Replacement of rice-wheat cropping system with alternative diversified systems concerning crop productivity and their impact on soil carbon and nutrient status in soil profile of north-west India

Abstract

The depth-wise depletion of soil organic carbon (OC), macro, micro, and secondary nutrients under the rice-wheat system has resulted in multi-nutrient deficiencies and a decline in crop productivity, emphasizing the replacement of rice-wheat with alternate cropping systems like maize-wheat, cotton-wheat, soybean-wheat, and moongbean-wheat to restore soil fertility and productivity. Long-term investigations (since 2016) revealed that there was a depth-wise decline in pH, EC, OC, and nutrients in soil profile (Udic Ustrochept, Inceptisols) among different cropping systems. The practice of deep-rooted cropping systems (maize-wheat and cotton-wheat) led to maximum OC, soluble calcium, and magnesium, while legume-based systems (especially soybean-wheat) led to maximum available phosphorus (30.86 kg ha⁻¹), boron (0.49 mg kg⁻¹), and DTPA-zinc (1.82 mg kg⁻¹) in soil profile (0–120 cm). This system also led to the maximum surface soil OC, available phosphorus, soluble magnesium, DTPA-zinc, and boron. From the production point of view, soybean-wheat system (115.65 q ha⁻¹) led to higher system grain productivity as compared to rice-wheat system (109.60 q ha⁻¹). Therefore, the practice of alternative cropping systems like soybean-wheat and cotton-wheat helps in the build-up of nutrient status by playing a pivotal role in influencing the surface and depth-wise distribution of organic carbon and nutrients in the soil.

Keywords:

• Inceptisols
• Udic Ustrochept
• cropping systems
• soil organic carbon
• soil properties
• system productivity

1. Introduction

Continuous practice of cereal–cereal, especially the rice-wheat (R-W) system, has led to a decline in productivity and fertility status of soils in north-west India. Rice-wheat system is the major cropping system adopted in the Indo-Gangetic Plains (IGP) of South Asia occupying an area of 13.5 M ha (Sandeep at al., 2020, Bhatt et al., 2016). The major challenge faced by researchers in the present scenario of agriculture dominated by this system is to ensure optimum functioning of the ecosystem with improved soil attributes because the sustainability of the exhaustive R-W system has declined due to depletion in levels of soil organic carbon (SOC), nitrogen (N), phosphorus (P), and potassium (K) macronutrients (Verma et al., 2017) and micronutrients like zinc (Zn), iron (Fe), manganese (Mn), copper (Cu), and boron (B). In India, 44–49, 3–8, 12–15, and 5–6% of soils from different states have shown deficiency of Zn, Cu, Fe, and Mn (Das et al. 2018, Sharma & Kumar, 2016). Dhaliwal et al. (2020) reported deficiencies of Zn (12.1%), Cu (4.2%), Fe (9.7%), Mn (25.5%), and B (12.1%) in agricultural soils of different regions of Punjab. In several areas with the practice of intensive rice-wheat cropping on light textured soils, Zn deficiency appeared initially and subsequently the deficiencies of Fe, Mn, and other micronutrients (B, Cu, and Mo) were reported (Khanday et al. 2017). Therefore, there is a need to shift towards agricultural systems that can enhance soil productivity and fertility because deficiency of soil macro, micro, and secondary nutrients has created a restriction in increasing the sustainability and productivity of soils.

In order to ameliorate nutrient deficiencies, there is a need to study the distribution, availability, solubility, as well as uptake of nutrients by the crops. A prerequisite to understand the depth-wise variation of soil nutrients is to study the various chemical fractions in which these nutrients are present in the soil and the conditions under which they become available to the plants. The availability of nutrients in the soil is governed by chemical fractions, soil pH, EC, soil organic matter (SOM), management practices, microbial activity, and soil–plant interactions (Agrawal et al., 2016). These factors influence nutrient solubilization processes aided by the presence of organic acids released during the decomposition of organic matter and root activity (Rafique & Tariq, 2016; Rahman & Schoenau, 2021; Rengel, 2015) as well as by the chelating action of the organic ligands (M. Sharma et al., 2014).

