Comparing the changes in physiological parameters in two rice genotypes due to infection with bakanae disease caused by Fusarium fujikuroi

Introduction

Rice (Oryza sativa L.) is the staple food crop for about half of the population of world. It is a major source of food along with wheat and maize throughout the world. Despite the significant increase in rice production and productivity witnessed in the last five decades, a significant proportion of rice production is lost every year due to various abiotic (viz., high or low temperature, drought and salinity) and biotic (viz., pathogen infection and insect herbivory) stress factors (Kreye et al., 2009; Devine, 2009) [27,14] . Diseases like bacterial blight (Xantomonas oryzae pv. oryzae), sheath blight (Rhizoctonia solani), blast (Magnaporthe oryzae), brown spot (Biplolaris oryzae), bakanae or foot rot (Fusarium fujikuroi), stem rot (Sclerotium oryzae) and false smut (Ustilaginoidea virens) are of major economic significance. Among these bakanae disease caused by Fusarium fujikuroi is emerging as a serious threat to production of rice (Singh and Sunder, 2012) [40] . Bakanae disease was first identified during 1828 in Japan. In India, Thomas (1931) [44] described it as foot rot disease. Hori (1898) [21] first time demonstrated the fungus Fusarium heterosporium Nees induced the bakanae symptom in rice plants. The disease is incited by Fusarium fujikuroi Nirenberg (Anamorph) (Teleomorph: Gibberella fujikuroi Sawada, Wollen worth). This disease is emerging as a major threat to rice cultivation in India, Japan, Taiwan and Thailand (Webster and Gunnell, 1992; Bashyal et al., 2016; Saremi, 2005; Kini et al., 2002) [46, 3, 39, 26] . It causes 20 to 50% crop loss in Japan (Ito and Kimura, 1931) [23] , 3.7 to 14.7% in Thailand (Kanjanasoon, 1965) [25] . In India, the prevalence and incidence of bakanae disease has been reported very recently particularly in basmati rice cultivars (Bashyal et al., 2014; Gupta et al., 2014) [2,20] . The yield losses ranging from 15-25% have been reported from UP, Assam, AP, TN, Haryana and Punjab states of India (Pavgi and Singh, 1964; Rathaiah et al., 1991; Pannu et al., 2012; Sunder et al., 2014) [32,34,31,42] . The disease produces different kind of symptoms including seedling blight, root rot, crown rot, stunting, and the most classical symptoms of etiolation, hypertrophy effect or excessive elongation of infected plants, foot rot, seedlings rot, grain sterility, grain discoloration.

