Introduction
Egypt's agriculture, like that of the rest of the world, confronts several issues, including water shortage and climate change (Sowers et al., Reference Sowers, Vengosh and Weinthal2011; Abdelghany et al., Reference Abdelghany, Zhang, Azam, Shaibu, Feng, Qi, Li, Li, Tian and Hong2021; Abd-Elaty et al., Reference Abd-Elaty, Kuriqi and Shahawy2022). One of the difficulties is to increase agricultural production to feed the world's population of 9.8 billion by 2050 (Gennari et al., Reference Gennari, Rosero-Moncayo and Tubiello2019; Elmardy et al., Reference Elmardy, Yousef, Lin, Zhang, Ali, Lamlom, Kalaji, Kowalczyk and Xu2021). Maize (Zea mays L.) is one of the world's most significant strategic and economic crops (Kandil et al., Reference Kandil, Abdelsalam, Mansour, Ali and Siddiqui2020; Gomaa et al., Reference Gomaa, Kandil, El-Dein, Abou-Donia, Ali and Abdelsalam2021; Guo et al., Reference Guo, Qian, Yang, Khaliq, Han, Hussain, Zhang, Cai, Jia, Chen and Ren2022; Siddique et al., Reference Siddique, Naveed, Yaseen and Shahbaz2022) followed by wheat and rice. It is regarded as the third most important crop in terms of economic value. Maize is a cereal crop that belongs to the Poaceae family and is one of the most productive and economically important crops (El-Sorady et al., Reference El-Sorady, El-Banna, Abdelghany, Salama, Ali, Siddiqui, Hayatu, Paszt and Lamlom2022; Yousaf et al., Reference Yousaf, Khan, Farooqi, Muhammad, Barros, Tamayo-Ramos, Iqbal and Yousaf2022).
The irrigation duration and cultivation method affect the distribution of water in the soil, and the water distribution in the soil affects the growth and distribution of the root system in its size, which then affects the growth of above and below-soil plants' parts (Hussain et al., Reference Hussain, Hussain, Aslam, Rafiq, Abbas, Saqib, Rauf, Hano and El-Esawi2021; Neupane et al., Reference Neupane, Adhikari, Bhattarai, Rana, Ahmed, Sharma and Adhikari2022). Silicon application has been reported to improve the agro-morphological and physiological characteristics of maize plants, including growth parameters like plant height, stem diameter and the number of leaves (Amin et al., Reference Amin, Ahmad, Ali, Hussain, Mahmood, Aslam and Lee2018; Galindo et al., Reference Galindo, Pagliari, Rodrigues, Fernandes, Boleta, Santini, Jalal, Buzetti, Lavres and Teixeira Filho2021). It also increased yield-related parameters such as cob length, number of grains per cob, 100-grain weight, grain yield and total yield in hybrid P-33H25 and FH-810 plants experiencing drought stress. Moreover, Si treatment boosted photosynthetic rate and reduced transpiration rate under drought conditions (Abdelsalam et al., Reference Abdelsalam, Abdel-Megeed, Ali, Salem, Al-Hayali and Elshikh2018; Amin et al., Reference Amin, Ahmad, Ali, Hussain, Mahmood, Aslam and Lee2018; Youssef et al., Reference Youssef, Yousef, Ali, Ahmed, Lamlom, Strobel and Kalaji2021). Drought stress in the root zone is one of the most detrimental stressors for plant growth, development and productivity (Gomaa et al., Reference Gomaa, Kandil, El-Dein, Abou-Donia, Ali and Abdelsalam2021, Reference Gomaa, Kandil, El-Dein, Abou-Donia, Ali and Abdelsalam2021; Sanjari et al., Reference Sanjari, Shobbar, Ghanati, Afshari-Behbahanizadeh, Farajpour, Jokar, Khazaei and Shahbazi2021; Zhao et al., Reference Zhao, Liu, Abdelsalam, Carver and Bai2021; Ahmad et al., Reference Ahmad, Aslam, Javed, Hussain, Raza, Shabbir, Mora-Poblete, Saeed, Zulfiqar, Ali and Nawaz2022). The exogenous application of inorganic fertilizers is a potential strategy to counteract the adverse impact of drought on plant growth and development (Ashraf and Foolad, Reference Ashraf and Foolad2007; Hassan et al., Reference Hassan, Ghareeb, Nawaz, Mahmood, Shah, Abdel-Megeed, Abdelsalam, Hashem, Alamri and Thabit2022; Nasar et al., Reference Nasar, Wang, Ahmad, Muhammad, Zeeshan, Gitari, Adnan, Fahad, Khalid and Zhou2022; Sattar et al., Reference Sattar, Sher, Abourehab, Ijaz, Nawaz, Ul-Allah, Abbas, Shah, Imam and Abdelsalam2022).
