N fertilizer (CI: 4.4–5.2) relative to the recommended fertilizer, while treatments without Zn inputs did not significantly increase the agronomic efficiency of N use (median: 1.4; CI: 1–2 kg per kg). In addition, Zn fertilization significantly increased the harvest index, which is a predictor of water productivity and nutrient use efficiency in crops 24 . Like Zn fertilization, Fe fertilization achieved significant increments in almost all variables except grain phytate concentrations relative to the recommended NPK fertilizer (Fig. 3 b). For example, Fe fertilization increased grain yields by 20%, grain Zn by 22%, grain Fe by 25%, Zn uptake by 39%, and N uptake by 29% (Fig. 3 b). It also increased protein concentrations by 31% and reduced phytate concentrations by 10% over the recommended fertilizer (Fig. 3 b). Fe fertilization also significantly increased the agronomic efficiency of N by 3.7 kg per kg of applied N (95% CI: 3.3–4.9) relative to the recommended fertilizer. The agronomic efficiency of soil-applied Zn fertilizer also increased by 50 kg per kg of applied Zn. The results above confirm our hypothesis that grain Zn and Fe concentrations can be increased with Zn and Fe fertilization. Variation with Zn, N, and P fertilizer rates The trends in response ratios indicated significant improvements in grain yield, grain Zn concentrations, Zn uptake, and N uptake with increasing Zn application to soil at rates up to 30 kg ha – 1 . Further increases in Zn application rates above 30 kg/ha appeared to reduce grain Zn uptake and N uptake (Figure S7). Since Zn is not easily available to smallholder farmers and is also costly, soil application of modest amounts (e.g. 5–10 kg ha – 1 Zn) may be recommended, as can be inferred from Figure S7. Zn oxide nanoparticles (ZnO NPs), a new class of Zn fertilizers, have been shown to be more effective than common Zn fertilizers due to their high specific surface area, and are easily absorbed and utilized by rice roots 32 . ZnO NPs have also been shown to increase rice yields, grain Zn content, and many quality traits compared to ionic Zn at the same application rate 32 , 33 . The LOESS (locally estimated scatterplot smoothing) regression also indicated that N application rates up to 150 kg ha – 1 can significantly improve grain yields, grain Zn and Fe concentrations, Zn, Fe, and N uptake beyond which response was flat (Figure S8a–f). On the other hand, grain protein concentrations did not significantly improve with increasing N application rates (Figure S8h). The response ratios of grain yield, and Zn uptake appear to diminish with increasing P application rates higher than 80 kg P ha – 1 (Figure S9a,c). Although P fertilization increases yield, it also reduces the bioavailability of Zn, Fe, and other nutrients to humans due to increased phytate content (it also has negative environmental impacts) 16 . This highlights the need for reconsideration of P fertilizer strategies. The trends in response ratios of Fe concentrations and Fe uptake with P application rates were somewhat idiosyncratic due to the small sample sizes (Figure S9d,e). Variation with production systems Global syntheses of the effects of rainfed and irrigated rice, which are two contrasting production environments, on grain nutrient concentrations are lacking. The magnitude of Zn and Fe fertilization on grain Zn and Fe concentrations in these systems are also not fully understood. In this meta-analysis, soil application of Zn significantly increased grain yields, grain Zn, and protein concentrations in both production systems (Fig. 4 a,b,d). Grain yield and grain protein concentrations did not significantly differ between rainfed and irrigated rice with soil-application of Zn (Fig. 4 a,d). On the other hand, grain Zn concentrations were significantly higher in irrigated rice (RR = 1.5) than in rainfed rice (RR = 1.36) (Fig. 4 b), while the reverse was true for grain Fe concentrations (Fig. 4 c). The significant increase in grain Zn concentrations with Zn fertilization in irrigated rice may be attributed to its widespread deficiency and lower bioavailability due to precipitation of Zn in flooded soils 34 . Thus, Zn fertilization is a critical agronomic practice in irrigated systems. On the other hand, flooding of the soil is known to increase the availability and total uptake of other nutrients such as N, P, K, S, Fe, and Mn 35 . The availability of Fe is high in flooded conditions because of the lower redox potential 36 , while the availability of Fe is limited in rainfed conditions, particularly when soil pH is high 37 . Variation with climatic factors Compared with the recommended NPK fertilizers, soil application of Zn significantly increased grain yield, grain Zn, and protein concentrations across all categories of aridity and climate zones (Fig. 4 a,b,d). This means that soil application of Zn can improve yield and grain Zn and protein concentrations over the recommended NPK fertilizer regardless of the climate and aridity zones. Greater increments in grain yields were achieved in subhumid zones and subtropical climates than in other zones (Fig. 4 a). On the other hand, greater increments in grain Zn concentrations were achieved in humid zones and tropical climates (Fig. 4 b). No clear patterns could be claimed in the case of grain Fe concentrations partly due to the small number of studies available for meta-analysis (Fig. 4 c). Variation with soil factors Soil application of Zn significantly increased grain yield, grain Zn, and protein concentrations across all categories of soil parent material, texture, soil pH, soil organic carbon (SOC), Olsen P, and soil Zn concentrations (Fig. 5 a,b,d). This means that soil application of Zn can improve yields and grain Zn and protein concentrations over NPK fertilizer regardless of the soil conditions. The exception was grain Fe concentrations, for which there were too few studies and observations to make firm conclusions. Significantly greater increments in grain yield were achieved on soils that are deficient in Zn than on those considered as having sufficient Zn (Fig. 5 a). This is probably because Zn is a yield-limiting soil nutrient whose deficiency is widespread especially in wetland rice 38 , 39 . On the other hand, grain Zn and protein concentrations were equally improved on both Zn-deficient and Zn-sufficient soils (Fig. 5 b,d). This implies that soil application of Zn can significantly improve grain Zn and protein concentrations regardless of the Zn status of the soil. Taken together, the results above confirm our hypothesis that “Zn and Fe fertilization significantly increases grain Zn, Fe, and protein concentrations over control (NPK fertilizer only) across climates, soils, and rice growing environments.” Greater improvements in grain yield, grain Zn, and protein concentrations, and N and P uptake due to soil application of Zn occurred where soil available Zn concentrations fell below 1.0 mg kg – 1 (diethylenetriaminepentaacetic acid [DTPA] method). This is consistent with the argument that the optimum range of soil available Zn for rice is 0.38–0.90 mg kg – 1 (DTPA-extractable Zn) 40 . Challenges faced and limitations of the meta-analysis The major challenges faced in this analysis were (1) the small number of studies reporting nutrient concentrations in white rice; (2) incomplete description of the study sites; (3) scarcity of information on the rice cultivar used, fertilizer rates, and irrigation regimes; (4) lack of directly measured soil properties and climate variables; and (5) selective reporting of results of statistical analyses. In many publications, main effects were reported and interaction effects were missing. These challenges have limited in-depth analysis of the association between grain nutrient concentrations and climate and soil variables. These limitations are presented in detail in the Supplementary Methods. Conclusion Based on the various analyses, it is concluded that (1) modern rice cultivars have inadequate concentrations of Zn, Fe, and protein for healthy human nutrition; (2) processing of brown rice to white rice significantly reduces Zn, Fe, and protein concentrations; (3) current rates of increments in grain Zn and Fe concentrations with genetic biofortification are not likely to achieve the target Zn and Fe concentrations without complementary use of agronomic interventions; (4) application of Zn and Fe fertilizers can significantly increase the nutritional value of rice grains while also increasing grain yields; and (5) soil application of Zn can improve yields and grain Zn and protein concentrations over the recommended NPK fertilizer regardless of production systems, climate zones, soil conditions, and soil Zn status. We recommend, (1) complementary efforts in genetic and agronomic fortification of rice to increase grain Zn and Fe concentrations; (2) emphasis on consumption of parboiled rice among populations whose staple diet is rice; and (3) reducing milling and polishing of rice grains to reduce loss of micronutrients in rice grain. Materials and Methods Scope and context of this review and meta-analysis The scope of this meta-analysis is global, covering all rice growing regions, cultivated rice species and their crosses, subspecies, and cultivars. Rice was chosen for this analysis because of its strategic importance for food security especially in Asia and Africa 41 , 42 . For example, rice ranks first in Asia, and second in Africa in terms of area and production in 2022. 43 Historically cultivated rice is diverse, and consists of two species, namely, Oryza sativa (Asian rice) and O. glaberrima (African rice) 44 and their crosses. Asian rice is dominant in terms of area under cultivation, whereas African rice remains important in some parts of Africa. The latter is also an essential source of genes for resistance to biotic and abiotic stresses 44 . Asian rice consists of two sub-species, namely O. sativa indica (Indica hereafter) and O. sativa japonica (Japonica), each with different ecotypes 45 , 46 . Indica rice is mainly found in tropical and subtropical rice growing regions, at low latitudes or low altitudes, whereas Japonica rice is mostly cultivated in high-latitude temperate regions 45 , 46 . Literature search, retrieval, and data extraction We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA-S) 47 guidelines for literature searches in systematic reviews and meta-analysis. We conducted searches in Google Scholar and Web of Science for peer-reviewed publications. In Google Scholar, we used the keywords: (Rice and mineral or ferti* and grain yield or produc*) and (grain quality or zinc or iron or protein or phosphorous or selenium), retrieving 998 titles. In Web of Science, the search string was: “Rice or (Oryza) (All Fields) AND Zinc or iron ferti* (All Fields) AND grain yield or grain produce or bio-forti* or bioforti or grain zinc or grain iron or grain phosphorous” resulting in 9,379 titles. After refining by document type (Article) and language (English), 8,998 titles were retained. Initial title screening excluded 8,184 publications (Figure S4). After removal of duplicate studies, 670 publications were subjected to abstract and full-text screening. At this stage, 388 studies were excluded based on the exclusion criteria. A total of 38 studies were found by reading the reference lists of the selected studies. In total, 245 publications were retained for data extraction (Figure S4). A map of the study sites included in this meta-analysis is presented in Figure S5. Two reviewers screened each record independently. No automation tools were used in the process. All retrieved publications were compiled into an MS Excel spreadsheet with metadata including author, title, journal name, publication year, DOI, geographic coordinates of the site, site name, and country. All relevant data were extracted from the 245 publications and organized in Excel after extracting information from tables directly and digitizing figures using Web PlotDigitizer. Studies reporting grain yield and mineral contents with clearly defined fertilizer management strategies were categorized as “Treatment”; otherwise, they were identified as “Germplasm” studies. In cases where the same data were reported in multiple publications by the same authors, we selected the most comprehensive source for data extraction. Where geographical coordinates of the experimental site were not provided, we used Google Maps ( https://www.google.com/maps/ ) to determine the latitude and longitude based on the location of the nearest city or the experimental station where the study was conducted. Where initial soil properties of the test site were not provided, we extracted some of them for the approximate study locations from the ISRIC Soilgrids. Inclusion and exclusion criteria The following criteria were used for inclusion of a study in the meta-analysis: (1) the study was published in a peer-reviewed journal; (2) the study compared application of either Zn or Fe alone or in combination with other micronutrients as treatments and/or rice genotypes; (3) the study reported rice grain yield and grain nutrient concentrations on a dry matter basis; (4) all recommended agronomic management practices were performed uniformly; (5) the study was performed in randomized and controlled field trials; (6) the treatments were replicated; (7) the study reported survey results that sampled crops directly from farmers’ fields on a dry mass basis. A study was excluded if it: (1) was conducted under greenhouse conditions, as a pot experiment, or in a hydroponic medium; (2) reported nutrient concentrations of rice grains sampled in the market; (3) reported nutrient concentrations of rice grains sampled after long storage period; (4) did not report treatment means, but reported other measures such as median and the corresponding author was not willing to share raw data; (5) is a review paper; (6) is not published in a peer-reviewed journal; (7) presented results in a manner that treatment means cannot be extracted, for example boxplots without mean values or in 3D graphs or in broken bar charts; (8) is based on genetically modified (transgenic) rice varieties (but genetically biofortified varieties were included); and (9) was retracted by the journal after publication. Quality control of the data used in this meta-analysis was assured by: (1) including only publications in peer-refereed journals; (2) ensuring that all selected publications are independently reviewed by the co-authors; (3) full article reading of the publications to substantiate each finding; (4) thorough training of the data extraction team; and (5) counterchecking the extracted data by co-authors before pre-processing for statistical analysis. Choice of response variables and calculation of effect sizes For meta-analysis, we used the response ratios (RR) of grain yield, grain Zn, Fe, protein, and phytate concentrations as the response variables. Particular attention was given to grain Zn, Fe, and protein concentrations in the meta-analysis because rice is deficient in these nutrients among the staple cereal crops 8 , 15 . We also chose harvest index and thousand-grain weight (TGW) as response variables. We chose harvest index specifically because it is considered as an informative indicator of the sink–source balance, and a measure of biological success in partitioning photosynthates to the harvestable product 48 . Over the years, breeding efforts have achieved great increases in yield by improving the harvest index. Therefore, the harvest index is considered as a useful indicator to consider in the breeding of new high-yielding varieties and improving resource use efficiency 48 , and the amount of nutrients recovered in harvested products is also a function of the harvest index. Data processing In preparation for statistical analysis, some variables were calculated from the available data and gaps were filled: harvest index, Zn, Fe, and protein concentrations, Zn and Fe bioavailability. Nutrient uptake and agronomic use efficiency of N fertilizer were not reported in some studies. These were calculated from studies that have reported the requisite variables. For example, where the harvest index (HI) was not provided, it was calculated from grain yield, straw yield, or total aboveground biomass as follows: HI = 100*(grain yield/total aboveground biomass) or HI = 100*(grain yield/(grain yield + straw yield)). In studies where only grain nutrient uptake was reported, grain N, Zn, and Fe concentrations were calculated from uptake as follows: N concentration (in % or g kg –1 ) = (Uptake*100)/grain yield (in kg ha – 1 ) Zn or Fe concentration (in mg kg –1 ) = (Uptake*1000)/yield (in kg ha – 1 ) In studies where protein concentration was not reported but grain N concentration was reported, grain protein concentration was calculated by multiplying N concentration (in %) by 5.95 as per Food and Agriculture Organization of the United Nations (FAO) guidelines 49 . Conversely, N concentration was calculated by dividing the protein concentration (%) by 5.9. In studies where nutrient yield alone was reported, grain Zn and Fe concentrations were calculated from nutrient yield as follows: Zn or Fe concentration (in mg kg –1 ) = ((Nutrient yield (kg ha –1 )/grain yield (in kg ha –1 ))/10. Grain N, Zn, Fe, P, and S uptake were calculated from their respective concentrations as follows: N, P, or S uptake (in kg ha –1 ) = N, P, or S concentration (in %)*grain yield (in kg ha –1 )/100 Zn or Fe uptake (in g ha –1 ) = concentration (in mg kg –1 )*grain yield (in kg ha –1 )/1000. The agronomic efficiency of N fertilizer was calculated as follows: (treatment yield – control yield)/N rate, where the control yield is the yield recorded in the recommended NPK treatment. To facilitate the meta-analyses, continuous explanatory variables were also converted into categories and dummy variables were created. Using the soil texture provided and/or the soil clay and silt concentrations reported, three soil texture classes (sandy, loam, and clayey) were created following Chivenge and co-workers 50 . Soils were classified as calcareous, silicic, or mafic based on their parent materials 51 . Calcareous parent materials include limestone, dolomite, calcareous shale, and sands with > 50% CaCO 3 or MgCO 3 , while silicic (also called acidic) refers to rocks that contain significant amounts of silica (> 68% Si), and intermediate parent materials contain 52–68% Si 51 . The term mafic (basic) is applied to parent material with relatively low amounts of silica (45–52% Si) 51 . Soil organic matter (SOM) data were converted to SOC by dividing SOM by 2 following Pribyl 52 . Then, the SOC values were grouped into two categories: low (SOC ≤ 1%) and medium to high (SOC > 1%). Wherever primary studies used different soil extraction methods of soil pH, available P, and cations, and reported in different units, these were converted into a single unit before combining the data for meta-analysis. All soil pH reported in different extraction methods were converted into pH in H 2 O. To facilitate meta-analysis, soil pH data were grouped into three categories as acidic (pH 7.5) in H 2 O following Xu and co-workers 53 . Similarly, P extracted using different methods were converted into Olsen P equivalents using regression equations. The Olsen P (bicarbonate extraction) was used in preference to the other methods as it is the most used indicator for soil P status 54 . Olsen P values were grouped into three categories: low ( 22 mg kg – 1 ) based on application in earlier studies. Following Wissuwa and co-workers 55 , we classified study sites as Zn-deficient (≤ 0.8 mg kg – 1 Zn) and Zn-sufficient (> 0.8 mg kg – 1 Zn) using the DTPA soil available Zn reported. Based on existing convention, climate zones were classified into tropical (between latitudes 23.5° north and 23.5° south), sub-tropical (23.5–35° north and south), temperate (35–50° north and south), and boreal (> 50° north) using the global positioning system (GPS) coordinates of the study sites. Study sites were also classified based on the aridity index (AI) as arid (AI 0.65) 56 . Rice is produced in two contrasting production systems: rainfed (aerobic conditions) and irrigated (mostly anaerobic). Rainfed rice is grown in unsaturated soil under rainfed conditions but sometimes with supplemental irrigation, while irrigated rice is conventionally produced in flooded and puddled soil, and seedlings are normally transplanted 2 , 41 . Accordingly, the rice production systems were grouped into rainfed and irrigated to facilitate the meta-analysis. Statistical analysis Quantifying baseline grain nutrient concentrations and antinutritional factors To establish the baseline grain Zn, Fe, and protein concentrations, the median values, their 95% confidence intervals (CIs), and the lower and upper quantiles (25th and 75th percentiles; Q1 and Q3) were estimated. The uncertainty around median values was represented by 95% CIs estimated using bias-corrected and accelerated (BCa) bootstrapping with 9,999 replicates. BCa is a non-parametric method that accounts for skewness and allows one to set CIs through resampling with replacement 57 . Violin and box plots were also generated to aid visualization and comparison of brown rice with white