Assessing rice yield response relative to soil phosphorus fractions under site-specific nutrient management
Abstract
In rice-wheat systems on Indian Vertisols, efficient phosphorus (P) management is pivotal for sustaining productivity under resource constraints. This study evaluated STCR-based fertilizer prescriptions, with and without farmyard manure (FYM), across yield-target regimes in a randomized block design with four replications and five treatments. Soils were fractionated to quantify major inorganic P pools (Ca-P, Red-P, Fe-P, Al-P, RS-P) and total P, while plant tissues and grain yield provided P uptake data; P balance was estimated to gauge soil fertility status under different regimes. The STCR dose targeting 22 q ha−1 consistently produced the highest grain yield and total P uptake, accompanied by increases in Ca-P and Red-P pools. FYM enhanced nutrient-use efficiency and amplified certain P fractions, though interactions with inorganic fertilizers varied across treatments. Across fractions, total P and its components rose with cumulative P inputs, aligning with improved P availability and uptake in rice and subsequent wheat. Regression analyses identified Red-P as the strongest predictor of grain yield, followed by available P fractions, with Total-P explaining less variation. Positive P balances under STCR strategies indicated sustained P supply, though regional and long-term validations are recommended. The findings underscore the value of STCR-based P management, particularly when integrated with FYM, to optimize P partitioning, yield targets, and soil fertility in Indian Vertisols; implications for on-farm soil testing, economic feasibility, and broader adoption warrant further investigation.
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Introduction
Rice (Oryza sativa L.) is a fundamental staple crop worldwide, underpinning food security and rural livelihoods for billions of people. Cultivation spans approximately 165.04 million hectares across about 100 countries, reflecting its central role in global agriculture and nutrition. This prominence, along with the crop's diverse production systems-from flooded paddies to rainfed uplands-highlights the importance of optimizing nutrient management to sustain yields while protecting soil health and environmental quality (Sawargaonkar et al. 2026) [8] . Phosphorus (P) deficiency remains one of the major constraints on plant growth and crop production worldwide. As a key macronutrient, phosphorus underpins critical biochemical and physiological processes, including energy transfer, nucleic acid synthesis (DNA and RNA), and the promotion of vegetative growth in rice as well as other cereals. Understanding P dynamics in soils and optimizing its management through soil testing and balanced fertilization is essential for sustaining yields, improving nutrient use efficiency, and safeguarding environmental health in diverse agroecosystems (Bird et al. 2001) [3] . Phosphorus (P) is a fundamental macronutrient essential for plant growth and development. It is a key component of phospholipids and nucleotides, which form cellular membranes and genetic material, and it participates in critical physiological processes including membrane function, photosynthesis, respiration, glycolysis, redox reactions, and signal transduction. Phosphorus availability also influences plant-microbe interactions, particularly under Pdeficient conditions, where signaling molecules and growth regulators coordinate adaptive responses. Understanding P dynamics and optimizing P nutrition through soil testing and balanced fertilization are therefore pivotal for sustaining crop productivity (Akiyama et al. 2005) [1] . Fertilizer strategies that combine chemical nutrients with organic amendments (e.g., farmyard manure, FYM) and those based on soil-test-crop response (STCR) prescriptions aim to optimize P use efficiency and align nutrient supply with crop demand. Despite advances, the relationship between rice yield and soil P fractions under STCR-guided management remains underexplored in Vertisols, where high pH, calcareous substrates, and specific mineralogy modulate P fixation and release. This study investigates (i) how STCR-based nutrient regimens influence the distribution of soil P fractions in a rice-wheat rotation, (ii) how changes in Ca-P, Red-P, Fe-P, Al-P, Saloid-P, and RS-P correlate with grain yield, and (iii) the role of FYM in modulating these relationships. By integrating fractionation data with yield outcomes in a field experiment, the work aims to inform precise, sustainable nutrient management strategies that optimize yield while conserving soil P resources in Indian Vertisols.
Material and Methods
Description and location of the study area
Raipur is the capital of Chhattisgarh state and it is situated in the center of Chhattisgarh at lies between 21 0 16'N latitude and 81 0 60'E longitude with altitude of 289.56 meters the mean sea level. The Instructional Farm, Indira Gandhi Agricultural University Raipur (C.G.) is situated on near to NH 6 in the eastern part of Raipur city and located between 20 0 4' North latitude and 81 0 39' East longitude with altitude of 293 m above mean sea level.
