Assessment of soil quality and crop productivity for reducing yield gaps in farmers’ fields

Introduction

Sustainable rice production is essential for meeting the growing demand for food under increasing competition for land, water and other agricultural resources. In India, substantial variation in rice productivity among farmers' fields remains a major constraint to sustainable production. Yield-gap analysis provides a useful approach for quantifying the difference between attainable yields and the yields actually achieved under farmers' management conditions. Balanced and efficient nutrient management is critical for exploiting the yield potential of modern rice genotypes. However, fertilizer practices in many regions continue to rely on generalized recommendations that do not adequately account for spatial variability in soil nutrient-supplying capacity, crop nutrient requirements and prevailing management practices. Such blanket recommendations may result in either insufficient or excessive nutrient application, leading to nutrient imbalance, inefficient fertilizer use and reduced sustainability. The introduction of high-yielding rice cultivars with greater yield potential further emphasizes the need for location-specific nutrient management. Therefore, assessment of soil fertility, soil quality and crop productivity at the farmers' field level is important for identifying yield constraints and developing appropriate fertilizer strategies. In this context, the present study was undertaken to evaluate soil quality and productivity variability across farmers' fields and to develop site-specific nutrient recommendations aimed at reducing yield gaps and improving the sustainability of rice production.

Materials and Methods

The investigation was carried out during kharif 2019 in farmers' fields located around six selected centres: Chinsurah (46 farmers), Titabar (30 farmers), Pantnagar (50 farmers), Kanpur (20 farmers), Kaul (24 farmers) and Karaikal (22 farmers).

The study was undertaken to characterize field-level variation in soil nutrient-supplying capacity and examine its association with rice productivity under farmers' existing fertilizer practices and prevailing recommended practices. Another objective was to refine nutrient requirements for achieving specific yield targets and assess the performance of targeted fertilizer recommendations under farmers' field conditions. A rapid roving survey was conducted in villages surrounding each centre. Information was collected from farmers on rice variety, sowing time, manure and fertilizer use, crop-management practices, yield, weather conditions and general soil characteristics. Based on recorded grain yields, farmers' fields were classified into low-and highproductivity groups. Soil and plant samples were collected after harvest and analyzed for relevant nutrient parameters. The collected information and analytical results were evaluated to identify the major factors associated with yield differences between low-and high-yielding fields. Based on the identified nutrient constraints and soil fertility status, site-specific fertilizer recommendations were formulated for the subsequent rice season. These recommendations were implemented in selected lowyielding farmers' fields to evaluate their effectiveness in improving rice productivity and maintaining soil health.

Results and Discussion

The study was conducted during kharif 2022 at Chinsurah, Titabar, Kanpur, Pantnagar and Kaul, while observations at Karaikal were recorded during both kharif 2022 and rabi 2022. The investigation covered irrigated and shallow lowland rice ecosystems and included a wide range of locally cultivated rice varieties. Farmers' fields were categorized into low-and high-yielding groups based on grain yield. Soil and plant samples collected after harvest were analyzed to assess soil fertility, nutrient uptake and soil quality, and the resulting information was used to develop site-specific nutrient recommendations.

Variation in Soil, Crop and Weather Parameters

Considerable variation was observed in soil properties among farmers' fields across the study locations. The fields differed with respect to soil reaction (pH), organic carbon, texture and available nutrient status. Such spatial variability in soil fertility indicates differences in the inherent nutrientsupplying capacity of individual fields and emphasizes the need for location-specific nutrient management.

The variation in soil characteristics was also reflected in crop performance. Differences in crop yield and nutrient uptake were observed between low-and high-yielding farmers' fields at all locations. These differences suggest that variation in soil nutrient supply, fertilizer management and crop nutrient demand contributed to the observed productivity differences.

Rice Yield and Nutrient Uptake

A wide range of grain yields was recorded across the farmers' fields. At Chinsurah, mean grain yield ranged from 2.38 t ha -1 in low-yielding fields to 4.73 t ha -1 in highyielding fields. At Titabar, yield increased from 3.10 to 4.75 t ha -1 , while at Kanpur it ranged from 4.76 to 6.59 t ha -1 . At Pantnagar, mean yield varied from 4.83 to 5.84 t ha -1 . Karaikal recorded a relatively narrow range of 3.83-4.36 t ha -1 , whereas Kaul showed the largest variation, with yields ranging from 1.44 to 8.80 t ha -1 . The substantial differences between low-and high-yielding fields were accompanied by marked variation in nutrient uptake. The uptake of major nutrients differed considerably among locations and between yield groups, indicating differences in crop nutrient demand and nutrient-use efficiency. In several locations, soil-test values did not consistently correspond with grain yield and nutrient uptake, particularly under flooded rice conditions. This suggests that conventional soil-test values alone may not fully explain nutrient availability and crop response in submerged soils. However, at some locations, the soil quality index showed a closer association with observed yield and nutrient uptake. These results are in conformity with earlier reported by Bharamappanavara et al. (2020) [1] , Kale et al. (2021) [4,5] , Gorlapalli et al. (2022) [3] and Sahoo et al. (2022) [14] .