rice in terms of distributions. To ensure rigorous statistical inferences, tests of equality were performed for both medians and the data distributions. Accordingly, two or more medians or distributions were deemed significantly different if the 95% confidence intervals of medians did and/or overlap and the tests of equality of medians and distributions yielded P < 0.05. For the equality of medians, the Mann-Whitney test was used for two samples and the Kruskal–Wallis test for several samples. The Kolmogorov–Smirnov test was used for equality of distributions. In all cases, the P values were generated via Monte Carlo permutation. The overall distributions of grain Zn, Fe, and protein concentrations were derived using all datasets covering field experiments and farm surveys. Farm survey data were not treated differently from experimental data since means of a given number of samples were reported on a dry mass basis. Before formal analysis of the distributions, outliers were identified using the Extreme Studentized Deviate test. Then, histograms of rice grain Zn, Fe, and protein concentrations were generated for brown rice and white rice separately. Histograms were generated by setting the number of bins to optimal following the zero-stage rule. The Gaussian kernel density and the normal distribution curves were added on the histograms. To test the first hypothesis that “grain Zn and Fe concentrations in rice cultivars are below the target concentrations for human nutrition,” the probability ( ϕ ) of exceeding the target values of Zn (28 mg kg – 1 ) and Fe (15 mg kg – 1 ) in brown and white rice were estimated using their cumulative frequency distributions. Correlations, G×E interactions, and heritability traits Although the effects of genotype-by-environment (G×E) interactions on grain yield, grain Zn, Fe, and protein concentrations and the heritability of these traits in rice have been widely studied, syntheses were lacking. To gain insights into the genetic and environmental control of grain yield, grain Zn, Fe, and protein concentrations, we reviewed studies that have quantified G×E interactions and heritability, and we compiled the variance explained by the genotype, environment, and the G×E interactions and the broad-sense (H 2 ) and narrow-sense (h 2 ) heritability estimates. Details of the steps taken are described in detail in the Supplementary Methods. In addition, we performed correlation analyses between grain yield, grain Zn, Fe, and protein concentrations, and grain N uptake, Zn uptake, Fe uptake, P uptake, and S uptake using the data compiled for the meta-analysis. We limited the correlation analysis to studies that have reported two target variables measured concurrently and where the number of means was more than 10 to correctly estimate the Pearson correlation coefficient. We then performed a random effects meta-analysis of the correlation coefficients between grain yield, and concentrations of Zn, Fe, protein, and phytate using the Fisher r-to-z transformed values as the outcome measure. We performed the random effects modeling using restricted maximum likelihood method (REML) implemented in the MAJOR module of the JAMOVI software. This is an open-source software that performs random effects meta-analysis using an extension of the metafor package of R 58 . The random effects meta-analysis provides an estimate of the pooled correlation coefficient, the amount of heterogeneity (i.e. T 2 , I 2 , H 2 , and Cochran’s Q statistics), and tests of publication bias (Rosenthal’s fail-safe N, Begg and Mazumdar Rank Correlation, Egger’s Regression and Trim and Fill Number of Studies). The procedure uses Studentized residuals and Cook’s distances to identify outliers and/or influential. To check for funnel plot asymmetry, the procedure uses rank correlation test and regression test using the standard error of the observed outcomes as the predictor. Variations in grain yield, grain Zn, Fe, and protein concentrations with variety age For this analysis, released rice varieties were classified by the year of release, and the median concentrations and their 95% CIs were determined. Due to uncertainty about some exact dates, release years were grouped into four 20-year periods: pre-1960, 1960–1980, 1981–2000, and 2001–2022 for varieties for which the years of release could be verified. Statistical inferences were based on medians and their 95% CIs, estimated using BCa bootstrapping with 9,999 replicates. Analysis of genotypic differences in grain Zn, Fe, and protein concentrations Of the 245 studies, 20 (8%) did not report the cultivar used in the study. In the remaining 225 studies, over 3,600 cultivars (including landraces, advanced lines, recombinant inbred lines, and high-yielding improved varieties) were used. It was not possible to make valid statistical comparisons between individual cultivars due to the large number of cultivars with small sample sizes. Therefore, cultivars were grouped into smaller and manageable categories to facilitate the comparison and establish genotypic differences in terms of grain Zn, Fe, and protein concentrations. Accordingly, rice cultivars were grouped based on known ecotypes of rice (i.e. aromatic, AUS, indica, japonica, admixtures vs. unknown), improvement status (landraces vs. improved cultivars), and biofortification status of improved cultivars (regular vs. genetically biofortified). Admixtures comprised crosses between Asian and African rice species (e.g. New Rice for Africa [NERICA]) and crosses between indica and japonica, indica and AUS, etc. Cultivars that could not be reliably placed in any one of these categories were categorized as “unknown.” To facilitate statistical comparisons, the median values, their 95% CIs, and the lower and upper quantiles (Q1 and Q3) were presented. Violin plots were also generated to aid visualization of the distributions. Estimating loss of nutrients through milling and polishing The magnitude of loss in grain nutrients was estimated using two methods. Method 1 involved estimations using data from 26 studies that have reported nutrient concentrations of brown rice and the corresponding values for white rice from the same sample. Method 2 involved calculations of the loss from the median concentration in brown rice and white rice. In both cases, losses were estimated as: 100*(concentration in white rice – concentration in brown rice)/(concentration in brown rice). Although Method 1 is expected to be more reliable than Method 2, estimates from Method 2 are expected to give an approximate idea of the potential loss in the absence of directly measured data. Estimating bioavailability of Zn and Fe The bioavailability of Zn and Fe in grains was estimated as the phytate (PA)-to-Zn (PA: Zn) and phytate-to-Fe (PA: Fe) molar ratios calculated as follows: PA: Zn = ((phytic acid content in mg kg –1 )/660)/((Zn content in mg kg –1 )/65.38) PA: Fe = ((Phytic acid content in mg kg –1 )/660)/((Fe content in mg kg –1 )/55.845) Meta-analysis of response to fertilization The meta-analysis focused on quantifying the magnitude of effect on the target variables following a given treatment. The bulk of the meta-analysis focused on grain yield, grain Zn, Fe, and protein concentrations because the majority of the studies involved treatments with either Zn and Fe fertilizers or their combination. Additional meta-analyses were performed on response ratios of grain Zn uptake, Fe uptake, and N uptake. The number of studies and total number of observations used in the meta-analysis for each treatment are summarized in Table S6. Meta-analyses were performed on a subset of the data where the desired “control” defined as the treatment receiving the recommended inorganic nitrogen, phosphorus, and potassium (NPK) fertilizer could be reliably identified. The recommended NPK fertilizer was specifically chosen as the control because Zn, Fe, and other micronutrients are almost always combined with NPK. As such, the recommended NPK fertilizer is expected to provide a reasonable baseline against which gains in crop yields and nutrient concentrations in response to the micronutrient inputs can be compared. Grain yield, grain Zn, Fe, and protein concentrations were chosen as the target variables for most of the meta-analysis because sufficient data were available. Other variables such as the harvest index, thousand-grain weight, and the phytate and amylase concentrations were used in the meta-analysis only where sufficient data existed. Meta-analysis of phytate concentrations was not performed due to lack of sufficient data. All meta-analyses were performed using the response ratio (RR) as the effect size metric. The RR was calculated as the ratio of the value of the target variable in the treatment and the corresponding control. To normalize the RR, its natural logarithm (lnRR) was calculated as follows: $$\:lnRR=ln\left(\frac{T}{C}\right)$$ where T and C are the values of the target variable from the treatment and control, respectively. Once analyses were completed, lnRR and its 95% CIs were back-transformed into the arithmetic domain. The effect sizes estimated in percentage terms were used for inference because percentage change can be readily understood by non-technical readers. For this purpose, lnRR values were re-expressed in percentage change in the target variables as follows: $$\:\%\:change\:=100\times\:\left({e}^{lnRR}-1\right)\:or\:100\times\:\left(RR-1\right)$$ Accordingly, an RR value of 1.0 represents no change, while 1.1 represents a 10% increment over the recommended NPK fertilizer. Prior to meta-analysis, we evaluated publication bias using funnel plots of the response ratios (Figure S6) generated using the random effects models implemented in the MAJOR module of the JAMOVI software. Figure S6 also presents tests of heterogeneity (i.e. T 2 , I 2 , H 2 , and Cochran’s Q statistics) and publication bias (Rosenthal’s fail-safe N, Kendal’s tau, and Egger’s Regression) for the variables presented in the funnel plots. P values of these tests greater than 0.05 indicate no publication bias in the effect sizes, and these are indicated by “ns” (Figure S6). We also performed sub-group analyses within a linear mixed-effects modeling framework to estimate the variation in effect sizes with genotypic, agronomic practice, soil, and climate variables. The genotypic variables were subspecies (e.g. AUS, indica, japonica) and genetic biofortification status (biofortified vs. unfortified). Comparison of rice species (Asian vs. African) was not possible because African rice was reported in only one study. To ensure the comparability of results, a subset of studies on regular and genetically fortified cultivars under the same growth conditions under the same agronomic management were analyzed. The agronomic practices included methods of Zn and Fe application (e.g. seed coating, soil application, foliar application, and a combination), application of manures, Zn solubilizing bacteria (ZnSolB) and/or growth promoting rhizobacteria (GPR), and growth condition (aerobic vs. anaerobic) (Table S6). To test the hypothesis that Zn and Fe fertilization significantly increase grain Zn, Fe, and protein concentrations over the recommended fertilizer, all treatments involving Zn and Fe were grouped into WithZn and WithFe, respectively, and compared with those that did not contain any Zn or Fe input (WithoutZn and WithoutFe). This grouping was also aimed at overcoming statistical artefacts that arise due to small sample sizes when modes of application (foliar, seed, soil, etc.) were analyzed individually. Other treatments for which sample sizes were too small were bulked together as “Other” to reduce data fragmentation and small sample size artefacts. Meta-analysis of the effect of production-system, climatic, and soil factors was limited to soil application of Zn because there were adequate numbers of studies (n = 93) and observations (S = 1013). A comparable meta-analysis could not be performed on the effect of the other treatments due to the small number of studies (see Table S6). The focus on soil application of Zn was further motivated by the fact that Zn deficiency is a significant factor limiting grain yield of wetland rice 59 . Zn deficiency has also increased due to the substitution of traditional rice varieties by modern varieties that are less tolerant to Zn deficiency, removal of large amounts of Zn by modern high-yielding cultivars, high phosphorus fertilization, and other factors such as pH 59 . Meta-analysis of effects of climate variables was limited to climate zone and aridity of the study sites (arid, semiarid, subhumid, humid). Meta-analysis of the effects of soil variables was performed primarily on soil texture (sandy, loamy, clay), parent material (calcareous, mafic, intermediate, silicic), soil pH (acidic, alkali, neutral), Olsen P (high, low, medium), and soil organic carbon levels (high, low, medium). The climatic and soil variables mentioned above were entered into the model as fixed effects. The studies (each publication) were entered as the random effect because they represent the clustering structure in the population. In all presentations, the marginal least square means and the uncertainty around means were represented by the 95% CI estimated using linear mixed effects models. Uptake of soil-applied N, Zn, and Fe by crops and their subsequent translocation to the grain may depend on rate of application and the interactions between N, P, and Zn (synergistic or antagonistic). To reveal the trends in response ratios with Zn, N, and P application rates, we isolated treatments involving soil-applied Zn and we performed locally estimated scatterplot smoothing (LOESS), a non-parametric regression analysis. LOESS regression was performed in preference to linear or non-linear regression because a parametric form of the relationships could not be established in the exploratory analyses. Declarations Acknowledgements This work is part of the CGIAR Research Initiative on Excellence in Agronomy and Sustainable Farming Science Program. We would like to acknowledge all funders who supported this research through their contributions to the CGIAR Trust Fund. Funding information This study was funded through the Excellence in Agronomy initiative and the Sustainable Farming Science Program of One CGIAR. Author contributions Conceptualization and design: SK, GWS, DM, JK, PSB Data collection, curation and analysis: DM, GWS, PP, JFD Technical review and writing: SK, GWS, KS, MD, PP, DM, AI, SAN, PSB, JK All authors read and approved submission of the manuscript to npj Sustainable Agriculture . Competing interests The authors declare no competing interests. Additional information All additional information has been supplied in the Supplementary Materials Data availability The raw data used for this analysis, template used for data collection forms, and any other materials used in the review will be made available upon reasonable request. References Badoni, S. et al. Multiomics of a rice population identifies genes and genomic regions that bestow low glycemic index and high protein content. Proc. Nat. Acad. Sci. USA 121, e2410598121 https://doi.org/10.1073/pnas.2410598121 (2024). Devkota, K. P. et al. Economic and environmental indicators of sustainable rice cultivation: a comparison across intensive irrigated rice cropping systems in six Asian countries. Ecol. Indic. 105, 199–214 https://doi.org/10.1016/j.ecolind.2019.05.029 (2019). Elert, E. Rice by the numbers: a good grain. Nature 514, S50–S51 https://doi.org/10.1038/514S50a (2014). Wairich, A., Ricachenevsky, F. K. & Lee, S. A. tale of two metals: biofortification of rice grains with iron and zinc. Front. Plant Sci. 13, 944624 https://doi.org/10.3389/fpls.2022.944624 (2022). Carrijo, D. R., Lundy, M. E. & Linquist, B. A. Rice yields and water use under alternate wetting and drying irrigation: a meta-analysis. Field Crop. Res. 203, 173–180 https://doi.org/10.1016/j.fcr.2016.12.002 (2017). IRRI. The importance of rice: 1. Rice as a global staple food. http://www.knowledgebank.irri.org/ericeproduction/Importance_of_Rice.htm (2024). Kihara, J. et al. Maize grain zinc and iron concentrations as influenced by agronomic management and biophysical factors: a meta-analysis. Food Secur. 16, 1147–1173 https://doi.org/10.1007/s12571-024-01478-5 (2024). Mahender, A. et al. Rice grain nutritional traits and their enhancement using relevant genes and QTLs through advanced approaches. Springerplus 5(1), 2086 https://doi.org/10.1186/s40064-016-3744-6 (2016). Debnath, S. et al. Historical shifting in grain mineral density of landmark rice and wheat cultivars released over the past 50 years in India. Sci. Rep. 13, 21164 https://doi.org/10.1038/s41598-023-48488-5 (2023). Devkota, M. et al. Grain zinc, iron and protein concentrations of contemporary wheat cultivars fall short of targets for human health: global meta-analysis. Nat. Food. 7, 283–295. https://doi.org/10.1038/s43016-026-01314-3 (2026). Bouis, H. E. et al. Biofortification: a new tool to reduce micronutrient malnutrition. Food Nutr. Bull. 32, S31–S40 https://doi.org/10.1177/15648265110321S105 (2011). Andersson, M. S. et al. Progress update: crop development of biofortified staple food crops under HarvestPlus. Afr. J. Food Agric. Nutr. Dev. 17, 11905–11935 https://doi.org/10.18697/ajfand.78.HarvestPlus05 (2017). Juliano, B. O. Rice: role in diet. In Encyclopedia of Food and Health , Caballero, B., Finglas, P. M. & Toldrá, F. (eds.), 641–645 https://doi.org/10.1016/B978-0-12-384947-2.00595-X (Academic Press, 2016). Nakandalage, N. et al. Improving rice zinc biofortification success rates through genetic and crop management approaches in a changing environment. Front. Plant Sci. 7, 764 https://doi.org/10.3389/fpls.2016.00764 (2016). Wairich, A., Ricachenevsky, F. K. & Lee, S. A tale of two metals: biofortification of rice grains with iron and zinc. Front. Plant Sci. 13, 944624 https://doi.org/10.3389/fpls.2022.944624 (2022). Bindraban, P. S., Dimkpa, C. O. & Pandey, R. Exploring phosphorus fertilizers and fertilization strategies for improved human and environmental health. Biol. Fertil. Soils 56, 299–317 https://doi.org/10.1007/s00374-019-01430-2 (2020). Chen, P. et al. The genetic basis of grain protein content in rice by genome-wide association analysis. Mol. Breed. 43, 1 https://doi.org/10.1007/s11032-022-01347-z (2023). Shi, S. et al. Comprehensive evaluation of 17 qualities of 84 types of rice based on principal component analysis. Foods 10, 2883 https://doi.org/10.3390/foods10112883 (2021). Yang, Y. et al. Natural variation of OsGluA2 is involved in grain protein content regulation in rice. Nat. Commun. 10, 1949 https://doi.org/10.1038/s41467-019-09919-y (2019). Rietra, R. P. J. J. et al. Effects of nutrient antagonism and synergism on yield and fertilizer use efficiency. Commun. Soil Sci. Plant Anal . 48, 16 https://doi.org/10.1080/00103624.2017.1407429 (2017). Senguttuvel, P. et al. Rice biofortification: breeding and genomic approaches for genetic enhancement of grain zinc and iron contents. Front. Plant Sci. 14, 1138408 https://doi.org/10.3389/fpls.2023.1138408 (2023). Kumar, A. et al. Bioavailability of iron and zinc as affected by phytic acid content in rice grain. J. Food Biochem. 41, e12413 https://doi.org/10.1111/jfbc.12413 (2017). Fan, M.-S. et al. Evidence of decreasing mineral density in wheat grain over the last 160 years. J. Trace Elem. Med. Biol. 22, 315–324 https://doi.org/10.1016/j.jtemb.2008.07.002 (2008). Yang, J. & Zhang, J. Simultaneously improving grain yield and water and nutrient use efficiencies by enhancing the harvest index in rice. Crop Environ. 2, 157–164 https://doi.org/10.1016/j.crope.2023.07.001 (2023). Longvah, T. et al. Nutrient variability of rice landraces ( Oryza sativa L.) from Manipur, northeast India and its nutrients supply potential in rice-based diets. Nutr. Food Sci. 52, 1100–1115 https://doi.org/10.1108/NFS-10-2021-0296 (2021). Carcea, M. Value of wholegrain rice in a healthy human nutrition. Agriculture 11, 720 https://doi.org/10.3390/agriculture11080720 (2021). Xia, Q. et al. Innovative processing techniques for altering the physicochemical properties of wholegrain brown rice ( Oryza sativa L.) – opportunities for enhancing food quality and health attributes. Crit. Rev. Food Sci. Nutr. 59, 3349–3370 https://doi.org/10.1080/10408398.2018.1491829 (2018). Lee, H. H. et al. Impact of phytic acid on nutrient bioaccessibility and antioxidant properties of dehusked rice. J. Food Sci. Technol. 52, 7806–7816 https://doi.org/10.1007/s13197-015-1918-9 (2015). Bhar, S. et al. A perspective on the benefits of consumption of parboiled rice over brown rice for glycaemic control. Eur. J. Nutr. 61, 615–624 https://doi.org/10.1007/s00394-021-02694-x (2022). Ndindeng, S. A. et al. Rice milling and parboiling trade-offs for economic and nutritional gains with special attention to sub-Saharan Africa: a comprehensive review. Appl. Food Res. 5, 101274 https://doi.org/10.1016/j.afres.2025.101274 (2025). Castro-Alba, V. et al. Phytate, iron, zinc, and calcium content of common Bolivian foods and their estimated mineral bioavailability. Food Sci. Nutr. 7, 2854–2865 https://doi.org/10.1002/fsn3.1127 (2019). Mi, K. et al. Zinc oxide nanoparticles enhanced rice yield, quality, and zinc content of edible grain fraction synergistically. Front. Plant Sci. 14, 1196201 https://doi.org/10.3389/fpls.2023.1196201 (2023). Yang, G. et al. Effect of ZnO nanoparticles on the productivity, Zn biofortification, and nutritional quality of rice in a life cycle study. Plant Physiol. Bioch. 