Unraveling the Impact of Cumin-Centric Cropping Sequences on Cumin Yield, Economic Viability, and Dynamics of Soil Enzymatic Activities in Hot Arid Climatic Conditions

Abstract

A comprehensive study spanning three kharif and rabi seasons (2018–2019, 2020–2021, and 2021–2022) was conducted to investigate the intricate interactions among different cropping sequences and their impacts on cumin yield, financial outcomes, and soil microbial dynamics. The experiment was designed using a randomized block design, comprising eight distinct treatment combinations, each replicated three times. The results revealed compelling insights into the potential of specific cropping sequences to enhance multiple aspects of agricultural sustainability. The results revealed that the highest cumin yield (averaging 592 kg ha⁻¹ over the three years) was achieved when cumin was cultivated subsequent to pearl millet, showcasing significant increases of 14.28% and 23.07% over the cumin–fallow and cumin–cotton cropping systems, respectively. When it came to cumin equivalent yield, the cumin–cotton cropping sequence (985 kg ha⁻¹) emerged as the most favorable, closely followed by cumin–groundnut (968 kg ha⁻¹). Furthermore, analyzing net realizations and benefit–cost ratios demonstrated that the cumin–pearl millet cropping sequence stood out with the maximum values (₹88,235 ha⁻¹ and 2.7, respectively), followed by the cumin–mung bean cropping system (₹84,164 ha⁻¹ and 2.47, respectively). Among the various cropping sequences studied, cumin–mung bean, cumin–cluster bean, cumin–pearl millet and cumin–groundnut were recorded as statistically similar in terms of soil microbial enzymatic activities viz. fluorescein diacetate (FDA), alkaline phosphatase (ALP), dehydrogenase activity (DHA), and microbial biomass carbon and were at par over the cumin–sorghum, cumin–sesame, cumin–cotton and cumin–fallow cropping systems. These findings emphasize the significance of strategic crop sequencing for sustainable agriculture practices that simultaneously optimize productivity and maintain soil health.

1. Introduction

The hot arid zone of India, particularly western Rajasthan, is characterized by its challenging agricultural environment, marked by erratic monsoons, scorching hot and dry summers, and persistent winds [1]. In this context, kharif crops such as sesame, pearl millet, mung bean, cluster bean, groundnut, and sorghum, along with rabi crops like cumin, fennel, and mustard, play a crucial role in sustaining the livelihoods of farmers. Cumin, (Cuminum cyminum L.) stands out as a vital short-duration cash crop in this region, due to its lower input requirements, including fertilizers, irrigation, and labor, compared to other rabi crops [2]. This seed spice, a prominent member of the Apiaceae family within the Apiales order, holds substantial significance. It commands over 22% of the spice crop area and constitutes more than 48% of the seed spice area in India [3]. Major cumin growing states are Gujarat and Rajasthan, which represent more than 95 per cent of cumin production of India. Considering the potential area and production, Rajasthan is dominant over other states and the majority of the production of this state is undertaken by the Jodhpur, Barmer, Jalore, Nagour, and Sirohi districts [4]. Cumin’s culinary applications are diverse and global. It plays a pivotal role in mixed spices, curry powders, soups, sausages, pickles, and various other dishes. This seed spice is used in a variety of cuisines, including Iranian, Mexican, Turkish, Cuban, Indian, Southeast Asian, and Egyptian. Cumin is widely used in confectionery, beverages, medications, liquors, sausages, meat, perfumes, and bread production [3]. Cumin seeds additionally possess a noteworthy content of essential oil, typically ranging from 2.5% to 3.6%. The aromatic richness of cumin’s essential oil further contributes to its significance beyond culinary applications [4,5,6].

Agronomically, successful cumin cultivation hinges on factors such as planting dates, rotation or cropping sequences, seeding rates, labor demands, and judicious fertilizer application [7,8]. The concept of cropping sequences encompasses the types and order of crops cultivated, along with the techniques employed [9]. Cropping systems exert a pivotal role in both diversifying and intensifying crop cultivation practices. This can be achieved through a strategic selection of key crops and the incorporation of cash crops, synergistically optimizing the utilization of residues from each crop. This approach facilitates efficient resource utilization and contributes to the overall enhancement of agricultural productivity [10]. Crop rotation within agricultural systems holds substantial sway over crop yields. The incorporation of leguminous plants within rotation patterns typically translates to a notable surge in the availability of soil nitrogen for subsequent crops. Traditionally, cropping systems have been formulated with the primary goal of maximizing crop yields [11]. However, the contemporary landscape demands a reevaluation of cropping-system design. It is imperative to holistically consider emerging socioeconomic, ecological, and environmental concerns. Modern cropping systems must strike a balance that goes beyond mere yield maximization. They should be economically viable, optimizing resource inputs while being cognizant of the local climate dynamics encompassing factors like soil type, water availability, temperature patterns, and rainfall characteristics. A forward-looking approach to cropping systems is now vital, one that holistically integrates productivity, cost effectiveness, and environmental sustainability.