For a sandy soil, irrigation intervals for a long period of about 15 days recorded the greatest maize yield, its components and water saved (Ahmed et al., Reference Ahmed, Salih, Eltaib, Fageer, Fadul, Mohamed and Mustafa2020). One way to enhance the efficiency of water use in maize farming is by irrigating the crops every 14 days without causing any reduction in maize production (Abbasi et al., Reference Abbasi, Sufyan, Ashraf, Zaman, Haq, Ahmad, Saleem, Hashmi, Jaremko and Abdelsalam2022; Abdelghany et al., Reference Abdelghany, El-Banna, Salama, Ali, Al-Huqail, Ali, Paszt, El-Sorady and Lamlom2022; Ma et al., Reference Ma, Tong, Kang, Wang, Wu, Cheng and Li2022). It has been well established that regulating irrigation with varying irrigation intervals and fertilizer levels has had a significant impact on crop performance (Muhammad et al., Reference Muhammad, Yang, Ahmad, Farooq, Al-Ghamdi, Khan, Zeeshan, Elshikh, Abbasi and Zhou2022). Although Si is not generally included in the list of essential elements, it is considered one of the important beneficial nutrients for plant growth and physiochemical process (Laing et al., Reference Laing, Gatarayiha and Adandonon2006; Kandil et al., Reference Kandil, Abdelsalam, Mansour, Ali and Siddiqui2020; Abdelsalam et al., Reference Abdelsalam, Balbaa, Osman, Ghareeb, Desoky, Elshehawi, Aljuaid and Elnahal2022b). Despite its deposition on cell walls, its active involvement in a multitude of physiological and metabolic processes is also evident (Moussa, Reference Moussa2006). In general, crops belonging to the family Poaceae accumulate much more Si than that other species belonging to other families (Abbasi et al., Reference Abbasi, Sufyan, Ashraf, Zaman, Haq, Ahmad, Saleem, Hashmi, Jaremko and Abdelsalam2022). Exogenous application of Si has also been reported to improve crop performance even under stressful conditions (Kojić et al., Reference Kojić, Pajević, Jovanović-Galović, Purać, Pamer, Škondrić, Milovac, Popović and Grubor-Lajšić2012). Tuna et al. (Reference Tuna, Kaya, Higgs, Murillo-Amador, Aydemir and Girgin2008) studied the supplements of Si to plants subjected to salt-affected soils and reported beneficial improvement in crop tolerance to stress conditions.
Zinc is an important micronutrient because it plays a key role in photosynthesis-related enzymatic processes (Bashir et al., Reference Bashir, Rehim, Liu, Imran, Liu, Suleman and Naveed2019; Hassan et al., Reference Hussain, Hussain, Aslam, Rafiq, Abbas, Saqib, Rauf, Hano and El-Esawi2021; Hassain et al., Reference Hussain, Hussain, Aslam, Rafiq, Abbas, Saqib, Rauf, Hano and El-Esawi2021; Zafar et al., Reference Zafar, Ahmed, Munir, Zafar, Saqib, Sarwar, Iqbal, Ali, Akhtar, Ali and Hussain2023). It is a cofactor and structural component of a variety of enzymes involved in a wide variety of metabolic processes. Also, it is involved in photosynthesis, glucose metabolism, protein metabolism, pollen generation, auxin metabolism, membrane integrity maintenance and stress tolerance induction in plants (Alloway, Reference Alloway2008; Jan et al., Reference Jan, Anwar-Ul-Haq, Javed, Hussain, Ahmad, Sumrah, Iqbal, Babar, Hafeez, Aslam and Akbar2023). Furthermore, it also has been reported to boost germination rates, product quality and crop productivity (Kausar et al., Reference Kausar, Hussain, Javed, Zafar, Anwar, Hussain, Zahra and Saqib2023). Its involvement in the acceleration of catalytic actions to enhance growth and development throughout critical periods of development is also well reported (Naeem, Reference Naeem2015).
Plant growth, developmental processes and productivity can benefit from Zn treatment. It helps in the control of pests including plant insects and diseases, the restriction of pollutant absorption and the tolerance of environmental stress (Rehman et al., Reference Rehman, Farooq, Ozturk, Asif and Siddique2018). It is also required for the regulation of gene expression required for plant biotic as well as abiotic stress tolerance. Zinc supplementation helps to increase the transpiration rate; a shortage would lower a leaf's transpiration efficiency (Sarwar et al., Reference Sarwar, Rafique, Gill and Khan2017).
Furthermore, Zn supplementation is important for increasing the efficiency of water utilization. It was shown that adequate nutritional practices resulted in a 20–25% increase in water use efficiency (Waraich et al., Reference Waraich, Ahmad, Ashraf, Saifullah and Ahmad2011; Iqbal et al., Reference Iqbal, Javad, Naz, Shah, Shah, Paray, Gulnaz and Abdelsalam2022; Mustafa et al., Reference Mustafa, Athar, Khan, Chattha, Nawaz, Shah, Mahmood, Batool, Aslam and Jaremko2022). Zinc's action is crucial for enhanced seed yield and quality (Estrada-Urbina et al., Reference Estrada-Urbina, Cruz-Alonso, Santander-González, Méndez-Albores and Vázquez-Durán2018). Zinc application has a greater impact on chlorophyll formation and carbonic anhydrase activity, which helps the transfer of CO2 from the liquid phase of a cell into the chloroplast, hence enhancing the photosynthetic rate (Hernández et al., Reference Hernández, Hernández, Contreras, Saldaña, Velázquez, Escudero and Cué2020).