The preservation of SOM is crucial to ensure the long-term sustainability of agricultural ecosystems (Fageria, 2012). The adoption of cropping systems that have legume or deep-rooted as a component crop may help in the addition of soil organic matter through leaf litter and root distribution which further improves soil physical, chemical, and biological properties (Pries et al., 2018; Tekin et al., 2017). Long-term cultivation adds organic matter to the soil which significantly influences the SOC and nutrient status of the soil (Moharana et al., 2017). The agro-forestry system also adds organic matter to soil, whereas in the rice-wheat system, an extensive portion of the crop is removed as grain and straw which results in substantial loss of nutrients. Poplar-based agroforestry increased the levels of total Zn, Cu, Fe, and Mn compared to the rice-wheat system as it added micronutrients through leaf litter and root turnover (Dhaliwal, Naresh, Mandal et al., 2019a). The most straightforward approach to improve the status of nutrients and productivity under different cropping systems is to try new combination of crops (cereals + legumes or cereals + deep-rooted crops). There are some cropping systems like cotton-wheat, moongbean-wheat, soybean-wheat, and moongbean-raya that play a pivotal role in the build-up of nutrient status and amelioration of their deficiencies by influencing the depth-wise distribution of nutrients in the soil. With these considerations in view, the present study was carried out to investigate the replacement of rice-wheat cropping system with alternative diversified systems to study their impact on crop productivity, soil carbon, and nutrient status in the soil profile.

2. Materials and methods

2.1. Experimental site

The present long-term investigation has been carried out since 2016 at the Farm Research area of the Department of Soil Science, Punjab Agricultural University, Ludhiana to investigate the replacement of rice-wheat cropping system with alternative diversified systems and their impact on soil carbon and nutrient status in the soil profile. The experimental site is located at 30° 31ˊ N latitude and 75° 78ˊ E longitude. The layout of the experimental site is given in Figure 1. The climate is subtropical with hot, wet summers and dry winters. The annual rainfall falls in the range of 400 to 600 mm, and around 70% of rainfall is received between July and September. The soil belongs to Inceptisols with sandy loam texture. The initial soil properties (in 2016) were 8.28 pH, 0.17 dS m⁻¹ EC, 0.18% OC, 24.40 kg ha⁻¹ available P, 133.00 kg ha⁻¹ available K, 14.84 Cmol (⁺) kg⁻¹ soluble Ca, 12.68 Cmol (⁺) kg⁻¹ soluble Mg, 26.42 mg kg⁻¹ available S. The DTPA-Zn, Fe, Mn, Cu, and Ni were 0.48, 1.40, 4.27, 0.26, and 0.50 mg kg⁻¹, respectively, and available B was 0.48 mg kg⁻¹.

Figure 1. Layout of experimental site.

2.2. Treatment description

The experiment under study comprised six treatments, where a particular cropping system was practiced in a given treatment. All treatments were replicated eight times in a randomized block design in plots of an area of 6.25 m². The six cropping systems followed (2021) were as follows: (1) rice-wheat, (2) maize-wheat, (3) cotton-wheat, (4) moongbean-wheat, (5) soybean-wheat, and (6) moongbean-raya. The details of the experiment are provided in Table 1. All the management practices were as per the recommendations given in the package of practices, PAU, Ludhiana. Nitrogen was applied through urea, phosphorus was applied through diammonium phosphate (in rice, maize, cotton, wheat, and raya) and single super phosphate (in moongbean and soybean), and potassium was applied through muriate of potash. Irrigation and other management practices were done as and when required.

Table 1. Treatment details and management practices of individual crops grown under different cropping systems

Cropping systems	Kharif season					Rabi season
	Varieties	Fertilizer recommendation (kg ha^−1)			FYM (t ha^−1)	Varieties	Fertilizer recommendation (kg ha^−1)			FYM (t ha^−1)
		N	P2O5	K2O			N	P2O5	K2O
Rice-Wheat	Rice PR126	105	-	30	15	Wheat PBW725	125	62.5	-	-
Maize-Wheat	Maize PMH13	125	57.6	30	15	Wheat PBW725	125	62.5	-	-
Cotton-Wheat	Cotton RCH773	105	30	-	-	Wheat PBW725	125	62.5	-	-
Moongbean-Wheat	Moongbean ML2056	12.50	40	-	-	Wheat PBW725	125	62.5	-	-
Soybean-Wheat	Soybean SL958	31.25	80	-	-	Wheat PBW725	125	62.5	-	-
Moongbean-Raya	Moongbean ML2056	12.50	40	-	-	Raya RLC3	100	30	-	-