Pathogen infection impacts plants photosynthesis by causing leaf yellowing, which leads to a reduction in chlorophyll content and decreases the photosynthetically active leaf area. This results in the downregulation of CO2 fixation rates in the remaining green tissues (Bingham et al., 2009) [7] . The decline in photosynthetic efficiency has been linked to several factors, including self-shading, stomatal limitations, and other metabolic disruptions (Boote et al., 1983; Johnson, 1987; Bowden et al., 1990; Bassanezi et al., 2002) [8,27,10,4] . Consequently, the loss of photosynthetic capacity under disease conditions has been strongly associated with reduced plant growth and yield (Schwartz et al., 1981; Widin and Schipper, 1981; Spitters et al., 1990) [50,47,41] . Genotypic variation in rice has long been reported for WUE, measured either as intrinsic WUE, i.e. the ratio of net photosynthesis rate to stomatal conductance (A/gs) or by carbon isotope discrimination (Δ) (Samejima, 1985; Dingkuhn et al., 1991) [35,16] . Dingkuhn et al. (1991) [16] found that Δ was highly correlated with in situ measurements of A/gs. Varietal differences in stomatal response to decreasing leaf water potential have also been reported (Dingkuhn et al., 1989) [15] . Comparing WUE values of tropical japonica with those of indica cultivars based on leaf gas exchange rates (A/E) under irrigated conditions, Peng et al. (1998) [33] found that indica cultivars had generally higher E than tropical japonica lines, and the A/E ratio was 25-30% higher for the tropical japonica than for indica. Moreover, lower Δ values in the tropical japonica compared to indica confirmed the observed differences in A/E. Impa et al. (2005) [22] confirmed the relationship between gravimetrically determined WUE and Δ among rice germplasm, indicating that WUE is genetically variable in rice and hence can be exploited through breeding. Plants infected with pathogens exhibit increased respiration rates and reduced light-use efficiency, alongside a decline in photosynthetic activity. Pathogen infection causes the downregulation of most genes related to photosynthesis and chloroplast synthesis, as well as their transcriptional activity (Bozso, 2009) [11] . During the grain-filling period, the majority of carbon stored in mature rice grains is derived from CO2 assimilation, with the flag leaf serving as the primary site of photosynthesis (Yoshida, 1981; Murchie et al., 1999) [49,30] . Any reduction in the photosynthesis rate of the flag leaf during this critical period can significantly limit grain yield (Dingkuhn et al., 1989) [15] . Plants possess several mechanisms to regulate water use with water availability. Regulation of diffusive conductances is known to affect WUE by modulating both transpiration and photosynthesis rates. Regulation of photosynthetic rates also strongly affects WUE. The fractionation of carbon isotopes during photosynthesis depends on biochemical and physical phenomena, mainly associated with CO 2 diffusion and carboxylation reactions. In fact, the depletion of the heavy isotope 13 C in plant tissues, with respect to its abundance in the atmospheric CO 2 , is directly related to the ratio of Cc to atmospheric CO 2 concentration (Ca); this ratio represents the equilibrium between the availability and the requirement of CO 2 at the leaf level, that is the set point for gas exchange activity (Ehleringer, 1993) [18] . Understanding the physiological changes of a pathogen's host during the infection process can help to predict the effects of diseases on crop growth and yield (Bastiaans, 1993; Boote et al., 1980) [6,9] . Photosynthesis, a major driver of crop yield, is the key physiological process affected by foliar pathogens (Bassanezi et al., 2002; Bastiaans, 1991; Dallagnol et al., 2011; Debona et al., 2014; Resende et al., 2012) [4,5,12,13,35] . Therefore, the proper assessment of the photosynthetic performance of plants under pathogen infection can provide crucial insight into the mechanisms underlying their interactions, with the potential for identifying novel strategies for crop protection (Rolfe and Scholes 2010) [37] . There is an imminent need to investigate the detailed physiological functions under bakanae infection for better understanding of plant physiology during pathogen infection in the rice genotypes having varying degree of resistance against the pathogen. The present state of knowledge on photosynthetic leaf gas exchange traits in bakanae infected rice is preliminary and the responses of some of the important gas exchange characteristics including stomatal conductance to CO 2 , leaf transpiration rate and internal CO 2 concentration have not yet been investigated in detail during bakanae infection in rice. The present study has, therefore, been designed to study the impact of bakanae infection on various physiological parameters related to leaf gas exchange activities in order to understand dynamic responses in leaf assimilation physiology in rice genotypes having contrasting response to disease infection.

Material and Methods a) Pathogen and plant material:

The experiment was conducted at division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi. Fusarium fujikuroi, the causative pathogen, was isolated and purified from infected field samples. The purified pathogen culture was then mass-produced on autoclaved sorghum in a BOD incubator maintained at 25±2 ºC. The study utilized two rice genotypes with contrasting levels of disease resistance: PB1121 (susceptible) and GP50 (resistant). b) Inoculum preparation and inoculation: The pathogen inoculum was prepared by mixing the sorghum-based pathogen culture with distilled water and manually mashing it. The mixture was then filtered through muslin cloth, and the spore concentration was adjusted to 10 6 spores per milliliter. Seeds of both genotypes were inoculated by immersing them in the spore suspension for 24 hours. A separate set of seeds was immersed in distilled water for 24 hours to serve as a control. Following inoculation, the seeds were sown in plastic pro trays filled with autoclaved soil. All treatments were conducted in triplicate. c) Plant growth and disease assessment: After sowing the plants were kept under glass house conditions. The germination percentage in each treatment was recorded seven days after inoculation. The disease scoring was done at 7, 14 and 21 days post inoculation and disease incidence was calculated. The average height of plants was also measured at every interval viz. 7, 14 and 21 days post inoculation. Each observation was taken in three replications. d) Evaluation of physiological changes: The IRGA LI-6400XT Portable Photosynthesis System was used to record the physiological parameters. The physiological parameters such as Transpiration rate, stomatal conductance, photosynthesis rate and internal CO 2 concentration were recorded as per manufacturer's protocol. To setup the IRGA instrument, the sensor head was connected to the console by uncoiled cable and the CO 2 cartridge was attached. Before using the instrument, calibration was done and before each observation zeroing was done (Evans and Santiago, 2014) [19] . The leaf area standard was set 2 ×3 cm leaf chamber, light source (6400-02B LED light) and the flow rate of 300-400 μmol m -2 s -1 . The CO 2 entering the leaf chamber was 415 μmol mol -1 that maintains 400 μmol mol -1 in the leaf chamber head (Mujawamariya et al., 2018) [29] . The leaf was securely placed in the head chamber, ensuring there were no leaks. Physiological parameters were allowed to stabilize to minimize variability caused by environmental fluctuations. Measurements were taken once the values for photosynthesis and stomatal conductance reached a steady state.