Nanoparticles have unique features that distinguish them from their bulk counterparts, such as higher solubility, surface area and reactivity, making them potentially useful in mitigating the negative impacts of abiotic and biotic stresses on crop production (Javed et al., Reference Javed, Shabbir, Hussain, Naseer, Ejaz, Ali, Ahmar and Yousef2022). They can improve crop stress tolerance by addressing nutritional deficits, increasing enzyme activities and promoting the adherence of plant growth-promoting bacteria under abiotic stresses. This has led to a new era of using nanoparticles to enhance agricultural production, but their potential harmful effects on the environment and vegetation should not be ignored (Iqbal et al., Reference Iqbal, Waheed and Naseem2020; Abdelghany et al., Reference Abdelghany, El-Banna, Salama, Ali, Al-Huqail, Ali, Paszt, El-Sorady and Lamlom2022). Silver nanoparticles (AgNPs) have a lot of potential in agriculture, especially when it comes to increasing the pace and development of diploid and triploid seeds (Khafaga et al., Reference Khafaga, Fouda, Alwan, Abdelsalam, Taha, Atta and Dosoky2022; Sabra et al., Reference Sabra, Alaidaroos, Jastaniah, Heflish, Ghareeb, Mackled, El-Saadony, Abdelsalam and Conte-Junior2022; Abdelsalam et al., Reference Abdelsalam, Abdel-Megeed, Ghareeb, Ali, Salem, Akrami, Al-Hayalif and Desoky2022a). Capping phytochemicals in green nanoparticle manufacturing has a positive impact on agriculture. They can improve plant growth, crop production and seed germination without affecting the plant's intrinsic characteristics (Acharya et al., Reference Acharya, Jayaprakasha, Crosby, Jifon and Patil2020). Nanoparticles are being used in plants to promote their development, which is a new strategy in agriculture. It is, without a doubt, an innovative and promising technique for safeguarding the plant while it is under stress (Gohari et al., Reference Gohari, Mohammadi, Akbari, Panahirad, Dadpour, Fotopoulos and Kimura2020). AgNPs improved the growth characteristics of the fenugreek plant (e.g., shoot length, number of leaves/plant and dry weights) and increased photosynthetic pigment (i.e., chlorophyll and carotenoid contents) and indole acetic acid contents, which in turn resulted in increased yield and quality (Sadak, Reference Sadak2019). The hypothesis of this work was whether the improved water stress resistance by Si, Zn and AgNPs and their combination are mediated via the enhanced photosynthetic rate and lowered transpiration in drought stressed maize crop. The aim of this study was to investigate the effect of foliar application of Si, Zn and AgNPs on yield and quality traits and their ability to counter water deficiency effects on maize.
Materials and methods
Experimental setup
Two field experiments were conducted at El-Horreya village, Abou El-Matamir, El-Behira governorate, Egypt, during the 2020 and 2021 growing seasons to study the role of Si, Zn and AgNPs to promote biofortification of maize growth, yield, yield components and quality under different irrigation intervals and amount (Table 1). The preceding crop was Egyptian clover (Berseem) in both seasons. The climate of the study site is characterized by a hot summer and mild winters (Fig. 1). Some physical and chemical soil properties of the surface layer (0–60 cm) of the experimental site were determined before sowing according to the method described by Chapman and Pratt (Reference Chapman and Pratt1962), and illustrated in Table 2.
Table 1. Irrigation water applied at different growth stages (days after sowing) under different irrigation treatments during two seasons 2020 and 2021
DAS, days after sowing; VE, emergence stage; Vn, vegetative stages; VT, tasseling stage; R1, silking stage; R2, blister stage; R3, milking stage; R4, dough stage; R5 dent stage; RM, maturity stage.
Figure 1. Weather conditions (minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (%) and rainfall) during the two growing seasons of maize cultivation. Error bars refer to the standard deviation.
Note: no rainfall was received during these months.
Table 2. Soil physical and chemical properties of the experimental sites in 2020 and 2021 seasons
EC (dS/m) (1:2), electrical conductivity. The (1:2) ratio refers to the soil-to-water ratio.
Yellow maize hybrid (single cross Pioneer 3444/SC P3444) was obtained from Pioneer Seed Co. and sown on 15th of May in 2020 and 2021 seasons. The field plants were hand thinned at 21 days from sowing to maintain one plant/hill. The field was sprayed with two herbicides (Harness 84% EC at 2.5 litres/ha and Gesaprim 80% WP at 1.8 kg/ha) after sowing, then irrigated on the same day. Other agricultural practices were carried out as recommended by the Ministry of Agriculture and Land Reclamation, Egypt.