2.3. Collection of plant samples for calculation of biological yield and system productivity

The grain and straw yields of the individual crops were recorded after harvesting the crops to calculate the biological yield (in q ha⁻¹) of component crops. The equivalent yield of crop was calculated as:

Wheat equivalent yield of rabi crops (q ha⁻¹) = $\frac{GrainYieldofcropxMarketpriceofcrop}{Marketpriceofwheatgrain}$

Rice equivalent yield of kharif crops (q ha⁻¹) = $\frac{GrainYieldofcropxMarketpriceofcrop}{Marketpriceofricegrain}$

The equivalent yield of component crops was used to calculate system grain productivity by adding wheat equivalent yields of respective kharif and rabi season crops.

2.4. Collection and analysis of soil samples

The soil profile samples were collected after the harvesting of rabi crops (2022) from five different depths (0–15, 15–30, 30–60, 60–90, and 90–120 cm) under all the cropping systems. The soil samples collected in each plot were mixed thoroughly to obtain a representative soil sample. The samples were air dried, grounded in a wooden mortar, and passed through a 2-mm plastic sieve and stored for further analysis. The standard methodology was followed for the laboratory analysis of the processed soil samples. The soil pH and EC were determined from the soil: water suspension (in 1:2 ratio) as described by Jackson (1973) and Richard (1954). The soil OC was determined by the wet combustion method (Walkley & Black, 1934). The available P was determined by the method given by Olsen (1954), and available K was measured by the neutral normal ammonium acetate method given by Merwin and Peech (1950). The soluble Ca and Mg in soil were measured by the versenate method (0.01 N) given by Cheng and Bray (1951), and available S was determined by the turbidimetric method given by Chesnin and Yien (1951). The diethylene triamine pentaacetic acid (DTPA) extractable micronutrients (Zn, Fe, Mn, Cu, and Ni) in the soil were determined by DTPA-method given by Lindsay and Norvell (1978) using atomic absorption spectrophotometer, while available B in soil was determined by the hot-water soluble method given by Berger and Truog (1939).

2.5. Statistical analysis

The data was subjected to statistical analysis by using randomized block design—two-way analysis of variance (ANOVA) in SPSS v 25.0 (SPSS Inc., Chicago, USA). The least significant difference (LSD) at 5% level was used to analyze the depth-wise distribution of various soil parameters under different cropping systems. Duncan’s multiple range test (DMRT) was used to compare treatment means of various cropping systems.

3. Results and discussions

3.1. Impact of cropping systems on depth-wise distribution of soil physico-chemical properties

The soil pH decreased significantly with depth from 8.55 (0–15 cm layer) to 8.34 (90–120 cm layer) and soil EC (mean values) declined from 0.11 to 0.07 dS m⁻¹ down the soil profile (Table 2 and Figure 2). There was no significant variation in soil pH and EC under different cropping systems. Organic matter addition by legume-based cropping system acted as a buffer in the soil and their mineralization and decomposition released free cations, resulting in soil reaction and EC stability (Meena et al., 2020). Under the rice-wheat system, there is an increase in the leaching losses and reduced salt accumulation which leads to a depth-wise decrease in EC values (Sandhu et al., 2020), whereas legume-based cropping systems accumulate more organic matter and nutrients in the plough layer (0–15 cm) through leaf litter and root biomass contribution (Hazra et al., 2014). Dhaliwal et al. (2020) also observed non-significant changes in soil pH under rice-wheat and cotton-wheat system. Similar variations in soil pH and EC under varying cropping systems were reported by Dhumgond et al. (2017), Moharana et al. (2017), and Dhaliwal et al. (2019a).

Figure 2. Surface and depth-wise distribution (up to 120 cm) of soil pH, EC, OC, available P and K, DTPA-Zn, Fe, Mn, Cu, Ni and B, soluble Ca and Mg and available S under different cropping systems.

Within columns of the same parameter, the values followed by the same letter are nonsignificant at p < 0.05 by Duncan’s Multiple Range Test (DMRT).