Transpiration rate

The rate of transpiration depicted as E is calculated as differences in H2O concentration, based on the readings of the IRGAs in the reference and sample circuits, as per following formula: E = F (W e -W o )/L a . Where, W e and W o are the H2O mole fraction at the chamber entrance and output respectively; F-flow and L a -the leaf area surface ( m2 ).

Stomatal conductance

The transpiration rate (E) and relative humidity in the substomatal cavity is used to calculate the stomatal conductance to water vapour (gs) by using the first Fick's law of diffusion (Douthe et al., 2018) [17] , as per following formula: gsw = E/(Wi-Wa). Where, Wi -H 2 O in the substomatal cavity, Wa-H 2 O in the atmosphere (chamber head) and E-rate of transpiration.

Photosynthesis rate

The difference of CO 2 and water flux between reference and sample circuit is measured and is used to calculate rate of photosynthesis (A N ) or rate of CO 2 assimilation (Douthe et al., 2018; von Caemmerer and Farquhar, 1981) [17,45] .

Where, C e and C o are the CO 2 mole fraction at the chamber entrance and output, respectively; u e is the incoming flow air (mol air s -1 ), La is the leaf area surface (m 2 ), and E is the transpiration rate (mol H2O m -2 s -1 ).

Internal CO2 concentration:

Stomatal conductance is expressed as gsw in terms of H 2 O and gsc in terms of CO 2 .

The CO 2 concentration in the substomatal cavities (Ci) is calculated from CO 2 concentration in the atmosphere, rate of photosynthesis and stomatal conductance in terms of CO 2 by using following formula: Ci = Ca -A N /gsc. Where, Ca is the atmospheric CO 2 (in chamber head) (Von Caemmerer and Farquhar, 1981) [45] .

Statistical data analysis:

The differences between treatment means for various growth and physiological parameters were analyzed using one-way ANOVA and the Student's t-test. A significance level of (p< 0.05 \) was used to determine statistical significance. The physiological data were analyzed using OPSTAT software.

Results and Discussion

Germination percentage and growth parameters: The germination percentage was assessed seven days after inoculation. The highest germination rate (93.33%) was observed in the control plants of the resistant genotype (GP50), while the lowest germination rate (78.67%) was recorded in the Fusarium fujikuroi-inoculated susceptible genotype (PB1121) (Table 1). No significant difference in germination was found between inoculated and noninoculated plants of the resistant genotype, whereas a significant reduction in germination was observed in the inoculated plants of the susceptible genotype compared to its control. Similar results were reported by Yadav et al.

(2020) [48] , who found that Fusarium inoculation led to seed colonization, thereby inhibiting germination and reducing germination rates. Plant height was consistently highest in the inoculated plants of the susceptible genotype PB1121 across all time intervals compared to its control. A significant difference in plant height was observed between inoculated and non-inoculated PB1121 plants, attributed to stem elongation induced by pathogen infection. In contrast, no significant difference in plant height was noted between inoculated and control plants of the resistant genotype GP50, as no disease symptoms were observed. Root length was significantly reduced in the pathogen-inoculated plants of PB1121 compared to its control, likely due to Fusarium fujikuroi infection causing tissue blockage and impaired root growth. However, no significant difference in root length was recorded between control and inoculated plants of the GP50 genotype (Table 1). These findings align with those reported by Yadav et al. (2020) [48] . Disease incidence and Area under Disease Progress Curve: Highest disease incidence was observed in the PB1121 inoculated with Fusarium fujikuroi at all-time intervals. At 21 DPI pathogen inoculated PB1121 plants showed a disease incidence of 85.91%. Whereas no to very little disease was observed in pathogen inoculated GP50 plants (Fig. 1). At 21 DPI pathogen inoculated GP50 plants showed a disease incidence of 3.33%. No disease incidence was observed in the non-inoculated plants of both genotypes. After 21 days post inoculation highest AUDPC (885.88) was observed in pathogen inoculated PB1121 plants whereas at same time point an AUDPC of 23.34 was observed in pathogen inoculated GP50 plants (Table 2). Photosynthesis rate: At 7 days post-inoculation (DPI), the rate of photosynthesis was initially higher in inoculated plants of both genotypes; however, it later declined in these plants compared to their respective controls. The net carbon assimilation rate dropped significantly in the pathogeninoculated susceptible genotype (PB1121) compared to the control, with the lowest photosynthesis rate observed at 21 DPI (5.27 μmol m⁻² s⁻¹) in the inoculated PB1121 plants (Fig. 2 A), corresponding to the severity of the disease. In GP50, the photosynthesis rate initially showed a slight increase before declining, but the reduction was not as pronounced as in the susceptible genotype. No significant changes in photosynthesis were observed in the noninoculated plants of either genotype. These findings are consistent with the results reported by Kumar et al. (2013) [28] and Tatagiba et al. (2015) [43] . The reduced photosynthesis rate in inoculated plants is likely due to decreased activity of Rubisco and carbonic anhydrase, reduced mesophyll conductance to CO₂ diffusion, increased respiratory and photorespiratory activities, and biochemical damage (Ribeiro et al., 2004) [36] .