Notably, cropping sequences can influence soil microbial communities through mechanisms involving root exudates, organic matter input, and rhizosphere interactions [12]. Plant-derived carbon-rich exudates stimulate microbial activity, thereby aiding organic matter decomposition and nutrient cycling [13,14]. Additionally, rotations featuring leguminous crops foster nitrogen fixation and impact microbial diversity [12]. According to Sarrntonio and Gallandt [15], increased cropping-sequence diversity and cover crops encourage increased microbial biomass and more fungal-based community structures, resulting in increased microbial-derived organic matter [16]. Enhanced microbial activities within certain cropping sequences can lead to numerous benefits for soil health, including improved nutrient availability, organic-matter decomposition, and soil structure. Increased microbial activity contributes to nutrient cycling, making essential elements more accessible to plants [17,18]. This, in turn, enhances plant growth and overall agricultural productivity. Moreover, soil enzymes such as dehydrogenase (DHA), alkaline phosphatase (ALP), and fluorescent diacetate (FDA) can also be used as indicators of healthy soil, in light of the fact that the vast majority of the soil enzymes are secreted by microbes [19]. Soil microbial biomass carbon (MBC) and soil enzymes are important soil biological parameters that can be used to monitor the fluctuations in soil ecosystems or microbial activities due to agronomic practices like tillage, cropping sequences, or cropping systems [20,21]. Overall, understanding the impact of cropping sequences on soil microbial activities is pivotal for promoting sustainable and resilient agricultural systems. It provides insights into strategies that harness the microbial potential to enhance soil health, nutrient cycling, and plant productivity in various climatic and environmental conditions [22,23,24].

There is a lack of comprehensive research on the impact of diverse cropping sequences on cumin yield, economic returns, and soil microbial activities in hot arid climates. While the importance of soil microbes is acknowledged, there is a gap in understanding how different cropping sequences influence specific microbial activities in these conditions. Previous studies have not deeply explored the economic implications of various cropping sequences for cumin production in hot arid regions. There is a need for practical recommendations on the most suitable cropping sequences that can enhance both cumin yield and soil health under hot arid climatic conditions. We hypothesized that different cumin-based cropping sequences will have varying effects on cumin yield, economic outcomes, and soil microbial activities in hot arid climatic conditions. Certain sequences may prove more effective in optimizing yield and economic returns while also positively influencing soil microbial dynamics. Keeping the above facts in view, the present study was aimed at investigating the effects of cumin-based cropping sequences on cumin yield, economic viability, and soil microbial activities like microbial biomass carbon (MBC), dehydrogenase activity (DA), alkaline phosphatase, and fluorescein diacetate (FDA) within the challenging context of a hot arid climatic condition.

2. Materials and Methods

2.1. Soil Characterization

Soil samples were collected from various locations within the experimental field using a screw auger, focusing on the 0–30 cm depth range. The objective was to assess the physico-chemical properties and fertility status of the soil. The soil was characterized as sandy-loamy in texture, with a bulk density of 1.74 Mg m⁻³ and a stable particle density of 2.65 Mg m⁻³. The three primary elements (N, P, K) initially present in the soils were analyzed by using the alkali potassium permanganate method [25], the Olsen method [26], and the flame photometer method [27], respectively. The pH [28] and organic carbon [29] were also measured. In addition, the available sulfur in the soil was determined by using the CaCl₂-extractable S method [30]. The results revealed a low level of available nitrogen (174 kg N ha⁻¹), a medium level of phosphorus (22.5 kg P₂O₅ ha⁻¹), and a high level of potassium (325 kg K₂O ha⁻¹) in the soil of the experimental field. In addition, the soil of the experimental site was moderately alkaline (pH 8.2) in reaction, and low in organic carbon (0.13%).

