Studies on effect of different levels of phosphorus and microbial inoculants on growth and yield of potato (Solanum tuberosum L.)

Introduction

Potato (Solanum tuberosum L.) is one of the important vegetable crops belongs to the solanaceae family popularly known as 'The king of vegetables' (Miyu et al). Cultivated potato are tetraploid (2n=4x=48) and origin of potato is believed to be South America (Peru). The 100 g of fresh potato tubers contain 70 -80% water, 20.6 % carbohydrate, 2.1% protein, 0.3% fat, 1.1% crude, 0.9% ash (Bist and Sharma) and fair amount of essential amino acids such as isoleucine, leucine and tryptophan. It is a heavy feeder, requires high amount of nitrogen, phosphorus and potassium fertilizers and other soil nutrients but due to its poorly developed and shallow root system it can't get access to deeper soils. Phosphorus is important element involved in numerous vital plant processes, including root development, energy transfer and disease resistance. It is necessary for the synthesis of DNA (Deoxyribonucleic acid), RNA (Ribonucleic Acid), and ATP (Adenosine triphosphate) which is responsible for energy storage and transfer within cells. Phosphorus helps plants establish strong root systems, enhance early plant vigor, improves tuber formation and increases yield and quality. Insufficient phosphorus levels can result in stunted growth, dark green or purplish leaves, delayed tuber formation and reduced yield. (Gruber et al). In India approximately 42% of soils are deficient in plant-available phosphorus and 38% have medium availability (Annappa et al), where available phosphorus content in the Chhattisgarh region are found to be varied from 5.37 to 39.42 kg/ha with average content of 19.79 kg/ha. (Motghare et al). Application of phosphorus effectively results in growth promoting attributes of the plants, in order to save the soils natural nutrient condition, reduce chemical burden on environment and also increase the production and quality of potato without putting an extra financial burden on farmers, the judicious use of combined application of inorganic fertilizers along with incorporation of bio-fertilizers (Phosphate solubilizing bacteria and arbuscular mycorrhizal fungi) is the need of the time. Hence, it's a matter of great interest to find out the best combination of phosphorus dose through inorganic fertilizers, and their combined effect with different biofertilizers for obtaining better yield and quality of potato tubers.

Methods and Materials

The experiment was conducted at Research cum Demonstration Farm, College of Agriculture, Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh during year 2023-24 and 2024-25 rabi season to investigate the effect of different levels of phosphorus and microbial inoculants on growth and yield of potato (Solanum tuberosum L.) cv. Kufri Pukhraj. The soil of the experimental field is clayloam, which is locally known as "Dorsa". Soil samples of experimental field were gathered at random using an auger from 4-5 locations upto a depth of 20 cm and properly mixed to create a composite sample. The composite sample was analysed to determine the soil initial fertility status. The plots were labelled uniformly and were laid down as per the design of the experiment. The trial was laid down in a randomized block design (RBD) corresponding to 13 treatments and three replications. The experimental area was divided into 39 plots, each measuring 4.8 m × 3.4 m, and ridges of 20 cm height were prepared at 60 cm spacing with a tractor-mounted ridger. Nitrogen, phosphorus, and potassium were applied in the form of urea, diammonium phosphate (DAP) as per as treatment and muriate of potash (MOP), respectively. Half of the nitrogen along with the full dose of phosphorus and potassium was applied as a basal dose at planting, and the remaining nitrogen was top-dressed at 30 days after planting (DAP) during earthing-up. For uniform sprouting, seed tubers were kept under diffused light for seven days after removal from cold storage. To prevent fungal contamination, sprouted tubers were dipped in Dithane M-45 solution @ 2.5 g L⁻¹ for 15 minutes, shadedried, and planted on ridges at 20 cm spacing. Gap filling was done at 15 DAP using treated sprouted tubers. Earthingup was carried out at 30 and 60 DAP, coinciding with topdressing. The crop was irrigated using the furrow method, with a pre-emergence irrigation applied immediately after planting and subsequent irrigations at 12-day intervals.

Weeding was done manually at 30 DAP during earthing-up.