Stomatal conductance:

The stomatal conductance was observed to be decreasing in inoculated plants as compared to their non-inoculated counterparts in both genotypes. However the rate of decrease was low in resistant genotype whereas, it was very high in susceptible genotype. The lowest stomatal conductance activity was recorded in pathogen inoculated PB1121 plants at 21 DPI (0.14 mol m -2 s -1 ) (Fig. 2 B). No significant difference was observed in control plants of both resistant and control genotypes. Our findings are in accordance with the findings of Kumar et al., 2013 [28] .

Transpiration rate: The transpiration rate was initially observed to increase in pathogen-inoculated plants compared to their non-inoculated counterparts at 7 DPI. However, it began to decline in inoculated plants relative to their control counterparts in both resistant and susceptible genotypes. The reduction in the resistant genotype was minimal, whereas the susceptible genotype showed a significant and rapid decline. The lowest transpiration rate was recorded at 21 DPI (0.26 mmol m⁻² s⁻¹) in the inoculated susceptible genotype. No significant changes in transpiration rate were observed in the control plants of either genotype (Fig. 2 C). These findings align with previous studies by Kumar et al. (2013) [28] , Alves et al. (2011) [1] , and Resende et al. (2012) [15] . Internal CO2: Highest internal CO 2 (275 μmol m -2 s -1 ) was observed in PB1121 inoculated plants at 21 DPI. The internal CO 2 activity was initially decreasing in inoculated plants but it was reported to increase at later stages. In control plants no significant changes were observed in both genotypes (Fig. 2 D). Tatagiba et al (2015) [43] also observed similar results in their experiment.

Conclusions

The study findings indicate that disease severity significantly impacts the leaf gas exchange activities of plants. Genotypes with differing responses to pathogen infection exhibit distinct physiological changes when challenged by the pathogen. Infected plants generally experience a decline in the photosynthesis rate or net carbon assimilation, attributed to various underlying factors. However, in resistant genotypes with little to no disease, these factors have minimal impact, resulting in no significant changes. Similarly, other physiological activities such as transpiration rate, water use efficiency, internal CO₂ concentration, and stomatal conductance are affected differently based on the genotype's susceptibility to the pathogen.

Fututre Scope

Further studies are required to gain a deeper understanding of the mechanisms underlying each physiological parameter and the factors influencing these processes. Such research could provide valuable insights into the host's defense mechanisms and aid in developing more robust resistance sources.

Acknowledgement

Acknowledgement:The authors wish to acknowledge the In charge national containment facility, head plant pathology division and head fruits & horticultural division, IARI for the facilities needed to carry out the study.