2.2. Field Experiment

A field experiment spanning three kharif and rabi seasons (2018–2019, 2020–2021, and 2021–2022) took place at the Agricultural Research Station, Mandor, affiliated with the Agriculture University, Jodhpur. Unfortunately, the experiment faced challenges in the 2019–2020 season, leading to its failure due to environmental constraints. Geographically, the field was situated between 26°15′ N to 26°45′ north latitude and 73°00′ E to 73°29′ east longitude, at an altitude of 231 m above mean sea level. This research station fell within agro–climatic zone Ia, classified as the arid western plains zone in Rajasthan. The region experienced an average annual rainfall of approximately 367 mm (CV 52%), with the majority (85 to 90%) occurring from June to September during the kharif season, facilitated by the southwest monsoon. The present investigation was a study of a suitable and profitable cropping sequence for cumin. The experiment involved eight different cropping sequences, each replicated three times in a randomized block design. A consistent plot size of 4.0 m × 3.6 m was maintained throughout the entire experiment. A total of eight cropping sequences were taken to constitute eight treatments for different cumin-based cropping sequences, viz. T₁: cumin–pearl millet; T_2: cumin–sorghum; T₃: cumin–mung bean; T₄: cumin–cluster bean; T₅: cumin–sesame; T₆: cumin–groundnut; T₇: cumin–cotton; and T₈: cumin–fallow. GC 4 (cumin), MPMH 17 (pearl millet), local (sorghum), GM 4 (mung bean), RGM 112 (cluster bean), RT 351 (sesame), HNG 10 (groundnut), Ajeet 155 (cotton) verities were taken for the experiment. The recommended doses of fertilizer (RDF) were given to each crop under the cropping sequences. Agronomic practices like seed rate, date of sowing, spacing, fertilizers, and irrigation practices used in each crop are provided in

Table 1. Details of agronomic practices for the crops used in cropping sequence.

Crop	Seed Rate (kg ha−1)	Date of Sowing	Spacing	Fertilizer	Irrigation
Cumin	12	1st week of November	30 × 7 cm	30 kg N: 20 kg P: 15 kg K 10 kg ZnSO4	Eight: 1st—just after sowing, 2nd—after 7 days of sowing, 3rd—7 days after 2nd irrigation; after that, 5 irrigations at 15–25 days interval
Pearl millet	4	1st week of July	45 × 15 cm	60 kg N: 30 kg P	Three—at the time of plant growth, earhead formation, and grain formation
Sorghum	10	1st week of July	45 × 15 cm	80 kg N: 40 kg P	Two—at the time of plant growth and earhead formation
Mung bean	15	1st week of July	30 × 10 cm	15 kg N: 40 kg P	One
Cluster bean	15	1st week of July	30 × 10 cm	10 kg N: 40 kg P	Two—20 days after sowing and after 20 days again if no rainfall
Sesame	2.5	1st week of July	30 × 10 cm	40 kg N: 25 kg P 250 kg gypsum	One
Groundnut	125	3rd week of June	30 × 10 cm	15 kg N: 60 kg P 250 kg gypsum	Two—at the time of flower formation and grain/pod formation
Cotton	1.8	3rd week of May	90 × 60 cm	150 kg N: 40 kg P	Six (first irrigation 30–35 days after sowing; after that, 20–25 days interval)

. The cropping history of the experimental fields for five years prior to the present experiment is provided in Supplementary Table S1. The weekly agro–meteorological parameters were recorded and presented in chart format during the years 2018–2019, 2020–2021, and 2021–2022, respectively (Supplementary Tables S2–S4).

2.3. Cumin Equivalent Yield (CEY)

The cumin equivalent yield (CEY) within cumin-based cropping sequences was determined using the following formula.

$$\mathrm{C}\mathrm{u}\mathrm{m}\mathrm{i}\mathrm{n} \mathrm{e}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d} (\mathrm{k}\mathrm{g} {\mathrm{h}\mathrm{a}}^{-1})=[\frac{\mathrm{Y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d} \mathrm{o}\mathrm{f} \mathrm{c}\mathrm{r}\mathrm{o}\mathrm{p}(\mathrm{k}\mathrm{g} {\mathrm{h}\mathrm{a}}^{-1})\times \mathrm{P}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e} \mathrm{o}\mathrm{f} \mathrm{c}\mathrm{r}\mathrm{o}\mathrm{p}(\mathrm{₹}{\mathrm{q}}^{-1})}{\mathrm{P}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e} \mathrm{o}\mathrm{f} \mathrm{c}\mathrm{u}\mathrm{m}\mathrm{i}\mathrm{n}(\mathrm{₹}{\mathrm{q}}^{-1})}]$$

(1)

2.4. Economics

The cost of cultivation for various treatments was computed based on the inputs utilized and their respective prevailing costs. Gross return (₹ ha⁻¹) was determined by considering the seed and straw yield in conjunction with their current market prices. These values were then employed to ascertain the net return (₹ ha⁻¹). The benefit-to-cost ratio was calculated by dividing the gross return by the cost of cultivation.

2.5. Analysis of Soil Enzymatic Activities and Soil Microbial Biomass Carbon

Soil samples from surface depth 0–30 were taken in small polythene bags from each plot by a core sampler over two consecutive years—specifically, in 2020–2021 and 2021–2022. The soil samples were air-dried, ground, passed through a 2 mm mesh-sieve [25], and analyzed for microbial parameters.