The furrow irrigation requirements in the two seasons are presented in Table 1. The average values of seasonal applied water (m3/ha) of maize plants were 7925 m3/ha (at 15 days irrigation interval) which represents the total amount applied over seven irrigations with variable amounts. The irrigation with 20-day interval received a total amount of 5690 m3/ha irrigation water divided over five irrigations with variable amounts while the irrigation interval of 25 days received a total amount of 4564 m3/ha irrigation water divided over four irrigations with variable amounts. The first irrigation amount was the same (796.5 m3/ha) for all irrigation intervals and applied on day 21 after sowing. Thereafter, irrigation numbers 2, 3, 4, etc., were applied on different days but with the same amount of irrigation water.
The amount of actual irrigation water applied under each irrigation treatment was determined using the following equation:
where I.Ra is the total actual irrigation water applied (mm/interval); ETc is the crop evapotranspiration estimated from the Penman–Monteith equation using CROPWAT model 8.0; Lf is the leaching factor (10%) and Er is the irrigation system efficiency.
Experimental design
A split plot design with three replications was used in this experiment. The three irrigation intervals of 15, 20 and 25 days were allocated to the main plots and foliar treatments of (i) water spray (control), (ii) Si (at 150 mg/l by using potassium silicate (K₂O₃Si; MW = 154.3 g/m; pH = 12.7)), (iii) Zn (at 5 g/l from zinc sulphate heptahydrate (ZnSO4·7H2O; MW = 287.5 g/m)), (iv) AgNPs (at 50 mg/l), (v) Si + AgNPs, (vi) Si + Zn, (vii) Zn + AgNPs and (viii) Si + Zn + AgNPs, applied four times at 30 DAS (vegetative stage), 50 DAS (tasseling stage), 70 DAS (silking stage) and 90 DAS (milking stage). Each subplot consisted of six ridges of 3.50 m in length and 70 cm in width and the plot area was 14.7 m2.
The application of three types of fertilizer and their interaction was carried out in a liquid form using a backpack sprayer (foliar application) on maize plants. This was done four times, with foliar spraying done at sunset to avoid damage from strong sunlight and high temperatures. The spraying application was done at the rate of 750 litres/hectare where each plot received 1.2 litres/spraying time. The study consisted 63 plots, consisting of nine control plots and nine plots each for Zn (270 g/plot/season), silicon (8 g/plot/season), AgNPs (2.7 g/plot/season), Si (8 g/plot/season) + Zn (270 g/plot/season), Zn (270 g/plot/season) + AgNPs (2.7 g/plot/season) and Si (8 g/plot/season) + Zn (270 g/plot/season) + AgNPs (2.7 g/plot/season).
Application of fertilizers
Potassium sulphate (K2SO4) was applied at the rate of 120 kg/ha during both seasons. Phosphorus fertilizer at the rate of 60 kg P2O5/ha was applied before planting in the form of calcium superphosphate (15.5% P2O5). Ammonium nitrate (33.5% N) at the rate of 288 kg/ha was used as the N source and applied in two equal doses, the first dose was applied at sowing and the second one was added at 21 DAS during both cropping seasons.
Data collection
Growth parameters
At harvest, five random plants from each experimental plot were harvested to measure plant height. Measurement was taken from the soil surface to the top of the plant. Leaf area index (LAI), the ratio of leaf area to the ground area occupied by the crop plants, was calculated at 90 DAS according to Radford (Reference Radford1967) as follows:
where leaf area = K (L × W), where K is the constant value (0.75), L is the maximum leaf length (cm) and W is the maximum leaf width (cm).
The crop growth rate was calculated using dry weight of the two periods (60–75 and 75–90 DAS) according to the formula suggested by Radford (Reference Radford1967).
where W1 and W2 are plant dry weights at T1 and T2 corresponding days.
Total chlorophyll index was calculated using a SPAD meter (SPAD 502 Meter) based on ten random leaves taken from each subplot at 90 DAS, following the method described by Minolta (Reference Minolta1989).
Yield and yield characteristics
Yield and its components were determined at 120 DAS. Ten plants from each subplot were harvested to measure the ear height, ear length, the number of rows/ears and the number of grains/rows. One hundred-grain weight was obtained from three samples of each subplot. The total yield was calculated as the weight of grains and straw in each subplot. The grain yield was determined from all plants in each subplot. The straw yield (SY) was recorded according to the following formula:
The harvest index was calculated according to the following formula:
Chemical analyses
At harvest, the chemical constituents of leaves/grains were determined as follows:
Crude grain protein content was calculated using the following formula of Salo-väänänen and Koivistoinen (Reference Salo-väänänen and Koivistoinen1996) as:
Nitrogen content in maize grain was determined by the Micro-Kjeldhal method as described by Helrich (Reference Helrich1990).
Leaf proline content: Three fresh leaf samples were taken at 90 DAS between 11:00 and 14:00 h. Firstly, leaf disks were taken from two plants in each plot. The leaf disks were immersed immediately in a cooled proline extraction solution (3% aqueous sulfosalicylic acid solution). Next, the samples were kept refrigerated prior to extraction and determination of leaf proline content, following the method of Bates et al. (Reference Bates, Waldren and Teare1973). The proline content was determined spectrophotometrically.
