Introduction

Food and nutrition insecurity are significant challenges facing the global population, particularly in regions such as Africa and Asia1. Current global food systems rely heavily on a few major staple crops and cereals, such as rice, wheat, and maize, which have relatively low concentrations of essential mineral elements. With the projected global population reaching 9.8 billion by 2050, there is a pressing need to ensure sustainable agriculture and healthy diets to address the increasing demand for food2. The cultivation of a variety of crops, particularly minor and regionally significant ones like small millets, plays a vital role in enhancing dietary diversity and securing food and nutrition. Focusing predominantly on major crops has restricted research and development opportunities, underscoring the need to investigate the potential of small millets in boosting food and nutritional security3. Millets are cultivated mainly in the low-fertility soils of India for food and fodder. In China, minor millets are cultivated over 4.58 lakh hectares, and the production of small millets is 3.70 lakh tonnes, with an average productivity of 809 kg ha−1. The important small millet cultivation methods are Madhya Pradesh, Chhattisgarh, Uttarakhand, Karnataka, Maharashtra and Tamil Nadu4. Kodo millet (Paspalum scrobiculatum L.) is an important nutri cereal crops, which is cultivated mainly in India. The continuous intake of Kodo millet prevents cardiovascular diseases and reduces blood pressure and high cholesterol5. Kodo millet is grown on marginal lands and produces high grain yields even under limited water. Currently, it is cultivated only in India on a limited acreage (0.20 million ha)3,6; however, it has great potential and could be introduced to other semiarid regions in the world. The decline in the production of minor millets in India, such as kodo millet, highlights the urgency for advancements in crop enhancement techniques and production methods to increase yields. Farmers can increase both the yield and the nutritional value of kodo millet through the adoption of efficient nutrient management strategies, thereby supporting food security and increasing standards of living4.

Soil testing is important for ensuring balanced fertilizer application and helps farmers use fertilizers according to crop needs. Many algorithms and methods, which are widely used throughout the world, have been developed for use in nutrient and crop management7. Examples include crop growth models8 and site-specific nutrient management9. However, site-specific critical values of soil nutrient-supply capacity and fertilizer use efficiency should be considered when making fertilizer recommendations. Among the various approaches of fertilizer recommendation, soil test crop response (STCR)-targeted yield fertilizer application is an approach that considers the crop needs and nutrients present in the soil10. The soil test crop response fertilizer recommendation, which is based on soil test values and targeted yields, has been recognized as crucial for not only achieving the targeted yield of crops but also enhancing the soil fertility status. STCR-based fertilizer recommendations are valid and apt against the debated approaches, namely, ‘fertilizing the soil’ versus ‘fertilizing the crop’. Yield targeting-based fertilizer recommendation is a unique cultivation package, as it not only indicates soil test-based fertilizer recommendation but also considers the level of yield that a farmer is expecting/targeting11. Kodo millet is a drought-resistant crop that thrives in tropical and sub-tropical climates. The study region experiences a tropical savanna climate with distinct wet and dry seasons. The average annual temperature ranges from 24 to 28 °C, which is suitable for kodo millet cultivation. Hence, the present study was conducted on Alfisols for kodo millet under rainfed conditions to.

  1. (a)

    Monitor kodo millet yield and nutrient uptake under various soil fertility conditions.

  2. (b)

    Develop fertilizer prescription equations.

  3. (c)

    Test/validate equations by comparing them with other approaches.

Materials and methods

Site characteristics

The study area is characterized by a dry tropical savanna climate, with hot summers and cool winters. Three consecutive field trials, viz., a fertility gradient experiment in 2020, a test crop experiment in 2021 and a validation trial in 2022, were conducted at Zonal Agricultural Research Station, University of Agricultural Sciences, Bangalore, Karnataka, India. The research station is located at a latitude of 13° 04′ 55.2′′ N and a longitude of 77° 34′ 10.0′′ E, at an elevation of 930 m above sea level. The annual precipitation level recorded at the site was 1180.0 mm in 2020, increasing to 1328.40 mm in 2021 and further to 1556.80 mm in 2022. Over the three years, the maximum temperatures varied, ranging from 29.07 to 18.28 °C in 2020, 28.89 to 18.13 °C in 2021, and 28.41 to 17.78 °C in 2022, as depicted in Fig. 1.

Fig. 1
figure 1

Variation in monthly rainfall and maximum and minimum temperatures during the field experiments from 2020–2022 (RF: rainfall; T max: maximum temperature; T min: minimum temperature).

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The well-drained red soil at the research site belongs to the taxonomically defined large group known as Typic Kandic Paleustalfs, which is a member of the fine mixed Isohyperthermic family. Prior to the start of the experiment, soil samples were taken randomly from the top 15 cm of the experimental field. These samples were then air-dried in the shade, processed, and analyzed for their chemical properties. The analysis revealed that the soil pH was 5.93, with an electrical conductivity of 0.21 dS m−1. The organic carbon content was found to be 4.95 g kg−1, while the levels of available nitrogen, phosphorus, and potassium were 220.17 kg ha−1, 76.19 kg ha−1, and 149.36 kg ha−1, respectively.