2.5.1. Fluorescein Diacetate (FDA) Hydrolysis

A Fluorescein Diacetate (FDA) hydrolysis assay was conducted using 100 mg of soil suspended in potassium phosphate buffer (pH 7.6) and FDA solution (0.5 mg/mL). Following 2-h incubation at 37 °C, the reaction was halted using acetone. The resultant solution was filtered through Whatman No. 1 filter paper, and the absorbance of the supernatant was measured at 490 nm, utilizing fluorescein as the standard [31]. The obtained values were expressed as µg fluorescein per gram of soil per hour (µg fluorescein g soil⁻¹ h⁻¹).

2.5.2. Alkaline Phosphatase (ALP)

The ALP activity analysis followed Tabatabai and Bremener’s [32] method. It involved preparing a substrate solution with buffered disodium p-nitrophenyl phosphate hexahydrate. Soil samples were mixed with this solution and incubated at 37 °C for an hour, enabling ALP reactions in the soil. ALP released p-nitrophenol (p-NP) due to enzymatic activity. Adding sodium hydroxide developed a yellow color, representing p-NP release. A spectrophotometer measured color intensity at 480 nm, directly linked to p-NP quantity. Enzyme activity was quantified using a calibration curve relating color intensity to known p-NP concentrations. ALP activity was expressed as µg p-nitrophenol phosphate g soil⁻¹ h⁻¹.

2.5.3. Dehydrogenase Activity

Dehydrogenase activity was assessed by monitoring the rate of tri-phenyl formazon (TPF) production from tri-phenyl tetrazolium chloride (TTC), serving as an electron acceptor. The method outlined by Klein et al. [33] was adhered for the dehydrogenase activity assay. In a 15 mL screw-capped tube, one gram of air-dried soil sample was placed. To create an anaerobic environment, 0.2 mL of 3% TTC and 0.5 mL of 1% glucose were added, and the tubes were incubated at 28 °C for 24 h. Following incubation, 10 mL of methanol was introduced, and the mixture was shaken for precisely one minute. After standing in the dark for 6 h, the pink color developed, and its intensity was measured at 485 nm (using a blue filter) with a spectrophotometer. The TPF produced in the samples was calculated from the standard curve drawn in the range of 0.004 to 0.4 mg TPF/10 mL of methanol. Dehydrogenase activity was expressed as µg TPF per gram of soil per day (µg TPF g soil⁻¹ day⁻¹).

2.5.4. Microbial Biomass Carbon

The microbial biomass carbon content in the soil was determined using the fumigation-extraction method. A 17.5 g moist soil sample was taken in duplicate in a Schott bottle. One bottle with soil was placed inside a vacuum desiccator and fumigated with fresh ethanol-free chloroform for 24 h. Excess chloroform was removed by repeated back suction. Both the fumigated and non-fumigated soil samples were then extracted with 70 mL of 0.5 M K₂SO₄. The carbon content in the extracts was determined by following the modified procedure outlined by Vance et al. [34].

$$\mathrm{M}\mathrm{B}\mathrm{C} (\mathrm{\mu }\mathrm{g} {\mathrm{g}}^{-1}\mathrm{o}\mathrm{f} \mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l})=\frac{(\mathrm{O}.\mathrm{D}.\mathrm{o}\mathrm{f} \mathrm{f}\mathrm{u}\mathrm{m}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{s}\mathrm{o}\mathrm{i}-\mathrm{O}.\mathrm{D}.\mathrm{o}\mathrm{f} \mathrm{n}\mathrm{o}\mathrm{n}\mathrm{f}\mathrm{u}\mathrm{m}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l})}{\mathrm{A}\mathrm{m}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l} \mathrm{u}\mathrm{s}\mathrm{e}\mathrm{d}}\times \mathrm{15,487}$$

(2)

2.6. Statistical Analysis

All data obtained from the experiments were utilized to analyze the mean values of three replications for each treatment. Statistical analysis was performed using Minitab 17 statistical software, employing a one-way analysis of variance (ANOVA). Grouping information between the mean values of the obtained data for each experiment was determined using the Fisher LSD method with a 95% confidence level (p ≤ 0.05). This approach allowed for a robust statistical assessment and comparison of the experimental results.

3. Results

3.1. Effect of Cumin-Based Cropping Sequences on Cumin-Seed Yield

Table 2. Effect of cumin-based cropping sequence on seed yield.