Haulm cutting was done at 90 DAP, and harvesting was carried out manually seven days later using spades and kudali. After harvest, tubers were graded into three categories: small (75 g), with marketable tubers defined as >25 g. Observations were recorded on growth and yield parameters from five randomly selected plants per plot.

Growth observations included plant emergence percentage at 30 DAP, plant height, number of shoots and number of compound leaves. Yield attributing traits included number of tubers per plant, fresh and dry tuber weights, grade-wise tuber yield and number, marketable and unmarketable yield, total tuber yield, and tuber counts. The analysis of variance approach was used to examine the analytical data in this experiment as described by Gomez and Gomez (1984) [18] .

Results and Discussion

During the first year of experiment, data indicated that the maximum plant emergence (92.33 %) was observed in T 13 . Whereas, the treatment withT 9 recorded for minimum plant emergence (87.33 %). In second year, the maximum plant emergence (96.34 %) was noticed in case of treatment T 7 . However, minimum plant emergence (93.33 %) was observed under treatment T 1 . Findings based on pooled mean data showed that the treatment T 3 was recorded for highest plant emergence (94.17 %). The lowest plant emergence (90.67 %) was noticed in treatment T 9 . Similarly, verma et al. (2013) [16] and Banjare et al. (2012) [19] reported that the fertilizer and micro inoculants treatments had no effect on the tuber emergence of potato. [16] reported that the use of microbial inoculants on potato enhance the plant height.

Number of shoots plant-1

The number of shoots per plant was recorded at 45, 60, and 75 DAP. The differences were significant across first years and pooled data but found to be non-significant on second year. At 75 DAP, the highest number of shoots plant-1 was (7.07) was recorded in treatment T 9 . However, the treatment T 1 was recorded minimum (4.77). During the second year the maximum number of shoots plant-1 (7.50) was recorded under treatment T 9 and the treatment T 1 was recorded minimum (4.93). Similarly, the pooled the highest number of shoots plant-1 (7.60) at 60 DAP was recorded with treatment T 9 and the lowest (4.85) was obtained with the treatment T 1 . Various research works also reported the similar finding viz., (Raghuwanshi et al) and Verma et.al.

Number of compound leaves plant -1

The number of compound leaves plant-1 was recorded at 45, 60, and 75 DAP. The differences were significant across first years and pooled data but found to be non-significant on second year. At 75 DAP, the highest number of compound leaves plant-1 was (58.30) was recorded in treatment T 9 . However, the treatment T 1 was recorded minimum number of compound leaves plant-1 (38.13).

During the second year the number of compound leaves plant-1 (56.73) was recorded under treatment T 9 and the treatment T 1 was recorded minimum (37.73). Similarly, the pooled the highest number of compound leaves plant-1 (57.52) at 75 DAP was recorded with treatment T 9 and the lowest (37.93) was obtained with the treatment T 1 . Various research works also reported the similar finding viz., Choudhary et al. and Verma et.al, (2013) [16] .

Yield attributes of potato Yield of potato of different grades

The yield attributing characters of different grades were found to be significant across the years and pooled mean. Tuber yield of grade 0-25g was found maximum under T 9 (6.89 t/ha) and treatment T 1 (4.40 t/ha) was found to be minimum, grade 25-50 g was found maximum under T 9 (8.40 t/ha) and treatment T 1 (4.68 t/ha) was found to be minimum, grade 50-75 g was found maximum under T 10 (7.13 t/ha) and treatment T 1 (4.68 t/ha) was found to be minimum, tuber grade >75 g was found maximum under T 10 (9.40 t/ha) and treatment T 1 (5.44 t/ha) was found to be minimum.

Marketable, unmarketable and total yield tuber yield

The yield attributing characters of different marketable, unmarketable and total yield were found to be significant across the years and pooled mean. Tuber yield of marketable was found maximum under T 10 (24.59 t/ha) and treatment T 1 (13.78 t/ha) was found to be minimum, unmarketable yield was found maximum under T 6 (8.90 t/ha) and treatment T 10 (5.94 t/ha) was found to be minimum, grade total yield was found maximum under T 9 (32.55 t/ha) and treatment T 1 (21.15 t/ha) was found to be minimum. It might be response of optimum availability of plant developing nutrient which might be facilitate developing plant for higher photosynthetic activity and production of more photoassimilates and its utilization in plant growth, development and accumulation of dry matter in developing tubers. Higher plant growth viz., plant height, number of branches and leaves also facilitate the plant for interception of solar radiation and more potato synthesis. compound leaves plant⁻¹ (57.52 at 75 DAP) and total tuber yield (32.55 t/ha). The treatment T 10 was observed with significantly higher yields across all tuber size grades, resulting in the greatest marketable yield (24.59 t/ha).