Fertility gradient experiment with fodder maize

In 2020, an experiment was conducted to reset the soil fertility levels and establish a controlled fertility gradient in relation to NPK availability before the test crop experiment. Three uniform plots were established, each receiving different amounts of nitrogen (N), phosphorus (P), and potassium (K) fertilizers: 0–0–0 kg NPK ha−1, 150–135–200 kg NPK ha−1, and 300–270–400 kg NPK ha−1 respectively. Details on the fertilizer quantities and the experimental design can be found in Supplementary Fig. 1. The standard dose of nitrogen fertilizers was fixed on the basis of the recommended dose of fertilizers for fodder maize (150 kg ha−1). The phosphorus and potassium doses were fixed on the basis of the P- and K-fixing capacity (135 and 200 kg ha−1, respectively) of the soil. To equalize and artificially induce fertility levels, the fodder maize variety ‘African tall’ was cultivated. Following a 60-day growth period, the crop was harvested, and surface soil samples (0–15 cm depth) were collected from each plot. These samples were then air-dried, finely ground, and analyzed to measure available nitrogen via the alkaline KMnO4 method12, phosphorus through Bray’s extractant and the ascorbic acid method13, and potassium via ammonium acetate extraction and flame photometry14 to evaluate the progression of the fertility gradient.

Test crop experiment with Kodo millet

During the Kharif season of 2021, a test crop experiment was carried out using kodo millet after confirming the establishment of the fertility gradient to assess the impact of graded doses of FYM and NPK fertilizer combinations on crop yield. Each fertility strip was divided into three blocks of different levels of FYM: none (F0), the recommended dose (F1), and twice the recommended dose (F2). These blocks were further subdivided into eight plots, where seven unique nitrogen (N), phosphorus (P), and potassium (K) fertilizer combinations, along with one control plot, were tested. This resulted in a total of 24 plots per fertility strip, resulting in a total of 72 plots across the entire field. The fertilizer treatments were administered in four varying quantities for N (0, 10, 20, and 30 kg ha−1), P2O5 (0, 10, 20, and 30 kg ha−1), and K2O (0, 2.5, 5, and 7.5 kg ha−1), alongside three levels of FYM (0, 6.25, and 12.50 t ha−1). From these, 21 different NPK combinations were selected by incorporating seven treatment combinations and one control for each FYM level. These combinations were then randomly assigned across the remaining two fertility strips and within the FYM blocks. The experimental design and nutrient levels are depicted in Supplementary Fig. 2. Subsequent to the layout, soil samples were collected from the surface (0–15 cm depth) to ascertain the available N, P, and K contents in each plot before sowing, following the standard soil analysis procedure mentioned in the gradient experiment. Nitrogen was sourced from urea, phosphorus from single super phosphate, and potassium from muriate of potash. FYM was applied two weeks before sowing, and half of the nitrogen and the entire recommended amounts of phosphorus and potassium were used as basal fertilizers. Standard cultivation practices were followed for growing the crop. The remaining nitrogen was distributed evenly across the two applications at 30 and 60 days after sowing. Upon maturity, the crop was harvested, and the yield from each plot was calculated and expressed in kg per hectare. The plant samples from each plot were initially air-dried, followed by oven drying at 65 °C. These samples were then ground via a Willey mill. The nitrogen content was quantified via the micro Kjeldahl technique15. Following Jackson’s 1973 protocol, a diacid solution was prepared with nitric and perchloric acid mixture at a ratio of 9:4. Predigesting involved treating the sample with 10 mL of nitric acid per gram. This solution was then utilized to quantify the phosphorus and potassium levels in the samples. Phosphorus was quantified via the vanadomolybdate method for a yellow phosphoric acid color reaction16, whereas potassium levels were determined via flame photometry16.

$$Nutrient~\;upatke~\left( {{\text{kg~h}}{{\text{a}}^{-1}}} \right)=\frac{{\left[ {Nutrient\;~content~\left( \% \right)~ \times ~Dry~\;weight~\left( {{\text{kg~h}}{{\text{a}}^{{\text{--1}}}}} \right)} \right]}}{{100}}$$

From the data on the soil test values, crop dry matter yield and nutrient uptake, basic parameters such as nutrient requirements (NR), and the contributions of nutrients from soil (CS), fertilizer (CF) and organic manure (C-OM) were calculated via the following formulae10.

