Diversification of Rice-Based Cropping System for Improving System Productivity and Soil Health in Eastern Gangetic Plains of India

Abstract

Mono-cropping in the farming system decline in farm profit, climate change, and food insecurity are some of the major concerns that lead to unsustainability in the agricultural production system in the Eastern Gangetic Plains. A study was conducted for three years from June 2019 to June 2022 at Dr. Rajendra Prasad Central Agricultural University, Pusa, Bihar, India, to assess the profitable and best rice-based cropping system through crop diversification for sustainable agriculture. Ten different cropping sequences were exploited using randomised block design and replicated thrice, with the system productivity ranging from 8.70 to 24.95 t ha⁻¹ under the different cropping sequences. The system productivity was increased by 187% and profitability by 299.52% in the maize − Cole crops − sesame cropping system over the rice − wheat cropping system. A diversified cropping system with black gram − maize + vegetable pea − sesbania possessed significantly more soil organic carbon (0.49%), bacterial population (47.85 × 10⁶ cfu/g soil), azotobacter population (42.96 × 10⁴ cfu/g soil), phosphate solubilising bacteria (20.72 × 10⁶ cfu/g soil), dehydrogenase activity (4.39 µg TPF/g/h), fluorescein diacetate hydrolytic activity (17.28 µg fluorescein/g/h) and acid phosphatase activity (451.46 µg pNP/g/h), as well as urease activity (47.21 µg NH₄⁺/g/h), relative to the rice–wheat cropping system. Therefore, the adoption of vegetables and legumes as diversified crops are viable options for enhancing productivity, profitability and soil health in the EGPs.

1. Introduction

The rice–wheat cropping system plays an important role in global food security to the world’s population [1]. It is extensively cultivated over a 13.5-million-hectare area in Asia, with 57% in South Asia [2]. Furthermore, more than 85% of the rice–wheat cropping system area is distributed in the Indo–Gangetic Plains of South Asia [3]. It is the most important cropping system, followed by India with an about 10.5-million-hectare area [4]. The cropping intensity of Eastern India (Bihar) is as low as 140%, and it needs to be increased to meet the food and nutritional demands of the ever-burgeoning population. In recent years, the sustainability of the rice–wheat cropping system has been adversely affected, as the productivity of both the cereals are either stagnant or declining due to the deterioration of soil health, the resurgence of insect pests, diseases, new weed flora and a reduction in profit margins [5,6]. The large-scale occurrence of second-generation problems such as the overmining of soil nutrients, decline of factor productivity, lowering of ground water tables and build-up of new diseases and pests and the cereal-based production system, which are threatening agricultural sustainability [7].

Crop diversification in rice-based cropping systems has been recognised as an effective strategy for fulfilling the objectives of enhancing productivity for food security, judicious uses of resources and sustainable agriculture for the marginalised group of farmers [8]. It shows a lot of promise for alleviating the problems of food and nutritional insecurity, imbalance fertilisation, irregular farm income, weather aberrations and hazardous emissions of gases that are polluting the environment. Crop diversification through the inclusion of highly remunerative crops through broadening of the base of the cropping system utilises various techniques, such as inter-cropping and other efficient management practices [9]. Crop diversification through crop substitution or mixed cropping/intercropping may be a useful tool for mitigating the problems associated with aberrant weather conditions. The diverse agro-ecosystem of India is favourable for cultivating several pulses, oilseeds, vegetables, fodder and aromatic, as well as medicinal, crops. An increase in demand for oilseeds and pulses can be successfully met through crop diversification in the rice–wheat cropping system. A recent meta-analysis was performed by Lui et al. [10], who showed that the inclusion of legumes enhanced the soil’s organic carbon content. With the intensive cropping of rice–wheat, the deficiency of zinc, iron and manganese emerged as a huge threat to the sustainable level of food crop production [11]. The ability of legumes to fix atmospheric nitrogen makes it a viable option for developing more sustainable production systems by providing nitrogen to the component, as well as subsequent crops [12]. Moreover, legumes are an important source of protein, minerals, human diets and animal feed for small and marginal landholders [13]. The inclusion of vegetable crops in rice-based cropping systems increased the net profitability of the farmers [14]. The interest is in the use of green manure as a part of the cropping system improving soil health by ameliorating soil pH, soil structure, water-holding capacity and nitrogen addition and also helps in raising the organic matter content in the soil [15]. Green manuring also favourably improves the availability of nutrients to plants [16].

Thus, efforts are being made to promote crop diversification in the rice–wheat cropping system in the eastern zone of the country for sustaining productivity and food security. Therefore, the present study was carried out to find the location-specific best cropping system for more sustainable productivity, profitability and soil health through resource use-efficient technology.

2. Materials and Methods

2.1. Experimental Site

The experiment was conducted over three years (2019–2022) in the Research Farm of Tirhut College of Agriculture, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar (25°39′ N latitude and 85°40′ E longitude). Prior to commencing the study, the soil was sampled from 0 to 15 cm deep and was analysed after making a composite sample. The experimental soil was alluvial in nature and sandy loam texture (International Pipette Method) [17] and was characterised by a high content of free calcium carbonate varying from 20 to 40%. The topsoil (0–15 cm) was low in organic carbon content, available nitrogen and available phosphorus and medium in available potassium (

Table 1. Initial soil properties of the experimental site.