163, 87–94 https://doi.org/10.1016/j.plaphy.2021.03.053 (2021). Johnson-Beebout, S. E., Lauren, J. G. & Duxbury, J. M. Immobilization of zinc fertilizer in flooded soils monitored by adapted DTPA soil test. Commun. Soil Sci. Plant Anal. 40, 1842–1861 https://doi.org/10.1080/00103620902896738 (2009). Dass, A. et al. Agronomic fortification of rice grains with secondary and micronutrients under differing crop management and soil moisture regimes in the north Indian Plains. Paddy Water Environ . 15, 745–760 https://doi.org/10.1007/s10333-017-0588-9 (2017). Fageria, N. K. et al. Chemistry of lowland rice soils and nutrient availability. Commun. Soil Sci. Plant Anal. 42, 1913–1933 https://doi.org/10.1080/00103624.2011.591467 (2011). Kreye, C. et al. Biotic and abiotic causes of yield failure in tropical aerobic rice. Field Crops Res. 112, 97–106 https://doi.org/10.1016/j.fcr.2009.02.005 (2009). Awio, T. et al. Micronutrients in East African lowlands: are they needed to intensify rice production? Field Crop. Res. 270, 108219 https://doi.org/10.1016/j.fcr.2021.108219 (2021). Senthilkumar, K. et al. Rice yield and economic response to micronutrient application in Tanzania. Field Crop. Res. 270, 108201 https://doi.org/10.1016/j.fcr.2021.108201 (2021). Takkar, P. N., Singh, M. V. & Ganeshmurthy, A. N. A critical review of plant nutrient supply needs, efficiency and policy issues for Indian agriculture for the year 2000: micronutrient and trace elements. In Plant Nutrient, Supply and Policy Issues: 2000–2025 , Kanwar, J. S. & Katyal, J. C. (eds.), 238–264 (NAAS, 1997). Ibrahim, A. et al. Thirty years of agronomy research and development in irrigated rice-based cropping systems in the West African Sahel: achievements and perspectives. Field Crop. Res . 266, 108149 https://doi.org/10.1016/j.fcr.2021.108149 (2021). Trijatmiko, K. R. et al. Biofortified indica rice attains iron and zinc nutrition dietary targets in the field. Sci. Rep. 6, 1–13 https://doi.org/10.1038/srep19792 (2016). FAOSTAT. Crops and livestock products. https://www.fao.org/faostat/en/#data/QCL (2024). Manful, J. T. & Graham-Acquaah, S. African rice ( Oryza glaberrima ): a brief history and its growing importance in current rice breeding efforts. In Encyclopedia of Food Grains , Second Edition, Wrigley, C. et al. (eds.), 140–146 https://doi.org/10.1016/B978-0-12-394437-5.00016-4 (Academic Press, 2016). Lu, B. R., Cai, X. & Xin, J. Efficient indica and japonica rice identification based on the InDel molecular method: its implication in rice breeding and evolutionary research. Prog. Natur. Sci. 19, 1241–1252 https://doi.org/10.1016/j.pnsc.2009.01.011 (2009). Shi, S. et al. Relationship between rice grain protein content and key phenotypes in rice. Agron. J. 115, 1257–1264 https://doi.org/10.1002/agj2.21250 (2023). Rethlefsen, M. L. et al. PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. Syst. Rev. 10, 39 https://doi.org/10.1186/s13643-020-01542-z (2021). Saito, H. et al. Two novel QTLs for the harvest index that contribute to high-yield production in rice ( Oryza sativa L.). Rice 14, 18 https://doi.org/10.1186/s12284-021-00456-1 (2021). FAO. Food Energy – Methods of Analysis and Conversion Factors . FAO Food and Nutrition Paper 77. https://www.fao.org/4/y5022e/y5022e03.htm (Food and Agriculture Organization of the United Nations, 2003). Chivenge, P., Vanlauwe, B. & Six, J. Does the combined application of organic and mineral nutrient sources influence maize productivity? A meta-analysis. Plant Soil 342, 1–30 https://doi.org/10.1007/s11104-010-0626-5 (2011). Gray, J. M., Humphreys, G. S. & Deckers, J. A. Distribution patterns of World Reference Base soil groups relative to soil forming factors. Geoderma 160, 373–383 https://doi.org/10.1016/j.geoderma.2010.10.006 (2011). Pribyl, D. W. A critical review of the conventional SOC to SOM conversion factor. Geoderma 156, 75–83 https://doi.org/10.1016/j.geoderma.2010.02.003 (2010). Xu, H. et al. Effects of biochar application on crop productivity, soil carbon sequestration, and global warming potential controlled by biochar C:N ratio and soil pH: a global meta-analysis. Soil Tillage Res. 213, 105125 https://doi.org/10.1016/j.still.2021.105125 (2021). Chen, X.-P. et al. Harvesting more grain zinc of wheat for human health. Sci. Rep. 7, 7016 https://doi.org/10.1038/s41598-017-07484-2 (2017). Wissuwa, M., Ismail, A. M. & Graham, R. D. Rice grain zinc concentrations as affected by genotype, native soil-zinc availability, and zinc fertilization. Plant Soil 306, 37–48 https://doi.org/10.1007/s11104-007-9368-4 (2008). Zomer, R. J., Xu, J. & Trabucco, A. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Sci. Data 9, 409 https://doi.org/10.1038/s41597-022-01493-1 (2022). Efron, B. & Narasimhan, B. The automatic construction of bootstrap confidence intervals. J. Comput. Graph. Stat. 29, 608–619 https://doi.org/10.1080/10618600.2020.1714633 (2020). JAMOVI. The jamovi project. jamovi . (Version 2.6) [computer software]. Retrieved from https://www.jamovi.org (2024). Fahad, S. et al. Effects of tire rubber ash and zinc sulfate on crop productivity and cadmium accumulation in five rice cultivars under field conditions. Environ. Sci. Pollut. Res. 22, 12424–12434 https://doi.org/10.1007/s11356-015-4518-3 (2015). Additional Declarations No competing interests reported. Supplementary Files SUPPLE1.doc SUPPLE2.doc SupplementaryMethods.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 01 May, 2026 Reviews received at journal 28 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 30 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9267207","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":621725219,"identity":"f31b16d4-19ba-4a95-88e3-dc6e346b714a","order_by":0,"name":"Kalimuthu Senthilkumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIie3RsUvDQBTH8VcOLstPu1ZSvH+hIRiXko7+G08C3aUgHQMFXQqu/TMqgpPDQcAuQVdDRJRCJodCF0UHz1gX5ayj4H2HC4F8eO8Ikcv1B2uRd28eGpuo37tEYi1Bh4g15Afp/4rQO6EVydYvtpWiMT94um1L5DvT54trpfZEtaDhXZxaiE/ePJxwBbkxjopxVQanmQwnlA8SG9kminxwBtlEdANdcjCikBpHnNgWM2T35ZMUr/rKEG/5I/HNFFETs1gJrVkJ1FNi6/VHCH30DcHlYdnWSTAVGBDnzDbSmh0/LNHNek0k58WjjpU6mZ3RYsg9G/n+4zraHGbEfmo1X1OrT+1TXC6X67/1BhFxTrn6phJPAAAAAElFTkSuQmCC","orcid":"","institution":"Africa Rice Center (AfricaRice)","correspondingAuthor":true,"prefix":"","firstName":"Kalimuthu","middleName":"","lastName":"Senthilkumar","suffix":""},{"id":621725220,"identity":"55f58c5c-adb2-4f58-8346-c71b54db6e5e","order_by":1,"name":"Dominic Mutambu","email":"","orcid":"","institution":"International Center for Tropical Agriculture (CIAT), ICIPE Duduville Complex, Nairobi, Kenya","correspondingAuthor":false,"prefix":"","firstName":"Dominic","middleName":"","lastName":"Mutambu","suffix":""},{"id":621725221,"identity":"468533f1-632e-4426-aa59-3c48e732581a","order_by":2,"name":"Gudeta Weldesemayat Sileshi","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Gudeta","middleName":"Weldesemayat","lastName":"Sileshi","suffix":""},{"id":621725223,"identity":"906bfcc5-3c7d-466a-bd69-9b365e4e62ee","order_by":3,"name":"Panneerselvam Peramaiyan","email":"","orcid":"","institution":"International Rice Research Institute, South Asia Regional Center, Varanasi, 221106, India","correspondingAuthor":false,"prefix":"","firstName":"Panneerselvam","middleName":"","lastName":"Peramaiyan","suffix":""},{"id":621725224,"identity":"fd51e1cb-0bf3-4cb7-b15d-803ebb8e2af0","order_by":4,"name":"Kazuki Saito","email":"","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Kazuki","middleName":"","lastName":"Saito","suffix":""},{"id":621725226,"identity":"8ee80881-5a63-481f-b66d-7926d3210ed0","order_by":5,"name":"Mina Devkota","email":"","orcid":"","institution":"International Center for Agriculture Research in Dry Areas (ICARDA)","correspondingAuthor":false,"prefix":"","firstName":"Mina","middleName":"","lastName":"Devkota","suffix":""},{"id":621725228,"identity":"10fa298e-fcea-4344-822a-52ea203453f9","order_by":6,"name":"Ali Ibrahim","email":"","orcid":"","institution":"AfricaRice","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Ibrahim","suffix":""},{"id":621725229,"identity":"7d41f07c-f857-4f2d-8642-bb04b92d00cf","order_by":7,"name":"Justin Fagnombo Djagba","email":"","orcid":"","institution":"AfricaRice","correspondingAuthor":false,"prefix":"","firstName":"Justin","middleName":"Fagnombo","lastName":"Djagba","suffix":""},{"id":621725230,"identity":"3bab631b-4843-4b5b-a0cf-4ba6532c215c","order_by":8,"name":"Sali Atanga Ndindeng","email":"","orcid":"","institution":"AfricaRice","correspondingAuthor":false,"prefix":"","firstName":"Sali","middleName":"Atanga","lastName":"Ndindeng","suffix":""},{"id":621725231,"identity":"94ce415a-4e68-4594-a880-24c74ddc10f5","order_by":9,"name":"Prem S Bindraban","email":"","orcid":"","institution":"AfricaRice","correspondingAuthor":false,"prefix":"","firstName":"Prem","middleName":"S","lastName":"Bindraban","suffix":""},{"id":621725232,"identity":"efddfe9a-cec4-41a2-bff0-bdcb417c2fda","order_by":10,"name":"Job Kihara","email":"","orcid":"","institution":"International Center for Tropical Agriculture (CIAT), ICIPE Duduville Complex, Nairobi, Kenya","correspondingAuthor":false,"prefix":"","firstName":"Job","middleName":"","lastName":"Kihara","suffix":""}],"badges":[],"createdAt":"2026-03-30 12:56:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9267207/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9267207/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106805244,"identity":"040a688a-c3c7-4be5-b1a2-48cdce6f389a","added_by":"auto","created_at":"2026-04-13 15:21:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104756,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of hypothesized relationships between uptake of various nutrients, grain yield, and concentrations of the nutrients in the grain. Solid and dashed lines indicate direct and indirect relationships.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9267207/v1/e8173dd77e21a09f3d2d04ef.png"},{"id":106805249,"identity":"e05a1a9d-41ce-471a-84be-2dd81a0a40a8","added_by":"auto","created_at":"2026-04-13 15:21:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1126919,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in the response ratios of grain yield, grain Zn, Fe, and protein concentrations with soil treatments. Horizontal bars represent the 95% confidence intervals (CIs). CIs falling above the red line (RR = 1) indicate significant improvement in response to the treatment over the control (e.g. soil application of Zn over the recommended fertilizer). Figures in brackets in front of each category represent the number of studies (n) and total sample size (s) available for the meta-analysis. Figures on the left sides of the horizontal bars represent the marginal mean RRs. The RR for Znseed is missing in (c) because the number of observations were too few to estimate the CIs. Explanations for abbreviation of treatments are in Table S6.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9267207/v1/d1600845c2aa20068de77a96.png"},{"id":106805251,"identity":"9ea40877-5506-4036-9c87-4a0ac1a2da61","added_by":"auto","created_at":"2026-04-13 15:21:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1161223,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Zn fertilization (WithZn) with treatments without Zn (WithoutZn) (a) and with Fe and without Fe (b) in terms of the response ratios of grain yield, harvest index (HI), thousand-grain weight (1000 GW), grain nutrient concentrations and uptake, grain protein, amylose and phytate concentrations, and the benefit-to-cost ratios (BCR). Horizontal bars represent the 95% confidence intervals (CIs) of means. CIs falling above the red line (RR = 1) indicate significant improvement in response to soil application of Zn over the recommended fertilizer. Figures in brackets beside each variable represent the number of studies (n) and total sample size (s) available for the meta-analysis. Figures on the left side of the horizontal bars represent the marginal means.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9267207/v1/8823482a72654b72cf134d5f.png"},{"id":106960739,"identity":"8ae360fb-92f1-4326-8d09-5ce9b70b065a","added_by":"auto","created_at":"2026-04-15 09:22:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1056435,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in the response ratios of grain yield, grain Zn, Fe, and protein concentrations with production system (irrigated vs. rainfed), climate zone, and aridity of study site where Zn fertilizer was soil-applied. Horizontal bars represent the 95% confidence intervals (CIs) of means. CIs falling above the red line (RR = 1) indicate significant improvement in response to soil application of Zn over the recommended NPK fertilizer. Figures in brackets in front of each category represent the number of studies (n) and total sample size (s) available for the meta-analysis. Figures on the left side of the horizontal bars represent the marginal means.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9267207/v1/e7837a35567e844b702573c6.png"},{"id":106994059,"identity":"23025d85-36c4-4f22-b63a-624ed38db6c4","added_by":"auto","created_at":"2026-04-15 15:03:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1507685,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in the response ratios of grain yield, grain Zn, Fe, and protein concentrations with soil parent material, texture, pH, soil organic carbon (SOC), available phosphorus (Olsen P), and soil available Zn concentrations (DTPA) of the study sites where Zn fertilizer was soil-applied. Horizontal bars represent the 95% confidence intervals (CIs) of means. CIs falling to the right of the red line (RR = 1) indicate significant improvement in response to soil application of Zn over the recommended fertilizer. Figures in brackets in front of each category represent the number of studies (n) and total sample size (s) available for the meta-analysis. Figures on the left side of the horizontal bars represent the marginal means.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9267207/v1/1b4049524d139b3b6f9431d3.png"},{"id":106995899,"identity":"fcc5cc96-5466-40cf-ad4a-35f236de00c3","added_by":"auto","created_at":"2026-04-15 15:25:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6556246,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9267207/v1/e75f525f-a643-4c21-8935-8f28f01b21e2.pdf"},{"id":106960815,"identity":"f09b830b-6177-4e8d-9cf9-f11cc6f9795c","added_by":"auto","created_at":"2026-04-15 09:23:15","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3095393,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLE1.doc","url":"https://assets-eu.researchsquare.com/files/rs-9267207/v1/d0d6d9871e6060422f5e818f.doc"},{"id":106805246,"identity":"29e8b5fc-3e2c-4a5b-872c-ecbbc59b8d4f","added_by":"auto","created_at":"2026-04-13 15:21:34","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4881672,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLE2.doc","url":"https://assets-eu.researchsquare.com/files/rs-9267207/v1/029f66d1021601f87ecfe8fc.doc"},{"id":106805248,"identity":"8f8a8192-413f-408f-b7b1-97673be10c98","added_by":"auto","created_at":"2026-04-13 15:21:34","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":22091,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-9267207/v1/8e6cb1f8b7bfc822653bdfc5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combining genetic and agronomic fortification is essential to meet human health targets for zinc, iron, and protein concentrations in rice grains: A meta-analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRice is a staple food for more than half of the world\u0026rsquo;s population, and the single largest food source for the poor\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. It provides 19\u0026ndash;21% of the global per capita energy and about 15% of the protein requirement for humans\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Rice is consumed primarily in a polished form which is loaded with easily digestible starch (~\u0026thinsp;90% of dry weight)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, but low in zinc (Zn), iron (Fe), and protein concentrations compared with other staple cereals such as wheat and maize\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In the past, research and development efforts have focused most on yield improvement, and less on improvement of the nutritional quality. Decades of breeding for higher yields have led to a decline in grain concentrations of Zn and Fe in staple cereals such as rice and wheat\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The grain Zn and Fe concentrations reported in the literature are often lower than the breeding targets proposed for human health\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Initially, the CGIAR biofortification challenge program (HarvestPlus) set the breeding target at 15 mg Fe kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e in polished (white) rice grains\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, this breeding target appears to have been abandoned and no longer appears in the recent literature\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The breeding target for Zn is 28 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e in polished rice\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These targets are aspirational goals set to achieve a realistic level of the daily requirement of Zn and Fe for human health. However, these targets do not consider the bioavailability of Zn and Fe in the human gut. The bioavailability and uptake of Zn, Fe, calcium (Ca), magnesium (Mg), and manganese (Mn) in the human gut depend on the phytic acid concentrations in the processed grain\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Plants often store excess phosphorus (P) as phytate (the salt form of phytic acid), which accounts for 60\u0026ndash;80% of total P in cereal grains\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. While it has beneficial health effects as an anticarcinogen, antioxidant, and inhibitor of kidney stone formation, phytate is the most abundant anti-nutritional factor in cereal grains with regard to the uptake of bivalent cations\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Earlier work has emphasized the need for improvement of the nutritional value of cereals through reduction of phytate and increased Zn and Fe concentrations in the grain\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Generally, whole-grain rice also has one of the lowest protein concentrations among cereals\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The protein, nitrogen (N), and amylose concentrations in rice grains also play a key role in the nutritional, cooking, and eating qualities of rice\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Nevertheless, there are no breeding targets for grain protein or phytate concentrations in rice. Recent findings in breeding rice varieties with higher protein content and low glycemic index are opening new opportunities for breeding more nutritious rice\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. These discoveries highlight the potential to develop rice varieties with improved protein content and better health features, especially for people with diabetes.\u003c/p\u003e \u003cp\u003eOur ability to improve grain yield, grain Zn, Fe, and protein concentrations will depend on understanding the complex interactions and correlations between traits in soils and plants. The interaction between two elements in soils may be antagonistic or synergistic, thereby influencing nutrient uptake and use efficiency\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. For example, P fertilization is expected to increase crop yields due to increased P uptake (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which may increase phytate concentrations in grains, but depresses Zn uptake\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The plant physiological process of absorption by phytate, decreases the availability of Zn even further. However, information on the entire spectrum of how nutrient uptake and physiological processes would interact with regards to P, Zn, Fe, sulfur (S) (relevant for amino acids in proteins), and proteins, and hence their concentrations in rice grains is lacking. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a schematic representation of our hypothesized relationships between nutrient uptake, grain yield, and the concentrations of nutrients, protein, and phytate in rice grains. Quantifying the correlations between these variables could inform efforts to simultaneously improve yield and minerals such as Zn and Fe\u003csup\u003e12,21\u003c/sup\u003e. According to Senguttuvel and co-workers\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, the correlation between grain yield and grain Zn concentrations is a key factor in identifying and releasing Zn-biofortified rice varieties. Genotype-by-environment (G\u0026times;E) interactions also play a crucial role in identifying stable lines for Zn and Fe concentrations\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, the strength of correlations and the effect of G\u0026times;E interactions on the traits targeted for rice biofortification are not fully understood. It is also unclear to what degree agronomic and breeding efforts have achieved the initial breeding targets for Zn and Fe in rice. The impact of the production environments (e.g. irrigation vs rainfed) and agronomic interventions (e.g. fertilization) on grain Zn, Fe, and protein concentrations has not been fully quantified.