$${\text{NR }}\left( {{\text{kg k}}{{\text{g}}^{ - {\text{1}}}}} \right){\text{ }}=\frac{{{\text{Nutrient uptake }}\left( {{\text{NPK}}} \right){\text{ by grain }}+{\text{ straw }}\left( {{\text{kg h}}{{\text{a}}^{ - {\text{1}}}}} \right)}}{{{\text{Grain yield or any economic produce }}\left( {{\text{kg h}}{{\text{a}}^{ - {\text{1}}}}} \right)}}$$
$$\% {\text{ CS}}~~=\frac{{{\text{Nutrient uptake }}\left( {{\text{kg h}}{{\text{a}}^{ - {\text{1}}}}} \right){\text{ by grain }}+{\text{ straw in control plot}}}}{{{\text{Soil test values }}\left( {{\text{Av}}.{\text{ NPK}}} \right){\text{ in control plot }}\left( {{\text{kg h}}{{\text{a}}^{ - {\text{1}}}}} \right)}} \times 100$$
$$\% {\text{ CF}}~~=\frac{{({\text{Nutrient uptake bygrain }}+{\text{ straw in treated plot}}) - {\text{Soil test values in treated plot}} \times \frac{{\% {\text{ Contribution}}~~~\left( {{\text{NPK}}} \right){\text{ from soil}}}}{{100}}}}{{{\text{Fertilizer nutrient dose applied in treated plot }}\left( {{\text{kg h}}{{\text{a}}^{ - {\text{1}}}}} \right)}} \times 100$$
$$\% {\text{ C-OM}}~~~=\frac{{{\text{Total uptake of NPK in organic plot }}\left( {{\text{kg h}}{{\text{a}}^{ - {\text{1}}}}} \right) - {\text{Soil test values in}}~~{\text{OM plot}}~ \times \frac{{\% {\text{ Contribution }}\left( {{\text{NPK}}} \right){\text{ from soil}}}}{{{\text{1}}00}}~}}{{{\text{Quantity of OM added in organic plot }}\left( {{\text{kg h}}{{\text{a}}^{ - {\text{1}}}}} \right)}} \times 100$$

By using basic parameters, the fertilizer nutrient requirements for the targeted productivity of kodo millet were calculated as follows:

  1. (i)

    Fertilizer N, P and K alone through inorganics (without FYM).

$$FN=\left( {\frac{{NR}}{{CF}} \times 100 \times T} \right) - \left( {\frac{{CS}}{{CF}} \times SN} \right)$$
$$FP=\left( {\frac{{NR}}{{CF}} \times 100 \times T} \right) - \left( {\frac{{CS}}{{CF}} \times SP} \right)$$
$$FK=\left( {\frac{{NR}}{{CF}} \times 100 \times T} \right) - \left( {\frac{{CS}}{{CF}} \times SK} \right)$$
  1. (ii)

    Fertilizer N, P and K along with FYM for integrated plantrient supply (with FYM).

$$FN=\left( {\frac{{NR}}{{CF}} \times 100 \times T} \right) - \left( {\frac{{CS}}{{CF}} \times SN} \right) - \left( {\frac{{C - OM}}{{CF}} \times OM} \right)$$
$$FP=\left( {\frac{{NR}}{{CF}} \times 100 \times T} \right) - \left( {\frac{{CS}}{{CF}} \times SP} \right) - \left( {\frac{{C - OM}}{{CF}} \times OM} \right)$$
$$FK=\left( {\frac{{NR}}{{CF}} \times 100 \times T} \right) - \left( {\frac{{CS}}{{CF}} \times SK} \right) - \left( {\frac{{C - OM}}{{CF}} \times OM} \right)$$

where FN, fertilizer N (kg ha−1); FP, fertilizer P (kg ha−1); FK, fertilizer K (kg ha−1); SN, soil test value for available N (kg ha−1); SP, soil test value for available P (kg ha−1); SK, soil test value for available K (kg ha−1); C-OM, contribution of nutrients from the FYM; and OM, quantity of the FYM applied (t ha−1).

Validation trial/follow-up trial

During Kharif 2022, a field study was carried out on kodo millet to assess the effectiveness of soil test-based crop response-based equations aimed at achieving targeted yields. These equations were measured against other fertilizer recommendation methods, such as the soil fertility rating approach and the General Recommended Dose (GRD), using a randomized block design with three repetitions. The GRD is derived from extensive trials across multiple locations in which various levels of nitrogen, phosphorus, and potassium fertilizers are tested to determine the most cost-effective dosage for the maximum yield of a specific crop. The soil fertility rating method aligns medium fertility levels with the GRD. In situations of extremely low or very high soil fertility, the fertilizer amount is adjusted up or down by 25–30% from the GRD17. The treatments included T1—STCR NPK TY 17 kg ha–1, T2—STCR NPK + FYM TY 17 kg ha–1, T3—STCR NPK TY 15 kg ha–1, T4—STCR NPK + FYM TY 15 kg ha–1, T5—general recommended dose (GRD), T6—soil fertility rating (SFR) and T7—absolute control. A composite soil sample was collected at 0–20 cm depth from each plot after the plan was laid out and before the start of the experiment. On the basis of the soil test values, NPK fertilizer nutrients were applied for specific yield targets in the STCR and SFR approaches. The crop was grown according to standard agronomic practices and harvested at the full maturity stage, and the grain and straw yields were computed from the net plot and expressed in kg ha−1. Grain and straw samples were collected from each treatment and analyzed for total NPK content via the same procedure, and uptake was determined as described in the test crop experiment. The response yard stick (RYS), percent deviation and value cost ratio (VCR) were computed via the standard formulae shown below10.