Parameters	Value	Interpretation	Methods Used
Bulk density (Mg m−3)	1.38	Moderate	Core sampler method [17]
pH	8.32	Calcareous	Glass electrode pH meter [18] (Soil:Water, 1:2.5)
EC (dS/m)	0.34	Moderate	Conductivity Bridge [18] (Soil:Water, 1:2.5)
Organic Carbon (%)	0.46	Low	Walkley and Black method [19]
Available N (kg ha−1)	172.35	Low	Alkaline potassium permanganate method [20]
Available P (kg ha−1)	16.75	Low	Olsen’s method [21]
Available K (kg ha−1)	129.40	Low	Flame photometric method [18]
Fe (ppm)	9.87	High	DTPA Extractable method [22]
Mn (ppm)	4.67	Moderate	DTPA Extractable method [22]
Zn (ppm)	0.52	Low	DTPA Extractable method [22]

). Before set-up, the experimental site had a history of paddy, rapeseed and mustard and green gram cropping mainly.

The total rainfall per annum over the experimental period, i.e., 2019–2020, 2020–2021 and 2021–2022, was 1297.3 mm, 1606.6 mm and 1753.8 mm, respectively. The rainfall was evenly distributed. The average rainfall during the three-year research work was 1552.57 mm per annum. During the research study, the average mean minimum and maximum temperature ranged from 29.61 to 29.93 °C and 18.56 to 19.26 °C, respectively (Figure 1).

2.2. Experimental Design and Treatments

The treatments were organised in a randomised block design (RBD) and replicated thrice in production scale plots. The existing rice–wheat cropping system was compared with diversified cropping sequences as follows: (T₁) Rice (Oryza sativa) − Wheat (Triticum aestivum) (Farmer’s practice), (T₂) Direct seeded rice (Oryza sativa) − Wheat (Triticum aestivum) − Green gram (Vigna radiata), (T₃) Direct seeded rice (Oryza sativa) − Mustard (Brassica juncea) − Green gram (Vigna radiata), (T₄) Direct seeded rice (Oryza sativa) − Lentil (Lens culinaris) − Okra (Abelmoschus esculentus), (T₅) Finger millet (Eleusine coracana) − Maize (Zea mays) + Potato (Solanum tuberosum) − Sesbania (Sesbania aculeata), (T₆) Red gram (Cajanus cajan) + Turmeric (Curcuma longa) − Green gram (Vigna radiata), (T₇) Maize (Zea mays) − Cole crops (Brassica oleracea) − Sesame (Sesamum indicum), (T₈) Maize (Zea mays) − Wheat (Triticum aestivum) − Black gram (Vigna mungo), (T₉) Maize (Zea mays) + Black gram (Vigna mungo) − Chick pea (Cicer arietinum) − Sesbania (Sesbania aculeata) and (T₁₀) Black gram (Vigna mungo) − Maize (Zea mays) + Vegetable Pea (Pisum sativum) − Sesbania (Sesbania aculeata). The production scale plot size was 6 m × 5 m.

All the base and component crops were sown as per the standard sowing/planting time. Before starting the experiment, the land was chisel-ploughed to a depth of 30 cm and levelled. The recommended dose of fertilisers was applied to each crop. The weed management was achieved by the application of glyphosate (41% SL) at a rate of 1.5 litres/hectare before one week of seeding/planting. Selective pre- and post-emergence (PE/POE) herbicides were used to manage the weeds. Bispyribac sodium (POE) at 20 days after sowing for rice, atrazine (PE) for maize, Pendimethalin (PE) for green gram/black gram/lentil/finger millet and mustard and Clodinafop propagyl (POE) at 30 days after sowing in wheat were used for effective weed management. All the crops of the Kharif season were sown during June and harvested in October, except red gram and turmeric. All rabi crops (e.g., wheat, oilseeds and pulses) were sown during October/November and harvested during March/April. Summer crops (green gram/black gram/okra/sesame/sesbania) were sown and harvested during March/April and June, respectively, and sesbania was incorporated into the soil as green manure. Other agronomic practices were kept at the optimum (

Table 2. Details of agronomic practices for different crops in the cropping systems.