Table 2. Variation of soil OC and available macronutrients in soil profile (mean values of 0–15, 15–30, 30–60, 60–90, and 90–120 cm) under different cropping systems

	Soil parameters
	pH	Electrical conductivity (EC)	Organic carbon (OC)	Available macronutrients
				Phosphorus (P)	Potassium (K)
		dS m^−1	%	kg ha^−1	kg ha^−1
Cropping systems*
Rice-Wheat	8.43^a	0.09^a	0.27^c	24.11^c	93.52^a
Maize-Wheat	8.46^a	0.08^a	0.39^a	26.62^b	94.84^a
Cotton-Wheat	8.44^a	0.08^a	0.40^a	25.77^bc	96.63^a
Moongbean-Wheat	8.41^a	0.09^a	0.37^a	25.45^bc	97.06^a
Soybean-Wheat	8.43^a	0.08^a	0.33^b	30.86^a	94.62^a
Moongbean-Raya	8.44^a	0.09^a	0.33^b	29.69^a	96.49^a
L.S.D. (p < 0.05)	NS	NS	0.04	2.22	NS
Depth (cm)^#
D1 (0–15 cm)	8.55^a	0.11^a	0.59^a	31.92^a	146.95^a
D2 (15–30 cm)	8.48^b	0.10^b	0.38^b	28.72^b	96.28^b
D3 (30–60 cm)	8.43^c	0.08^c	0.31^c	26.39^c	82.69^c
D4 (60–90 cm)	8.37^d	0.07^d	0.24^d	25.04 ^cd	78.71^c
D5 (90–120 cm)	8.34^d	0.07^d	0.24^d	23.33^d	73.08^d
L.S.D. (p < 0.05)	0.06	0.01	0.04	2.03	5.47

Within columns, the values followed by same letter are nonsignificant at p < 0.05 by Duncan’s Multiple Range Test (DMRT).

*Values represent mean values of soil profile (0–15, 15–30, 30–60, 60–90, and 90–120 cm under a given cropping system.

^#Values represent mean values of six cropping systems at a given depth.

3.2. Impact of cropping systems on depth-wise distribution of soil organic carbon

The soil organic carbon decreased from 0.59 to 0.24 (mean values in %) down the soil profile under different cropping systems, where the maximum levels of SOC were reported under the leguminous crops in the surface soils (0.64% under soybean which was at par with 0.63% under moongbean) whereas the deep-rooted crops gave the maximum levels of SOC in deeper soil layers (0.29% under cotton and 0.28% under maize) as depicted in Table 2 and Figure 2. The decline in OC with soil depth might be attributed to the decline in the soil organic matter down the soil profile (Dhaliwal, Naresh, Mandal et al., 2019a). loHigher surface soil OC under the soybean-wheat system might be attributed to higher litter fall, root biomass contribution, closer spacing, N fixation, and fertilizer application (Prasad et al., 2016). Organic matter addition by legumes raised SOC after long-term integration of organics, crop residues, and biofertilizers in legume-based systems (Dhaliwal, Naresh, Mandal et al., 2019a). The changes in soil microenvironment from root proliferation might have also resulted in increased organic matter content of soils (Venkatesh et al., 2013). Cotton-wheat system led to higher soil organic carbon in the soil profile as compared to the rice-wheat system which might be due to the higher content of total carbon in soils under the cotton-wheat system than rice-wheat system (Singh & Benbi, 2021). Singh et al. (2021) also reported higher SOC under the cotton-wheat system as compared to the rice-wheat system which might be due to the accumulation of phenolic compounds and substances derived from lignin in soils under rice crop that led to the conversion of carbon into recalcitrant forms. Lower SOC under rice-wheat system might be linked to puddled conditions in field that destroy soil structure and expose soil organic matter to oxidation which leads to a decline in soil fertility (Bhatt et al., 2016). Replacement of the rice-wheat system with nutrient-enriching leguminous or deep-rooted crops will serve as a viable alternative to preserve soil fertility by influencing the depth-wise distribution of SOC which will further influence the levels of soil nutrients (Dhaliwal, Sharma, Sharma et al., 2021b).

3.3. Impact of cropping systems on depth-wise distribution of available macronutrients

There was a significant decline in the level of macronutrients with an increase in soil depth, where available P (mean values in kg ha⁻¹) declined from 31.92 to 23.33 and available K (mean values in kg ha⁻¹) declined from 146.95 to 73.08 under different cropping systems (Table 2 and Figure 2). The decline in the levels of available macronutrients down the soil profile might be attributed to higher levels of organic matter, microbial activity in mobilizing soil P, application of various inorganic fertilizers in the surface layers (Rajneesh et al., 2017), greater exposure of clay minerals to weathering agencies at the surface than in subsoils (Khanday et al., 2018; Kunlanit, 2018), more cation exchange capacity of clay minerals, P fixation by clay minerals and sesquioxides, slow weathering and fixation of released K in sub-surface soil (Rosemary et al., 2017).