$${\text{RYS~=}}\frac{{{\text{Yield~response~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right)}}{{{\text{Total~nutrient~applied~~~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right)}}$$
$${\text{Percent~deviation~=}}\frac{{\left[ {{\text{Actual~yield~obtained~~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right) - {\text{Targeted~yield~~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right)} \right]}}{{{\text{~~Targeted~yield~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right)}}~~ \times 100$$
$${\text{VCR~ = }}\frac{{\left[ {{\text{Yield~in~treated~plot~}}\left( {{\rm kg}{\text{~ha}}^{{ - {\text{1}}}} } \right){\text{~}} - {\text{Yield~in~control~plot~}}\left( {{\rm kg}{\text{~ha}}^{{ - {\text{1}}}} } \right)} \right]}}{{{\text{~Cost~of~fertilizers~and~FYM~applied~to~treated~plot}}}} \times {\text{Cost }}{\rm kg}^{{ - 1{\text{~}}}} {\text{of~grain}}$$

The nutrient (N/P/K) use efficiency parameters, viz., apparent recovery efficiency (RE), agronomic nutrient use efficiency (AE), and reciprocal internal utilization efficacy (RIUE), were calculated via the following formulae18 to determine the crop response to the added fertilizers and to compare the use efficiency of added nutrients and economics under different fertilizer recommendation approaches.

$${\text{RE}}\left( {{\rm kg}~{\rm k}{{\rm g}^{ - 1}}} \right){\text{~=~}}\frac{{\left[ {{\text{Total~nutrient~uptake~in~treated~plot~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right) - {\text{Total~nutrient~uptake~in~control~plot~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right)} \right]}}{{{\text{Fertilizer~nutrient~~applied~~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right)}}$$
$${\text{AE}}\left( {{\rm kg}~{\rm k}{{\rm g}^{ - 1}}} \right){\text{~=~}}\frac{{\left[ {{\text{Grain~~yield~in~treated~plot~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right) - {\text{Grain~~~yield~in~control~plot~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right)} \right]}}{{{\text{Fertilizer~nutrient~~applied~~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right)}}$$
$${\text{RIUE~=}}\frac{{{\text{Total~uptake~~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right)}}{{{\text{Grain~~yield~}}\left( {{\text{kg~h}}{{\text{a}}^{ - {\text{1}}}}} \right)}}$$

Statistical analysis

The basic data from the test-crop experiments were used for developing fertilizer prescription equations. The fertility gradient created is displayed in a box-and-whisker plot, and test crop experimental data were summarized via descriptive statistics with SPSS 16.0 software. A standard regression procedure was used to relate the soil test values and fertilizer dose with the crop yield response19. The data from the validation trial was subjected to analysis of variance (ANOVA) with a randomized block design20. The online statistical program OPSTAT was used for the data analysis (validation trial), and least significance difference (LSD) values at α = 0.05 were used to compare treatment means. Pearson correlation coefficient (r) values were calculated via Microsoft Excel 2016.

Results

Fertility gradient experiment with fodder maize

A gradient experiment was conducted to minimize the factors related to soil and other management practices that could affect crop yield. The postharvest soil analysis revealed variations in soil fertility across the different strips, as shown in Fig. 2. The average concentrations of the soil available N, P, and K were 216.92, 169.33, and 164.49 kg ha–1 respectively, in strip I; 234.87, 189.44, and 195.88 kg ha–1 respectively, in strip II; and 255.66, 222.65, and 266.74 kg ha–1, respectively, in strip III. The lowest nutrient distribution centers for N, P, and K were found in strip I, with values of 211.85, 162.90, and 161.18 kg ha–1, respectively, in contrast to strips II and III. In strips I and III, the upper tails of the whisker and half-box plots are longer than the lower tails are, indicating positive skewness for the N and K distributions, whereas P shows negative skewness. Conversely, in strip II, the N and P distributions are positively skewed, and K shows a negative skewness, as depicted in Fig. 2.

Fig. 2
figure 2

Box and whisker plots of (a) available nitrogen, (b) available phosphorus, and (c) available potassium (0–15 cm) after the soil fertility gradient experiment.

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Test crop experiment with kodo millet

The results in Table 1 show the range and average values of grain and straw yield and nutrient uptake by kodo millet in relation to the three fertility gradients. The overall grain yields were found to be in the range of 467–2348 kg ha–1 with means of 1477 kg ha–1, 1980 kg ha–1, and 1966 kg ha–1, in strips I, II and III, respectively. A similar trend was observed with respect to straw yield. There was an increasing trend in yield and nutrient uptake from strips I (low-fertility strips) to III (high-fertility strips). The standard deviation (SD) and coefficient of variation (CV%) were greater in strip I and declined under strips II and III, indicating that yield and nutrient uptake variations were greater in strip I due to soil-test variations. The crop responses to N, P, K, and FYM fertilizers are depicted in Fig. 3, which shows a significant (P < 0.01) crop response to NPK fertilizer application. The relationships between kodo millet yield and different nutrients were derived via regression analysis to evaluate the yield variations due to the application of different fertilizer nutrients, the results of which are presented in Table 2. The effect of nitrogen fertilizer is significant, explaining 67% of yield variation, while a quadratic model indicates an even better fit at 70%, suggesting diminishing or increasing returns after a certain point. Phosphorus fertilizer influences yield, explaining 51% of the variation, with a quadratic model increasing the explanation to 53%. Potassium’s effect is somewhat lower, explaining 45% of the yield variation, with its quadratic model slightly better at 48%. When combining nutrients, models that include both nitrogen and phosphorus explain 67% of the yield variation, while including potassium as well increases the explanation to 69%. The most comprehensive model includes nitrogen, phosphorus, potassium, and farmyard manure, explaining 70% of the yield variation. This relationship revealed a greater efficiency of N and P fertilizer with FYM application.