Soil Quality Index

The soil quality index differed among the study locations and farmer categories, reflecting differences in overall soil health and nutrient-supplying capacity. [15] , Kale et al. (2021) [4,5] , Rathod et al. (2021) [10] , Parmar et al. (2022) [9] and Patel et al. (2025) [7, 8] .

Nutrient Requirement and Site-Specific Fertilizer Recommendations

The nutrient requirement per tonne of grain production varied considerably among the study locations. These estimates were used to determine nutrient demand in relation to grain production and to develop fertilizer prescriptions for specific yield targets. Based on the maximum yield recorded under recommended fertilizer management, target yields were set at 5.0 t ha -1 for Chinsurah, 5.0 t ha -1 for Titabar, 6.5 t ha -1 for Pantnagar, 10.0 t ha -1 for Kaul, 6.5 t ha -1 for Kanpur and 5.0 t ha -1 for Karaikal. The location-specific fertilizer prescriptions demonstrated the need to move beyond blanket fertilizer recommendations. Differences in soil nutrient supply, crop nutrient uptake and nutrient requirement per unit of grain production resulted in varying fertilizer requirements among locations. The relatively high estimated requirements of phosphorus and potassium may be associated with low recovery of applied phosphorus and greater potassium accumulation by rice. These findings indicate considerable scope for improving nutrient-use efficiency through sitespecific nutrient management. These results are in conformity with earlier reported by Sharma et al. (2022) [11] , Patel et al. (2025) [7,8] and Kumar et al. (2025) [6] , Brajendra et al. (2025) [2] and Zoureen et al. (2025) [17] .

Yield Gap Analysis

A substantial yield gap was observed between low-and highyielding farmers' fields at all study locations. The highest yield gap was recorded at Kaul (83.6%), followed by Chinsurah (49.6%), Kanpur (27.8%), Titabar (27.7%), Pantnagar (17.3%) and Karaikal (17.3%). The magnitude of these differences indicates considerable potential for increasing rice productivity through improved nutrient management and adoption of suitable production practices. The wide yield differences were associated with variations in nutrient uptake, soil quality and fertilizer management. In both recommended and farmers' fertilizer practices, substantial variation in grain yield was observed, indicating that fertilizer doses were not always adequately matched with field-specific nutrient-supplying capacity and crop requirements. Therefore, integrating soil quality assessment, nutrient uptake and yield-gap analysis can provide a more effective basis for refining fertilizer recommendations and improving rice productivity in farmers' fields. These results are in conformity with earlier reported by Sharma et al.

(2020) [12] , Sharma et al. (2022) [11] , Patel et al. (2025) [7,8] and Kumar et al. (2025) [6] .

Conclusion

The study demonstrated substantial variability in soil nutrient-supplying capacity, nutrient uptake and rice productivity among farmers' fields across the selected locations. The differences between low-and high-yielding fields resulted in considerable yield gaps, with the largest gap recorded at Kaul. Soil quality indices also varied among locations, indicating differences in overall soil fertility and productivity potential. The observed mismatch between soiltest values, nutrient uptake and grain yield highlights the limitations of relying solely on generalized fertilizer recommendations under diverse flooded rice conditions. Site-specific fertilizer prescriptions based on soil nutrient supply, crop nutrient uptake and nutrient requirement per unit of grain yield can therefore provide a more effective approach for improving nutrient-use efficiency and reducing yield gaps. The study emphasizes the importance of fieldspecific nutrient management for enhancing rice productivity while maintaining soil quality and long-term sustainability.