Available P varied significantly from 24.11 to 30.86 kg ha⁻¹ (mean values) under different cropping systems, whereas there was no significant variation in available K with different cropping systems. The soybean-wheat system resulted in the maximum available P which might be attributed to changes in rhizospheric activity (Moharana et al., 2017), litter fall and root biomass contribution (Venkatesh et al., 2017), N fixation, and release of organic acids during organic matter decomposition under soybean that directly contributed to increasing levels of available P in soils (Meena et al., 2020). Lower soil available macronutrients under the rice-wheat system can be linked to the burning of rice residues that result in nutrient loss which compels the farmers to apply fertilizers at higher rates, thereby increasing the cost of cultivation and decreasing soil fertility and productivity (Bhatt et al., 2016). Banjara et al. (2021) also reported lower levels of available P and K under the rice-wheat system which might be due to higher P and K uptake by rice grains making rice-wheat to be a nutrient-exhaustive system (Shweta & Malik, 2017). Continuous practice of R-W system leads to depletion of available K when rice residue is not returned to the soil because 80% to 85% of absorbed K remains in rice straw (Chauhan et al., 2012). Diversification of the rice-wheat system with alternative cropping systems like soybean-wheat and cotton-wheat will help to retain soil nutrient status by increasing availability of nutrients in soils.

3.4. Impact of cropping systems on depth-wise distribution of micronutrients

Among the different cropping systems, there was a significant depth-wise decline in the concentration of micronutrients from 4.90 (0–15 cm) to 0.18 mg kg⁻¹ (90–120 cm) for Zn, 6.17 (0–15 cm) to 4.25 mg kg⁻¹ (90–120 cm) for Mn, 12.07 (0–15 cm) to 9.54 mg kg⁻¹ (90–120 cm) for Fe, 0.79 (0–15 cm) to 0.25 mg kg⁻¹ (90–120 cm) for Cu, 0.17 (0–15 cm) to 0.08 mg kg⁻¹ (90–120 cm) for Ni and 0.62 (0–15 cm) to 0.19 mg kg⁻¹ (90–120 cm) for B as shown in Table 3 and Figure 2. The maximum levels of DTPA-Fe and Mn (mean values of 11.68 and 5.52 mg kg⁻¹, respectively) were reported under the rice-wheat system, whereas the highest levels of DTPA-Zn and B (with mean values of 1.82 and 0.49 mg kg⁻¹, respectively) were under soybean-wheat system, whereas DTPA-Cu and Ni did not show any significant variation under different cropping systems.

Table 3. Variation of soil micro nutrients in soil profile (mean values of 0–15, 15–30, 30–60, 60–90, and 90–120 cm) under different cropping systems

	Micronutrients (mg kg^−1)
	DTPA-Zn	DTPA-Fe	DTPA-Mn	DTPA-Cu	DTPA-Ni	Available B
Cropping systems*
Rice-Wheat	1.11^c	11.68^a	5.52^a	0.49^a	0.15^a	0.33^e
Maize-Wheat	1.30^abc	10.75^b	5.33^a	0.45^a	0.14^a	0.37^d
Cotton-Wheat	1.29^ab	10.97^ab	5.33^a	0.43^a	0.15^a	0.40^c
Moongbean-Wheat	1.74^ab	10.35^b	4.04^b	0.47^a	0.11^a	0.34^de
Soybean-Wheat	1.82^a	10.43^b	4.36^b	0.42^a	0.11^a	0.49^a
Moongbean-Raya	1.17^c	10.33^b	4.44^b	0.43^a	0.10^a	0.44^b
L.S.D. (p < 0.05)	0.54	0.75	0.87	NS	0.05	0.04
Depth (cm)^#
D1 (0–15 cm)	4.90^a	12.07^a	6.14^a	0.79^a	0.17^a	0.62^a
D2 (15–30 cm)	1.36^b	11.33^ab	4.99^b	0.58^b	0.15^a	0.48^b
D3 (30–60 cm)	0.35^c	10.69^bc	4.52^bc	0.35^c	0.12^b	0.40^c
D4 (60–90 cm)	0.24^c	10.28^c	4.29^c	0.28^d	0.11^b	0.29^d
D5 (90–120 cm)	0.18^c	9.54^c	4.25^c	0.25^d	0.08^c	0.19^e
L.S.D. (p < 0.05)	0.49	0.69	0.79	0.05	0.04	0.03

Within columns, the values followed by same letter are nonsignificant at p < 0.05 by Duncan’s Multiple Range Test (DMRT).