Treatments	Seed Yield (kg ha−1)
	2018–2019		2020–2021		2021–2022		Pooled
	Cumin	Kharif Crops	Cumin	Kharif Crops	Cumin	Kharif Crops	Cumin	Kharif Crops
Cumin–Pearl millet	577	1932	628	1847	571	2114	592	1964
Cumin–Sorghum	532	1505	606	1504	550	1790	563	1600
Cumin–Mung bean	514	656	575	597	509	841	533	698
Cumin–Cluster bean	541	861	591	748	526	912	553	840
Cumin–Sesame	466	580	564	503	473	512	501	532
Cumin–Groundnut	510	1503	557	1457	446	1514	504	1491
Cumin–Cotton	483	1382	537	1424	422	1469	481	1425
Cumin–Fallow	485	0	539	0	529	0	518	0
S.Em.±	22.7	47.1	24.4	45.0	32	37	15.5	25.0
C.D. (p = 0.05)	68.9	142.7	74.0	136.4	98	113	44.2	71.2
CV (%)	7.7	7.7	7.4	7.7	11.1	5.6	8.8	7.0

presents the seed yield for various cumin-based cropping sequences across three consecutive years (2018–2019, 2020–2021, and 2021–2022), as well as the pooled data for these years. The cumin–pearl millet-based cropping system showed significantly higher cumin-seed yield compared with the cumin–mung bean-, cumin–fallow-, cumin–cotton-, cumin–groundnut-, and cumin–sesame-based cropping systems. However, it was statistically similar to the cumin-sorghum and cumin–cluster bean cropping sequences. The cumin–pearl millet sequence consistently yielded a higher cumin-seed yield compared to the other sequences over the three years, with an average of 592 kg ha⁻¹. The cumin–sorghum and cumin–cluster bean sequences also exhibited relatively competitive cumin-seed yields, averaging around 563 and 553 kg ha⁻¹, respectively. The cumin–sorghum cropping sequence was significantly better than the cumin–cotton-, cumin–fallow-, cumin–groundnut-, and cumin–sesame-based cropping systems with respect to cumin-seed yield. In addition, the cumin–cluster bean-based cropping system resulted in a significantly higher cumin-seed yield, compared to the cumin–cotton-, cumin–groundnut-, and cumin–sesame-based cropping systems. The cumin–mung bean-based cropping system was statistically significantly better than the cumin–cotton-based cropping system with respect to cumin-seed yield. The minimum cumin-seed yield (481 kg ha⁻¹) was recorded with the cumin–cotton-based cropping system, followed by the cumin–groundnut- (504 kg ha⁻¹) and cumin–sesame (501 kg ha⁻¹)-based cropping systems over the three years. The cumin–cotton-, cumin–fallow-, cumin–groundnut-, and cumin–sesame-based cropping systems were statistically similar in relation to cumin-seed yield.

3.2. Effect of Cumin-Based Cropping Sequences on CEY

Table 3. Effect of cumin-based cropping sequence on CEY.

Treatments	CEY (kg ha−1)
	2018–2019	2020–2021	2021–2022	Pooled
Cumin–Pearl millet	812	882	809	835
Cumin–Sorghum	760	870	796	809
Cumin–Mung bean	800	866	848	838
Cumin–Cluster bean	746	798	777	774
Cumin–Sesame	693	789	660	714
Cumin–Groundnut	969	1068	866	968
Cumin–Cotton	927	1054	973	985
Cumin–Fallow	485	539	596	540
S.Em.±	25.7	27.9	29	16
C.D. (p = 0.05)	78.0	84.6	88	45
CV (%)	5.8	5.6	6.3	5.9

presents the crop equivalent yield (CEY) for various cumin-based cropping sequences across three consecutive years (2018–2019, 2020–2021, and 2021–2022), as well as the pooled data for these years. All cumin-based cropping systems showed a significantly higher crop equivalent yield (CEY) than that of the cumin–fallow system. The cumin–groundnut and cumin–cotton cropping sequences showed significantly similar CEYs and showed significantly higher CEY values than those of the cumin–pearl millet-, cumin–sorghum-, cumin–mung bean-, cumin–cluster bean-, cumin–sesame-, and cumin–fallow-based cropping systems. However, the cumin–cotton sequence system showed the highest CEY (985 kg ha⁻¹), followed by cumin–groundnut (968 kg ha⁻¹), over the three years. Cumin–sesame (714 kg ha⁻¹) showed least CEY value, followed by cumin–cluster bean (774 kg ha⁻¹). The cumin–pearl millet, cumin–sorghum, and cumin–mung bean cropping sequences were statistically similar and showed significantly higher CEY values than those of the cumin–cluster bean-, cumin–sesame-, and cumin–fallow-based cropping systems.

3.3. Effect of Cumin-Based Cropping Sequences on Economics

Table 4. Effect of cumin-based cropping sequence on economics.