References

1. Table 6: Effect of different doses of phosphorus fertilizer and microbial inoculants on on grade wise tuber yield (t ha -1 ) of potato Treatments Yield of tuber 0-25g (t ha -1 ) Yield of tuber 25-50g (t ha -1 ) Yield of tuber 50-75g (t ha -1 ) 2023-24 2024-25 Pooled 2023-24 2024-25 Pooled 2023-24 2024-25 Pooled T1: Without P fertilizer (NK) (control)
2. Păun A, Păun M, Rodríguez-Patón A. On the Hopcroft's minimization technique for DFA and DFCA. Theoretical Computer Science. 2009;410(24-25):24242430.
3. Aho AV, Sethi R, Ullman JD. Compilers: Principles, Techniques, and Tools. 1st ed. Reading, MA, USA: Addison-Wesley; 1986.
4. Ravikumar B, Eisman G. Weak minimization of DFA-An algorithm and applications. Theoretical Computer Science. 2004;328(1-2):113-133.
5. Knuth DE. The Art of Computer Programming, Volume 1: Fundamental Algorithms. 3rd ed. Reading, MA, USA: Addison-Wesley; 1997.
6. Xing G. Minimized Thompson NFA. 2008.
7. Watson H. A taxonomy of finite automata minimization algorithms. Computing Science Technical Report, Eindhoven University of Technology; 1995.
8. Baburin I, Cotterell R. A close analysis of the subset construction. In: Proceedings of the International Conference on Descriptional Complexity of Formal Systems (DCFS); 2025. Springer.
9. Hedayati Somarin I. DFA minimization algorithms in Map-Reduce. M.S. thesis, Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada; 2016.
10. Brzozowski JA. Canonical regular expressions and minimal state graphs for definite events. Mathematical Theory of Automata. 1962;12:529-561.
11. Hopcroft JE. An n log n algorithm for minimizing states in a finite automaton. Theory of Machines and Computations. Academic Press; 1971. p. 189-196.
12. Becchi M, Crowley P. Efficient regular expression evaluation: Theory to practice. In: Proceedings of the 4th ACM/IEEE Symposium on Architecture Networking and Communication Systems (ANCS); 2008. San Jose, CA, USA. p. 50-59.
13. Rabin MO, Scott D. Finite automata and their decision problems. IBM Journal of Research and Development. 1959;3(2):114-125.
14. Verma RM. A visual and interactive automata theory course emphasizing breadth of automata. In: Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE); 2005. p. 325-329.
15. Yu S. Regular languages. In: Handbook of Formal Languages, Vol. 1. Springer; 1997. p. 41-110.
16. Pai TV, Devi AJ, Aithal PS. A systematic literature review of lexical analyzer implementation techniques in compiler design. International Journal of Applied Engineering Management Letters. Dec 2020.
17. Verma SP, Pandarinath K, Verma SK, Agrawal S. Fifteen new discriminant-function-based multidimensional robust diagrams for acid rocks and their application to Precambrian rocks. Lithos. 2013 May 1;168:113-23.
18. Nizamuddin MA, Balister PJ, Tranter WH, Reed JH. Nonlinear tapped delay line digital predistorter for power amplifiers with memory. In2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003. 2003 Mar 16 (Vol. 1, pp. 607-611). IEEE.
19. Gomez KA, Gomez AA. Statistical procedures for agricultural research. John wiley & sons; 1984 Feb 17.
20. Banjare L, Prasad AK, Naik ML. Boerhaavia diffusa from traditional use to scientific assessment-a review. Int J Pharm Biol Arch. 2012;3(6):1346-54.