Crops	Variety	Sowing Time	Seed Rate (kg ha−1)	Spacing (cm × cm)	Fertilisers (N:P:K:S kg ha−1)	Irrigation Depth (mm)	Harvesting Time
Rice	Sahbhagi	20 June–30 June	25	20	120:60:40 100% P & K + 50% N as basal + 25% N at active tillering and 25% at Panicle Initiation stage	360	10 October–20 October
Wheat	HD 2967	1 November–10 November	100	22.5	120:60:40 100% P & K + 50% N as basal +25% N at CRI stage and 25% N at Panicle Initiation stage	180	20 March–30 March
Greengram	IPM 2-3	25 March–30 March	20	30 × 10	20:40:20:20 100% NPKS as basal	100	10 June–20 June
Mustard	Rajendra Suflam	20 October–25 October	5	30 × 10	20:20:0:15 100% P, K & S + 50% N as basal + 50 % N at flowering stage	100	5 March–15 March
Lentil	HUL 57	25 October–5 November	30	30 × 10	20:45:20:20 100% NPKS as basal	50	25 February–5 March
Okra	Kashi Bhairav	1 March–10 March	15	30 × 15	120:60:60 100% P & K + 50% N as basal + 50 % N at earthing up	150	5 June–10 June
Finger millet	RAU 8	20 June–30 June	8	22.5 × 10	40:20:20 100% P & K + 50% N as basal +25% N at tillering stage and 25% N at panicle initiation stage	-	10 October–20 October
Maize (kharif)	Shaktiman 5	20 June–30 June	20	60 × 20	120:60:40 100% P & K + 50% N as basal +25% N at knee high stage and 25% at silking stage	100	15 October–25 October
Maize (Rabi)	Shaktiman 5	20 June–30 June	20	60 × 20	120:60:40 100% P & K + 50% N as basal +25% N at knee high stage and 25% at silking stage	180	20 March–30 March
Potato	Kufri Jyoti	10 October–20 October	2000	60 × 20	150:90:100 100% P & K + 50% N as basal and 50%N at earthing up stage	180	10 February–20 February
Sesbania	Local	15 April–20 April	30	Broadcast	-	-	5 June–15 June
Red gram	Rajendra Arhar1	25 June–5 July	15	60 × 20	20:45:20:20 100% NPKS as basal	100	15 March–25 March
Turmeric	Rajendra Sonia	25 June–5 July	2200	30 × 20	120:60:100 100% P & K + 50% N as basal and 50%N at earthing up stage	120	5 February–15 February
Cabbage	Express Green	20 October–30 October	-	60 × 40	120:80:60 100% P & K + 50% N as basal and 50 % at earthing up stage	150	25 February–5 March
Sesamum	Krishna	25 March–5 April	4	30 × 10	40:20:20:20 100% P & K + 50% N as basal and 50%N at flowering stage	100	10 June–20 June
Black gram (Kharif)	Pant U31	25 June–5 July	20	30 × 10	20:45:20:20 100% NPKS as basal	-	25 September–5 October
Black gram (Summer)	Pant U31	25 March–30 March	20	30 × 10	20:45:20:20 100% NPKS as basal	50	15 June–25 June
Chick pea	GNG-1958	25 October–5 November	75	30 × 10	20:45:20:20 100% NPKS as basal	50	20 March–30 March
Vegetable pea	Azad P-3	20 October–25 October	75	20 × 5	20:45:20:20 100% NPKS as basal	100	25 December–5 January

).

2.3. Soil Sampling and Analysis

Soil sampling and analysis were made after the harvest of the cropping system (after three years) to determine the physicochemical and biological properties of the soil. Soil samples were collected randomly from five places in each plot of 30 m² at a 0–15-cm soil depth using soil auger after the end of the three-year-long experiment. The samples collected from each plot were mixed together to make a composite sample of 500 g. Thereafter, the fresh soil samples were divided into two parts, in which a part of the soil sample was air-dried, ground and sieved (through 2-mm mesh) for the physicochemical analysis [17,18,20,21,22]. The soil organic carbon was determined by the Walkley and Black method [19]. The viable and cultural microbial population was enumerated by adopting the standard serial dilution plate technique with a selective medium for specified groups of soil microorganisms. Such an enumeration of bacteria was done using nutrient agar containing 50 mg/L cycloheximide [23], Azotobacter by Ashby’s N free mannitol agar and phosphate solubilising bacteria (PSB) by Pikovskaya’s agar media [24]. The dehydrogenase activity (DHA) in the soil was estimated as per the method given by Tabatabai [25], which is based on the reduction of iodonitrotetrazolium chloride (TTC) into triphenyl formazan (TPF), thereafter examining the sample using a spectrophotometer at a wavelength of 485 nm. The estimation of the fluorescein diacetate hydrolytic activity (FDA) was done following the standard procedure mentioned by Schnurer and Rosswall [26], in which the fluorescein released after the reduction was measured with a spectrophotometer at a wavelength of 490 nm. Urease activity (UA) was determined by quantifying the NH4+ released during 2 h of soil incubation at 370 °C after distillation with magnesium oxide (MgO) using a boric acid indicator and back titration with 0.005 N sulphuric acid [27]. The acid phosphatase (ACP) activities in soil were estimated through the detection of p-nitrophenol (PNP) released after incubation for 1 h at 37 °C at pH 6.5 of soil with p-Nitrophenyl phosphate disodium [28].

2.4. Rice Equivalent Yield, System Production Efficiency, Land Use Efficiency and Economics

For comparing the results of different cropping sequences, the yields of Kharif, rabi and summer crops were converted into rice equivalent yields using the formula cited by Kumar et al. [29]:

REY (t ha⁻¹) = {Yield of first crop (t ha⁻¹) × Price of first crop (Rs/t)/Price of paddy (Rs/t)} + {Yield of second crop (t ha⁻¹) × Price of second crop (Rs/t)/Price of paddy (Rs/t)} + {Yield of third crop (t ha⁻¹) × Price of third crop (Rs/t)/Price of paddy (Rs/t)}

The rice equivalent yield (REY) was calculated to compare the system performance by converting the non-rice crop’s yield into an equivalent paddy yield based on price.

The system production efficiency (kg ha⁻¹ day⁻¹) was calculated by dividing the total economic yield (REY) by the total duration of crop in the cropping system [29].

The relative production efficiency was computed by the formula cited by Kumar et al. [29]:

Relative Production Efficiency (%) = {Total productivity (TP) of diversified cropping system − TP of existing cropping system/TP of existing cropping system} × 100

The land use efficiency was computed by dividing the total number of days occupied by the respective crop by 365 days and multiplying by 100.