*Values represent mean values of soil profile (0–15, 15–30, 30–60, 60–90, and 90–120 cm under a given cropping system.

^#Values represent mean values of six cropping systems at a given depth.

The depth-wise decline in the levels of DTPA-extractable micronutrients is linked to lower levels of SOC in deeper soil layers (Dhaliwal, Naresh, Mandal et al., 2019a). Variations in soil texture, pH, EC, organic matter, calcium carbonate, and other soil factors like the composition of the parent materials (Narender et al., 2016; Rahman & Schoenau, 2021) and soil-forming processes influence levels of DTPA-micronutrients in soil (Mandal et al., 2018). The behavior of Fe in soil is governed mainly by two factors: redox potential and soil pH because precipitation of Fe minerals is aided by neutral pH, whereas the mobilization occurs under acidic environments (Dhaliwal, Naresh, Walia et al., 2019b). Higher levels of DTPA-Mn in surface soils might be due to the addition of organic matter through leaf litter and the release of organic acids (Cremer & Prietzel, 2017). The variations in DTPA-Cu content can be due to the lower availability of Cu-based minerals and root activity down the soil profile (Rutkowska et al., 2014). The decline in the levels of soil B down the soil profile might be attributed to the binding of B that lowered the availability of B down the soil profile (Rengel, 2015).

Changes in the soil microenvironment under different cropping systems may have caused the release of plant-available micronutrients, resulting in the rise of DTPA-Fe under different crops (Moharana et al., 2017). The chelation of micronutrients with organic ligands reduces their adsorption, fixation, and precipitation in the soil, thereby resulting in release of micronutrients in the soil (Puniya et al., 2019). The chelating effect of organic matter on DTPA-micronutrients and increased recycling of nutrients under the soybean-wheat system due to the action of various microbes involved in organic matter decomposition increases DTPA-Zn and B under legume-based cropping systems as compared to other systems (Dhaliwal, Naresh, Mandal et al., 2019a; Rajneesh et al., 2017). Submerged conditions created under rice crop make Fe and Mn available to the crop by converting it from higher valent forms (Fe ³⁺ and Mn⁴⁺) to lower valent form (Fe ²⁺ and Mn²⁺; Dhaliwal, Naresh, Mandal et al., 2019a; Haque et al., 2016). Dhaliwal et al. (2020) also reported higher surface DTPA-Fe under the rice-wheat system as compared to the cotton-wheat system. The content of DTPA-Cu was observed to be lower under the legume-based system which might be due to the antagonistic effect of organic matter on soil Cu levels, whereas in deeper layers, the content of organic matter decreases so the levels of DTPA-Cu increase under this system (S. Sharma et al., 2019). Fungi and actinomycetes predominate in rice-cultivated soils and help in the degradation of stable organic matter and the release of higher amount of accessible Cu content (Meena et al., 2020). Continuous practice of the rice-wheat cropping system has resulted in micronutrient deficiencies due to a reduction in the recycling of crop residues, excessive removal of nutrients from soils, and depletion of organic matter status. Rice crop is highly nutrient exhaustive and extracts higher quantities of micronutrients from soil than wheat crop, thus creating an imbalance of micronutrients in soil and decreasing the sustainability of rice-wheat cropping system (Nawaz et al., 2019).

3.5. Impact of cropping systems on depth-wise distribution of secondary nutrients

The levels of secondary nutrients declined down the soil profile under different cropping systems, where soluble Ca (mean values in Cmol (⁺) kg⁻¹) declined from 18.42 to 12.33, soluble Mg (mean values in Cmol (⁺) kg⁻¹) declined from 13.21 to 7.79 and available S (mean values in mg kg⁻¹) declined from 30.94 to 11.88 down the soil profile (Table 4 and Figure 2). There was no significant variation in the levels of secondary nutrients with different cropping systems. The available Ca and Mg content varied greatly with depth, with the highest levels in the middle layers of soils, possibly due to calcareous parent material (Wani et al., 2017). The decrease in the S content with depth might be attributed to the effect of various factors in influencing S content like organic matter, pH, clay minerals, hydrous oxides, mineralization, immobilization, practice of different cropping systems, application of sulphur-containing fertilizers like single superphosphate and varying parent material. Higher levels of soil sulphur were obtained under the legume-based and deep-rooted crops at all soil levels as compared to rice-based cropping systems, although the levels of soil sulphur were statistically at par with each other under different cropping systems. Also, there is an increased addition of sulphur through pulse residue, making pulse-based rotation better than cereal–cereal rotation (Venkatesh et al., 2017).