Physico-chemical properties of experimental soil
The study site features a clayey Vertisol with a texture comprising 26% sand, 28% silt, and 44% clay, which classifies it as a clayey Vertisol. The bulk density is 1.35 Mg m-3 . The soil exhibits an alkaline pH of 7.88 and electrical conductivity of 0.20 dS m -1 at 25 °C. The organic carbon content is 0.47%, Available nutrient pools are: nitrate-nitrogen 217.34 kg ha -1 , available phosphorus as P 2 O 5 19.32 kg ha -1 , and available potassium as K 2 O 498.05 kg ha - 1 . Micronutrients show adequate to sufficient levels, with iron (Fe) 27.54 mg kg -1 , zinc (Zn) 11.57 mg kg -1 , copper (Cu) 2.57 mg kg -1 , and manganese (Mn) 3.25 mg kg -1 .
Experimental design and treatments
The field experiment was conducted on a Vertisol using a randomized block design with four replications and five rice treatments. The treatments were arranged to compare a nonfertilized control (T 1 ) with a gradation of nutrient management and yield-target strategies: T 2 (GRD) used a balanced NPK ratio of 100:60:40; T 3 applied the STCR dose corresponding to a yield target of 5 t/ha; T 4 employed the STCR dose corresponding to a yield target of 6 t/ha; and T 5 used the STCR dose for 6 t/ha yield target combined with farmyard manure (FYM).
Collection, preparation and analysis of plant samples 2.4.1 Plant samples collection and preparation
Plant samples of rice collected at maturity just before the harvesting and were air dried. Plot wise grain and straw samples were grinded separately and used for the chemical analysis.
Plant sample analysis for N, P and K content 2.4.2.1 Total nitrogen content
Grinded samples of straw and grain were digested separately with 10 ml conc. H 2 SO 4 and 3.0 gm mixture of K 2 SO 4 and CuSO 4 salts (9:1). Total N were determine by micro Kjeldahl's method (using alkaline potassium permagnate).
Total phosphorus and potassium content
For the P and K analysis, 1.0 g plant materials were digested at 250 o C in a digestion chamber with 10 ml of di-acid mixture of HNO 3 and HClO 4 acids (ration of 9:4). The completion of digestion was confirmed when the liquid becomes colourless. After the digestion, the digested material was filtered and transferred in to a 100 ml volumetric flask and the volumes made up to the 100 ml by distil water. Aliquots of this diluted digest were used for the determination of P and K content. Total P was determine by vanadomolybdate phosphoric acid yellow colour method at 420 nm (Jackson; 1967) [4] and potassium was determine by using Flame-photometer from the extract obtained by the digestion.
Phosphorus Uptake
P uptake was calculated using the grain and straw yields and potassium content.
Yields (kg ha -1 ) x P content (%) Total P uptake (kg ha -1 ) = 100
Phosphorus balance
Phosphorus balance sheet was calculated by total quantity of P removed by rice was subtracted from the sum of fertilizers nutrient applied and initially available soil status.
Statistical analysis
The experimental data were analyzed by the software "OPSTAT". All the parameters were analyzed in a Randomized block design to list the variance of different treatments at 5 per cent level of significance.
Results and Discussion
Effect of nutrient management practices on Phosphorus balance in soil 3.2.1 Phosphorus uptake
The result on P uptake by rice is presented in Fig. 1. Results revealed that P uptake by rice grain, straw as well as their total significantly affected by different fertilizer P application. The P uptake in grain varied from 3.45 kg/ha in T 1 (control) to 14.30 kg/ha T 5 (YT 6 t ha -1 + FYM). The highest grain P uptake was noticed in T 5 (YT 6 t ha -1 + FYM) followed by T 4 (YT 6 t/h), T 2 (GRD), and lowest in T 1 (control) treatment. Rice straw P uptake also followed the similar trend as with that of grain P uptake. All the plots receiving either chemical fertilizer alone or in combination with organics significantly increased P uptake over the control. It is evident from the data that the total P uptake also followed the similar trend as rice grain and straw since the total P uptake is their sum total. It is varied from 4.01 kg/ha to 17.49 kg/ha in T 1 and T 5 , respectively. P uptake is the multiple of yield and P content and hence the trends of P uptake followed the yield trends. Similar results found in Sharma (2005) [10] , Prakash (2012) [7] , Nagumo et al. (2013) [6] .
