Legume–Cereal Cover Crops Improve Soil Properties but Fall Short on Weed Suppression in Chickpea Systems

Abstract

Chickpea is a highly weed-prone crop with limited herbicide options and high labor demands, raising the following question: Can fall-planted legume–cereal cover crops (CCs) improve soil properties while reducing herbicide use and manual weeding pressure? To explore this, we evaluated the effect of fall-planted winter rye (WR) alone in 2021 and mixed with hairy vetch (HV) in 2022 and 2023 at Randolph farm in Petersburg, Virginia. The objectives were two-fold: (a) to examine the effect of CCs on soil properties using monthly growth dynamics and biomass harvested from fifteen 0.25 m²-quadrants and (b) to evaluate the efficiency of five termination methods: (1) green manure (GM); (2) GM plus pre-emergence herbicide (GMH); (3) burn (BOH); (4) crimp mulch (CRM); and (5) mow-mulch (MW) in suppressing weeds in chickpea fields. Weed distribution, particularly nutsedge, was patchy and dominant on the eastern side. Growth dynamics followed an exponential growth rate in fall 2022 (R² ≥ 0.994, p < 0.0002) and a three-parameter sigmoidal curve in 2023 (R² ≥ 0.972, p < 0.0047). Biomass averaged 55.8 and 96.9 t/ha for 2022 and 2023, respectively. GMH consistently outperformed GM in weed suppression, though GM was not significantly different from no-till systems by the season’s end. Kabuli-type chickpeas under GMH had significantly higher yields than desi types. Pooled data fitted well to a three-parametric logistic curve, predicting half-time to 50% weed coverage at 35 (MM), 38 (CRM), 40 (BOH), 46 (GM), and 53 (GMH) days. Relapses of CCs were consistent in no-till systems, especially BOH and MW. Although soil properties improved, CCs alone did not significantly suppress weed.

1. Introduction

Fall-planted cover crops (CCs) have long been recognized as a sustainable agricultural practice serving multiple purposes. These benefits include enhancing biodiversity, improving soil health, reducing erosion, and suppressing pests, diseases, and weeds [1]. CCs play a pivotal role in maintaining and enhancing soil health by contributing to soil organic matter, improving soil structure, mitigating nutrient depletion, and stimulating microbial activity in both tilled and no-tilled systems [2,3]. Incorporating CCs results in better water infiltration, reduced soil compaction, and improved nutrient availability, all of which are essential for sustainable crop production [2,3,4,5]. Recent studies across diverse agroecological zones highlight their effectiveness, demonstrating significant improvements in soil properties, overall soil health, and crop yields through mechanisms such as organic matter incorporation, nutrient cycling, and microbial community enhancement, leading to nutrient mineralization and soil structure stability [6,7]. Consequently, the global agricultural community is increasingly integrating CCs into food crop production systems for their multifaceted benefits [7].

A meta-analysis by Poeplau and Don [8] revealed that CCs increase soil organic carbon stocks, with cumulative effects over long-term applications. Leguminous CCs, alone or mixed with grasses, contribute nitrogen to the soil through biological nitrogen fixation, reducing dependency on synthetic fertilizers [6,9]. These processes collectively improve water retention and aggregate stability, mitigating soil erosion and drought stress. However, not all studies align with these positive outcomes. Blanco-Canqui et al. [3] observed less pronounced benefits of cover crops from some regions under water-limited conditions or with improper species selection. Likewise, certain cover crops may compete with cash crops for water and nutrients, potentially reducing yields in resource-limited environments [10,11]. While these findings highlight the importance of adaptive management and context-specific strategies, the growing body of research largely supports the service role of CCs as a critical component of sustainable agriculture [12].

In addition to soil benefits, CCs play a crucial role in suppressing weeds as a living plant or mulch through direct competition, allelopathy, or by providing a physical barrier [13]. However, the interaction of these benefits with different tillage practices under different environments remains poorly understood. This is relevant to this study, which involves chickpea (Cicer arietinum L.) production, where weed pressure and nutrient demands are critical determinants of yield. While several studies have demonstrated the weed-suppressive potential of CCs, results are often inconsistent across different cropping systems and regions [14]. For instance, some research has found that CCs can reduce weed biomass by creating a physical barrier or through allelopathic effects, whereas others report limited weed suppression due to factors such as poor cover crop establishment or inadequate biomass production [15]. Evaluating the effectiveness of CCs for weed control in chickpea production in humid and rainy conditions is critical for outlining the efficiency of each system and developing best management practices.