Table 4. Variation of soil secondary nutrients in soil profile (mean values of 0–15, 15–30, 30–60, 60–90, and 90–120 cm) under different cropping systems

	Soil secondary nutrients
	Soluble calcium	Soluble magnesium	Available sulphur
	(Ca)	(Mg)	(S)
	C mol (^+) kg^−1		mg kg^−1
Cropping systems*
Rice-Wheat	14.45^a	9.80^a	18.46^a
Maize-Wheat	15.80^a	10.95^a	18.88^a
Cotton-Wheat	15.50^a	10.90^a	22.66^a
Moongbean-Wheat	14.45^a	10.40^a	22.68^a
Soybean-Wheat	14.20^a	9.00^a	20.21^a
Moongbean-Raya	14.40^a	9.95^a	19.99^a
L.S.D. (p < 0.05)	NS	NS	NS
Depth (cm)^#
D1 (0–15 cm)	18.42^a	13.21^a	30.94^a
D2 (15–30 cm)	15.83^b	11.21^b	24.36^b
D3 (30–60 cm)	14.42^cd	9.58^c	19.48^c
D4 (60–90 cm)	13.00^d	8.75^cd	15.17^d
D5 (90–120 cm)	12.33^e	7.79^d	11.88^e
L.S.D. (p < 0.05)	1.48	1.55	3.24

Within columns, the values followed by same letter are nonsignificant at p < 0.05 by Duncan’s Multiple Range Test (DMRT).

*Values represent mean values of soil profile (0–15, 15–30, 30–60, 60–90, and 90–120 cm under a given cropping system.

^#Values represent mean values of six cropping systems at a given depth.

3.6. Correlation analysis among various soil parameters

A correlation analysis of the soil parameters is presented in Table 5 and indicates that the soil parameters were significantly correlated with each other. The soil OC positively correlated with available P and K (r = 0.965 and 0.988, respectively), soluble Ca and Mg (r = 0.981 and 0.968, respectively), available S (r = 0.959), DTPA-Zn (r = 0.978), Mn (r = 0.998), and Fe (r = 0.925), suggesting a strong affinity of organic matter with soil micronutrients that led to the chelate formation (Sandhu et al., 2020). Soil availability of K was positively correlated (r = 0.952) with soluble Ca. Soil availability of S was positively correlated with soluble Mg (r = 0.997) and Ca (r = 996). DTPA-Zn was positively correlated with macronutrients (available P (r = 0.911) and K (r = 996)). The correlation of soil micronutrients with PK suggests that the variation in levels of macronutrients will significantly affect the distribution as well as the availability of micronutrients. There were significant correlations among micronutrients—DTPA-Mn and Zn (r = 0.987), DTPA-Zn and Cu (r = 0.936), DTPA-Fe with Mn (r = 0.918), Cu (r = 0.955), Ni (r = 0.996), and B (r = 0.996), suggesting that their availability is interdependent and will be affected by levels of other micronutrients (Dhaliwal, Sharma, Sharma et al., 2021b).

Table 5. Correlation analysis of soil parameters

	pH	EC	OC	Available P	Available K	Soluble Ca	Soluble Mg	Available S	DTPA-Zn	DTPA-Fe	DTPA-Mn	DTPA-Cu	DTPA-Ni	Available B
pH	1	0.975**	0.954*	0.993**	0.912*	0.994**	0.992**	0.999**	0.879*	0.989**	0.941*	0.972**	0.978**	0.995**
EC		1	0.942*	0.970**	0.895*	0.973**	0.976**	0.974**	0.874	0.951*	0.937*	0.988**	0.950*	0.952*
OC			1	0.965**	0.988**	0.981**	0.968**	0.959*	0.978**	0.925*	0.998**	0.975**	0.901*	0.935*
Available P				1	0.940*	0.995**	0.999**	0.998**	0.911*	0.992**	0.960**	0.983**	0.983**	0.990**
Available K					1	0.952*	0.942*	0.924*	0.996**	0.895*	0.994**	0.951*	0.87	0.898*
Soluble Ca						1	0.996**	0.996**	0.927*	0.979**	0.973**	0.985**	0.964**	0.985**
Soluble Mg							1	0.997**	0.915*	0.988**	0.964**	0.988**	0.980**	0.987**
Available S								1	0.892*	0.992**	0.950*	0.978**	0.983**	0.995**
DTPA-Zn									1	0.856	0.987**	0.936*	0.829	0.859
DTPA-Fe										1	0.918*	0.955*	0.996**	0.996**
DTPA-Mn											1	0.977**	0.896*	0.922*
DTPA-Cu												1	0.949*	0.953*
DTPA-Ni													1	0.985**
Available B														1