Table 1 Descriptive statistics of yield and nutrient uptake of kodo millet in the main experiment.
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Fig. 3
figure 3

Relationships between kodo millet grain yield and (a) fertilizer nitrogen, (b) fertilizer phosphorus and (c) fertilizer potassium doses in the main experiment.

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Table 2 Selected regression model to account for yield variation in kodo millet.
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A close association was observed between grain yield and total N, P, and K uptake of nutrients. This relationship was used to estimate the nutrient requirements of kodo millet, as shown in Table 3. The nutrient requirement (NR) is defined as the amount of nutrients required to produce a unit amount of yield. The NR can be given by the regression coefficient (b1) of yield (Y) and total nutrient uptake (U):

Table 3 Relationships of grain yield (Y) with total nutrient uptake (U) and basic data for calculating fertilizer doses with NPK alone and NPK + FYM for the targeted yield of kodo millet.
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\({\text{Y}}\,=\,{{\text{b}}_{\text{1}}}{\text{U or U}}\,=\,{\text{1}}/{{\text{b}}_{\text{1}}}*{\text{Y}}\)

The amount of nutrients absorbed by a crop influences biomass production. The amounts of nutrients required to produce one kg of grain yield were 0.049 kg N, 0.0047 kg P, and 0.035 kg K under NPK alone and 0.050 kg N, 0.0046 kg P and 0.037 kg K under the NPK + FYM approach. The yield and nutrient uptake relationships for kodo millet are depicted in Fig. 4, which shows a close association between crop yield and nutrient uptake with an almost linear relationship. A good correlation (p < 0.001) was observed for total nitrogen uptake (r2 = 070), followed by total phosphorus uptake (r2 = 0.53) and total potassium uptake (r2 = 0.48) (Fig. 4).

Fig. 4
figure 4

Linear correlations between grain yield and total uptake of (a) N, (b) P and (c) K in kodo millet during the main experiment.

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The contribution of nutrients from the soil is expressed as the capacity of the crop to extract nutrients from the soil. The percent contributions of NPK from the soil were 15.64, 2.67 and 21.19, respectively. The nutrient contributions from fertilizers under NPK alone and NPK + FYM were 124.35% N, 25.33% P, and 298.84% K and 136.43% N, 33.53% P, and 380.81% K, respectively, toward the uptake by kodo millet. Similarly, the contributions of NPK from the FYM were 0.51, 0.15 and 0.73%, respectively.

On the basis of the basic parameters (viz., nutrient requirements, fertilizer efficiency, soil test, and organic source), the fertilizer adjustment equations were calibrated for kodo millet to achieve a definite yield target. The equations derived were as follows:

STCR-NPK equations alone (Without FYM)

$${\text{FN}}\,=\,{\text{1}}.{\text{37 T}} - \,0.{\text{38SN}}$$
(1)
$${\text{FP}}\,=\,0.{\text{57T}}-0.{\text{15SP}}$$
(2)
$${\text{FK}}\,=\,0.{\text{6}}0{\text{T}} - \,0.{\text{24SK}}$$
(3)

STCR-NPK + FYM equations (With FYM)

$${\text{FN}}\,=\,{\text{1}}.{\text{14 T}} - \,0.{\text{33SN}} - \,0.{\text{67OM}}$$
(4)
$${\text{FP}}\,=\,0.{\text{52T}}-0.{\text{19SP}} - \,0.{\text{62OM}}$$
(5)
$${\text{FK}}\,=\,0.{\text{56T}} - \,0.{\text{23SK}} - \,0.{\text{86OM}}$$
(6)

where FN, FP and FK are the N, P and K fertilizers in kg ha–1, respectively; T is the yield target at t ha–1; SN, SP and SK are the available soil nutrients in kg ha–1; and OM is the amount of organic manure (FYM) added at t ha–1.

The formulated fertilizer equations revealed that nitrogen had the highest nutrient contribution ratio from the soil to the fertilizer at 0.38:1, followed by potassium at 0.24:1, with phosphorus contributing the least at 0.15:1.

Validation/follow-up trial

The validation trial is important because the calibration of results obtained from a research farm must be tested for their validity under farmers’ field conditions. The objectives of this trial are to assess the validity of the main experimental results before they are recommended for adoption by extension agencies, as well as to persuade farmers that the soil test-based fertilizer recommendation is more profitable and efficient than the general recommended dose.