Treatments	Gross Returns (₹ ha−1)				Net Returns (₹ ha−1)				B:C Ratio
	2018–2019	2020–2021	2021–2022	Pooled	2018–2019	2020–2021	2021–2022	Pooled	2018–2019	2020–2021	2021–2022	Pooled
Cumin–Pearl millet	129,988	127,949	161,769	139,902	80,488	78,449	105,769	88,235	2.63	2.58	2.89	2.70
Cumin–Sorghum	121,666	126,192	159,148	135,669	69,436	73,962	100,148	81,182	2.33	2.42	2.70	2.48
Cumin–Mung bean	128,073	125,522	169,598	141,064	73,473	70,922	108,098	84,164	2.35	2.30	2.76	2.47
Cumin–Cluster bean	119,319	115,663	155,432	130,138	65,819	62,163	94,932	74,305	2.23	2.16	2.57	2.32
Cumin–Sesame	110,804	114,470	131,919	119,064	58,004	61,670	72,419	64,031	2.10	2.17	2.22	2.16
Cumin–Groundnut	155,080	154,909	173,179	161,056	64,080	63,909	73,179	67,056	1.70	1.70	1.73	1.71
Cumin–Cotton	148,382	152,773	194,595	165,250	64,382	68,773	99,595	77,583	1.77	1.82	2.05	1.88
Cumin–Fallow	77,600	78,203	119,174	91,659	41,600	42,203	77,174	53,659	2.16	2.17	2.84	2.39
S.Em.±	4115.8	4042.6	5785	2723	4115.8	4042.6	5785	2723	-	-	-	-
C.D.(p = 0.05)	12,484.0	12,262.0	17,548	7762	12,484.0	12,262.0	17,548	7762	-	-	-	-
CV (%)	5.8	5.6	6.3	6.0	11.0	10.7	11.0	11.1	-	-	-	-

presents the economic performance of various cumin-based cropping sequences over the three-year period. The parameters evaluated included gross returns (₹-ha⁻¹), net returns (₹ ha⁻¹), and the benefit-cost (B:C) Ratio. All cropping sequences showed statistically significant gross returns and net returns compared to the cumin–fallow system. In addition, the cumin–fallow system showed a higher B:C ratio (2.39) than that of the cumin–cotton (1.88), cumin–groundnut (1.71), cumin–sesame (2.16), and cumin–cluster bean (2.32) cropping sequences. Cumin–groundnut and cumin–cotton were recorded as equally significant in preference to the cumin–fallow, in relation to gross returns and net returns. However, both these systems showed the lowest B:C ratios. The cumin–sesame cropping sequence was recorded as statistically similar to cumin–groundnut with respect to net return. However, it exhibited a greater B:C ratio compared to that of the cumin–groundnut and cumin–cotton cropping sequences. The lowest net return was recorded with the cumin–fallow system (₹53,659 ha⁻¹), followed by cumin–sesame (₹64,031 ha⁻¹). The cumin-pearl millet, cumin–sorghum, and cumin–mung bean cropping sequences consistently exhibited statistically similar gross returns and net returns and were at par with the other cumin-based cropping sequences. The highest net return and highest B:C ratio were recorded with the cumin–pearl millet (₹88,235 ha⁻¹ and 2.70, respectively) cropping sequence, followed by cumin–sorghum (₹8182 ha⁻¹ and 2.48, respectively) and cumin–mung bean (₹84,164 ha⁻¹ and 2.47, respectively).

3.4. Effect of Cumin-Based Cropping Sequences on Soil Microbial Activities and Soil Microbial Biomass Carbon

The cropping sequences involving cumin–mung bean, cumin–cluster bean, cumin–pearl millet, and cumin–groundnut exhibited significantly higher levels of fluorescein diacetate (FDA), alkaline phosphatase (ALP), microbial biomass carbon (MBC), and dehydrogenase activity (DHA) in comparison to the cumin–sorghum, cumin–sesame, cumin–cotton, and cumin–fallow cropping systems (Figure 1 and Figure 2). Notably, there were no significant differences among the cumin–pearl millet, cumin–mung bean, cumin–cluster bean, and cumin–groundnut cropping sequences in relation to their FDA, ALP, MBC, and DHA levels. Conversely, the cumin–sorghum, cumin–sesame, cumin–fallow and cumin–cotton treatments displayed relatively lower values for FDA, ALP, MBC, and DHA. These treatments exhibited statistical similarity concerning these soil parameters, as depicted in Figure 1 and Figure 2. The cumin–fallow systems recorded the minimum MBC and FDA values, which were 33% and 27.70% lower, respectively, when compared to those of the cumin–pearl millet cropping system. In contrast, ALP and DHA were at their minimum in the cumin–cotton and cumin–sesame cropping sequences, respectively. These values were lower by 52% and 29.39%, respectively, when compared to that of the cumin–pearl millet cropping sequence.