* Correlation is significant at the 0.05 level (2-tailed).

** Correlation is significant at the 0.01 level (2-tailed).

3.7. System productivity and biological yield of crops under different cropping systems

There was a significant variation in system (wheat equivalent) grain productivity from 98.72 to 115.65 q ha⁻¹ among different cropping systems (Figure 3). Soybean-wheat system led to the maximum system grain productivity (115.65 q ha⁻¹) which was statistically at par with the system productivity of cotton-wheat (114.08 q ha⁻¹), whereas the system productivity of the rice-wheat system (109.60 q ha⁻¹) was significantly lower than these two systems. The biological yield of respective kharif and rabi season crops under different cropping systems varied from 62.70 to 256.10 q ha⁻¹ (kharif season) and 50.74 to 170.03 q ha⁻¹ (rabi season). In kharif season, maize crop recorded the maximum biological yield (256.10 q ha⁻¹), whereas the maximum biological yield in rabi season was recorded by wheat crop (170.03 q ha⁻¹) under soybean-wheat system, whereas moongbean-raya system led to the minimum biological yield of raya crop (50.74 q ha⁻¹).

Figure 3. Effect of different cropping systems on rice equivalent grain yield (q ha⁻¹), wheat equivalent grain yield (q ha⁻¹), system grain (wheat equivalent) productivity (q ha⁻¹), and biological yield (q ha⁻¹) of kharif and rabi season crops.

The inclusion of leguminous crops in the main cropping system is likely to affect the system productivity because the addition of organic matter into soil through leaf litter leads to an increase in nutrient uptake due to the solubilization of nutrients in soils (Hazra et al., 2014). This study suggests that higher system productivity of soybean-wheat and cotton-wheat systems is not only due to higher nutrient uptake and market price of soybean and cotton over cereals but also the succeeding effect of leguminous and deep-rooted crops in improving wheat grain yield. Higher grain yields, root biomass contribution, leaf litter, N fixation, and root exudates ultimately improve the soil health under the soybean-wheat system (Hazra et al., 2018). The higher grain and straw yield under the soybean-wheat system led to higher biological yield as well as harvest index in this system. The increased system productivity upon inclusion of legumes in main cropping systems has been reported in many studies (Gan et al., 2015; Ghosh et al., 2020; Gurr et al., 2016; Liu et al., 2020).

4. Conclusions

The inclusion of leguminous and deep-rooted crops in the main cropping systems helped in the elevation of soil organic carbon as well as soil macro, micro, and secondary nutrients in the soil profile. The study further suggests that the practice of soybean-wheat system led to the maximum levels of available phosphorus, soluble magnesium, DTPA-zinc, and boron in surface soils. Higher nutrient availability under this system increased nutrient uptake and ultimately increased system productivity as compared to other cropping systems. Also, cotton-wheat system led to higher organic carbon in the soil profile (0–120 cm) and improved nutrients status of the soil, thereby influencing system productivity. Therefore, the existing pre-dominant and nutrient exhaustive rice-wheat cropping system can be diversified with alternative cropping systems like soybean-wheat and cotton-wheat systems that play a pivotal role in the build-up of nutrient status and amelioration of their deficiencies by influencing the surface and depth-wise distribution of nutrients in the soil, thereby improving soil sustainability and crop productivity.

Author’s contributions

Sharanjit Kaur Brar and Salwinder Singh Dhaliwal set up the experiment, Vivek Sharma conducted the experiment, Sandeep Sharma and Manpreet Kaur provided the material of experiment, Sharanjit Kaur Brar and Salwinder Singh Dhaliwal wrote this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this research.