\u003c/p\u003e \u003cp\u003eAlthough several primary studies have measured concentrations of Zn, Fe, and protein in rice grain at the local level, a global synthesis of this information is lacking. Such syntheses are urgently needed for setting target concentrations and identifying management practices to increase the concentration of essential micronutrients and reduce the concentrations of antinutritional factors such as phytic acid in rice grain. Therefore, the overarching aim of this synthesis is to inform global efforts in genetic and agronomic fortification toward achieving the target concentrations\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e of Zn (28 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) and Fe (15 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) and increasing protein concentration in rice grains. The specific objectives of this synthesis are to: (1) determine the probability of achieving the target concentrations of Zn and Fe in white rice grains; (2) determine trait correlations, the influence of G\u0026times;E interactions and heritability toward the simultaneous improvement of grain yield, grain Zn, Fe, and protein concentrations; (3) quantify variations in response ratios of grain yield, grain Zn, Fe, and protein concentrations to genotypes, production environments, agronomic practices, and soil and climate variables. The main hypotheses being tested in this analysis are: (1) grain Zn and Fe concentrations in rice cultivars are below the target concentrations for human nutrition; (2) grain Zn and Fe concentrations can be increased with genetic biofortification and Zn and Fe fertilization; (3) the correlations between grain yield, grain Zn, Fe, and protein concentrations are positive across rice genotypes and fertilization treatments; and (4) Zn and Fe fertilization significantly increase grain Zn, Fe, and protein concentrations across climates, soils, and rice growing environments.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDistributions of grain Zn, Fe, protein, and phytate concentrations\u003c/h2\u003e \u003cp\u003eBy consolidating data from 245 publications in studies conducted across 34 countries, and using over 3,600 rice genotypes, this analysis has established benchmark concentrations of Zn, Fe, and protein in rice grain on dry matter basis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). So far, such baselines have not been available, except for Zn and Fe, which were based on limited data generated in the early 2010s. Grain Zn, Fe, and protein concentrations were available in 191, 94, and 89 studies, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and these were used to establish their empirical distributions (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe mean, median, 25th percentile (Q1), and 75th percentile (Q3) concentrations and the coefficients of variation (CV) of grain zinc, iron, nitrogen, phosphorus, sulfur, protein, and phytate concentrations in rice across both brown and white rice samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElements (unit)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo. studies and observations\u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eConcentration on dry matter basis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e)\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[191; 9801]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron (mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[94; 7104]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e103.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[89; 1652]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[48; 5282]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfur (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[21; 3888]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[89; 1652]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[27; 813]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e73.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCV, coefficient of variation; Q1, lower quantile; Q3, upper quantile.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026dagger; Figures in brackets represent the number of studies (n) and total sample size (s) available for analysis\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026Dagger; 1 mg kg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e = 1 \u0026micro;g g\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing data from a total of 38 and 21 studies reporting measurements in polished rice on Zn and Fe concentrations, we estimated the median grain concentrations in white rice at 17.5 mg Zn kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (confidence interval [CI]: 17.0\u0026ndash;18.0) and 3.6 mg Fe kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (CI: 3.4\u0026ndash;3.8). These values are much lower than the breeding targets (28 mg Zn kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and 15 mg Fe kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e)\u003csup\u003e11\u003c/sup\u003e. We estimated the probability of exceeding the targets of Zn and Fe in white rice using their cumulative frequency distributions. This was 4.0% in white rice for grain Zn concentration, and 10.5% for grain Fe in the absence of Zn and/or Fe fertilization. These low figures indicate that the progress toward the targets through genetic biofortification has been slow. Where Zn and/or Fe fertilizers were applied, the probabilities of achieving the targets were 41.3% for Zn and 67.7% for Fe. On the basis of these findings, we argue the necessity of complementary efforts in breeding and agronomic interventions to create synergistic effects.\u003c/p\u003e \u003cp\u003eThe median phytate concentration in rice grain was 0.72% across the 27 studies. The median phytate concentration in white rice (0.38%) was significantly lower than concentrations in brown rice (0.92%) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). According to Kumar and colleagues\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, high Zn bioavailability is associated with rice cultivars with low grain phytate concentrations (\u0026lt;\u0026thinsp;0.82%), while those with 2.62% or more phytate concentrations have low Zn and Fe bioavailability. Our analysis reveals that the processing of rice also impacts phytate concentrations, and consequently the bioavailability of Zn and Fe.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTrait correlations, G×E interactions, and heritability\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e provide an overview of the relationships and correlations between grain yield and nutritional qualities. The analysis of data aggregated across studies on germplasm and fertilization revealed significantly positive correlations between grain yield and grain Zn concentrations (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.091; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), grain yield and Fe concentrations (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.198; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and grain Zn and Fe concentrations (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.418; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not between grain yield and protein or phytate concentrations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Significant positive correlations were also observed between grain yield and grain Zn uptake (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.741; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), grain yield and grain Fe uptake (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.494; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), grain yield and grain P uptake (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.941; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), grain Zn uptake and Fe uptake (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.688; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), grain Zn uptake and N uptake (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.714; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), grain Zn uptake and P uptake (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.643; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), grain yield and grain S uptake (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.671; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and grain Fe uptake and Zn uptake (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.688; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ea provides the 95% confidence intervals and information on the robustness of the trait correlations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation coefficients of the associations between grain yield, grain Zn, Fe, protein, and phytate concentrations (conc), grain Zn uptake, Fe uptake, N uptake, P uptake, and S uptake in rice grains (for details of tests of statistical significance please refer to Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ea)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrain yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZn conc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFe conc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProtein conc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZn uptake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFe uptake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP uptake\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn conc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.091***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe conc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.198***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.418***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein conc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.179***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.097*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytate conc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.067ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.069*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.371***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.268**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.555***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.026ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP conc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.346***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.058ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.119ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.125ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.192**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.642***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS conc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.142*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.213**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.423***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.164*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.051ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.751***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.434***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn uptake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.741***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.729***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.468***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.207***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.688***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe uptake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.494***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.555***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.944***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.266***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.688**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN uptake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.797***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.364***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.192**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.603***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.714***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.301**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.580***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP uptake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.941***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.140ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.077ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.073ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.643***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.396***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS uptake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.671***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.149*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.030ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.035ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.340***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.631***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.745***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eStatistical significance: * α\u0026thinsp;=\u0026thinsp;0.05; ** α\u0026thinsp;=\u0026thinsp;0.001; *** α\u0026thinsp;=\u0026thinsp;0.0001; ns\u0026thinsp;=\u0026thinsp;not significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe findings above generally confirm the relationships depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We were unable to establish the correlations between concentrations of grain phytate and P uptake or S uptake due to lack of matching data on phytate concentrations. Given the correlations between traits, we posit that improvement of nutritional quality is possible through complementary use of agronomic and genetic fortification of rice. Genetic fortification relies on the inherent genetic potential of crop varieties to accumulate nutrients in the grains. However, the effectiveness of this process depends on nutrient availability, which must be ensured through agronomic fortification via soil and/or foliar application, as their uptake and subsequent accumulation in grains are influenced by soil properties and prevailing weather conditions. In the individual studies that evaluated rice germplasm, the correlations between grain yield, grain Zn, Fe, and protein concentrations were significantly positive in 25\u0026ndash;33% of the studies and non-significant in 35\u0026ndash;75% of the studies. On the other hand, the correlation between grain Zn and Fe was significantly positive in 59% of the studies. In the meta-analysis, the majority (88%) of Fisher r-to-z transformed correlation coefficients between grain Zn and Fe were positive and the random-effects model estimate (0.368) was significantly different from zero. However, between-study heterogeneity was significant (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eFrom the individual studies on fertilization effects, the correlations between grain yield, grain Zn concentrations, Fe concentrations, and protein concentrations were all significant. The fail-safe number, a tests of publication bias, indicate that the pooled effect size estimates are robust. However, significant between-study heterogeneity was evident in all estimates (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eb). Taken together these positive correlations suggest that grain yield, grain Zn, Fe, and protein concentrations can be simultaneously improved through Zn and Fe fertilization, and (but to a lesser extent) by breeding.\u003c/p\u003e \u003cp\u003eWe found 7, 5, and 4 studies on G\u0026times;E interactions for grain yield, grain Zn, and grain Fe concentrations, respectively. The review of these studies suggests that the environment and the G\u0026times;E interactions account for a significant percentage of the variance in grain yield, grain Zn, and grain Fe concentrations than the genotype effect (Table S4a). In almost all cases, the environment and G\u0026times;E interaction effects on Zn and Fe concentrations were statistically significant (Table S4a). This indicates a strong influence of the environment on the expression of grain Zn and Fe concentrations.\u003c/p\u003e \u003cp\u003eOur review of 20 studies on heritability indicated low to high broad-sense heritability (H\u003csup\u003e2\u003c/sup\u003e) for grain yield (median 80%; range: 12\u0026ndash;95%), grain Zn concentrations (median 86.5%; range: 7\u0026ndash;99.5%), and grain Fe concentrations (median 72.8%; range: 7\u0026ndash;99.5%) (Table S4b). Low heritability (H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;40%) was found in less than 20% of the observations (Table S4b). In the majority of cases, heritability appears to be high enough for genetic biofortification of Zn and Fe through conventional breeding techniques.\u003c/p\u003e\n\u003ch3\u003eVariations in grain yield, grain Zn, Fe, and protein concentrations with varietal release year\u003c/h3\u003e\n\u003cp\u003eGrain yields of newly released rice varieties significantly increased between 1960 and 2024 (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea), while grain Zn concentrations significantly declined over the same period (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). Grain Fe concentrations were significantly higher in varieties released after 2000 than earlier years (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ec) but with a very wide confidence interval reflecting greater uncertainty in varieties released during the 2001\u0026ndash;2024 period. Grain protein concentrations did not vary significantly with the year of release of varieties (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ed). Fan and colleagues\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e report a similar decline in micronutrient concentrations of Zn, Fe, copper (Cu), and Mg in wheat over the years.\u003c/p\u003e\n\u003ch3\u003eEcotypic differences\u003c/h3\u003e\n\u003cp\u003eOnly one study reported measurements of nutrient concentrations in African rice (\u003cem\u003eOryza glaberrima\u003c/em\u003e). Therefore, the rest of this analysis focuses on comparing ecotypes and admixtures of Asian rice (\u003cem\u003eO. sativa\u003c/em\u003e) in terms of grain yield and nutrient concentrations in brown rice. Data on white rice were excluded because of the small sample size available for the different ecotypes. Violin plots and Kruskal\u0026ndash;Wallis test revealed significant differences between sample medians for all variables used in the comparison of rice ecotypes (Table S5). The highest median grain yield was recorded in Japonica cultivars (8.3 t ha\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) followed by admixtures (6.1 t ha\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e), which were significantly higher than yield recorded in AUS (3.2 t ha\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) and Indica cultivars (3.3 t ha\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) (Table S5). This may be attributed to the fact that significant increases in harvest index (HI) have been achieved in Japonica cultivars (Table S5) contributing to the improvements in grain yield\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Grain Zn concentrations were significantly higher in AUS (27.7 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) and Japonica cultivars (27.5 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) than in Indica cultivars (23.9 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) (Table S5) across all treatments. Indica cultivars had significantly lower Fe concentrations than the other ecotypes (Table S5). Grain protein concentrations did not differ significantly between the different ecotypes of rice (Table S5).