Statistical analysis
All collected data were subjected to analysis of variance (ANOVA) according to the method of Gomez and Gomez (Reference Gomez and Gomez1984), using the CoStat computer software package (CoStat, Reference CoStat2005). The least significant difference (LSD at 5% probability) was used to compare the treatment means.
Results
There was a significant (P < 0.05) effect of the studied factors, i.e. irrigation intervals, foliar application treatments and their interaction on plant height, LAI, chlorophyll contents (SPAD value), ear height, leaf proline content, ear length, the number of grains/row, number of grains/ear, 100-grain weight, grain yield, SY, total yield, harvest index and grain protein content of maize cv. SC P3444 during both study years (Tables 3–6).
Table 3. Plant height, leaf area index and chlorophyll content of maize (Zea mays L. cv. SC P3444) as affected by irrigation intervals, foliar application of silicon (Si), zinc (Zn) and silver nanoparticles (AgNPs), and their interaction in both seasons 2020 and 2021
CK, control treatment. Means in the same column (s)/row(s) followed by the same letters are not significantly different at 5% probability level.
Table 4. Ear length, ear height and leaf proline content of maize (Zea mays L. cv. SC P3444) as affected by irrigation intervals, foliar application of silicon (Si), zinc (Zn) and silver nanoparticles (AgNPs), and their interaction in both seasons, i.e. 2020 and 2021
CK, control treatment. Means in the same column (s)/row(s) followed by the same letters are not significantly different at 5% probability level.
Table 5. Number of grains and 100-grain weight of maize (Zea mays L. cv. SC P3444) as affected by irrigation intervals, foliar application of silicon (Si), zinc (Zn) and silver nanoparticles (AgNPs), and their interaction in both seasons, i.e. 2020 and 2021
CK, control treatment. Means in the same column (s)/row(s) followed by the same letters are not significantly different at 5% probability level.
Table 6. Grain, straw and total yield of maize (Zea mays L. cv. SC P3444) as affected by irrigation intervals, foliar application of silicon (Si), zinc (Zn) and silver nanoparticles (AgNPs), and their interaction in both seasons, i.e. 2020 and 2021
CK, control treatment. Means in the same column (s)/row(s) followed by the same letters are not significantly different at 5% probability level.
Concerning the significant effect of irrigation intervals on the studied parameters, the data revealed that an irrigation interval of 15 days with the highest amount of irrigation water applied recorded the highest mean values of growth, yield and yield components. Irrigation interval of 15 days increased plant height by 5.23 and 6.53%, LAI by 30.7 and 24.1%, chlorophyll content by 21.4 and 26.1%, ear height by 9.58 and 10.3%, ear length by 13.8 and 14.4%, number of grains/row by 4.90 and 5.24%, number of grains/ear by 8.93 and 11.0%, 100-grain weight by 4.83 and 5.19%, grain yield by 16.5 and 21.1%, SY by 13.9 and 16.4% and total yield by 15.5 and 18.5% in 2020 and 2021, respectively, compared with irrigation interval of 25 days which depicted minimum values of these traits while consumed lowest amount of irrigation water. This was followed by an irrigation interval of 20 days, which received less amount of irrigation amount than the 15 days intervals but more than the 20 days interval (Tables 3–6).
Regarding the significant effect of foliar spraying treatments on the studied parameters, the recorded data showed that the highest mean values of growth, yield and yield components such as plant height, LAI, ear height, leaf proline content and ear length during both years were recorded with foliar application of Si + Zn + AgNPs. Nonetheless, Zn + AgNPs depicted significantly the highest values of chlorophyll content during both study years (Tables 3 and 4).
The highest values for the number of grains/row, number of grains/ear, 100-grain weight (Table 5), grain yield, SY, total yield, harvest index and grain protein content during both study years were recorded for Zn + AgNPs treatment followed by Si + Zn + AgNPs treatment. Meanwhile, the lowest values for the growth, yield and its components and protein content were obtained with the control treatment in which no Si, Zn and AgNPs were applied.
The highest values of harvest index (47.8 and 47.4% in 2020 and 2021, respectively) were obtained with an irrigation interval of 20 days together with Si + Zn + AgNPs treatment (Table 7). In addition, irrigation of 15 days together with Si + Zn + AgNPs treatment recorded the highest grain protein content (9.47 and 9.67%, in the first and second season, respectively) followed by Zn + AgNPs treatment which had no significant difference with Si + Zn + AgNPs in most of the studied parameters. Meanwhile, the lowest values for harvest index and protein content were obtained with irrigation of 25 days which received the lowest amount of irrigation water in both seasons. Moreover, there was no significant difference between irrigation of 15 and 20 days and foliar spray of Si + Zn + AgNPs for most of the studied parameters during both seasons.