References

1. Alves AA, Guimarães LMS, Chaves ARM, DaMatta FM, Alfenas AC. Leaf gas exchange and chlorophyll a fluorescence of Eucalyptus urophylla in response to Puccinia psidii infection. Acta Physiol Plant. 2011;33:1831-1839.
2. Bashyal BM, Aggarwal R, Sharma S, Gupta S, Rawat K, Singh D, et al. Occurrence, identification and pathogenicity of Fusarium species associated with bakanae disease of basmati rice in India. Eur J Plant Pathol. 2016;144(2):457-466.
3. Bassanezi RB, Amorim L, Bergamin Filho A, Bergerv RD. Gas exchange and emission of chlorophyll fluorescence during the monocycle of rust, angular leaf spot and anthracnose on bean leaves as a function of International Journal of Advanced Biochemistry Research https://www.biochemjournal.com their trophic characteristics. J Phytopathol. 2002;150:37-47.
4. Bastiaans L. Ratio between virtual and visual lesion size as a measure to describe reduction in leaf photosynthesis of rice due to leaf blast. Phytopathology. 1991;81:611-615.
5. Bastiaans L. Effects of leaf blast on photosynthesis of rice. 1. Leaf photosynthesis. Neth J Plant Pathol. 1993;99:197-203.
6. Bingham IJ, Walters DR, Foulkes MJ, Paveley ND. Crop traits and the tolerance of wheat and barley to foliar disease. Ann Appl Biol. 2009;154(2):159-173.
7. Boote KJ, Jones JW, Mishoe JW, Berger RD. Coupling pests to crop growth simulators to predict yield reductions. Phytopathology. 1983;73:1821-1587.
8. Boote KJ, Jones JW, Smerage GH, Barfield CS, Berger RD. Photosynthesis of peanut canopies as affected by leaf spot and artificial defoliation. Agron J. 1980;72:247-252.
9. Bowden RL, Rouse DI, Sharkey TD. Mechanism of photosynthesis decrease by Verticillium dahliae in potato. Plant Physiol. 1990;94(3):1048-1055.
10. Bozsó Z, Maunoury N, Szatmari A, Mergaert P, Ott PG, Zsíros LR, et al. Transcriptome analysis of a bacterially induced basal and hypersensitive response of Medicago truncatula. Plant Mol Biol. 2009;70:627-646.
11. Dallagnol LJ, Rodrigues FA, Martins SCV, Cavatte PC, DaMatta FM. Alterations on rice leaf physiology during infection by Bipolaris oryzae. Australas Plant Pathol. 2011;40:360-365.
12. Debona D, Rodrigues FA, Rios JA, Martins SCV, Pereira LF, DaMatta FM. Limitations to photosynthesis in leaves of wheat plants infected by Pyricularia oryzae. Phytopathology. 2014;104:33-39.
13. Dingkuhn M, De Datta SK, Dorffling K, Javellana C, Datta S. Varietal differences in leaf water potential, leaf net CO2 assimilation, conductivity and water use efficiency in upland rice. Aust J Agric Res. 1989;40(6):1183-1192.
14. Dingkuhn M, Farquhar GD, De Datta SK, O'Toole JC, Datta S. Discrimination of 13C among upland rices having different water use efficiencies. Aust J Agric Res. 1991;42(7):1123-1131.
15. Evans JR, Santiago LS. Prometheus Wiki Gold Leaf Protocol: gas exchange using LI-COR 6400. Funct Plant Biol. 2014;41:223.
16. Gupta AK, Singh Y, Jain AK, Singh D. Prevalence and incidence of bakanae disease of rice in Northern India. J Agric Res. 2014;52(4):233-237.
17. Hori S. Researches on bakanae disease of rice plants. Nijji Shiken Seiseki. 1898;12:110-119.
18. Impa SM, Nadaradjan S, Boominathan P, Shashidhar G, Bindumadhava HY, Sheshshayee MS. Carbon isotope discrimination accurately reflects variability in WUE measured at a whole plant level in rice. Crop Sci. 2005;45(6):2517-2522.
19. Ito S, Kimura J. Studies on the 'bakanae' disease of the rice plant. Rep Hokkaido Natl Agric Exp Stn. 1931;27:1-95.
20. Johnson KB. Defoliation, disease and growth: a reply. Phytopathology. 1987;77:1495-1497.
21. Kanjanasoon P. Studies on the bakanae disease of rice in Thailand. Doc Agric. 1965.
22. Kini KR, Let V, Mathur SB. Genetic variation in Fusarium moniliforme isolated from seeds of different host species from Burkina Faso based on random amplified polymorphic DNA analysis. J Phytopathol. 2002;150:209-212.