Suppression of weeds and diseases is a priority for chickpea, a legume ranked as the third most important pulse crop next to beans and peas in terms of global production and highly popular for its nutritional value, longer shelf life, and efficient atmospheric nitrogen fixation. For one, chickpeas exhibit slow initial growth and modest plant height and possess an open erect plant architecture, leading to late canopy cover [16]. The extent of damage by weeds and diseases on chickpeas is exacerbated in the southeastern US, where humidity is high and the weather is erratic during the peak growing season. This further complicates the tri-directional interaction of chickpea with biotic constraints (weeds and pathogens) on one hand and CCs on the other. Traditionally, farmers in cool and dry areas have relied on tillage or herbicides for weed control in chickpeas, but these practices can have negative environmental impacts, including soil erosion, the loss of organic matter, and the contamination of water resources [17]. Recent research underscores the importance of integrated pest management (IPM) strategies, including the use of CCs, to address these challenges [18].

The success of no-till production systems depends heavily on cover crop termination methods. Some studies report effective weed suppression and improved soil health with these methods, while others highlight challenges such as incomplete termination and weed resurgence [19,20]. The density of CCs is known to significantly influence the effectiveness of weed suppression and soil health improvement. Recent research indicates that dense cover crop stands can significantly enhance their utility. Whereas high-density CCs provide better ground coverage, reducing light availability for weed germination and growth, poor stands may lead to insufficient weed control [21]. Despite the numerous benefits, the implementation of CCs poses challenges, including the selection of appropriate species, the timing of planting and termination, and the potential competition with main crops for resources [22]. Addressing these challenges through research and adaptive management practices is crucial for optimizing the benefits of CCs [23] in sustainable food production systems [24].

In this study, mow-mulching, burning, and crimp mulching were tested in a no-till chickpea production system over a three-year period following the planting of either a grass mono-culture (winter rye (WR)) or a mixed-culture with a legume (hairy vetch (HV)). Two additional methods in a tilled system, green manure (GM) and GM plus pre-emergence herbicide (GMH), were added, bringing the total number of termination methods to five. Given that chickpea is a highly weed-prone crop with limited herbicide options and high labor demands, this study was guided by the hypothesis that fall-planted legume–cereal CCs could improve soil properties while reducing the need for herbicides and manual weeding. Accordingly, our objectives were two-fold: (a) to examine the effect of CCs on soil properties using soil sampling and monthly growth dynamics and biomass accumulation from replicated 0.25 m² quadrants and (b) to evaluate the efficiency of the five termination methods in suppressing weeds in chickpea fields. The current study, along with future research efforts, will contribute to optimizing cover crop mixtures, planting time, and termination approaches while promoting the integration of CCs into sustainable production practices.

2. Materials and Methods

Study Site, Cover Crop Planting, and Maintenance: This study was conducted on a 0.71-hectare plot of land at Virginia State University’s Randolph Farm located ar 37.227096° N, −77.439227° W. The site has fine sandy loam soil consisting of Norfolk (94%) and Faceville (6%) soil series with a loamy marine deposit parent material. The slope ranges from 0 to 6%. Further descriptions of the field are available in the Natural Resource Conservation Service (NRCS) Web Soil Survey. Weather parameters contrasting the three seasons in terms of monthly average soil temperature (°C), ambient temperature (°C), precipitation (mm), relative humidity (%), and wind speed (m/s) are acquired for the study duration from the U.S. National Oceanic and Atmospheric Administration (NOAA).

Winter rye (Secale cereale L.), a grass monocot, was planted as a monoculture on 15 December 2021. In two subsequent fall plantings, hairy vetch (Vicia villosa L.), a leguminous dicot, was added into the cover crop system as part of a mixed planting procedure on 15 November 2022 and 13 October 2023. A 2.13 m wide tractor-propelled Great Plains 706NT no-till drill (Ashland, VA, USA) was used for all plantings. The seeding rates were 135 kg/ha for winter rye and 22.4 kg/ha for hairy vetch to ensure optimal establishment and growth [25]. Cover crops were fertigated uniformly across the study area using 22.68 kg of 34-46-60 NPK fertilizer per application using individual bags applied at 3, 3 and 4 months after fall planting in 2021, 2022, and 2023, respectively.

Cover Crop Growth Dynamics and Harvest: A 0.71-hectare plot, with a respective length and width measurement of 62.48 m and 112.78 m, was measured. The cross section (128.92 m) was then divided into 11 equally spaced transectional sampling spots using a measuring tape to represent systematic and representative data (Figure 1). Growth dynamics of the cereal (winter rye (WR)) and legume (hairy vetch (HV)) were visually monitored [13,15] on a monthly basis, counted from the respective planting dates. During each sampling event, a 0.5 m × 0.5 m quadrant was placed at a designated sampling spot, and a high-resolution photograph was taken from knee height to document growth as a time series until termination. The images were then contrasted with the percentage area covered by the CCs (WR, HV), weed growth (WD), and bare ground (BG). Based on historical observations on the spotty nature of weed infestation—particularly of nutsedge—on the majority of the southeastern portions of the study area, 11 of the samples were partitioned into western, eastern (4 samples each), and middle (3 samples) portions of the study area.