Economics was computed based on the prevailing market prices of the inputs during their respective crop seasons. Gross returns were computed based on the grain and straw yields and their minimum support prices [30] and prevailing market prices during their respective crop seasons (

Table 3. Minimum support price and prevailing market price of produce during the respective year of experimentation [30].

Crops	2019–2020 (Rs t−1)	2020–2021 (Rs t−1)	2021–2022 (Rs t−1)
Rice	18,150	18,680	19,400
Wheat	19,250	19,750	20,150
Green gram	70,500	71,960	72,750
Mustard	44,250	46,500	50,500
Lentil	48,000	51,000	55,000
Okra	20,000	22,000	25,000
Finger millet	31,500	32,950	33,770
Maize	17,600	18,500	18,700
Potato	12,000	15,000	15,000
Red gram	58,000	60,000	63,000
Turmeric	30,000	30,000	35,000
Cabbage	12,000	10,000	15,000
Sesamum	64,850	68,550	73,070
Black gram	57,000	60,000	63,000
Chickpea	48,750	51,000	52,300
Vegetable pea	40,000	45,000	52,000

).

Net returns (Rs ha⁻¹) were computed by subtracting gross returns (Rs ha⁻¹) from the total cost of cultivation (Rs ha⁻¹). The benefit:cost ratio was obtained using the formula:

$$\mathrm{Benefit} \mathrm{Cos}\mathrm{t} \mathrm{ratio}=\frac{{\mathrm{Netprofit}(\mathrm{Rsha}}^{-}{}^1)}{{\mathrm{Cost} \mathrm{of} \mathrm{cultivation}(\mathrm{Rsha}}^{-}{}^1)}$$

The system profitability was calculated by the formula cited by Kumar et al. [29]:

System profitability (Rs ha⁻¹ day⁻¹) = Net return (Rs ha⁻¹)/365 days

The relative economic efficiency was computed by the following formula cited by Kumar et al. [29]:

Relative Economic Efficiency (%) = {Net returns (NR) of diversified cropping system−NR of existing cropping system/NR of existing cropping system} × 100

2.5. Statistical Analysis

The recorded data were analysed statistically according to the two-way analysis of variance (ANOVA) method for randomised block design [31] to determine the effects of the treatment of the cropping systems on the system productivity and soil health indicators. The significance of the sources of variation was tested by the error mean square of the ‘F’ test [32] at a probability level of 5%. For comparison of the mean values of different parameters tested in this experiment, the least significant difference (LSD) test at the 5% probability level was performed. Data analyses were performed using SPSS Windows version 20.0 (IBM Corp., Armonk, NY, USA).

3. Results and Discussion

3.1. Rice Equivalent Yield as Affected by the Diversified Cropping System

Crop diversification in the rice–wheat cropping system significantly influenced the system productivity (rice equivalent yield) (

Table 4. Rice equivalent yield is affected by the diversified cropping system.

Cropping Systems	REY (t ha−1) (Pooled)
Rice − Wheat (Farmer’s practice)	8.70
Direct seeded rice (DSR) − Wheat − Green gram	12.72
Direct seeded rice (DSR) − Mustard/Rai − Green gram	12.98
Direct seeded rice (DSR) − Lentil − Okra	13.35
Finger millet − Maize + Potato − Sesbania	19.37
Red gram + Turmeric − Green gram	22.62
Maize − Cole crops − Sesame	24.95
Maize − Wheat − Black gram	14.24
Maize + Black gram − Chick pea − Sesbania	13.48
Black gram − Maize + Vegetable pea − Sesbania	12.09
SEM (±)	0.813
LSD (p ≤ 0.05)	2.429

).

The highest rice equivalent yield (24.95 t ha⁻¹) was recorded in the maize − cabbage − sesame cropping system, which was significantly superior to the rest of the cropping system. However, it was statistically at par with the red gram + turmeric − green gram (22.62 t ha⁻¹) cropping system. This might be due to the higher yield and value of cabbage and turmeric. A previous study conducted by Singh et al. [33] also reported that a vegetable-dominated cropping system produced considerably higher system productivity compared with other systems. Similarly, Rathore and Bhatt [34] also mentioned that system productivity (rice equivalent yield) in the rice–garlic–maize cropping system (50.8 t ha⁻¹) was significantly higher than in other cropping systems. Mandal et al.’s [35] results revealed that total system productivity increased by 273% in the rice–tomato–okra cropping system compared to rice–fallow–rice. In view of our results, others also noted that leguminous crops have the potential to recycle nutrients through a deep root system, improved soil structure, add nutrients by biological nitrogen fixation or by leaf fall and showed better nutrient use efficiency, resulting in higher system productivity [36,37]. Similar findings were also reported by Mishra et al. [38] that the inclusion of vegetables and pulses in the rice-based cropping system enhanced the system productivity.

3.2. System Production Efficiency, Relative System Production Efficiency and Land Use Efficiency as Affected by the Diversified Cropping System

The system production efficiency varied significantly due to a diversified cropping system. The system production efficiency was found to be highest in the maize − cabbage − sesame (81.79 kg ha⁻¹ day⁻¹) cropping system, which was significantly superior to the rest of the cropping system. Different cropping systems significantly affect the value of the relative system production efficiency. Similarly, the maximum value of the relative system production efficiency (186.85%) was recorded in the maize − cabbage − sesame cropping system, which surpassed the rest of the cropping system (

Table 5. System production efficiency, relative system production efficiency and land use efficiency as affected by the diversified cropping system.