\u003c/p\u003e\n\u003ch3\u003eDifferences between genetically biofortified and regular cultivars\u003c/h3\u003e\n\u003cp\u003eA total of 43 cultivars have been reported to be genetically biofortified with Zn and one cultivar (NSIC Rc172 [MS 13]) biofortified with Fe. Data on Zn and Fe concentrations in brown rice were available for only 11 cultivars from 13 field studies comparing biofortified with regular cultivars side by side. Comparisons of grain yields were reported in 7 studies, grain Zn concentrations in 13 studies, and grain Fe concentrations in 4 studies that trialed biofortified and regular cultivars side by side. All of the studies report grain yields and grain Zn and Fe concentrations in brown rice but not in white rice. Our analysis did not reveal significant differences in grain yield (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eg), and a marginal difference in Fe concentrations between the genetically biofortified and regular cultivars (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On the other hand, the median grain Zn concentration of genetically biofortified cultivars (24.8 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e; CI: 24.3\u0026ndash;25.7 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) was significantly higher than that in regular cultivars (22.6 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e; CI: 22.2\u0026ndash;23.0 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e), but the difference was only 9.7 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eh).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences between genetically biofortified and regular cultivars in median grain yield, grain Zn and Fe concentrations of brown rice for studies that tested biofortified and regular cultivars side by side in the same trial.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiofortified cultivars\u003c/p\u003e \u003cp\u003e(95% CI)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegular cultivars\u003c/p\u003e \u003cp\u003e(95% CI) \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003cp\u003e(M-W test)\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003cp\u003e(K-S test) \u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain yield (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.5 (3.2; 3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.7 (3.4; 4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.071\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.016\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain Zn (mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.8 (24.3; 25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.6 (22.2; 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain Fe (mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.4 (7.8; 11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.3 (11.1; 11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.011\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.007\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e Figures in parentheses represent the 95% confidence intervals (CI) of median values estimated using bias-corrected and accelerated boot strapping. Two or more medians are deemed significantly different if their 95% CIs do not overlap and the Mann-Whitney test and the Kolmogorov-Smirnov tests produce P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003eThe \u003cem\u003eP\u003c/em\u003e values are for the Mann-Whitney (M-W) test of equality medians and the Kolmogorov\u0026ndash;Smirnov (K-S) test of equality of distributions\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLoss of nutrients with grain processing\u003c/h2\u003e \u003cp\u003eUsing two independent estimation methods, we found that milling and polishing can reduce grain Zn concentrations by 21.2\u0026ndash;30.9%, Fe concentrations by 69.3\u0026ndash;70.3%, protein concentrations by 6.5\u0026ndash;7.1%, and phytate concentrations by 47.4\u0026ndash;59.3% in rice grain (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage loss of grain concentrations of zinc, iron, phosphorus, protein, and phytate with processing of brown rice to white rice estimated using two different methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNutrient [n, s] \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethod 1*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethod 2*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% loss (95% CI) \u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% loss\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc [25; 1366]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-21.2 (-21.9; -20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-30.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron [9; 640]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-70.3 (-71.2; -68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-69.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus [1; 6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-52.3 (-54.0; -49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-63.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein [3; 36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-6.5 (-8.1; -4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytate [8; 146]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-47.4 (-51.0; -46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-59.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Method 1 is based on percentage changes between mean values from brown and white rice reported in the same study, while Method 2 is based on the percentage change between the median values in brown rice and white rice from all studies.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003e Figures in brackets represent the total number of studies (n) and the total sample size (s) available for the analysis in Method 1. See the sample size for Method 2 in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e Figures in parentheses represent the 95% confidence intervals (CI) of median values estimated using bias-corrected and accelerated boot strapping.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSome studies show that limiting the degree of milling to 5% can reduce losses of Zn and Fe substantially. For example, in an analysis of 30 landraces from Manipur in India, Longvah and co-workers\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e found 12.5% reduction in Zn concentrations with 5% milling but 19.8% reduction with 10% milling. In the case of Fe concentrations, they found 41.8% reduction with 5% milling but 61.3% reduction with 10% milling\u003csup\u003e25\u003c/sup\u003e. There is growing interest in promoting the consumption of whole-grain brown rice\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, brown rice is often considered as \u0026ldquo;peasant food,\u0026rdquo; and only consumed by the elderly among Asians\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Meanwhile, parboiled rice is known to have higher nutrient and vitamin B6 concentrations but lower phytic acid concentrations than non-parboiled rice\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Parboiling involves soaking of brown rice in hot water, followed by steaming and drying to a moisture content of 12\u0026ndash;14%. During the parboiling process, vitamins and micronutrients are infused into the starch endosperm, thereby reducing nutrient losses during milling and making parboiled rice nutritionally superior to non-parboiled white rice\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBioavailability of Zn and Fe\u003c/h3\u003e\n\u003cp\u003eThe bioavailability of Fe and Zn in rice grain is estimated at 10\u0026ndash;25%\u003csup\u003e11\u003c/sup\u003e, which is largely determined by the concentration of chelating molecules such as phytate (phytic acid) in the grain. The phytate-to-Zn and phytate-to-Fe molar ratio is considered the first proxy for bioavailability of Zn, Fe, and P to humans\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Phytate-to-Zn molar ratios greater than 15 are associated with low bioavailability, while ratios of 5\u0026ndash;15 and below 5 are associated with moderate and high Zn bioavailability\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Zn fertilization reduced the phytate-to-Zn molar ratio by 29.4% and phytate-to-Fe molar ratio by 55% in rice grain relative to treatments without Zn fertilization (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This is because Zn fertilization significantly increases grain Zn uptake without significantly affecting grain phytate concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The negative correlation between grain Zn uptake and phytate concentrations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) is also supportive of this finding. Similarly, Fe fertilization reduced the phytate-to-Zn and phytate-to-Fe molar ratios by 60 and 51%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e further indicate significant improvements in the bioavailability of Zn and Fe with Fe fertilization.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of phytate-to-zinc and phytate-to-iron molar ratios in rice grain without Zn fertilization (Without Zn) and with Zn fertilization (With Zn) or without Fe fertilization (Without Fe) and with Fe fertilization (With Fe).\u0026dagger;\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolar ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatments [n; s]\u0026Dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEquality of medians\u0026sect;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEquality of distributions\u0026sect;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytate : Zn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout Zn [25; 427]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.3 (28.6; 33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith Zn [13; 322]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.1 (21.0; 23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout Fe [25; 717]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.1 (26.2; 28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith Fe [5; 32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.8 (9.7; 17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytate : Fe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout Zn [18; 321]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.2 (31.7; 42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith Zn [6; 103]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.2 (11.6; 14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout Fe [18; 380]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.7 (30.5; 39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith Fe [6; 44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.9 (14.9; 19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026dagger; The median value of 31.3 indicates that for each mole of Zn there were 31.3 mols of phytate. The Mann-Whitney test of equality of medians and Kolmogorov\u0026ndash;Smirnov test of equality of distributions are shown on the right.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026Dagger; Figures in brackets represent the number of studies (n) and the total sample size (s) available for analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026sect; \u003cem\u003eP\u003c/em\u003e values estimated with 999 Monte Carlo permutations.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eVariations in response ratios\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVariation with nutrient inputs\u003c/h2\u003e \u003cp\u003eAmong the various nutrient inputs tested (Table S6), significantly greater increments in grain yield were achieved with Zn seed treatment (response ratio [RR]\u0026thinsp;=\u0026thinsp;1.29), followed by a combination of soil and foliar application of Zn (RR\u0026thinsp;=\u0026thinsp;1.25), and soil application of Fe (RR\u0026thinsp;=\u0026thinsp;1.24); these gave 29, 25, and 24% increase in grain yield over the recommended NPK fertilizer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The highest increment in grain Zn concentrations (RR\u0026thinsp;=\u0026thinsp;1.88) was achieved with the combination of soil and foliar application of Zn (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). This was followed by foliar application of Zn (RR\u0026thinsp;=\u0026thinsp;1.36), seed treatment with Zn (RR\u0026thinsp;=\u0026thinsp;1.34), and soil application of Zn (RR\u0026thinsp;=\u0026thinsp;1.23). The highest increment in grain Fe concentrations (RR\u0026thinsp;=\u0026thinsp;1.40) was achieved with growth-promoting rhizobacteria/zinc solubilizing bacteria (GPR\u0026thinsp;+\u0026thinsp;ZSB), followed by soil application of Fe (RR\u0026thinsp;=\u0026thinsp;1.27), and foliar application of Fe (RR\u0026thinsp;=\u0026thinsp;1.24) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Greater increments in grain protein concentrations were achieved with soil application of Fe (RR\u0026thinsp;=\u0026thinsp;1.17), foliar application of Zn (RR\u0026thinsp;=\u0026thinsp;1.13), and foliar application of Fe (RR\u0026thinsp;=\u0026thinsp;1.12) than the other treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Treatments involving other inputs (\u0026ldquo;Other\u0026rdquo;) did not significantly increase grain yields over the recommended fertilizer, but slightly increased grain Zn, Fe, and protein concentrations. Significant reduction in grain yield and grain Zn, Fe, and protein concentrations was noted in the absence of external inputs (\u0026ldquo;Noinput\u0026rdquo;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings highlight the value of applying Zn, Fe, and other micronutrients in addition to the recommended NPK fertilizer.\u003c/p\u003e \u003cp\u003eApplication of Zn fertilizer (\u0026ldquo;WithZn\u0026rdquo;) together with the recommended NPK fertilizer achieved 17% increment in grain yields (RR\u0026thinsp;=\u0026thinsp;1.17) relative to the recommended NPK fertilizer alone. But the 2% yield increment (RR\u0026thinsp;=\u0026thinsp;1.02) achieved with all other treatments without Zn (\u0026ldquo;WithoutZn\u0026rdquo;) relative to the recommended NPK fertilizer was negligible (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Zn fertilization also increased grain Zn concentrations by 44%, Fe concentrations by 5%, and protein concentrations by 7%, and reduced phytate concentrations by 8% compared with the recommended fertilizer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Between-study heterogeneity was high for all effect sizes with both with and without Zn (Figure S6). The fail-safe numbers from the random effects models indicated that the effect size estimates are all robust (Figure S6). It is likely that Zn fertilization increases grain yields and grain protein concentrations via increased grain Zn uptake, N uptake (see positive correlations in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and better agronomic use efficiency of the applied N fertilizers. For example, grain Zn uptake increased by 68% and N uptake by 29% with Zn fertilization (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Zn fertilization also increased the agronomic efficiency of N fertilizers by 4.8 kg per kg of applied N fertilizer (CI: 4.4\u0026ndash;5.2) relative to the recommended fertilizer, while treatments without Zn inputs did not significantly increase the agronomic efficiency of N use (median: 1.4; CI: 1\u0026ndash;2 kg per kg). In addition, Zn fertilization significantly increased the harvest index, which is a predictor of water productivity and nutrient use efficiency in crops\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLike Zn fertilization, Fe fertilization achieved significant increments in almost all variables except grain phytate concentrations relative to the recommended NPK fertilizer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). For example, Fe fertilization increased grain yields by 20%, grain Zn by 22%, grain Fe by 25%, Zn uptake by 39%, and N uptake by 29% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). It also increased protein concentrations by 31% and reduced phytate concentrations by 10% over the recommended fertilizer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Fe fertilization also significantly increased the agronomic efficiency of N by 3.7 kg per kg of applied N (95% CI: 3.3\u0026ndash;4.9) relative to the recommended fertilizer. The agronomic efficiency of soil-applied Zn fertilizer also increased by 50 kg per kg of applied Zn.\u003c/p\u003e \u003cp\u003eThe results above confirm our hypothesis that grain Zn and Fe concentrations can be increased with Zn and Fe fertilization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eVariation with Zn, N, and P fertilizer rates\u003c/h2\u003e \u003cp\u003eThe trends in response ratios indicated significant improvements in grain yield, grain Zn concentrations, Zn uptake, and N uptake with increasing Zn application to soil at rates up to 30 kg ha\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Further increases in Zn application rates above 30 kg/ha appeared to reduce grain Zn uptake and N uptake (Figure S7). Since Zn is not easily available to smallholder farmers and is also costly, soil application of modest amounts (e.g. 5\u0026ndash;10 kg ha\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Zn) may be recommended, as can be inferred from Figure S7. Zn oxide nanoparticles (ZnO NPs), a new class of Zn fertilizers, have been shown to be more effective than common Zn fertilizers due to their high specific surface area, and are easily absorbed and utilized by rice roots\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. ZnO NPs have also been shown to increase rice yields, grain Zn content, and many quality traits compared to ionic Zn at the same application rate\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe LOESS (locally estimated scatterplot smoothing) regression also indicated that N application rates up to 150 kg ha\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e can significantly improve grain yields, grain Zn and Fe concentrations, Zn, Fe, and N uptake beyond which response was flat (Figure S8a\u0026ndash;f). On the other hand, grain protein concentrations did not significantly improve with increasing N application rates (Figure S8h). The response ratios of grain yield, and Zn uptake appear to diminish with increasing P application rates higher than 80 kg P ha\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (Figure S9a,c). Although P fertilization increases yield, it also reduces the bioavailability of Zn, Fe, and other nutrients to humans due to increased phytate content (it also has negative environmental impacts)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. This highlights the need for reconsideration of P fertilizer strategies. The trends in response ratios of Fe concentrations and Fe uptake with P application rates were somewhat idiosyncratic due to the small sample sizes (Figure S9d,e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eVariation with production systems\u003c/h2\u003e \u003cp\u003eGlobal syntheses of the effects of rainfed and irrigated rice, which are two contrasting production environments, on grain nutrient concentrations are lacking. The magnitude of Zn and Fe fertilization on grain Zn and Fe concentrations in these systems are also not fully understood. In this meta-analysis, soil application of Zn significantly increased grain yields, grain Zn, and protein concentrations in both production systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea,b,d). Grain yield and grain protein concentrations did not significantly differ between rainfed and irrigated rice with soil-application of Zn (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea,d). On the other hand, grain Zn concentrations were significantly higher in irrigated rice (RR\u0026thinsp;=\u0026thinsp;1.5) than in rainfed rice (RR\u0026thinsp;=\u0026thinsp;1.36) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), while the reverse was true for grain Fe concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The significant increase in grain Zn concentrations with Zn fertilization in irrigated rice may be attributed to its widespread deficiency and lower bioavailability due to precipitation of Zn in flooded soils\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Thus, Zn fertilization is a critical agronomic practice in irrigated systems. On the other hand, flooding of the soil is known to increase the availability and total uptake of other nutrients such as N, P, K, S, Fe, and Mn\u003csup\u003e35\u003c/sup\u003e. The availability of Fe is high in flooded conditions because of the lower redox potential\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, while the availability of Fe is limited in rainfed conditions, particularly when soil pH is high\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eVariation with climatic factors\u003c/h2\u003e \u003cp\u003eCompared with the recommended NPK fertilizers, soil application of Zn significantly increased grain yield, grain Zn, and protein concentrations across all categories of aridity and climate zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea,b,d). This means that soil application of Zn can improve yield and grain Zn and protein concentrations over the recommended NPK fertilizer regardless of the climate and aridity zones. Greater increments in grain yields were achieved in subhumid zones and subtropical climates than in other zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). On the other hand, greater increments in grain Zn concentrations were achieved in humid zones and tropical climates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). No clear patterns could be claimed in the case of grain Fe concentrations partly due to the small number of studies available for meta-analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eVariation with soil factors\u003c/h2\u003e \u003cp\u003eSoil application of Zn significantly increased grain yield, grain Zn, and protein concentrations across all categories of soil parent material, texture, soil pH, soil organic carbon (SOC), Olsen P, and soil Zn concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea,b,d). This means that soil application of Zn can improve yields and grain Zn and protein concentrations over NPK fertilizer regardless of the soil conditions. The exception was grain Fe concentrations, for which there were too few studies and observations to make firm conclusions. Significantly greater increments in grain yield were achieved on soils that are deficient in Zn than on those considered as having sufficient Zn (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). This is probably because Zn is a yield-limiting soil nutrient whose deficiency is widespread especially in wetland rice\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. On the other hand, grain Zn and protein concentrations were equally improved on both Zn-deficient and Zn-sufficient soils (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb,d). This implies that soil application of Zn can significantly improve grain Zn and protein concentrations regardless of the Zn status of the soil. Taken together, the results above confirm our hypothesis that \u0026ldquo;Zn and Fe fertilization significantly increases grain Zn, Fe, and protein concentrations over control (NPK fertilizer only) across climates, soils, and rice growing environments.