Yield and uptake of kodo millet in the validation trial

The grain and straw yields of kodo millet ranged from 527 kg ha–1 and 805 kg ha–1 under absolute control to 1710 kg ha–1 and 2708 kg ha–1 under the STCR NPK + FYM approach, with a yield of 1700 kg ha–1, as shown in Table 4. Compared with the other treatments, the STCR approach with NPK + FYM, which resulted in a yield of 1700 kg ha–1 significantly outperformed the other treatments, achieving a grain yield of 1710 kg ha–1 and a straw yield of 2708 kg ha–1. This was followed by the STCR NPK alone for a 1700 kg ha–1 yield target, which yielded comparable results of 1670 kg ha–1 for grain and 2681 kg ha–1 for straw, surpassing other methods. Similar trend of results were noticed with respect to nutrient uptake where, Higher nitrogen and potassium uptakes of 69.07 and 92.04 kg ha–1, respectively, were recorded in the treatments with the application of fertilizer and the FYM based on the STCR approach for the targeted yield of 1700 kg ha–1 through NPK + FYM which was significantly greater than the general recommended dose (59.28 and 76.80 kg ha–1, respectively) and the soil fertility rating approach (56.86 and 76.75 kg ha–1, respectively). However, the maximum uptake of phosphorus was recorded with an STCR target of 1700 kg ha–1 through NPK + FYM (28.24 kg ha–1), which was significantly greater than that of the absolute control (14.94 kg ha–1). However, it was found on par with the other STCR treatments (Table 4). The general recommended dose and soil fertility rating approach produced lower grain yields of 1377 kg ha–1 and 1394 kg ha–1, respectively, demonstrating the superiority of the STCR approach. Furthermore, the STCR approach combined with FYM for the target yield of 1700 kg ha–1 increased the grain yield by 24.18% over the GRD and by 22.67% over the soil fertility rating approach.

Table 4 Influences of different nutrient recommendation approaches on yield, % deviation, value cost ratio (VCR) and nutrient uptake of kodo millet in a validation trial.
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Percent deviation and value cost ratio

The percent deviation indicates the yield variation from the target fixed or genetic potentiality of the crop (Table 4). The percent deviation in the present study from the fixed target was found to be positive for the STCR target of 1700 kg ha–1 through the NPK + FYM (0.57%) approach, where the yield obtained was higher than that of the fixed target. However, the deviations under the STCR approach at both targets were within ± 10%, confirming the validity of the targeted yield equations. A higher value cost ratio (VCR) of 15.03 was recorded where fertilizer nutrients were applied through the STCR NPK alone for a yield target of 1700 kg ha–1 followed by 14.96 in the STCR target of 1500 kg ha–1 through NPK alone (Table 4). The lower value cost ratio of 2.78 was recorded with the application of nutrients on the basis of the general recommended dose. However, the VCR was lower in the STCR NPK + FYM treatment than in the NPK alone treatment.

Nutrient use efficiency

The nutrient use efficiency (NUE) as influenced by various methods of nutrient management is depicted in Table 5. In general, the agronomic nutrient use efficiency was greater in the STCR treatments than in the other treatments. The highest agronomic efficiency of nitrogen (AEN) occurred with the STCR target of 1500 kg ha–1 through NPK + FYM (0.56 kg kg–1). The lowest AEN occurred in general recommended dose (0.21 kg kg–1). The AEP and AEK were greater under the STCR-based nutrient management practices than under the general recommended dose of fertilizer and the soil fertility rating approach. Similarly, the recovery efficiency of nitrogen (REN), phosphorus (REP) and potassium (REK) increased in the STCR treatments compared with the application of fertilizer nutrients on the basis of the soil fertility rating approach and general recommended dose. Higher REN and REP values were recorded for the STCR lower yield target of 1500 kg ha–1 through NPK + FYM (11.71 kg kg–1 and 16.78 kg kg–1 respectively), and higher REK values were reported with an STCR higher target of 1700 kg ha–1 through NPK alone (37.52 kg kg–1). In general, the reciprocal internal use efficiency (RIUE) followed the order RIUEK > RIUEN > RIUEP.

Table 5 Nutrient use efficiency of kodo millet as influenced by different approaches to fertilizer recommendation in the validation trial.
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Discussion

To establish a range of soil fertility levels for the main experiment, a fertility gradient treatment was conducted. Maize, known for its intensive nutrient extraction, leaves behind more stable nutrient concentrations in the soil, thereby forming a fertility gradient11. Despite this, some nutrients persist in the soil, acting as long-lasting sources of fertility. This phenomenon led to the creation of a fertility gradient within the experimental setup. The elevated soil test values (STVs) of NPK observed in strip III could be attributed to the application of twice the amount of NPK fertilizers in comparison to the control, along with the utilization of single-fold doses21. As a result of graded fertilization, strip III presented the highest STVs of P and K21. In addition, P fixation in soil, owing to its immobile nature, might be the reason for its higher STVs22. The variability of N (6.90–11.20%) was considerably lower than that of P (8.26–17.02%) and K (10.95–18.18%) contents, mainly because N was lost through denitrification, leaching, and ammonia volatilization (particularly in rice crops)23, and natural factors such as heavy irrigation and excessive rainfall above evapotranspiration wash away nitrate fertilizer beyond the root zone in cultivated soil in contrast to pasture conditions24.