4. Discussion

The findings of the present study provide valuable insights into the impact of different crop rotation systems on cumin-seed yield. The observed higher yields in the cumin–pearl millet sequence, followed by the cumin–sorghum and cumin–cluster bean sequences, suggest that these combinations are more favorable for cumin cultivation than other cropping systems. The consistent superiority of the cumin–pearl millet sequence in terms of seed yield may be attributed to synergies between cumin and pearl millet. These synergies could include efficient resource utilization, competition against weeds, and reduced pest pressure. The similarities in yield among cumin–pearl millet, cumin–sorghum, and cumin–cluster bean sequences may be linked to shared growth patterns, resource utilization strategies, and, possibly, more effective pest-management practices in these combinations. The contrast in yield between the cumin–cotton sequence and the more productive sequences like cumin–pearl millet suggests that resource competition and differences in growth requirements play a crucial role. The high resource demands of cotton may have led to competition with cumin for essential resources, resulting in lower cumin-seed yields in the cumin–cotton sequence. These findings align with previous research by Patel et al. [35], which also highlighted the significance of crop sequences in influencing cumin yield. The study supports the idea that certain crop combinations, such as green gram–cumin, can positively impact cumin yield compared to other sequences like sesame–cumin and sorghum–cumin. The regional variation in cumin yield reported by Patel et al. [36], where cumin after green gram outperformed other kharif crops, further emphasizes the importance of selecting appropriate crop sequences for optimal cumin production. This suggests that the choice of preceding crops can significantly influence cumin yield and should be considered in crop rotation planning. In our present study, the observed variations in yield among different sequences highlight the need for tailored crop management practices based on the compatibility of crops in rotation. This information can be valuable for farmers and agricultural practitioners looking to optimize cumin production through effective crop rotation strategies.

The introduction of the concept of combined equivalent yield (CEY) provides a comprehensive measure for evaluating the overall productivity of different cropping sequences. The comparison of cumin-based cropping sequences with the cumin–fallow system highlights the significance of continuous land utilization for cultivation rather than allowing it to remain fallow. The utilization of companion crops in cumin-based sequences contributes to increased overall yields, emphasizing the positive impact of crop rotation on land productivity. The finding that the cumin–cotton sequence exhibited the highest CEY suggests a synergistic interaction between cumin and cotton crops. The deep root system of cotton plants may have played a role in maintaining soil structure and fertility, ultimately benefiting subsequent cumin crops. This result supports the idea that certain crop combinations can enhance overall land productivity. The lower CEY values observed in cumin–sesame and cumin–cluster bean cropping sequences may be attributed to differences in growth habits, root systems, and potential resource competition between cumin and sesame or cluster bean. This underscores the importance of considering crop compatibility and resource requirements when designing cropping sequences. The consistent and competitive CEY values of cumin–pearl millet, cumin–sorghum, and cumin–mung bean sequences highlight their potential for sustainable and productive agricultural systems. This information can be valuable for farmers seeking to optimize land productivity through well-designed crop rotations. The higher equivalent yield in the green gram–cumin crop rotation, as reported by Patel et al. [35], aligns with the findings of the present study, emphasizing the significance of crop selection in rotation. The influence of cropping sequences on wheat yield, as investigated by Patel et al. [37], further reinforces the idea that thoughtful selection of crops in rotation is essential for maximizing overall crop productivity. Dogan et al. [38] also reported that appropriate selection of crops in rotation is important to increase crop yield.

All the cumin-based cropping sequences exhibited statistically significant higher gross returns and net returns than those of the cumin–fallow system. This emphasizes the economic importance of active land utilization and cultivating companion crops instead of leaving the land fallow. The economic evaluation of cumin-based cropping sequences highlights the advantages of active land utilization and cultivation of companion crops. Although some sequences showed higher returns, their B:C ratios were lower, possibly due to higher input costs. Cumin–sesame demonstrated a favorable B:C ratio, despite slightly lower returns. Surprisingly, the cumin–fallow system exhibited a higher B:C ratio (2.39) than that of certain cumin-based cropping sequences, including cumin–cotton, cumin–groundnut, cumin–sesame, and cumin–cluster bean sequences. This observation indicates that while the cumin–fallow system might have lower gross and net returns, the costs associated with it are also significantly lower, leading to a relatively higher B:C ratio. The study underscores the importance of selecting balanced and sustainable cropping sequences, as exemplified by cumin–pearl millet, cumin–sorghum, and cumin–mung bean, for optimizing economic returns while effectively utilizing available resources. Patel et al. [35] reported that the achieved maximum net realizations and BCR values were higher in the green gram–cumin crop sequence, closely followed by sorghum–cumin sequence.