Phosphorus Balance
Phosphorus balance was estimated by total quantity of P removed by rice deducted from the sum total of fertilizers P applied and initially available soil status. Results presented in Fig 2 show the P balance in relation to nutrient management practices. P balance estimated were found in the range from 6.13 to 25.4 kg/ha among different treatments. Positive balance showed nutrients build up in soil whereas the negative balance showed depletion of nutrition from the soil. The estimation of P balance is necessary for sustainable management of P fertilization. This is because the application of low P fertilizer can lead to a negative balance results decreased soil fertility, yield and profitability of system resources. Initially soil P was high in GRD (25.4 kg/ha) followed by YT 6 t/ha + FYM (24.36 kg/ha) and lowest in control (6.13 kg/ha) treatment. The P uptake by crop was higher in T 5 YT 6 t/ha + FYM (17.49 kg/ha) followed by T 4 YT 6 t/ha (16.16 kg/ha) and lowest in control (4.01 kg/ha) treatment. The P balances in soil varied from 2.12 kg/ha in T 1 (control) to 37.17 kg/ha in T 2 (GRD). Phosphorus balance in soil after the harvesting of rice was positive. The highest positive balance was observed in T 2 GRD (37.17 kg ha -1 ) followed by T 4 YT 6 t ha -1 (14.78 kg ha -1 ), T 5 YT 6 t/ha with FYM (11.58 kg ha -1 ) and lowest positive balance in control (2.12 kg/ha). The results were in conformity with the findings of Sharma (2005) [10] , Barla (2006) [2] , Chaudhary (2007) [5] Fig 2: Effect of nutrient management practices on Phosphorus balance in soil
Relationship between rice yield and P fractions
The relationship between rice yields and P fractions as independent variables were derived by regression analysis to yield variations due to various fractions and presented in figures 3 to 9. Results indicated that the large proportions of yield variations were accounted for Red-P fractions. Among different P fractions, Red-P was the most important P fractions contributing toward grain yield with 'R 2 'values 0.88. A critical examination of this equation indicated that Red-P was the most important variable computed to the yield variation observed by regression analysis. Reductant soluble P or occluded P is highly insoluble and this fraction is very important for rice soil under submergence. The R 2 value indicated that about 88% variations in grain yield were attributed only to this fraction of P. The second most important variable was Saloid-P in (87%) followed by Available-P (85.8%) and lowest in Total-P (77%). Similar results found in Verma (2002) [11] , Sepehya (2011) [9]
Conclusion
This study demonstrates that nutrient management, particularly STCR-based fertilizer prescriptions with or without FYM, markedly influences phosphorus (P) dynamics in a rice-wheat system and drives corresponding yield responses. Regression analyses indicated that P fractions, especially Red-P, explain a substantial portion of yield variation, highlighting the pivotal role of readily available P pools in sustaining rice productivity under flooded Vertisol conditions. Among P fractions, Red-P emerged as the most influential predictor of grain yield, followed closely by Saloid-P and available P, while Total-P showed comparatively weaker association. Treatments incorporating targeted P doses to achieve higher yield targets (e.g., 6 t/ha with FYM) enhanced P uptake in grain and straw and elevated soil P reserves (Ca-P, Red-P, Fe-P, Al-P, RS-P), contributing to improved nutrient use efficiency and potential long-term soil fertility. FYM generally augmented yield and P pools, though the magnitude of FYM-fertilizer interactions depended on the underlying fertilization regime.
Acknowledgements
The authors gratefully acknowledge the facilities and support provided by Indira Gandhi Krishi Vishwavidyalaya (IGKV), Raipur. We thank the Department of Soil Science and Agricultural Chemistry for enabling long-term field trials and for access to experimental plots and data management. Special thanks are due to the field staff and technicians at IGKV, Raipur, for their meticulous assistance in plot preparation, sample collection, and routine laboratory analyses. We also acknowledge the contributions of colleagues in the lab.
Author Contributions
Swati Sahu: formal analysis, methodology, writing-original draft; Lalit Kumar Srivastava: conceptualization, data curation, formal analysis, methodology, writing-original draft. Anusuiya Panda: formal analysis, data curation, methodology, writing-original draft, Vinay Bachkaiya: formal analysis, writing-original draft.
Conflict of Interest
The author declares that there is no conflict of interest regarding the publication of this manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial or any other non-profit sectors.
Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Consent to Participate
References
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