Cropping Systems	System Production Efficiency (kg ha−1/day)	Relative System Production Efficiency (%)	Land Use Efficiency (%)
Rice − Wheat (Farmer’s practice)	33.19	0.00	71.78
Direct seeded rice (DSR)−Wheat−Green gram	39.39	46.29	88.49
Direct seeded rice (DSR)−Mustard/Rai−Green gram	42.71	49.31	83.29
Direct seeded rice (DSR)−Lentil−Okra	39.63	53.57	92.33
Finger millet−Maize + Potato−Sesbania	60.53	122.77	87.67
Red gram + Turmeric−Green gram	65.57	160.14	94.52
Maize−Cole crops−Sesame	81.79	186.85	83.56
Maize−Wheat−Black gram	43.16	63.80	90.41
Maize + Black gram−Chick pea−Sesbania	42.78	55.00	86.30
Black gram−Maize + Vegetable pea−Sesbania	40.99	39.06	80.82
SEM (±)	2.37	5.38	-
LSD (p ≤ 0.05)	7.10	16.12	-

).

The system production efficiency (SPE) was significantly superior in rice − cabbage (83.8 kg ha⁻¹ day⁻¹) than in other rice-based cropping systems, as confirmed by Kumar et al. [29]. The highest relative system production efficiency (RSPE) of 1518% was obtained with rice − cabbage, followed by rice − tomato and rice − pea [29]. The red gram + turmeric − green gram (94.52%) cropping system recorded the highest land use efficiency, which might be due to the continuous standing of the crop in the field. In our study, a maximum land use efficiency of 94.52% was recorded with red gram intercropped with turmeric, followed by the green gram cropping sequence due to the crop duration (345 days). A recent study by Kumar et al. [29] also marked that higher land use efficiency was recorded with rice − cabbage (78.2%) due to the longer duration of sequences, followed by rice − pea (78.1%) and rice − tomato (76.4%). This confined that crop diversification utilises land resources efficiently, which would not only increase profitability but also generates more employment during the lean period. Similarly, Kumar et al. [39] found that diversification through the inclusion of vegetables and pulses/oilseeds in the cropping system increases land use efficiency. A maximum land use efficiency of 64.38% was observed in the rice–brinjal cropping sequence, followed by the rice–groundnut (63.01%) and rice–onion (63.01%) sequence due to its longer duration with less return [40]. Sharma et al. [41] also reported that crop diversification through the inclusion of vegetables and leguminous crops in the cropping system increased land use efficiencies.

3.3. Effect of Diversification in Rice–Wheat Cropping System on Economic Benefit

Gross returns and net returns were directly proportional to the economic yield of crops and the variable cost of cultivation. The highest gross return (Rs 4, 64 and 274 ha⁻¹); net returns (Rs 3, 64 and 024 ha⁻¹) and benefit–cost ratio (3.63) were recorded in the maize − cabbage − sesame cropping system, which was significantly higher over the rest of the cropping system. Similar findings were also confirmed by Kumar et al. [42] that the inclusion of vegetable crops in rice-based crop sequences increased profitability. The data presented in

Table 6. Effect of diversification in the rice–wheat cropping system on economics.

Cropping Systems	Gross Returns (Rsha−1)	Net Returns (Rsha−1)	Benefit-Cost Ratio	Profitability (Rs/ha/day)	Relative Economics Efficiency (%)
Rice−Wheat (Farmer’s practice)	161,868	91,118	1.29	249.64	0.00
Direct seeded rice (DSR)−Wheat−Green gram	236,808	148,808	1.69	407.69	63.31
Direct seeded rice (DSR)−Mustard/Rai−Green gram	241,713	165,363	2.17	453.05	81.47
Direct seeded rice (DSR)−Lentil−Okra	248,591	147,341	1.46	403.67	61.70
Finger millet−Maize + Potato−Sesbania	360,709	196,209	1.19	537.56	115.30
Red gram + Turmeric−Green gram	421,137	258,387	1.59	707.91	183.56
Maize−Cole crops−Sesame	464,274	364,024	3.63	997.33	299.52
Maize−Wheat−Black gram	265,186	166,186	1.68	455.30	82.37
Maize + Black gram−Chick pea−Sesbania	250,993	159,743	1.75	437.65	75.29
Black gram−Maize + Vegetable pea−Sesbania	225,137	134,637	1.49	368.87	47.75
SEM (±)	10,632	10,633	0.10	30.15	7.12
LSD (p ≤ 0.05)	31,835	31,836	0.30	86.28	21.31

shows that the relative economic efficiency (299.52%) was significantly higher in the maize − cabbage − sesame cropping system compared to the rest of the cropping systems. High-value crops such as oilseeds, pulses and vegetables are receiving more attention owing to higher prices due to increased demand by the consumers in the market. Similar results were also corroborated by Sharma et al. [43] in rice-based cropping systems.

The inclusion of these crops in a sequence enhances the profitability of the cropping sequences [44]. Samant [45] noted that the rice–fallow sequence has the lowest profitability (37.37 Rs ha⁻¹ day⁻¹), whereas the lowest relative economic efficiency (91.52%) was observed in the rice–black gram system due to the lower economic yield in paddies in spite of the higher market value. Subsequently, the highest net return and benefit:cost ratio were recorded with the rice–brinjal, followed by rice–tomato, sequence.