The gradient in soil available nutrients and variable doses of fertilizers markedly influence the nutrients acquired by crops. The high-fertility strip (strip III) presented the highest grain yield of kodo millet, which was attributed to the increased application of nutrients. This was due to the combined effect of the variations in fertility established after the gradient experiment and the various fertilizer amounts, which increased the fertility level25. In all the strips (I/II/III), the plots that were treated with NPK fertilizer and farmyard manure (FYM) outperformed those that received only NPK fertilizer in terms of yield. The superior yield from the combined treatment is likely due to FYM nutrient breakdown and release, along with indirect rhizosphere effects that might have increased microbial activity, thereby improving nutrient accessibility26. Furthermore, strip III also presented the highest total uptake of NPK, followed by strips II and I. The greater nutrient availability, which facilitated crop absorption, was likely due to the higher fertilizer application rates used on the more fertile strips27. The optimal level of nitrogen application, coupled with its increased uptake and accumulation, led to an increase in the production and assimilation of nitrogen, phosphorus, and potassium (NPK)28. The proper application of nitrogen (N) and its subsequent enhanced absorption led to increased yields and nutrient uptake, including nitrogen, phosphorus, and potassium (NPK)29. Strip III likely had an ample supply of N fertilizer, which facilitated positive nutrient uptake. The most significant phosphorus absorption in strip III could be due to improved root growth, which was possibly encouraged by the naturally higher phosphorus levels in that area28,30. Furthermore, a greater application of N seems to have promoted both vegetative growth and root exploration, necessitating additional phosphorus and potassium for the crops and thus increasing phosphorus absorption31 and a comparable pattern for potassium uptake, which may be linked to the increased use of potassium fertilizers32. The yield of kodo millet is largely influenced by nitrogen. The greater crop response could be attributed to the greater N requirement, and owing to the mobile nature of this element, it is accessible to the plant in the root system sorption zone10. Applying nitrogen in graded doses can enhance plant nutrient uptake, improve growth, and increase yield. Studies have shown that split application of nitrogen can optimize plant nutrient uptake and improve post-harvest soil nutrient availability46. The P and K fertilizers were next used to explain the variation in yield. P ions react very quickly with soil constituents to form insoluble compounds and are thus rendered immobile in the soil. Furthermore, the P nutrient requirement was lower than the N requirement. Phosphorus is essential for plant growth and development, but its availability in the soil can be limited due to fixation. Applying phosphorus in graded doses can help overcome this limitation by increasing the availability of phosphorus to plants. This can lead to improved plant height, root development, and overall yield47. The curvilinear nature of the kodo millet yield response to P application could therefore be attributed to these factors.

Soil test calibration was performed to obtain specific yield objectives for kodo millet via a targeted yield model. This approach aims to optimize fertilizer quantities through the integration of presowing soil test values, total nutrient uptake and the doses of N, P, and K fertilizers and organic manure (in the form of farmyard manure (FYM)) that are applied. The relationship between nutrient requirement, nutrient uptake, and crop yield is critical for maximizing agricultural productivity. Nutrient requirement refers to the specific amount of nutrients that crops need to grow optimally. These requirements vary based on the crop type, soil conditions, and growth stages. Nutrient uptake is the process by which plants absorb these essential nutrients from the soil through their roots. Effective nutrient uptake ensures that plants have the necessary building blocks for growth, development, and reproduction. The fundamental data essential for formulating the targeted yield equations included calculations for nutrient requirements (NR), the contribution of nutrients derived from the soil (CS), those from fertilizer (CF) and those from organic manure (COM). These computations were performed for nitrogen, phosphorus and potassium alone and NPK + FYM on the basis of data gathered from the main experiment. The NRs for N and K were 10.42 and 7.45 times greater than that for P, respectively, under NPK alone and 10.86 and 8.04 times greater than that for P, respectively, under the NPK + FYM approach. However, the requirement of N and K was higher under NPK + FYM approach due to higher yield associated with NPK + FYM application compare to the application of sole NPK fertilizers26.The nutrient requirement follows the trend of N > K > P, as it is mainly due to the interaction of the genotype of the variety with its surrounding environment in terms of nutrient availability, soil moisture and soil temperature33. The contribution of nutrients from soil follows the trend K > N > P, which might be due to the preferential absorption nature of crops24. The order of the percentage contribution of fertilizer nutrients to crop uptake to yield under the NPK alone and NPK + FYM approaches was K > N > P. The nutrient contribution originating from fertilizers exceeded that of the soil, mostly due to the faster and increased availability of nutrients in an inorganic form from the fertilizers. Notably, the NPK + FYM approach resulted in greater nutrient contributions from fertilizers than did NPK alone application. The contribution from organic manure was calculated with the help of data from the FYM-treated plots and followed the order N > K > P34. The variation in nutrient supply from indigenous sources across all plots, along with the effects of their interactions, could explain why the incorporation of CS, COM, and CF with NPK alone and of CF with NPK + FYM did not result in uniform percentage increases35. Fertilizer prescription equations are practically applied in real-world scenarios to optimize crop yields and ensure efficient use of fertilizers. These equations, often based on soil test crop response (STCR) methods, help determine the precise amount of nutrients (N, P, K) needed for specific crops and soil conditions. By tailoring fertilizer applications to the soil’s nutrient status and crop requirements, farmers can achieve targeted yields while minimizing waste and environmental impact. The performance of the formulated fertilizer prescription equations aimed at specific yield targets was evaluated through a comparative analysis with other established fertilizer recommendation practices, viz., the soil fertility rating approach and the general recommended dose. The increased production of kodo millet under the STCR target of 1700 kg ha–1 through NPK + FYM could be attributed to the equitable distribution of nutrients. This approach is grounded in soil testing and considers various factors, such as nutrient depletion by previous crops, baseline soil fertility, the efficacy of the soil’s inherent nutrients, the contributions from manure, and the nutrients introduced via fertilizers36. Moreover, the precision of the targeted yield methods likely meets the nutritional requirements of crops more effectively. These elements are presumed to ensure the timely provision of optimal nutrients, facilitating improved absorption, which in turn leads to increases in both dry matter accumulation and overall yield37. This observation is consistent with findings of the chili crop38. Inadequate fertilization techniques that disregard the nutrient requirements of the crop and the nutrient contributions from the soil, fertilizers, and farmyard manure (FYM) may be the cause of the reduced productivity under the soil fertility rating approach and general recommended dose. The percent deviation of yield was within ± 10% variation, confirming the validity of the developed equations33. The yield achieved via the NPK + FYM approach, with a yield target of 1700 kg ha–1, was relatively greater than that achieved via the NPK + FYM approach because of the higher yields achieved via the STCR NPK + FYM approach. The higher value cost ratio (VCR) under the STCR NPK alone approach for the targeted yields of 1700 and 1500 kg ha–1 could be mainly due to the application of the required dose of NPK fertilizer without FYM associated with higher yields39. Even though higher yields were recorded in the STCR NPK + FYM approach, the VCR was very low, mainly because of the high cost of FYM40.