Table 7. Harvest index and grain protein content of maize (Zea mays L. cv. SC P3444) as affected by irrigation intervals, foliar application of silicon (Si), zinc (Zn) and silver nanoparticles (AgNPs), and their interaction in both seasons 2020 and 2021
CK, control treatment. Means in the same column (s)/row(s) followed by the same letters are not significantly different at 5% probability level. Ns, not significant.
The analysis of variance over the two growing seasons
ANOVA for the combined effect of irrigation intervals/amount and foliar spraying treatments is shown in Tables 8 and 9. The results showed that the interaction of irrigation intervals/amount and foliar spraying treatments significantly (P ≤ 0.001) affected the plant height, LAI, chlorophyll content, 100-grain weight, ear height, proline content, number of grains per row, ear length, number of grains per ear, total yield, grain yield and SY during both seasons. The application of Si + Zn + AgNPs combined with 15-day irrigation interval treatment which has the highest amount of irrigation water recorded the highest value for plant height (202.7 cm), LAI (7.83 cm), number of grains per row (47.8), 100-grain weight (46.9 g), ear length (27.5 cm), number of grains per ear (733.1), total yield (18.1 t/ha), grain yield (8.63 t/ha) and SY (9.95 t/ha).
Table 8. Interactive effect of foliar application of silicon (Si), zinc (Zn) and silver nanoparticles (AgNPs), and irrigation intervals (days) on plant height, leaf area index, SPAD value, ear height, proline content and number of grains per row in maize during both study years
CK, control treatment. Means in the same column (s)/row(s) followed by the same letters are not significantly different at 5% probability level.
Table 9. Interactive effect of foliar application of silicon (Si), zinc (Zn) and silver nanoparticles (AgNPs), and irrigation intervals (days) on 100-grain weight, ear length, number of grains per ear, grain yield, straw yield, total yield, harvest index and protein content in maize during both study years
CK, control treatment; means in the same column (s)/row(s) followed by the same letters are not significantly different at 0.05 level of probability.
The AgNPs foliar application associated with 15-day irrigation interval treatment recorded the highest values for chlorophyll content (55.7), ear height (89.5 cm) and 100-grain weight (46.9 g). The results also showed that there was a non-significant difference between Si + Zn + AgNPs and Zn + AgNPs foliar application treatments for most of the measured parameters. The 25-day irrigation interval (received the lowest irrigation amount) together with AgNPs, Si + AgNPs, Si + Zn, Zn + AgNPs, Si + Zn + AgNPs foliar application treatments produced the highest values for proline content.
Correlations among crop parameters under the interaction of irrigation intervals and nano-foliar spraying treatments
An analysis of the interactions between irrigation and foliar spraying treatments was carried out using Pearson's correlation coefficients as well as a clustered map visualization (Figs 2 and 3). The correlation coefficients showed that there was a strong positive link among grain yield and total yield (r = 0.99), 100-grain weight (r = 0.81), plant height (r = 0.70), harvest index (r = 0.68), ear height (r = 0.84), LAI (r = 0.84) and protein content (r = 0.70) (Fig. 1). Crude protein was significantly and highly positively correlated with different measured parameters including total yield (r = 0.69), SY (r = 0.66) and ear length (r = 0.72) (Fig. 2). Negative correlations were detected between proline content and all measured parameters except protein content (Fig. 2).
Figure 2. Pearson correlation coefficient for growth traits, yield and yield-related traits under different irrigation intervals and foliar application of silicon, zinc and silver nanoparticles. The correlation key: The blue colour indicates negative correlation, an orange colour indicates positive correlation, a white colour means no correlation, a light colour indicates lesser significance and a dark colour circle indicates a greater significance. The colour intensity is relative to the correlation coefficients. TY, total yield (ton/ha); GY, grain yield(ton/ha); SY, straw yield(ton/ha); EL, ear length (cm); 100.KW, 100-kernel/grain weight (g); NGE, number of grains per ear; NGR, number of grains per row; Ch, chlorophyll index (SPAD); LAI, leaf area index %; PH, plant height (cm); EH, ear height (cm); HI, harvest index %.
Figure 3. Hierarchical clustering heat map visualization for 24 treatments' combinations (irrigation intervals and foliar spray of nanoparticles) for 14 agronomical parameters. The orange colour represents high values, and the blue colour represents low values. High to low values are scaled according to the key above. TY, total yield (ton/ha); GY, grain yield (ton/ha); SY, straw yield (ton/ha); EL, ear length (cm); KW, 100-kernel weight (g); NGE, number of grain/ear; NGR, number of grain/row; Ch, chlorophyll index (SPAD); LAI, leaf area index; PH, plant height (cm); EH, ear height (cm); HI, harvest index %.
The correlation coefficients also indicated that there was a highly and significant correlation among various parameters. For example, plant height was significantly and positively correlated with total yield (r = 0.81), SY (r = 0.80), ear length (r = 0.75), 100-grain weight (r = 0.58), chlorophyll content (r = 0.81) and grain yield (r = 0.79). Harvest index was highly and positively correlated with grain yield (r = 0.68), total yield (r = 0.55) and other growth and yield-related parameters except for proline content.