3.4. Bulk Density, pH, EC and Soil Organic Carbon

In the present experiment, the impact of crop diversification was evident across comparisons of the post-harvest soil status with the farmer’s practice (rice–wheat cropping system). The bulk density, pH and electrical conductivity were not significantly affected by different cropping systems. However, the organic carbon content was significantly influenced by the different cropping systems (

Table 7. Post-harvest physicochemical properties of the diversified cropping system plot.

Cropping Systems	Bulk Density (Mg m−3)	EC (dSm−1)	pH	Organic Carbon (%)
Rice − Wheat (Farmer’s practice)	1.38	0.34	8.33	0.45
Direct seeded rice (DSR) − Wheat − Green gram	1.38	0.35	8.32	0.47
Direct seeded rice (DSR) − Mustard/Rai − Green gram	1.38	0.34	8.32	0.47
Direct seeded rice (DSR) − Lentil − Okra	1.37	0.35	8.33	0.46
Finger millet − Maize + Potato − Sesbania	1.36	0.33	8.31	0.49
Red gram + Turmeric − Green gram	1.37	0.34	8.32	0.48
Maize − Cole crops − Sesame	1.38	0.35	8.32	0.45
Maize − Wheat − Black gram	1.38	0.35	8.32	0.46
Maize + Black gram − Chick pea − Sesbania	1.36	0.33	8.30	0.49
Black gram − Maize + Vegetable pea − Sesbania	1.36	0.33	8.31	0.49
SEM (±)	0.052	0.014	0.404	0.007
LSD (p ≤ 0.05)	NS	NS	NS	0.021

).

The minimum bulk density (1.36 Mg m⁻³) was recorded in the maize + black gram − chickpea − sesbania and black gram − maize + vegetable pea − sesbania cropping systems. This might be due to the continuous growing of leguminous crops and incorporation of crop residue of peas after picking and sesbania in the soil, which add organic matter to the soil, thereby reducing the bulk density. However, the lowest value of EC (0.34 dSm⁻¹) was recorded in the maize + black gram − chickpea − sesbania and black gram − maize + vegetable pea − sesbania cropping systems. Maize + black gram − chickpea − sesbania was recorded with a minimum pH of 8.30. This might be due to the incorporation of sesbania in the soil, which, after decomposition, releases certain organic acids that help to reduce the pH of the crop rhizosphere. Additionally, the continuous growth of the leguminous crop in the cropping system adds certain organic acid to the soil, which reduces the soil pH.

The data presented in the table revealed that the organic carbon content was increased with the inclusion of legumes in the cropping system and the incorporation of sesbania as green manure in the cropping system. The maximum organic carbon content was recorded in the maize + black gram − chickpea − sesbania (0.49%), black gram − maize + vegetable pea − sesbania (0.49%) and finger millet − maize + potato − sesbania (0.49%) cropping systems, which were significantly superior to the rest of the cropping systems, but it was found on par with the red gram + turmeric − green gram (0.48%), direct seeded rice − wheat − green gram (0.47%) and direct seeded rice − mustard/rai − green gram (0.47%) cropping systems. Legumes promote an increase in the primary productivity of plant communities through increased biological nitrogen fixation, which would lead to an increase in the soil organic carbon content [46], as well as positive effects of legumes on soil organic carbon, which can be attributed to low C/N ratios of legume residues more similar to soil microorganisms and soil organic matter than other plant residues [47]. Substrates with low C/N ratios can reduce the microbial nitrogen acquisition and thus increase the carbon use efficiency by the microbes, facilitating plant residue decomposition into soil organic matter [48].

3.5. Available Macronutrients

The availability of N, P and K was significantly influenced by the different cropping systems (

Table 8. Available N, P, K and DTPA extractable micronutrients in post-harvest soil of the diversified cropping system.

Cropping Systems	Macronutrients			Micronutrients
	Available N (kg ha−1)	Available P (kg ha−1)	Available K (kg ha−1)	Fe (mg kg−1)	Mn (mg kg−1)	Zn (mg kg−1)
Rice−Wheat (Farmer’s practice)	170.6	15.45	126.5	9.82	4.65	0.68
Direct seeded rice (DSR)−Wheat−Green gram	187.3	17.36	135.2	9.90	4.75	0.73
Direct seeded rice (DSR)−Mustard/Rai−Green gram	184.5	17.54	134.7	9.86	4.77	0.72
Direct seeded rice (DSR)−Lentil−Okra	180.3	17.10	130.8	10.05	4.78	0.70
Finger millet−Maize + Potato−Sesbania	191.3	22.78	130.5	10.50	4.96	0.76
Red gram + Turmeric−Green gram	192.4	17.87	136.4	10.12	4.79	0.75
Maize−Cole crops−Sesame	178.6	16.25	128.3	9.96	4.68	0.70
Maize−Wheat−Black gram	183.6	17.48	134.2	9.81	4.72	0.71
Maize + Black gram−Chick pea−Sesbania	201.5	24.55	139.3	10.62	5.05	0.78
Black gram−Maize + Vegetable pea−Sesbania	198.3	23.06	138.5	10.55	5.02	0.78
SEM (±)	5.514	0.547	1.737	0.143	0.086	0.01
LSD (p ≤ 0.05)	16.51	1.638	5.20	0.428	0.257	0.03

).