\u0026rdquo; Greater improvements in grain yield, grain Zn, and protein concentrations, and N and P uptake due to soil application of Zn occurred where soil available Zn concentrations fell below 1.0 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (diethylenetriaminepentaacetic acid [DTPA] method). This is consistent with the argument that the optimum range of soil available Zn for rice is 0.38\u0026ndash;0.90 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (DTPA-extractable Zn)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eChallenges faced and limitations of the meta-analysis\u003c/h2\u003e \u003cp\u003eThe major challenges faced in this analysis were (1) the small number of studies reporting nutrient concentrations in white rice; (2) incomplete description of the study sites; (3) scarcity of information on the rice cultivar used, fertilizer rates, and irrigation regimes; (4) lack of directly measured soil properties and climate variables; and (5) selective reporting of results of statistical analyses. In many publications, main effects were reported and interaction effects were missing. These challenges have limited in-depth analysis of the association between grain nutrient concentrations and climate and soil variables. These limitations are presented in detail in the Supplementary Methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on the various analyses, it is concluded that (1) modern rice cultivars have inadequate concentrations of Zn, Fe, and protein for healthy human nutrition; (2) processing of brown rice to white rice significantly reduces Zn, Fe, and protein concentrations; (3) current rates of increments in grain Zn and Fe concentrations with genetic biofortification are not likely to achieve the target Zn and Fe concentrations without complementary use of agronomic interventions; (4) application of Zn and Fe fertilizers can significantly increase the nutritional value of rice grains while also increasing grain yields; and (5) soil application of Zn can improve yields and grain Zn and protein concentrations over the recommended NPK fertilizer regardless of production systems, climate zones, soil conditions, and soil Zn status. We recommend, (1) complementary efforts in genetic and agronomic fortification of rice to increase grain Zn and Fe concentrations; (2) emphasis on consumption of parboiled rice among populations whose staple diet is rice; and (3) reducing milling and polishing of rice grains to reduce loss of micronutrients in rice grain.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eScope and context of this review and meta-analysis\u003c/h2\u003e \u003cp\u003eThe scope of this meta-analysis is global, covering all rice growing regions, cultivated rice species and their crosses, subspecies, and cultivars. Rice was chosen for this analysis because of its strategic importance for food security especially in Asia and Africa\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. For example, rice ranks first in Asia, and second in Africa in terms of area and production in 2022.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Historically cultivated rice is diverse, and consists of two species, namely, \u003cem\u003eOryza sativa\u003c/em\u003e (Asian rice) and \u003cem\u003eO. glaberrima\u003c/em\u003e (African rice)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e and their crosses. Asian rice is dominant in terms of area under cultivation, whereas African rice remains important in some parts of Africa. The latter is also an essential source of genes for resistance to biotic and abiotic stresses\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Asian rice consists of two sub-species, namely \u003cem\u003eO. sativa indica\u003c/em\u003e (Indica hereafter) and \u003cem\u003eO. sativa japonica\u003c/em\u003e (Japonica), each with different ecotypes\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Indica rice is mainly found in tropical and subtropical rice growing regions, at low latitudes or low altitudes, whereas Japonica rice is mostly cultivated in high-latitude temperate regions\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLiterature search, retrieval, and data extraction\u003c/h2\u003e \u003cp\u003eWe followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA-S)\u003csup\u003e47\u003c/sup\u003e guidelines for literature searches in systematic reviews and meta-analysis. We conducted searches in Google Scholar and Web of Science for peer-reviewed publications. In Google Scholar, we used the keywords: (Rice and mineral or ferti* and grain yield or produc*) and (grain quality or zinc or iron or protein or phosphorous or selenium), retrieving 998 titles. In Web of Science, the search string was: \u0026ldquo;Rice or (Oryza) (All Fields) AND Zinc or iron ferti* (All Fields) AND grain yield or grain produce or bio-forti* or bioforti or grain zinc or grain iron or grain phosphorous\u0026rdquo; resulting in 9,379 titles. After refining by document type (Article) and language (English), 8,998 titles were retained. Initial title screening excluded 8,184 publications (Figure S4). After removal of duplicate studies, 670 publications were subjected to abstract and full-text screening. At this stage, 388 studies were excluded based on the exclusion criteria. A total of 38 studies were found by reading the reference lists of the selected studies. In total, 245 publications were retained for data extraction (Figure S4). A map of the study sites included in this meta-analysis is presented in Figure S5.\u003c/p\u003e \u003cp\u003eTwo reviewers screened each record independently. No automation tools were used in the process. All retrieved publications were compiled into an MS Excel spreadsheet with metadata including author, title, journal name, publication year, DOI, geographic coordinates of the site, site name, and country. All relevant data were extracted from the 245 publications and organized in Excel after extracting information from tables directly and digitizing figures using Web PlotDigitizer. Studies reporting grain yield and mineral contents with clearly defined fertilizer management strategies were categorized as \u0026ldquo;Treatment\u0026rdquo;; otherwise, they were identified as \u0026ldquo;Germplasm\u0026rdquo; studies. In cases where the same data were reported in multiple publications by the same authors, we selected the most comprehensive source for data extraction. Where geographical coordinates of the experimental site were not provided, we used Google Maps (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.google.com/maps/\u003c/span\u003e\u003cspan address=\"https://www.google.com/maps/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to determine the latitude and longitude based on the location of the nearest city or the experimental station where the study was conducted. Where initial soil properties of the test site were not provided, we extracted some of them for the approximate study locations from the ISRIC Soilgrids.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eThe following criteria were used for inclusion of a study in the meta-analysis: (1) the study was published in a peer-reviewed journal; (2) the study compared application of either Zn or Fe alone or in combination with other micronutrients as treatments and/or rice genotypes; (3) the study reported rice grain yield and grain nutrient concentrations on a dry matter basis; (4) all recommended agronomic management practices were performed uniformly; (5) the study was performed in randomized and controlled field trials; (6) the treatments were replicated; (7) the study reported survey results that sampled crops directly from farmers\u0026rsquo; fields on a dry mass basis. A study was excluded if it: (1) was conducted under greenhouse conditions, as a pot experiment, or in a hydroponic medium; (2) reported nutrient concentrations of rice grains sampled in the market; (3) reported nutrient concentrations of rice grains sampled after long storage period; (4) did not report treatment means, but reported other measures such as median and the corresponding author was not willing to share raw data; (5) is a review paper; (6) is not published in a peer-reviewed journal; (7) presented results in a manner that treatment means cannot be extracted, for example boxplots without mean values or in 3D graphs or in broken bar charts; (8) is based on genetically modified (transgenic) rice varieties (but genetically biofortified varieties were included); and (9) was retracted by the journal after publication.\u003c/p\u003e \u003cp\u003eQuality control of the data used in this meta-analysis was assured by: (1) including only publications in peer-refereed journals; (2) ensuring that all selected publications are independently reviewed by the co-authors; (3) full article reading of the publications to substantiate each finding; (4) thorough training of the data extraction team; and (5) counterchecking the extracted data by co-authors before pre-processing for statistical analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eChoice of response variables and calculation of effect sizes\u003c/h2\u003e \u003cp\u003eFor meta-analysis, we used the response ratios (RR) of grain yield, grain Zn, Fe, protein, and phytate concentrations as the response variables. Particular attention was given to grain Zn, Fe, and protein concentrations in the meta-analysis because rice is deficient in these nutrients among the staple cereal crops\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. We also chose harvest index and thousand-grain weight (TGW) as response variables. We chose harvest index specifically because it is considered as an informative indicator of the sink\u0026ndash;source balance, and a measure of biological success in partitioning photosynthates to the harvestable product\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Over the years, breeding efforts have achieved great increases in yield by improving the harvest index. Therefore, the harvest index is considered as a useful indicator to consider in the breeding of new high-yielding varieties and improving resource use efficiency\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, and the amount of nutrients recovered in harvested products is also a function of the harvest index.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eData processing\u003c/h2\u003e \u003cp\u003eIn preparation for statistical analysis, some variables were calculated from the available data and gaps were filled: harvest index, Zn, Fe, and protein concentrations, Zn and Fe bioavailability. Nutrient uptake and agronomic use efficiency of N fertilizer were not reported in some studies. These were calculated from studies that have reported the requisite variables. For example, where the harvest index (HI) was not provided, it was calculated from grain yield, straw yield, or total aboveground biomass as follows: HI\u0026thinsp;=\u0026thinsp;100*(grain yield/total aboveground biomass) or HI\u0026thinsp;=\u0026thinsp;100*(grain yield/(grain yield\u0026thinsp;+\u0026thinsp;straw yield)). In studies where only grain nutrient uptake was reported, grain N, Zn, and Fe concentrations were calculated from uptake as follows:\u003c/p\u003e \u003cp\u003eN concentration (in % or g kg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) = (Uptake*100)/grain yield (in kg ha\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003eZn or Fe concentration (in mg kg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) = (Uptake*1000)/yield (in kg ha\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003eIn studies where protein concentration was not reported but grain N concentration was reported, grain protein concentration was calculated by multiplying N concentration (in %) by 5.95 as per Food and Agriculture Organization of the United Nations (FAO) guidelines\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Conversely, N concentration was calculated by dividing the protein concentration (%) by 5.9.\u003c/p\u003e \u003cp\u003eIn studies where nutrient yield alone was reported, grain Zn and Fe concentrations were calculated from nutrient yield as follows:\u003c/p\u003e \u003cp\u003eZn or Fe concentration (in mg kg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) = ((Nutrient yield (kg ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)/grain yield (in kg ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e))/10.\u003c/p\u003e \u003cp\u003eGrain N, Zn, Fe, P, and S uptake were calculated from their respective concentrations as follows:\u003c/p\u003e \u003cp\u003eN, P, or S uptake (in kg ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;N, P, or S concentration (in %)*grain yield (in kg ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)/100\u003c/p\u003e \u003cp\u003eZn or Fe uptake (in g ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) = concentration (in mg kg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)*grain yield (in kg ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)/1000.\u003c/p\u003e \u003cp\u003eThe agronomic efficiency of N fertilizer was calculated as follows:\u003c/p\u003e \u003cp\u003e(treatment yield \u0026ndash; control yield)/N rate,\u003c/p\u003e \u003cp\u003ewhere the control yield is the yield recorded in the recommended NPK treatment.\u003c/p\u003e \u003cp\u003eTo facilitate the meta-analyses, continuous explanatory variables were also converted into categories and dummy variables were created. Using the soil texture provided and/or the soil clay and silt concentrations reported, three soil texture classes (sandy, loam, and clayey) were created following Chivenge and co-workers\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Soils were classified as calcareous, silicic, or mafic based on their parent materials\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Calcareous parent materials include limestone, dolomite, calcareous shale, and sands with \u0026gt;\u0026thinsp;50% CaCO\u003csub\u003e3\u003c/sub\u003e or MgCO\u003csub\u003e3\u003c/sub\u003e, while silicic (also called acidic) refers to rocks that contain significant amounts of silica (\u0026gt;\u0026thinsp;68% Si), and intermediate parent materials contain 52\u0026ndash;68% Si\u003csup\u003e51\u003c/sup\u003e. The term mafic (basic) is applied to parent material with relatively low amounts of silica (45\u0026ndash;52% Si)\u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSoil organic matter (SOM) data were converted to SOC by dividing SOM by 2 following Pribyl\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Then, the SOC values were grouped into two categories: low (SOC\u0026thinsp;\u0026le;\u0026thinsp;1%) and medium to high (SOC\u0026thinsp;\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;1%).\u003c/p\u003e \u003cp\u003eWherever primary studies used different soil extraction methods of soil pH, available P, and cations, and reported in different units, these were converted into a single unit before combining the data for meta-analysis. All soil pH reported in different extraction methods were converted into pH in H\u003csub\u003e2\u003c/sub\u003eO. To facilitate meta-analysis, soil pH data were grouped into three categories as acidic (pH\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;6.5), neutral (pH 6.5\u0026ndash;7.5), and alkaline (pH\u0026thinsp;\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;7.5) in H\u003csub\u003e2\u003c/sub\u003eO following Xu and co-workers\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Similarly, P extracted using different methods were converted into Olsen P equivalents using regression equations. The Olsen P (bicarbonate extraction) was used in preference to the other methods as it is the most used indicator for soil P status\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Olsen P values were grouped into three categories: low (\u0026lt;\u0026thinsp;15 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e), medium (15\u0026ndash;22 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e), and high (\u0026gt;\u0026thinsp;22 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) based on application in earlier studies. Following Wissuwa and co-workers\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, we classified study sites as Zn-deficient (\u0026le;\u0026thinsp;0.8 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Zn) and Zn-sufficient (\u0026gt;\u0026thinsp;0.8 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Zn) using the DTPA soil available Zn reported.\u003c/p\u003e \u003cp\u003eBased on existing convention, climate zones were classified into tropical (between latitudes 23.5\u0026deg; north and 23.5\u0026deg; south), sub-tropical (23.5\u0026ndash;35\u0026deg; north and south), temperate (35\u0026ndash;50\u0026deg; north and south), and boreal (\u0026gt;\u0026thinsp;50\u0026deg; north) using the global positioning system (GPS) coordinates of the study sites. Study sites were also classified based on the aridity index (AI) as arid (AI\u0026thinsp;\u0026lt;\u0026thinsp;0.2), semiarid (AI\u0026thinsp;=\u0026thinsp;0.2\u0026ndash;0.5), subhumid (AI\u0026thinsp;=\u0026thinsp;0.51\u0026ndash;0.65), and humid (AI\u0026thinsp;\u0026gt;\u0026thinsp;0.65)\u003csup\u003e56\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRice is produced in two contrasting production systems: rainfed (aerobic conditions) and irrigated (mostly anaerobic). Rainfed rice is grown in unsaturated soil under rainfed conditions but sometimes with supplemental irrigation, while irrigated rice is conventionally produced in flooded and puddled soil, and seedlings are normally transplanted\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Accordingly, the rice production systems were grouped into rainfed and irrigated to facilitate the meta-analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eQuantifying baseline grain nutrient concentrations and antinutritional factors\u003c/h2\u003e \u003cp\u003eTo establish the baseline grain Zn, Fe, and protein concentrations, the median values, their 95% confidence intervals (CIs), and the lower and upper quantiles (25th and 75th percentiles; Q1 and Q3) were estimated. The uncertainty around median values was represented by 95% CIs estimated using bias-corrected and accelerated (BCa) bootstrapping with 9,999 replicates. BCa is a non-parametric method that accounts for skewness and allows one to set CIs through resampling with replacement\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Violin and box plots were also generated to aid visualization and comparison of brown rice with white rice in terms of distributions. To ensure rigorous statistical inferences, tests of equality were performed for both medians and the data distributions. Accordingly, two or more medians or distributions were deemed significantly different if the 95% confidence intervals of medians did and/or overlap and the tests of equality of medians and distributions yielded \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. For the equality of medians, the Mann-Whitney test was used for two samples and the Kruskal\u0026ndash;Wallis test for several samples. The Kolmogorov\u0026ndash;Smirnov test was used for equality of distributions. In all cases, the \u003cem\u003eP\u003c/em\u003e values were generated via Monte Carlo permutation.