Visualization and understanding of various treatment interactions through hierarchical clustering
The hierarchical clustering analysis (Fig. 3) clearly differentiated the interrelationship between combinations of foliar spraying treatments and irrigation intervals/amount of irrigation applied (24 combinations) according to their impact on yield, growth and chemical parameters (Fig. 3). Regarding the relationship between irrigation intervals/amount and foliar application treatments, two major clusters were characterized. The first cluster contains ten combinations which were divided into two subclusters where the first subcluster was formed by the combination of treatment A (25 days + Si), B (20 days + Si), C (25 days + Zn), D (20 days + Ag Nano), E (25 days + Ag Nano), F (25 days + Si + Nano), while the second subcluster recorded the lowest value of all parameters formed by the combination of G (25 days + water spray), H (15 days + water spray), I (20 days + water spray) and J (20 days + Zn). Treatments A, B and C give the lowest values for all measured parameters within this group, especially ear height and SY. For treatments D, E and F, the majority of parameters, except for proline content, expressed negative performance.
In the second subcluster of treatments G (25 days + water spray), H (15 days + water spray), I (20 days + water spray) and J (20 days + Zn), the combinations of fertilization treatments showed an opposite pattern to the treatment's combinations of the other clusters, as all studied parameters were negatively affected, especially for the G treatment which indicated the lowest value for all measured parameters.
With respect to the second cluster, 14 treatment combinations were clustered together and further were separated into two subclusters: the first subcluster included treatments K (20 days + Si + Nao), L (15 days + Si) and M (15 days + Si + Nano), the second subcluster included N (15 days + Si + Nano + Zn), O (15 days + Nano + Zn), P (15 days + Si + Zn), Q (25 days + Nano + Zn), R (25 days + Si + Nano + Zn), S (25 days + Si + Zn), T (20 days + Nano + Zn), U (20 days + Si + Zn), V (15 days + Zn), W (15 days + Ag Nano) and X (20 days + Si + Nano + Zn).
Based on the results, it was noted that the second cluster showed a discrepant effect on all recorded parameters, as the majority were positively affected by treatments K, L and M (first subcluster), while the combination of N gave the highest values for all recorded parameters within this group, especially plant height and ear length. For treatment O, the majority of parameters, except for proline content, showed high impact, especially for 100-kernel weight which showed the highest positive response, indicating the best parameters under such treatment. In contrast, the combination treatment R showed the highest positive effect on proline content followed by protein, while showed a negative effect on harvest index. Collectively, the treatment combination N resulted in the highest values for all parameters, followed by treatment combinations O and P.
Discussion
The main objective of this study was to examine the impact of different irrigation intervals/amount on the growth, yield and quality of maize, as well as to investigate the efficacy of some foliar treatments (i.e. Si, Zn, AgNPs and their combinations) in reducing the negative effects of water stress. There is no doubt that water shortage is a significant factor limiting plant growth, development and final yield (Hussain et al., Reference Hussain, Hussain, Qadir, Khaliq, Ashraf, Parveen, Saqib and Rafiq2019). Water stress conditions affect plants at every stage of their growth, especially at the vegetative stage (El-Gedwy, Reference El-Gedwy2020). The findings from the present study revealed that a significant reduction was noticed in different morphological and yield attributes due to using less irrigation water which was inconsistent with that found in previous investigations on maize, soybean, barley, wheat and rice (Hasanuzzaman et al., Reference Hasanuzzaman, Bhuyan, Nahar, Hossain, Mahmud, Hossen, Masud and Fujita2018; Gomaa et al., Reference Gomaa, Kandil, El-Dein, Abou-Donia, Ali and Abdelsalam2021).
Overall, according to the results of the current study, plant height and ear length had an increasing trend with increasing both foliar application and irrigation water applied during the growing seasons, while medium levels of both irrigation water and foliar application of Si, Zn and AgNPs recorded medium values of both traits. According to the results, the increase in irrigation interval up to 25 days and using less irrigation water led to a significant decrease in all the parameters under investigation, compared to the other intervals (15 and 20 days) that received more irrigation water and showed the highest values for all studied parameters. This suggests that appropriate irrigation management is crucial for achieving optimal maize growth and yield and that longer intervals between irrigations and reduced amount of irrigation water can have negative effects on plant performance (Çakir, Reference Çakir2004). Additionally, the foliar application of Si, Zn and AgNPs, either alone or in combination, may help to mitigate the negative impacts of water stress on maize production.