The data presented in the table revealed that the nitrogen content was increased with the addition of legumes in the cropping system and the incorporation of sesbania in the cropping system. The maximum nitrogen content was recorded in the maize + black gram − chickpea − sesbania (201.5 kg ha⁻¹) cropping system, which was significantly superior over the rest of the cropping system, but it was found on par with the black gram − maize + vegetable pea − sesbania (198 kg ha⁻¹), red gram + turmeric − green gram (192.4 kg ha⁻¹), finger millet − maize + potato − sesbania (191.3 kg ha⁻¹) and direct seeded rice − wheat − green gram (187.3 kg ha⁻¹) cropping systems. Green manuring crops not only transfer nutrients to soil but can also lead to a deep root system for nutrient uptake from deeper soil as a biological pump, thereby increasing the concentration of plant nutrients in the surface soil [49]. The maximum available nitrogen in the cropping system with the inclusion of legume crops might be due to legumes obtaining atmospheric nitrogen through N-fixation for their own requirements and subsequently release nitrogen into the soil as a result of nodulation, root, leaf, etc.; incorporation and decomposition throughout its growth, thus adding considerable quantities of N, P, and K to the soil [29,50].

The available phosphorous content was increased with the incorporation of sesbania as green manure in the cropping system. The maize + black gram − chickpea − sesbania cropping system recorded the highest phosphorous content (24.55 kg ha⁻¹) in the soil, which significantly surpassed the rest of the cropping system, but it was found on par with the black gram –maize + vegetable pea − sesbania (23.06 kg ha⁻¹) and Finger millet − Maize + Potato − Sesbania (22.78 kg ha⁻¹) cropping systems. It might be due to the incorporation of sesbania in the cropping system, which, after decomposition, releases organic acids, thereby making fixed phosphorous into an available form. Similar results were also corroborated by the findings of Ali et al. [51].

The maize + black gram − chickpea − sesbania cropping system recorded the highest potassium content (139.3 kg ha⁻¹) in the soil, which was significantly superior to the rest of the cropping system, but it was found on par with the black gram − maize + vegetable pea − sesbania (138.5 kg ha⁻¹), red gram + turmeric − green gram (136.4 kg ha⁻¹), direct seeded rice − wheat − green gram (135.2 kg ha⁻¹) and direct seeded rice − mustard − green gram (134.7 kg ha⁻¹) cropping systems.

3.6. Available Micronutrients

The data presented in the table revealed that the available micronutrients (Fe, Mn and Zn) were significantly affected by the different cropping systems. The utmost available iron (10.62 mg kg⁻¹) was recorded in the maize + black gram − chickpea − sesbania cropping system, which was significantly superior to the rest of the cropping system, but it was on par with the black gram − maize + vegetable pea − sesbania (10.55 mg kg⁻¹), finger millet − maize + potato − sesbania (10.50 mg kg⁻¹) and red gram + turmeric − green gram (10.12 mg kg⁻¹) cropping systems (Table 8). The utmost available iron (5.05 mg/kg) was recorded in the maize + black gram − chickpea − sesbania cropping system, which was significantly superior to rest of the cropping system, but it was found on par with the black gram − maize + vegetable pea − sesbania (5.02 mg kg⁻¹), finger millet − maize + potato − sesbania (4.96 mg kg⁻¹) and red gram + turmeric − green gram (4.79 mg kg⁻¹) cropping systems. The utmost available iron (0.78 mg kg⁻¹) was recorded in the maize + black gram − chickpea –sesbania and black gram –maize + vegetable pea − sesbania (0.78 mg kg⁻¹) cropping systems, which was significantly superior to the rest of the cropping system, but it was found on par with the finger millet − maize + potato − sesbania (0.76 mg kg⁻¹) and red gram + turmeric − green gram (0.75 mg kg⁻¹) cropping systems. It might be due to the addition of sesbania in the soil acting as a chelating material and increasing the availability of micronutrients. The incorporation of sesbania in the soil slightly reduces the pH of the soil, which increases the availability of iron, manganese and zinc. In our experiment, a cropping sequence with leguminous crops improved the soil organic carbon and other nutrient status. Similar to our results, Gangwar and Ram [52] also found that the available nitrogen, phosphorus, potassium and sulphur status increased in cropping sequences involving vegetable pea and green gram. Porpavai et al. [53] also found that taking a pulse crop as a component crop in a rice-based cropping system increased the available nitrogen, phosphorus, potash and organic carbon of the post-harvest soil due to the addition of several nutrients. Growing legume crops acts more as a soil fertility improver than as a grain crop due to a self-sufficient nitrogen supplier [54]. Leguminous crops grown either for green manure or fodder are well-known to play a major role in maintaining the soil fertility status. Thakur and Sharma [55] found that the maximum organic carbon was built up in the rice–green gram sequence (0.438%) and rice–black gram sequence (0.435%), which was attributed to residue accumulations of roots and leaves of legumes.

3.7. Soil Microbial Properties

The cropping system had a significant effect on the soil microbial count (

Table 9. Soil microbial population is influenced by a diversified cropping system.