23. Kreye C, Bouman BAM, Reversat G, Fernandez L, Cruz CV, Elazegui F, et al. Biotic and abiotic causes of yield failure in tropical aerobic rice. Field Crops Res. 2009;112(1):97-106.
24. Kumar A, Guha A, Bimolata W, Reddy AR, Laha GS, Sundaram RM, et al. Leaf gas exchange physiology in rice genotypes infected with bacterial blight: An attempt to link photosynthesis with disease severity and rice yield. Aust J Crop Sci. 2013;7(1):32-39.
25. Mujawamariya M, Manishimwe A, Ntirugulirwa B, Zibera E, Ganszky D, Bahati N, et al. Climate sensitivity of tropical trees along an elevation gradient in Rwanda. Forests. 2018;9:647. DOI:10.3390/f9100647.
26. Murchie EH, Chen YZ, Hubbart S, Peng S, Horton P. Interactions between senescence and leaf orientation determine in situ patterns of photosynthesis and photoinhibition in field-grown rice. Plant Physiol. 1999;119(2):553-564.
27. Pannu PPS, Jasmeet K, Gurdeep S, Jaspal K. Survival of Fusarium moniliforme causing foot rot of rice and its virulence on different genotypes of rice and basmati rice. Plant Dis Res. 2012, 26(2).
28. Pavgi MS, Singh J. Bakanae and foot rot of rice in Uttar Pradesh, India. Plant Dis Rep. 1964;48(5):340-342.
29. Peng S, Laza RC, Khush GS, Sanico AL, Visperas RM, Garcia FV. Transpiration efficiencies of indica and improved tropical japonica rice grown under irrigated conditions. Euphytica. 1998;103:103-108.
30. Rathaiah Y, Das GR, Singh KHU. Estimation of yield loss and chemical control of bakanae disease of rice. Oryza. 1991;28:509-512.
31. Resende RS, Rodrigues FA, Cavatte PC, Martins SCV, Moreira RM, Chaves ARM, et al. Leaf gas exchange and oxidative stress in sorghum plants supplied with silicon and infected by Colletotrichum sublineolum. Phytopathology. 2012;102:892-898.
32. Ribeiro RV, Machado EC, Oliveira RFD. Growth-and leaf-temperature effects on photosynthesis of sweet orange seedlings infected with Xylella fastidiosa. Plant Pathol. 2004;53(3):334-340.
33. Rolfe SA, Scholes JD. Chlorophyll fluorescence imaging of plant-pathogen interactions. Protoplasma. 2010;247:163-175.
34. Samejima M. Intraspecific variations of 13Cdiscrimination in Oryza sativa L. Bull Nat Inst Agrobiol Resour. 1985;1:63-84.
35. Saremi H. Fusarium, biology, ecology and taxonomy. Jihad Daneshgahi, Ferdossy Mashhad University, Iran; 2005. p. 152.
36. Singh R, Sunder S. Foot rot and bakanae of rice: an overview. Rev Plant Pathol. 2012;5:566-604.
37. Spitters CJT, Van Roermund HJW, Van Nassau HGMG, Schepers J, Mesdag J. Genetic variation in partial resistance to leaf rust in winter wheat: disease progress, foliage senescence and yield reduction. Neth J Plant Pathol. 1990;96:3-15.
38. Sunder S, Singh R, Dodan DS. Management of bakanae disease of rice caused by Fusarium moniliforme. Indian J Agric Sci. 2014;84(2):224-228.
39. Tatagiba SD, DaMatta FM, Rodrigues FA. Leaf gas exchange and chlorophyll a fluorescence imaging of rice leaves infected with Monographella albescens. Phytopathology. 2015;105(2):180-188.
40. Thomas KM. A new paddy disease in Madras. Madras Agric J. 1931;19:34-36.
41. Von Caemmerer S, Farquhar GD. Some relationships between the biochemistry of photosynthesis and the gas exchange of leaves. Planta. 1981;153:376-387. DOI:10.1007/BF00384257.
42. Widin KD, Schipper Jr AL. Effect of Melampsora medusae leaf rust infection on yield of hybrid poplars in the north-central United States. Eur J Forest Pathol. 1981;11(7):438-448.
43. Yadav J, Bashyal BM, Sinha P, Aggarwal R. Effect of different abiotic factors on symptom expression and severity of bakanae disease of rice (Oryza sativa). Indian J Agric Sci. 2020;90(2):386-391.
44. Yoshida S, Satake T, Mackil DS. High temperature stress in rice. IRRI Research Paper Series. 1981, 67.
45. Schwartz H, Davis SM. Matching corporate culture and business strategy. Organizational dynamics. 1981 Jun 1;10(1):30-48.