Our findings also suggest that water stress significantly reduces maize grain yield by decreasing the number of rows per ear, and grain weight, and shortening the length of the grain filling period, which leads to the development of small and wrinkled grains. The use of AgNPs has been established as a plant growth stimulator in previous studies (Ogutu et al., Reference Ogutu, Franssen, Supit, Omondi and Hutjes2018; Zhao et al., Reference Zhao, Xue, Jessup, Hou, Hao, Marek, Xu, Evett, O'Shaughnessy and Brauer2018; Yuan et al., Reference Yuan, Feng, Huo and Ji2019; Abdulhamed et al., Reference Abdulhamed, Abas, Noaman and Abood2021). Moreover, the current study indicates that the combined application of Zn, Si and AgNPs enhances the morphological and physiological characteristics of maize, particularly in alkaline calcareous soils where Zn deficiency is commonly found. Overall, these findings suggest that implementing appropriate irrigation and nutrient management strategies, along with the use of AgNPs and other growth-promoting agents, could potentially improve maize yield and quality in water-limited and nutrient-deficient environments (Abdelsalam et al., Reference Abdelsalam, Abdel-Megeed, Ali, Salem, Al-Hayali and Elshikh2018; Abdelsalam et al., Reference Abdelsalam, Balbaa, Osman, Ghareeb, Desoky, Elshehawi, Aljuaid and Elnahal2022b).
The improvement in growth characteristics of maize crop is due to the combined application of Si, Zn and AgNPs. Zn performs a critical role in the metabolic process, and protein synthesis in plants (Chaudhary et al., Reference Chaudhary, Dheri and Brar2017). Furthermore, Zn application results in a significant increase in plant leaf area, chlorophyll content and other photosynthetic pigments, thus causing growth improvement and yield (Karim et al., Reference Karim, Zhang, Zhao, Chen, Zhang and Zou2012). Similarly, Sultana et al. (Reference Sultana, Naser, Shil, Akhter and Begum2016) indicated that Zn countered the adverse impact of drought stress by remarkably enhancing wheat productivity. In another study on maize, Chattha et al. (Reference Chattha, Hassan, Khan, Chattha, Mahmood, Chattha, Nawaz, Subhani, Kharal and Khan2017) stated that Zn improved the yield and harvest index under drought stress. Moreover, Hera et al. (Reference Hera, Hossain and Paul2018) demonstrated that Zn as a foliar application reduced the negative influences of water deficit and enhanced the growth and yield of wheat. Zinc substantially enhances chlorophyll content and photosynthetic performance under drought stress (Peleg et al., Reference Peleg, Saranga, Yazici, Fahima, Ozturk and Cakmak2008; Ma et al., Reference Ma, Sun, Wang, Ding, Qin, Hou, Huang, Xie and Guo2017). Additionally, Zn improves chlorophyll content, starch content and grain yield. It is a fundamental component in maize crop for the biosynthesis of several proteins and enzymes (Balashouri and Prameeladevi, Reference Balashouri and Prameeladevi1995; Bhattarai et al., Reference Bhattarai, Midmore and Pendergast2008; Peleg et al., Reference Peleg, Saranga, Yazici, Fahima, Ozturk and Cakmak2008).
According to the results of the correlation study, plant height was strongly and substantially associated with yield and related parameters, demonstrating that growing maize at the optimal density may result in the maximum yield. Furthermore, leaf area and grain protein contents were strongly and positively correlated, which is likely due to the vital role of plant leaves in the process of photosynthesis and the formation of photosynthates. Based on these findings, decreasing the amount of applied water to maize crop and use of nanoparticles in the form of foliar spray may be effective ways to enhance grain yield in water-limited environments. However, it is important to note that these strategies should be implemented in a sustainable manner, taking into account factors such as soil type, weather conditions and other environmental variables (Rodrigues and Pereira, Reference Rodrigues and Pereira2009; Bijanzadeh et al., Reference Bijanzadeh, Tarazkar and Emam2021).
Conclusions
Our findings indicated that applying Si + Zn + AgNPs through the foliar application with a 15-day irrigation interval (7925 m3/ha irrigation water divided over seven irrigations) increased various growth and yield parameters of maize, including plant height, leaf area index, chlorophyll content (SPAD value), ear height, ear length, number of grains/row, number of grains/ear, 100-grain weight, grain yield, SY, total yield, harvest index and grain protein content. Additionally, highly significant correlations were observed between the recorded parameters, except for proline content and irrigation interval for 15 days + AgNPs + Zn. The highest positive response was observed in 100-kernel weight for this treatment. Overall, the treatment of an irrigation interval of 15 days + Si + AgNPs + Zn resulted in the highest values for all measurements, followed by an irrigation interval of 15 days + Si + AgNPs and an irrigation interval of 15 days + Si + Zn treatments.
Data
The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.
Acknowledgements
The authors are grateful to Alexandria University, Alexandria, Egypt for supporting the current research.
Author contributions
Conceptualization, E. E. K. and N. R. A.; methodology, E. E. K. and E.-S. M. S. G.; validation, T. J. and R. Y. G.; investigation, resources, supervision and project administration and funding, N. R. A.; data curation, S. F. L; writing – original draft preparation, E. E. K. and N. R. A.; writing – review and editing, S. F. L., E.-S. M. S. G., R. Y. G., T. J., S. H. and N. R. A.. All authors have read and agreed to the published version of the manuscript.
Financial support
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Competing interest
None.
Ethical standards
Not applicable.