The enhanced uptake of nitrogen, phosphorus, and potassium, observed in the STCR NPK + FYM approach aimed at achieving a yield of 1700 kg per hectare, can likely be attributed to the increased yield associated with increased NPK fertilizer application. This higher dosage may have improved the accessibility of these nutrients near the plant roots, potentially leading to greater nutrient uptake41. Similarly, the balanced application of nutrients considering crop requirements and contributions from soil, fertilizer, and FYM-based STCR treatments may be the reason for the greater yield with the STCR approach than with the general recommended dose and soil fertility rating approach25. Combining FYM with NPK fertilizers enhances nutrient availability and uptake by providing a balanced and continuous nutrient supply, improving soil structure and water retention, boosting microbial activity, and preventing nutrient leaching. This synergy between organic and inorganic sources leads to healthier soil and more robust plant growth. This might also be due to the balanced use of various plant nutrient sources which results in proper absorption, translocation and assimilation of nutrients, ultimately increasing the dry matter accumulation and nutrient contents of the crop42.

The increased yield with lower rates of fertilizer application in accordance with crop requirements may be the cause of the higher use efficiency of nutrients in the STCR approach with lower targets. An AEN of 6.8–34.243 and an AEK of 28.4–55.3 in rice crops have been reported44. The relatively higher AE with the STCR-based application of fertilizers than with the general recommended dose might be due to the balanced supply and efficient utilization of nutrients due to the synergistic effects of fertilizers and the applied FYM45. The RIUE for nitrogen and phosphorus was markedly greater with the STCR-based nutrient management practices than with the other approaches of fertilizer recommendation, and the RIUE for potassium was greater than the general recommended dose because of the lower potassium recommendation33.

Conclusion

The fertilizer prescription equations developed through the STCR approach are promising options for enhancing the yield, nutrient use efficiency and economics of kodo millet under various soil fertility levels of Alfisols in southern India. The results of the present study demonstrated a marked response of kodo millet to the application of NPK fertilizers. The magnitude of the response was greater when NPK fertilizers were integrated with FYM application on the basis of the STCR approach for yield targets of 1700 kg ha–1 and 1500 kg ha–1 relative to the application of NPK fertilizers based on general recommended dose and the soil fertility rating approach. The yield targets were achieved with a variance of ± 10%, confirming the reliability of these equations for determining the appropriate fertilizer amounts for kodo millet. Notably, the value cost ratio was higher for the combined STCR NPK + FYM approach, mainly because of the higher costs of farmyard manure. Therefore, it is advisable to promote STCR-based treatments that emphasize the importance of producing compost or farmyard manure locally. Encouraging farmers to create their own compost or manure can lead to numerous advantages, such as lowering production costs, maintaining soil fertility, and enhancing farmers’ financial outcomes. Further investigations has to be conducted to know the long-term impacts of STCR-based fertilization on soil health through field experiments over multiple growing seasons to monitor changes in soil properties such as organic carbon, microbial activity, nutrient availability, and pH levels. These studies help assess the sustainability of STCR practices and their effects on soil fertility and crop productivity over time.