In this present study, the FDA, ALP, MBC, and DHA levels in the soil varied across different cropping sequences. The variations in FDA, ALP, MBC, and DHA activity across different cropping sequences reflect differences in soil microbial communities’ composition and metabolic dynamics. Higher FDA, ALP, and DHA activity in certain sequences (cumin–mung bean, cumin–clusterbean, cumin–pearl millet, and cumin-–groundnut) indicates enhanced soil microbial metabolic potential, resulting in increased organic matter decomposition and nutrient release. Cropping sequences with higher MBC values, such as cumin–mung bean, cumin–cluster bean, cumin–pearl millet, and cumin–groundnut, may support increased nutrient cycling and organic matter breakdown, due to more abundant and active microbial populations. Conversely, the lower values observed in cumin–sorghum, cumin–sesame, and cumin–cotton treatments might suggest potential challenges or limitations in terms of supporting robust soil microbial communities and their associated activities. This could be due to factors such as resource competition, allelopathic effects, or less favorable soil conditions for microbial growth and activity. The observed variations in enzyme activities can be attributed to the influence of different companion crops and their interactions with soil microorganisms. These results can guide farmers in making informed decisions about cropping-sequence selection to enhance soil health and overall sustainability.

Previous studies have observed improvements in soil physical [39], biological [40] and chemical properties [41] with diverse rotations. Chary et al. [42] reported that the biological soil quality parameters, viz. DHA, MBC, and labile carbon, were found to be significantly influenced by cropping sequences. In this present study, soil microbial activities were observed least in the cumin–fallow system. Our results are corroborated by Gikonyo et al. [43], who reported that most enzyme activities were found to be higher in a frequently cultivated field than in a fallow field, and higher in crop-rotated fields than in single-crop patterns. Our results are also corroborated by the results of Singh et al. [44], who reported that a rice–wheat–mung bean cropping system showed more FDA, DHA and MBC than those of a rice–wheat cropping system. Patil and Puranik [45] also reported that legume intervention in crop rotation increased the microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) over those of a cereal–cereal cropping system. Qin et al. [46] found that the inclusion of legume in a cropping system had the potential to improve the soil biology, soil health, and soil fertility, and to alleviate continuous cropping obstacles and increase crop yield. McDaniel and Grandy [47] found that red clover in a corn–soybean–wheat cropping system enhanced soil microbial biomass and activity through the addition of labile residues and nitrogen. Similarly, Dou et al. [48] and Wacal et al. [49] also reported that soil enzyme activities such as urease, alkaline phosphatase, and d-glucosidase were lower in soils under monoculture than rotation cropping. In a study conducted in a tropical region of Colombia, [50] observed that a silvi–pastoral system stimulated soil MBC and enzymatic (β-glucosidase, UA, and alkaline and acid phosphatase) activities and provided a more favorable microbial habitat than that of a monoculture pasture.

In a study conducted in a tropical region of Colombia, ref. [50] observed that a silvi–pastoral system stimulated soil microbial biomass carbon and enzymatic activities, including β-glucosidase, urease, as well as alkaline and acid phosphatase. This system was found to provide a more favorable microbial habitat compared to monoculture pasture practices. Acosta-Martinez et al. [51] investigated that monoculture practices lead to a reduction in soil enzyme activities when compared to rotations involving leguminous crops.

5. Conclusions

Among the cropping sequences examined, it was found that cumin cultivation following pearl millet yielded the highest cumin output. Notably, the cumin–cotton sequence showcased a favorable cumin equivalent yield, closely trailed by cumin–groundnut. Economic analysis demonstrated that the cumin–pearl millet cropping sequence yielded the most significant net realizations (₹88,235 ha⁻¹) and an impressive benefit–cost ratio of 2.7. This was followed closely by the cumin–mung bean cropping system, underlining their economic viability in challenging climatic conditions. Remarkably, the study revealed that specific cropping sequences, such as those involving cumin–mung bean, cumin–cluster bean, cumin–pearl millet, and cumin–groundnut, contributed to significantly heightened levels of soil microbial activities. These included key enzymatic indicators like fluorescein diacetate (FDA), alkaline phosphatase (ALP), dehydrogenase activity (DHA), and soil microbial biomass carbon (MBC). These findings emphasize the potential of these cropping sequences to foster improved soil health and microbial functioning, even in the face of hot, arid climatic challenges. The study sheds light on sustainable agricultural practices that can enhance cumin yield, economic benefits, and the resilience of soil ecosystems in such demanding conditions.