\u003c/p\u003e \u003cp\u003eThe overall distributions of grain Zn, Fe, and protein concentrations were derived using all datasets covering field experiments and farm surveys. Farm survey data were not treated differently from experimental data since means of a given number of samples were reported on a dry mass basis. Before formal analysis of the distributions, outliers were identified using the Extreme Studentized Deviate test. Then, histograms of rice grain Zn, Fe, and protein concentrations were generated for brown rice and white rice separately. Histograms were generated by setting the number of bins to optimal following the zero-stage rule. The Gaussian kernel density and the normal distribution curves were added on the histograms. To test the first hypothesis that \u0026ldquo;grain Zn and Fe concentrations in rice cultivars are below the target concentrations for human nutrition,\u0026rdquo; the probability (\u003cem\u003eϕ\u003c/em\u003e) of exceeding the target values of Zn (28 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) and Fe (15 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) in brown and white rice were estimated using their cumulative frequency distributions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eCorrelations, G\u0026times;E interactions, and heritability traits\u003c/h2\u003e \u003cp\u003eAlthough the effects of genotype-by-environment (G\u0026times;E) interactions on grain yield, grain Zn, Fe, and protein concentrations and the heritability of these traits in rice have been widely studied, syntheses were lacking. To gain insights into the genetic and environmental control of grain yield, grain Zn, Fe, and protein concentrations, we reviewed studies that have quantified G\u0026times;E interactions and heritability, and we compiled the variance explained by the genotype, environment, and the G\u0026times;E interactions and the broad-sense (H\u003csup\u003e2\u003c/sup\u003e) and narrow-sense (h\u003csup\u003e2\u003c/sup\u003e) heritability estimates. Details of the steps taken are described in detail in the Supplementary Methods. In addition, we performed correlation analyses between grain yield, grain Zn, Fe, and protein concentrations, and grain N uptake, Zn uptake, Fe uptake, P uptake, and S uptake using the data compiled for the meta-analysis. We limited the correlation analysis to studies that have reported two target variables measured concurrently and where the number of means was more than 10 to correctly estimate the Pearson correlation coefficient. We then performed a random effects meta-analysis of the correlation coefficients between grain yield, and concentrations of Zn, Fe, protein, and phytate using the Fisher r-to-z transformed values as the outcome measure. We performed the random effects modeling using restricted maximum likelihood method (REML) implemented in the MAJOR module of the JAMOVI software. This is an open-source software that performs random effects meta-analysis using an extension of the \u003cem\u003emetafor\u003c/em\u003e package of R\u003csup\u003e58\u003c/sup\u003e. The random effects meta-analysis provides an estimate of the pooled correlation coefficient, the amount of heterogeneity (i.e. \u003cem\u003eT\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eH\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, and Cochran\u0026rsquo;s \u003cem\u003eQ\u003c/em\u003e statistics), and tests of publication bias (Rosenthal\u0026rsquo;s fail-safe N, Begg and Mazumdar Rank Correlation, Egger\u0026rsquo;s Regression and Trim and Fill Number of Studies). The procedure uses Studentized residuals and Cook\u0026rsquo;s distances to identify outliers and/or influential. To check for funnel plot asymmetry, the procedure uses rank correlation test and regression test using the standard error of the observed outcomes as the predictor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eVariations in grain yield, grain Zn, Fe, and protein concentrations with variety age\u003c/h2\u003e \u003cp\u003eFor this analysis, released rice varieties were classified by the year of release, and the median concentrations and their 95% CIs were determined. Due to uncertainty about some exact dates, release years were grouped into four 20-year periods: pre-1960, 1960\u0026ndash;1980, 1981\u0026ndash;2000, and 2001\u0026ndash;2022 for varieties for which the years of release could be verified. Statistical inferences were based on medians and their 95% CIs, estimated using BCa bootstrapping with 9,999 replicates.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of genotypic differences in grain Zn, Fe, and protein concentrations\u003c/h2\u003e \u003cp\u003eOf the 245 studies, 20 (8%) did not report the cultivar used in the study. In the remaining 225 studies, over 3,600 cultivars (including landraces, advanced lines, recombinant inbred lines, and high-yielding improved varieties) were used. It was not possible to make valid statistical comparisons between individual cultivars due to the large number of cultivars with small sample sizes. Therefore, cultivars were grouped into smaller and manageable categories to facilitate the comparison and establish genotypic differences in terms of grain Zn, Fe, and protein concentrations. Accordingly, rice cultivars were grouped based on known ecotypes of rice (i.e. aromatic, AUS, indica, japonica, admixtures vs. unknown), improvement status (landraces vs. improved cultivars), and biofortification status of improved cultivars (regular vs. genetically biofortified). Admixtures comprised crosses between Asian and African rice species (e.g. New Rice for Africa [NERICA]) and crosses between indica and japonica, indica and AUS, etc. Cultivars that could not be reliably placed in any one of these categories were categorized as \u0026ldquo;unknown.\u0026rdquo; To facilitate statistical comparisons, the median values, their 95% CIs, and the lower and upper quantiles (Q1 and Q3) were presented. Violin plots were also generated to aid visualization of the distributions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eEstimating loss of nutrients through milling and polishing\u003c/h2\u003e \u003cp\u003eThe magnitude of loss in grain nutrients was estimated using two methods. Method 1 involved estimations using data from 26 studies that have reported nutrient concentrations of brown rice and the corresponding values for white rice from the same sample. Method 2 involved calculations of the loss from the median concentration in brown rice and white rice. In both cases, losses were estimated as:\u003c/p\u003e \u003cp\u003e100*(concentration in white rice \u0026ndash; concentration in brown rice)/(concentration in brown rice).\u003c/p\u003e \u003cp\u003eAlthough Method 1 is expected to be more reliable than Method 2, estimates from Method 2 are expected to give an approximate idea of the potential loss in the absence of directly measured data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEstimating bioavailability of Zn and Fe\u003c/h3\u003e\n\u003cp\u003eThe bioavailability of Zn and Fe in grains was estimated as the phytate (PA)-to-Zn (PA: Zn) and phytate-to-Fe (PA: Fe) molar ratios calculated as follows:\u003c/p\u003e \u003cp\u003ePA: Zn = ((phytic acid content in mg kg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)/660)/((Zn content in mg kg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)/65.38)\u003c/p\u003e \u003cp\u003ePA: Fe = ((Phytic acid content in mg kg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)/660)/((Fe content in mg kg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)/55.845)\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eMeta-analysis of response to fertilization\u003c/h2\u003e \u003cp\u003eThe meta-analysis focused on quantifying the magnitude of effect on the target variables following a given treatment. The bulk of the meta-analysis focused on grain yield, grain Zn, Fe, and protein concentrations because the majority of the studies involved treatments with either Zn and Fe fertilizers or their combination. Additional meta-analyses were performed on response ratios of grain Zn uptake, Fe uptake, and N uptake. The number of studies and total number of observations used in the meta-analysis for each treatment are summarized in Table S6. Meta-analyses were performed on a subset of the data where the desired \u0026ldquo;control\u0026rdquo; defined as the treatment receiving the recommended inorganic nitrogen, phosphorus, and potassium (NPK) fertilizer could be reliably identified. The recommended NPK fertilizer was specifically chosen as the control because Zn, Fe, and other micronutrients are almost always combined with NPK. As such, the recommended NPK fertilizer is expected to provide a reasonable baseline against which gains in crop yields and nutrient concentrations in response to the micronutrient inputs can be compared.\u003c/p\u003e \u003cp\u003eGrain yield, grain Zn, Fe, and protein concentrations were chosen as the target variables for most of the meta-analysis because sufficient data were available. Other variables such as the harvest index, thousand-grain weight, and the phytate and amylase concentrations were used in the meta-analysis only where sufficient data existed. Meta-analysis of phytate concentrations was not performed due to lack of sufficient data.\u003c/p\u003e \u003cp\u003eAll meta-analyses were performed using the response ratio (RR) as the effect size metric. The RR was calculated as the ratio of the value of the target variable in the treatment and the corresponding control. To normalize the RR, its natural logarithm (lnRR) was calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:lnRR=ln\\left(\\frac{T}{C}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere T and C are the values of the target variable from the treatment and control, respectively. Once analyses were completed, lnRR and its 95% CIs were back-transformed into the arithmetic domain. The effect sizes estimated in percentage terms were used for inference because percentage change can be readily understood by non-technical readers. For this purpose, lnRR values were re-expressed in percentage change in the target variables as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\%\\:change\\:=100\\times\\:\\left({e}^{lnRR}-1\\right)\\:or\\:100\\times\\:\\left(RR-1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAccordingly, an RR value of 1.0 represents no change, while 1.1 represents a 10% increment over the recommended NPK fertilizer.\u003c/p\u003e \u003cp\u003ePrior to meta-analysis, we evaluated publication bias using funnel plots of the response ratios (Figure S6) generated using the random effects models implemented in the MAJOR module of the JAMOVI software. Figure S6 also presents tests of heterogeneity (i.e. \u003cem\u003eT\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eH\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, and Cochran\u0026rsquo;s \u003cem\u003eQ\u003c/em\u003e statistics) and publication bias (Rosenthal\u0026rsquo;s fail-safe N, Kendal\u0026rsquo;s tau, and Egger\u0026rsquo;s Regression) for the variables presented in the funnel plots. \u003cem\u003eP\u003c/em\u003e values of these tests greater than 0.05 indicate no publication bias in the effect sizes, and these are indicated by \u0026ldquo;ns\u0026rdquo; (Figure S6).\u003c/p\u003e \u003cp\u003eWe also performed sub-group analyses within a linear mixed-effects modeling framework to estimate the variation in effect sizes with genotypic, agronomic practice, soil, and climate variables. The genotypic variables were subspecies (e.g. AUS, indica, japonica) and genetic biofortification status (biofortified vs. unfortified). Comparison of rice species (Asian vs. African) was not possible because African rice was reported in only one study. To ensure the comparability of results, a subset of studies on regular and genetically fortified cultivars under the same growth conditions under the same agronomic management were analyzed. The agronomic practices included methods of Zn and Fe application (e.g. seed coating, soil application, foliar application, and a combination), application of manures, Zn solubilizing bacteria (ZnSolB) and/or growth promoting rhizobacteria (GPR), and growth condition (aerobic vs. anaerobic) (Table S6). To test the hypothesis that Zn and Fe fertilization significantly increase grain Zn, Fe, and protein concentrations over the recommended fertilizer, all treatments involving Zn and Fe were grouped into WithZn and WithFe, respectively, and compared with those that did not contain any Zn or Fe input (WithoutZn and WithoutFe). This grouping was also aimed at overcoming statistical artefacts that arise due to small sample sizes when modes of application (foliar, seed, soil, etc.) were analyzed individually. Other treatments for which sample sizes were too small were bulked together as \u0026ldquo;Other\u0026rdquo; to reduce data fragmentation and small sample size artefacts.\u003c/p\u003e \u003cp\u003eMeta-analysis of the effect of production-system, climatic, and soil factors was limited to soil application of Zn because there were adequate numbers of studies (n\u0026thinsp;=\u0026thinsp;93) and observations (S\u0026thinsp;=\u0026thinsp;1013). A comparable meta-analysis could not be performed on the effect of the other treatments due to the small number of studies (see Table S6). The focus on soil application of Zn was further motivated by the fact that Zn deficiency is a significant factor limiting grain yield of wetland rice\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Zn deficiency has also increased due to the substitution of traditional rice varieties by modern varieties that are less tolerant to Zn deficiency, removal of large amounts of Zn by modern high-yielding cultivars, high phosphorus fertilization, and other factors such as pH\u003csup\u003e59\u003c/sup\u003e. Meta-analysis of effects of climate variables was limited to climate zone and aridity of the study sites (arid, semiarid, subhumid, humid). Meta-analysis of the effects of soil variables was performed primarily on soil texture (sandy, loamy, clay), parent material (calcareous, mafic, intermediate, silicic), soil pH (acidic, alkali, neutral), Olsen P (high, low, medium), and soil organic carbon levels (high, low, medium). The climatic and soil variables mentioned above were entered into the model as fixed effects. The studies (each publication) were entered as the random effect because they represent the clustering structure in the population. In all presentations, the marginal least square means and the uncertainty around means were represented by the 95% CI estimated using linear mixed effects models.\u003c/p\u003e \u003cp\u003eUptake of soil-applied N, Zn, and Fe by crops and their subsequent translocation to the grain may depend on rate of application and the interactions between N, P, and Zn (synergistic or antagonistic). To reveal the trends in response ratios with Zn, N, and P application rates, we isolated treatments involving soil-applied Zn and we performed locally estimated scatterplot smoothing (LOESS), a non-parametric regression analysis. LOESS regression was performed in preference to linear or non-linear regression because a parametric form of the relationships could not be established in the exploratory analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis work is part of the CGIAR Research Initiative on Excellence in Agronomy and Sustainable Farming Science Program. We would like to acknowledge all funders who supported this research through their contributions to the CGIAR Trust Fund.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding information\u003c/p\u003e\n\u003cp\u003eThis study was funded through the Excellence in Agronomy initiative and the Sustainable Farming Science Program of One CGIAR.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Author contributions\u003c/p\u003e\n\u003cp\u003eConceptualization and design: SK, GWS, DM, JK, PSB\u003c/p\u003e\n\u003cp\u003eData collection, curation and analysis: DM, GWS, PP, JFD\u003c/p\u003e\n\u003cp\u003eTechnical review and writing: SK, GWS, KS, MD, PP, DM, AI, SAN, PSB, JK\u003c/p\u003e\n\u003cp\u003eAll authors read and approved submission of the manuscript to \u003cem\u003enpj Sustainable Agriculture\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Additional information\u003c/p\u003e\n\u003cp\u003eAll additional information has been supplied in the Supplementary Materials\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data availability\u003c/p\u003e\n\u003cp\u003eThe raw data used for this analysis, template used for data collection forms, and any other materials used in the review will be made available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBadoni, S. et al. Multiomics of a rice population identifies genes and genomic regions that bestow low glycemic index and high protein content. \u003cem\u003eProc. Nat. Acad. Sci. USA\u003c/em\u003e 121, e2410598121 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.2410598121\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2410598121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevkota, K. P. et al. Economic and environmental indicators of sustainable rice cultivation: a comparison across intensive irrigated rice cropping systems in six Asian countries. \u003cem\u003eEcol. Indic.\u003c/em\u003e 105, 199\u0026ndash;214 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2019.05.029\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2019.05.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElert, E. Rice by the numbers: a good grain. \u003cem\u003eNature\u003c/em\u003e 514, S50\u0026ndash;S51 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/514S50a\u003c/span\u003e\u003cspan address=\"10.1038/514S50a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWairich, A., Ricachenevsky, F. K. \u0026amp; Lee, S. A. tale of two metals: biofortification of rice grains with iron and zinc. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e 13, 944624 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2022.944624\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.944624\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarrijo, D. R., Lundy, M. E. \u0026amp; Linquist, B. A. Rice yields and water use under alternate wetting and drying irrigation: a meta-analysis. \u003cem\u003eField Crop. Res.\u003c/em\u003e 203, 173\u0026ndash;180 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2016.12.002\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2016.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIRRI. The importance of rice: 1. Rice as a global staple food. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.knowledgebank.irri.org/ericeproduction/Importance_of_Rice.htm\u003c/span\u003e\u003cspan address=\"http://www.knowledgebank.irri.org/ericeproduction/Importance_of_Rice.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKihara, J. et al. Maize grain zinc and iron concentrations as influenced by agronomic management and biophysical factors: a meta-analysis. \u003cem\u003eFood Secur.\u003c/em\u003e 16, 1147\u0026ndash;1173 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12571-024-01478-5\u003c/span\u003e\u003cspan address=\"10.1007/s12571-024-01478-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahender, A. et al. Rice grain nutritional traits and their enhancement using relevant genes and QTLs through advanced approaches. \u003cem\u003eSpringerplus\u003c/em\u003e 5(1), 2086 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40064-016-3744-6\u003c/span\u003e\u003cspan address=\"10.1186/s40064-016-3744-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebnath, S. et al. Historical shifting in grain mineral density of landmark rice and wheat cultivars released over the past 50 years in India. \u003cem\u003eSci. Rep.\u003c/em\u003e 13, 21164 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-48488-5\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-48488-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevkota, M. et al. Grain zinc, iron and protein concentrations of contemporary wheat cultivars fall short of targets for human health: global meta-analysis. \u003cem\u003eNat. Food. 7, 283\u0026ndash;295.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43016-026-01314-3\u003c/span\u003e\u003cspan address=\"10.1038/s43016-026-01314-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2026).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouis, H. E. et al. Biofortification: a new tool to reduce micronutrient malnutrition. \u003cem\u003eFood Nutr. Bull.\u003c/em\u003e 32, S31\u0026ndash;S40 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/15648265110321S105\u003c/span\u003e\u003cspan address=\"10.1177/15648265110321S105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersson, M. S. et al. Progress update: crop development of biofortified staple food crops under HarvestPlus. \u003cem\u003eAfr. J. Food Agric. Nutr. Dev.\u003c/em\u003e 17, 11905\u0026ndash;11935 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18697/ajfand.78.HarvestPlus05\u003c/span\u003e\u003cspan address=\"10.18697/ajfand.78.HarvestPlus05\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuliano, B. O. Rice: role in diet. \u003cem\u003eIn Encyclopedia of Food and Health\u003c/em\u003e, Caballero, B., Finglas, P. M. \u0026amp; Toldr\u0026aacute;, F. (eds.), 641\u0026ndash;645 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12-384947-2.00595-X\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-384947-2.00595-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Academic Press, 2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakandalage, N. et al. Improving rice zinc biofortification success rates through genetic and crop management approaches in a changing environment. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e 7, 764 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2016.00764\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2016.00764\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWairich, A., Ricachenevsky, F. K. \u0026amp; Lee, S. A tale of two metals: biofortification of rice grains with iron and zinc. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e 13, 944624 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2022.944624\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.944624\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBindraban, P. S., Dimkpa, C. O. \u0026amp; Pandey, R. Exploring phosphorus fertilizers and fertilization strategies for improved human and environmental health. \u003cem\u003eBiol. Fertil. Soils\u003c/em\u003e 56, 299\u0026ndash;317 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00374-019-01430-2\u003c/span\u003e\u003cspan address=\"10.1007/s00374-019-01430-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, P. et al. The genetic basis of grain protein content in rice by genome-wide association analysis. \u003cem\u003eMol. Breed.\u003c/em\u003e 43, 1 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11032-022-01347-z\u003c/span\u003e\u003cspan address=\"10.1007/s11032-022-01347-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi, S. et al. Comprehensive evaluation of 17 qualities of 84 types of rice based on principal component analysis. \u003cem\u003eFoods\u003c/em\u003e 10, 2883 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/foods10112883\u003c/span\u003e\u003cspan address=\"10.3390/foods10112883\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, Y. et al. Natural variation of \u003cem\u003eOsGluA2\u003c/em\u003e is involved in grain protein content regulation in rice. \u003cem\u003eNat. Commun.\u003c/em\u003e 10, 1949 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-019-09919-y\u003c/span\u003e\u003cspan address=\"10.1038/s41467-019-09919-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRietra, R. P. J. J. et al. Effects of nutrient antagonism and synergism on yield and fertilizer use efficiency. \u003cem\u003eCommun. Soil Sci. Plant Anal\u003c/em\u003e. 48, 16 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00103624.2017.1407429\u003c/span\u003e\u003cspan address=\"10.1080/00103624.2017.1407429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenguttuvel, P. et al. Rice biofortification: breeding and genomic approaches for genetic enhancement of grain zinc and iron contents. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e 14, 1138408 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2023.1138408\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2023.1138408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, A. et al. Bioavailability of iron and zinc as affected by phytic acid content in rice grain. \u003cem\u003eJ. Food Biochem.\u003c/em\u003e 41, e12413 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jfbc.12413\u003c/span\u003e\u003cspan address=\"10.1111/jfbc.12413\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan, M.-S. et al. Evidence of decreasing mineral density in wheat grain over the last 160 years. \u003cem\u003eJ. Trace Elem. Med. Biol.\u003c/em\u003e 22, 315\u0026ndash;324 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtemb.2008.07.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jtemb.2008.07.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, J. \u0026amp; Zhang, J. Simultaneously improving grain yield and water and nutrient use efficiencies by enhancing the harvest index in rice. \u003cem\u003eCrop Environ.\u003c/em\u003e 2, 157\u0026ndash;164 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.crope.2023.07.001\u003c/span\u003e\u003cspan address=\"10.1016/j.crope.2023.07.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLongvah, T. et al. Nutrient variability of rice landraces (\u003cem\u003eOryza sativa\u003c/em\u003e L.) from Manipur, northeast India and its nutrients supply potential in rice-based diets. \u003cem\u003eNutr. Food Sci.\u003c/em\u003e 52, 1100\u0026ndash;1115 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/NFS-10-2021-0296\u003c/span\u003e\u003cspan address=\"10.1108/NFS-10-2021-0296\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarcea, M. Value of wholegrain rice in a healthy human nutrition. \u003cem\u003eAgriculture\u003c/em\u003e 11, 720 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agriculture11080720\u003c/span\u003e\u003cspan address=\"10.3390/agriculture11080720\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia, Q. et al. Innovative processing techniques for altering the physicochemical properties of wholegrain brown rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) \u0026ndash; opportunities for enhancing food quality and health attributes. \u003cem\u003eCrit. Rev. Food Sci. Nutr.\u003c/em\u003e 59, 3349\u0026ndash;3370 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10408398.2018.1491829\u003c/span\u003e\u003cspan address=\"10.1080/10408398.2018.1491829\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, H. H. et al. Impact of phytic acid on nutrient bioaccessibility and antioxidant properties of dehusked rice. \u003cem\u003eJ. Food Sci. Technol.\u003c/em\u003e 52, 7806\u0026ndash;7816 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13197-015-1918-9\u003c/span\u003e\u003cspan address=\"10.1007/s13197-015-1918-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhar, S. et al. A perspective on the benefits of consumption of parboiled rice over brown rice for glycaemic control. \u003cem\u003eEur. J. Nutr.\u003c/em\u003e 61, 615\u0026ndash;624 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00394-021-02694-x\u003c/span\u003e\u003cspan address=\"10.1007/s00394-021-02694-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNdindeng, S. A. et al. Rice milling and parboiling trade-offs for economic and nutritional gains with special attention to sub-Saharan Africa: a comprehensive review. \u003cem\u003eAppl. Food Res.\u003c/em\u003e 5, 101274 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.afres.2025.101274\u003c/span\u003e\u003cspan address=\"10.1016/j.afres.2025.101274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastro-Alba, V. et al. Phytate, iron, zinc, and calcium content of common Bolivian foods and their estimated mineral bioavailability. \u003cem\u003eFood Sci. Nutr.\u003c/em\u003e 7, 2854\u0026ndash;2865 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/fsn3.1127\u003c/span\u003e\u003cspan address=\"10.1002/fsn3.1127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMi, K. et al. Zinc oxide nanoparticles enhanced rice yield, quality, and zinc content of edible grain fraction synergistically. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e 14, 1196201 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2023.1196201\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2023.1196201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, G. et al. Effect of ZnO nanoparticles on the productivity, Zn biofortification, and nutritional quality of rice in a life cycle study. \u003cem\u003ePlant Physiol. Bioch.\u003c/em\u003e 163, 87\u0026ndash;94 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.plaphy.2021.03.053\u003c/span\u003e\u003cspan address=\"10.1016/j.plaphy.2021.03.053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson-Beebout, S. E., Lauren, J. G. \u0026amp; Duxbury, J. M. Immobilization of zinc fertilizer in flooded soils monitored by adapted DTPA soil test. \u003cem\u003eCommun. Soil Sci. Plant Anal.\u003c/em\u003e 40, 1842\u0026ndash;1861 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00103620902896738\u003c/span\u003e\u003cspan address=\"10.1080/00103620902896738\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDass, A. et al. Agronomic fortification of rice grains with secondary and micronutrients under differing crop management and soil moisture regimes in the north Indian Plains. \u003cem\u003ePaddy Water Environ\u003c/em\u003e. 15, 745\u0026ndash;760 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10333-017-0588-9\u003c/span\u003e\u003cspan address=\"10.1007/s10333-017-0588-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFageria, N. K. et al. Chemistry of lowland rice soils and nutrient availability. \u003cem\u003eCommun. Soil Sci. Plant Anal.\u003c/em\u003e 42, 1913\u0026ndash;1933 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00103624.2011.591467\u003c/span\u003e\u003cspan address=\"10.1080/00103624.2011.591467\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKreye, C. et al. Biotic and abiotic causes of yield failure in tropical aerobic rice. \u003cem\u003eField Crops Res.\u003c/em\u003e 112, 97\u0026ndash;106 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2009.02.005\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2009.02.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwio, T. et al. Micronutrients in East African lowlands: are they needed to intensify rice production? \u003cem\u003eField Crop. Res.\u003c/em\u003e 270, 108219 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2021.108219\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2021.108219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenthilkumar, K. et al. Rice yield and economic response to micronutrient application in Tanzania. \u003cem\u003eField Crop. Res.\u003c/em\u003e 270, 108201 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2021.108201\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2021.108201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakkar, P. N., Singh, M. V. \u0026amp; Ganeshmurthy, A. N. A critical review of plant nutrient supply needs, efficiency and policy issues for Indian agriculture for the year 2000: micronutrient and trace elements. \u003cem\u003eIn Plant Nutrient, Supply and Policy Issues: 2000\u0026ndash;2025\u003c/em\u003e, Kanwar, J. S. \u0026amp; Katyal, J. C. (eds.), 238\u0026ndash;264 (NAAS, 1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIbrahim, A. et al. Thirty years of agronomy research and development in irrigated rice-based cropping systems in the West African Sahel: achievements and perspectives. \u003cem\u003eField Crop. Res\u003c/em\u003e. 266, 108149 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2021.108149\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2021.108149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrijatmiko, K. R. et al. Biofortified \u003cem\u003eindica\u003c/em\u003e rice attains iron and zinc nutrition dietary targets in the field. \u003cem\u003eSci. Rep.\u003c/em\u003e 6, 1\u0026ndash;13 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep19792\u003c/span\u003e\u003cspan address=\"10.1038/srep19792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAOSTAT. Crops and livestock products. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/faostat/en/#data/QCL\u003c/span\u003e\u003cspan address=\"https://www.fao.org/faostat/en/#data/QCL\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManful, J. T. \u0026amp; Graham-Acquaah, S. African rice (\u003cem\u003eOryza glaberrima\u003c/em\u003e): a brief history and its growing importance in current rice breeding efforts. \u003cem\u003eIn Encyclopedia of Food Grains\u003c/em\u003e, Second Edition, Wrigley, C. et al. (eds.), 140\u0026ndash;146 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12-394437-5.00016-4\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-394437-5.00016-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Academic Press, 2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, B. R., Cai, X. \u0026amp; Xin, J. Efficient \u003cem\u003eindica\u003c/em\u003e and \u003cem\u003ejaponica\u003c/em\u003e rice identification based on the InDel molecular method: its implication in rice breeding and evolutionary research. \u003cem\u003eProg. Natur. Sci.\u003c/em\u003e 19, 1241\u0026ndash;1252 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pnsc.2009.01.011\u003c/span\u003e\u003cspan address=\"10.1016/j.pnsc.2009.01.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi, S. et al. Relationship between rice grain protein content and key phenotypes in rice. \u003cem\u003eAgron. J.\u003c/em\u003e 115, 1257\u0026ndash;1264 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/agj2.21250\u003c/span\u003e\u003cspan address=\"10.1002/agj2.21250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRethlefsen, M. L. et al. PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. \u003cem\u003eSyst. Rev.\u003c/em\u003e 10, 39 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13643-020-01542-z\u003c/span\u003e\u003cspan address=\"10.1186/s13643-020-01542-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaito, H. et al. Two novel QTLs for the harvest index that contribute to high-yield production in rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.). \u003cem\u003eRice\u003c/em\u003e 14, 18 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12284-021-00456-1\u003c/span\u003e\u003cspan address=\"10.1186/s12284-021-00456-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO. \u003cem\u003eFood Energy \u0026ndash; Methods of Analysis and Conversion Factors\u003c/em\u003e. FAO Food and Nutrition Paper 77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/4/y5022e/y5022e03.htm\u003c/span\u003e\u003cspan address=\"https://www.fao.org/4/y5022e/y5022e03.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Food and Agriculture Organization of the United Nations, 2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChivenge, P., Vanlauwe, B. \u0026amp; Six, J. Does the combined application of organic and mineral nutrient sources influence maize productivity? A meta-analysis. \u003cem\u003ePlant Soil\u003c/em\u003e 342, 1\u0026ndash;30 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-010-0626-5\u003c/span\u003e\u003cspan address=\"10.1007/s11104-010-0626-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGray, J. M., Humphreys, G. S. \u0026amp; Deckers, J. A. Distribution patterns of World Reference Base soil groups relative to soil forming factors. \u003cem\u003eGeoderma\u003c/em\u003e 160, 373\u0026ndash;383 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geoderma.2010.10.006\u003c/span\u003e\u003cspan address=\"10.1016/j.geoderma.2010.10.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePribyl, D. W. A critical review of the conventional SOC to SOM conversion factor. \u003cem\u003eGeoderma\u003c/em\u003e 156, 75\u0026ndash;83 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geoderma.2010.02.003\u003c/span\u003e\u003cspan address=\"10.1016/j.geoderma.2010.02.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, H. et al. Effects of biochar application on crop productivity, soil carbon sequestration, and global warming potential controlled by biochar C:N ratio and soil pH: a global meta-analysis. \u003cem\u003eSoil Tillage Res.\u003c/em\u003e 213, 105125 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.still.2021.105125\u003c/span\u003e\u003cspan address=\"10.1016/j.still.2021.105125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, X.-P. et al. Harvesting more grain zinc of wheat for human health. \u003cem\u003eSci. Rep.\u003c/em\u003e 7, 7016 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-017-07484-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-017-07484-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWissuwa, M., Ismail, A. M. \u0026amp; Graham, R. D. Rice grain zinc concentrations as affected by genotype, native soil-zinc availability, and zinc fertilization. \u003cem\u003ePlant Soil\u003c/em\u003e 306, 37\u0026ndash;48 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-007-9368-4\u003c/span\u003e\u003cspan address=\"10.1007/s11104-007-9368-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZomer, R. J., Xu, J. \u0026amp; Trabucco, A. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. \u003cem\u003eSci. Data\u003c/em\u003e 9, 409 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41597-022-01493-1\u003c/span\u003e\u003cspan address=\"10.1038/s41597-022-01493-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEfron, B. \u0026amp; Narasimhan, B. The automatic construction of bootstrap confidence intervals. \u003cem\u003eJ. Comput. Graph. Stat.\u003c/em\u003e 29, 608\u0026ndash;619 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10618600.2020.1714633\u003c/span\u003e\u003cspan address=\"10.1080/10618600.2020.1714633\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJAMOVI. The jamovi project. \u003cem\u003ejamovi\u003c/em\u003e. (Version 2.6) [computer software]. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jamovi.org\u003c/span\u003e\u003cspan address=\"https://www.jamovi.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFahad, S. et al. Effects of tire rubber ash and zinc sulfate on crop productivity and cadmium accumulation in five rice cultivars under field conditions. \u003cem\u003eEnviron. Sci. Pollut. Res.\u003c/em\u003e 22, 12424\u0026ndash;12434 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-015-4518-3\u003c/span\u003e\u003cspan address=\"10.1007/s11356-015-4518-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-sustainable-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Agriculture](https://www.nature.com/npjsustainagric/)","snPcode":"44264","submissionUrl":"https://submission.springernature.com/new-submission/44264/3","title":"npj Sustainable Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Agronomic fortification, bioavailability, genetic biofortification, nutrient uptake","lastPublishedDoi":"10.21203/rs.3.rs-9267207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9267207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRice, consumed by half of the world\u0026rsquo;s population, is inherently low in zinc (Zn), iron (Fe), and protein. We synthesized data from 245 studies across 34 countries to evaluate the impact of genetic, agronomic, and processing interventions on rice grain Zn, Fe, and protein concentrations. Zn-biofortified cultivars had 9.8% higher grain Zn concentrations than non-biofortified cultivars, while Fe-biofortified cultivars did not exhibit a significant improvement in Fe content. The probabilities of achieving the breeding target concentrations of Zn (28 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) and Fe (15 mg kg\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) in polished rice were only 4.0% and 10.5%, while Zn and Fe fertilization increased the probability to 41.3% and 67.7% for Zn and Fe, respectively. Milling and polishing of brown rice grains reduced Zn, Fe, and protein concentrations by 21%, 70%, and 6.5%, respectively. Our findings emphasize the need for combined use of genetic and agronomic fortification, and consumption of parboiled rice to attain desired health impacts.\u003c/p\u003e","manuscriptTitle":"Combining genetic and agronomic fortification is essential to meet human health targets for zinc, iron, and protein concentrations in rice grains: A meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 15:21:30","doi":"10.21203/rs.3.rs-9267207/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-12T14:21:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T21:58:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T23:28:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T20:20:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245988946122334728411110483021477199959","date":"2026-04-12T15:02:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150398590352068770039811223366448316074","date":"2026-04-09T19:09:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57513004217048884217620416758557565938","date":"2026-04-07T18:55:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T13:25:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T22:35:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T07:36:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Sustainable Agriculture","date":"2026-03-30T12:43:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-sustainable-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Agriculture](https://www.nature.com/npjsustainagric/)","snPcode":"44264","submissionUrl":"https://submission.springernature.com/new-submission/44264/3","title":"npj Sustainable Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"116858a9-f55b-4c30-a386-1fda9b704567","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-12T14:21:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T21:58:45+00:00","index":25,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":66160355,"name":"Biological sciences/Biochemistry"},{"id":66160356,"name":"Biological sciences/Biotechnology"},{"id":66160357,"name":"Biological sciences/Genetics"},{"id":66160358,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-05-12T14:27:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 15:21:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9267207","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9267207","identity":"rs-9267207","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}