Cropping Systems	Bacteria (106 cfu/g Soil)	Azotobacter (104 cfu/g Soil)	PSB (106 cfu/g Soil)
Rice−Wheat (Farmer’s practice)	33.29	31.74	15.41
Direct seeded rice (DSR)−Wheat − Green gram	38.34	33.68	16.25
Direct seeded rice (DSR)−Mustard/Rai−Green gram	38.67	33.92	16.57
Direct seeded rice (DSR)−Lentil−Okra	38.16	33.26	16.08
Finger millet−Maize + Potato−Sesbania	43.58	39.71	18.96
Red gram + Turmeric−Green gram	39.26	35.49	16.84
Maize−Cole crops−Sesame	34.48	32.53	15.36
Maize−Wheat−Black gram	38.09	33.01	16.05
Maize + Black gram−Chickpea−Sesbania	45.92	41.57	20.24
Black gram − Maize + Vegetable pea−Sesbania	47.85	42.96	20.72
SEM (±)	2.40	1.67	0.93
LSD (p ≤ 0.05)	7.19	5.01	2.78

). The soil microbial count (Bacteria, Azotobacter and PSB)) were significantly higher in the black gram − maize + vegetable pea − sesbania cropping system compared with the other systems. The black gram − maize + vegetable pea − sesbania and the maize + black gram − chickpea − sesbania cropping systems, which had vegetable pea and sesbania, respectively, required higher phosphorus nutrition for nitrogen fixation. The legume crops can solubilise phosphorus quickly by secreting root exudates that enhance the growth and activities of PSB [56,57]. The bacterial population was higher where more legume crops were taken, because the availability of the growth substances for bacteria was higher from the root exudates, which led to increased bacterial growth.

3.8. Soil Enzyme Activities

The varied cropping systems significantly affect the enzymatic activities in the soil (

Table 10. Soil enzymes are influenced by a diversified cropping system.

Cropping Systems	DHA (µg TPF/g/h)	FDA (µg Fluoresce in/g/h)	AcP (µg pNP/g/h)	Urease (µg NH4+/g/h)
Rice − Wheat (Farmer’s practice)	3.12	14.56	402.68	32.92
Direct seeded rice (DSR) − Wheat − Green gram	3.98	16.28	421.87	35.16
Direct seeded rice (DSR) − Mustard/Rai − Green gram	4.01	16.53	428.56	35.71
Direct seeded rice (DSR) − Lentil − Okra	3.84	15.92	419.28	34.18
Finger millet − Maize + Potato − Sesbania	4.26	16.95	439.21	41.26
Red gram + Turmeric − Green gram	4.02	16.71	430.94	37.63
Maize − Cole crops − Sesame	3.10	14.52	401.49	32.85
Maize − Wheat − Black gram	3.42	15.86	412.78	33.94
Maize + Black gram − Chick pea − Sesbania	4.36	17.09	446.23	45.76
Black gram − Maize + Vegetable pea − Sesbania	4.39	17.28	451.46	47.21
SEM (±)	0.11	0.10	6.43	3.12
LSD (p ≤ 0.05)	0.34	0.31	19.25	9.34

).

The dehydrogenase (DHA) activity was significantly higher (4.39 µg TPF/g soil/h) in the black gram − maize + vegetable pea − sesbania cropping system compared with the other cropping systems. The crop residues and modulated crops returned to the soil definitely modified the soil microclimate, thereby manipulating the microbial metabolism [34] and leading to enhanced DHA activity in the soil. The fluorescein diacetate hydrolytic (FDH) activity was also significantly higher (17.28 µg fluorescein/g/h) black gram − maize + vegetable pea − sesbania; however, it was found on par with the maize + black gram − chickpea − sesbania cropping system (17.09 µg fluorescein/g/h). Similarly, the acid phosphatase (AcP) activity was significant in the black gram − maize + vegetable pea cropping system; the highest value of 451.46 µg pNP/g/h was recorded. The urease (UA) activity was significantly higher (47.21 µg NH₄⁺/g/h) in the black gram − maize + vegetable pea cropping system compared with the other systems, except for the maize + black gram − chickpea − sesbania system. The inclusion of legumes in a cereal-based cropping system was reported to improve the soil organic matter status, soil enzyme activity and soil respiratory activity [58,59].

4. Conclusions

The results from the present study suggest that crop diversification in the existing rice–wheat cropping system (farmer’s practice) with the introduction of maize, vegetables and oilseeds improved the system productivity, system production efficiency, relative system production efficiency and relative economic efficiency, which was statistically on par with red gram intercropped with turmeric, followed by green gram. The available N, P, K, Fe, Mn and Zn in the soil was significantly improved through diversification with the maize + black gram − chickpea − sesbania and black gram − maize + vegetable pea − sesbania cropping systems. However, the organic carbon content in the post-harvest soil was found the maximum in all the cropping sequences wherein the sesbania was green-manured. The soil microbial population (bacteria, azotobacter and PSB), as well as the soil enzymatic activities, were found highest in the black gram − maize + vegetable pea − sesbania cropping system that leads to improving the soil health. Although productivity and profitability were found the maximum in the maize − vegetables − oilseeds cropping system, the red gram + turmeric − green gram cropping system was the best for sustainability and higher profitability for the farmers under the current scenario. By following the crop diversified technology, farmers can adopt horticultural crops or pulses in their existing cropping sequences of rice–wheat only for improving their livelihood, as well as food security, and sustainable agriculture production system through higher soil quality in the Eastern Gangetic Plains of India.