References

1. Bharamappanavara M, Siddaiah AM, Ponnuvel S, et al. Mapping QTL hotspots associated with weed competitive traits in backcross population derived from Oryza sativa L. and O. glaberrima Steud. Scientific Reports. 2020;10:22103. doi:10.1038/s41598-02078675-7.
2. Brajendra M, Prasad Babu BB, Manasa V, Rao KV, Tejendra Kumar, Vinod Kumar, et al. Factor productivity, nutrient and harvest status of rice in farmers' fields of Ghagraghat. The Journal of Rural and Agricultural Research. 2025;25(2):57-60.
3. Gorlapalli A, Kallakuri S, Sreekanth PD, Patil R, Bandumula N, Ondrasek G, et al. Characterization and prediction of water stress using time series and artificial intelligence models. Sustainability. 2022;14:6690. doi:10.3390/su14116690.
4. Kale RR, Anila M, Mahadeva Swamy HK, Bhadana VP, Durga Rani CV, Senguttuvel P, et al. Morphological and molecular screening of rice germplasm lines for low soil P tolerance. Journal of Plant Biochemistry and Biotechnology. 2021;30:275286.
5. Kale RR, Durga Rani CV, Anila M, Mahadeva Swamy HK, Bhadana VP, Senguttuvel P, et al. Novel major QTLs associated with low soil phosphorus tolerance identified from the Indian rice landrace, Wazuhophek. PLoS ONE. 2021;16(7). doi:10.1371/journal.pone.0254526.
6. Kumar Vishal, Amarendra Kumar, Uday Kumar, Matlooba Naseem, Brajendra, Munna Lal. Field evaluation of rice (Oryza sativa L.) genotypes for resistance against brown spot disease (Drechslera oryzae). Acta Scientific Agriculture. 2025;9(10):1-8.
7. Patel Mukesh Kumar, Ramawatar Meena, Yadvir Singh, Ramjeet Meena, Munna Lal, Brajendra. Effect of phosphorus and bio-organics on yield and nutrient uptake by mung bean [Vigna radiata (L.) Wilczek] in an Inceptisol of Varanasi. The Journal of Rural and Agricultural Research. 2025;25(2):69-73.
8. Patel Mukesh Kumar, Ramawatar Meena, Yadvir Singh, Ramjeet Meena, Munna Lal, Brajendra. Enhancing mung bean [Vigna radiata (L.) Wilczek] growth with phosphorus and bio-organics in an Inceptisol. The Journal of Rural and Agricultural Research. 2025;25(2):74-77.
9. Parmar B, Vishwakarma A, Padbhushan R, Kumar A, Kumar R, Kumari R, et al. Hedge and alder-based agroforestry systems: potential interventions to carbon sequestration and better crop productivity in Indian Sub-Himalayas. Frontiers in Environmental Science. 2022;10:858948. doi:10.3389/fenvs.2022.858948.
10. Rathod S, Saha A, Patil R, Ondrasek G, Gireesh C, Anantha MS, et al. Two-stage spatiotemporal time series modelling approach for rice yield prediction and advanced agroecosystem management. Agronomy. 2021;11:2502. doi:10.3390/agronomy11122502.
11. Walp) and nutrient status in rainfed Alfisol soils. Communications in Soil Science and Plant Analysis. 2022;53(2):268-280. doi:10.1080/00103624.2021.1993883.
12. Sharma KL, Indoria AK, Srinivas K, Sammi Reddy K, Ravindra Charry G, Munna Lal, et al. Effect of longterm use of tillage, crop residue and N application on phosphorus fractions in soil under sorghum (Sorghum vulgare (L.)-castor system (Ricinus communis) in rainfed Alfisol soils. Communications in Soil Science and Plant Analysis. 2020;51(1):1-13. doi:10.1080/00103624.2019.1689252.
13. Sharma KL, Munna Lal, Sammi Reddy K, Indoria AK, Srinivas K, Vinod Kumar Singh, et al. Long term effect of different levels of surface crop residue application on hydrolyzable and non-hydrolyzable nitrogen fractions in sorghum (Sorghum bicolor (L.) Moench)-cowpea (Vigna unguiculata) system in rainfed Alfisols. Communications in Soil Science and Plant Analysis. 2022;53(3):337-345. doi:10.1080/00103624.2021.2008415.
14. Sahoo S, Mukhopadhyay P, Sinha AK, Bhattacharya PM, Rakesh S, Kumar R, et al. Yield, nitrogen-use efficiency, and distribution of nitrate nitrogen in the soil profile as influenced by irrigation and fertilizer nitrogen levels under zero-till wheat in the eastern Indo-Gangetic plains of India. Frontiers in Environmental Science. 2022;10:970017. doi:10.3389/fenvs.2022.970017.
15. Srilatha P, Yousuf F, Methre R, Vishnukiran T, Agarwal S, Poli Y, et al. Physical interaction of RTBV ORFI with D1 protein of Oryza sativa and Fe/Zn homeostasis play a key role in symptoms development during rice Tungro disease to facilitate the insect mediated virus transmission. Virology. 2019;526:117124.
16. Tuti MD, Rapolu MK, Sreedevi B, Bandumula N, Kuchi S, Bandeppa S, et al. Sustainable intensification of a rice-maize system through conservation agriculture to enhance system productivity in southern India. Plants. 2022;11:1229. doi:10.3390/plants11091229.
17. Zoureen S, Sufiya, Brajendra, Gobinath R, Mangal Deep Tuti, Munna Lal, et al. Effect of different growing environments on available macro and micro nutrient in soil under various establishment methods. The Journal of Rural and Agricultural Research. 2025;25(2):64-68.