During the second and third weeks of April, cover crops were terminated to record the biomass increase, study the weed suppression effect in a tilled or not-tilled chickpea system, and analyze its effect on soil properties. In 2023 and 2024, cover crop mixes were harvested from 15 quadrants, each measuring 0.5 m × 0.5 m. These quadrants were partitioned into the three density categories at harvest, namely, “dense”, “poor”, and “no growth”, with each represented by five randomly selected quadrants during sampling. For each quadrant, the percentage of area covered by weeds, cover crops, and bare ground was visually estimated. The average height of the cover crops within the quadrant was measured for three plants and multiplied by the area of the quadrant, which is 0.25 m², in order to estimate the total volume of cover crops. Following these assessments, the WR–HV mix within each quadrant was uprooted, cut at the soil line, and harvested. The harvested samples were then manually separated into grass (WR) and legume (HV) components to allow for species-specific analysis. Fresh biomass (g) and dry matter (g) of the harvest representing each density were recorded using a Sartorius digital laboratory balance.

Cover Crop Termination and Weed Suppression: The cover crop system (monocrop or mixed) was terminated in April using five methods primarily categorized into two main groups: the first group involved tilled/green-manured termination without (T-GM) and with (T-GMH) the pre-emergence herbicide application, and the second group comprised no-till (NT) methods including mowing–mulching (NT-MM), crimping (NT-CR), and burning with a (bio)herbicide (NT-BH). For NT-BH, the organic herbicide BioSafe weed and grass killer, with 40% ammonium nonanoate as the active ingredient (a.i.) (BioSafe Systems LLC, East Hartford, CT, USA) was used in 2023 and 2024 at the rate of 12% v/v in 187 L per ha. Glystar (@) Star (glyphosate as the isopropylamine salt, 41%), 450 g a.e. (acid equivalent)/L at 7 L/ha., was used in 2022. Whereas NT-CR was established using a compact 1.75 m width crimper model 6-1S equipped with a chevron blade pattern and mounted on a 3-point hitch-compatible tractor, NT-MM was implemented by mowing the cover crop at ankle height using a mechanical mower, allowing residues to form a surface mulch over the planting rows. Four chickpea varieties, two desi (Kala Chana, Myles) and two kabuli (Leader, Orion) types were planted on 25 April 2023 and 24 April 2024 using a tractor-mounted 38.1 cm row planter. A plot consisted of four rows spaced 0.38 m apart, each 3.05 m in length, at a planting density of approximately 54 plants/m². In 2022, the varieties Myles, Orion, and Frontier were planted on May 16. Chickpea seeds were treated with Obvius™ (BASF, Raleigh, NC, USA) before planting. At harvest, chickpea plants were manually pulled, and roots were snipped at the soil line, air-dried, and threshed using a Haldrup USA thresher to measure grain yield. This study was arranged in a split-plot design with three replications, where the termination type was the main plot and variety was the sub-plot.

Soil Sampling: Samples were collected at different times and sent to Waypoint Analytical Inc. (Richmond, VA, USA) for a comprehensive soil analysis. In March and May of 2024, soil samples were specifically collected from quadrants representing three categories of CCs: dense, poor, and bare ground. At each sampling unit, a stainless steel Varomorus soil probe (30.48 cm) was used to collect soil samples. These samples were further divided into three depth profiles, namely, upper (0–10.16 cm), middle (10.16–20.32 cm), and lower (20.32–30.48 cm), to provide a detailed assessment of the soil properties across the vertical profile.

Data Collection and Statistical Analysis: Weed coverage (%) was visually rated at 14, 21, 64, and 80 days after planting (DAP) in 2022; 14, 21, 45 and 92 DAP in 2023; and 14, 21, 58 and 70 DAP in 2024. In 2023 and 2024, the proportions of legume (HV) and cereal (WR) cover crops relapsed in each plot were estimated, since this continued to be a challenge in the rainy weather. The area under the weed growth curve (AUWGC) was calculated and then scaled to the relative AUWGC (rAUWGC) to account for the variability in the duration where data was collected in each year.

Statistical Analysis: Due to significant deviations from normality (Shapiro–Wilk p < 0.05), a rank transformation was applied to all dependent variables that involved percentages prior to ANOVA. The ranked values were then analyzed using PROC GLM in SAS (version 9.4; SAS Institute Inc., Cary, NC, USA) to assess the effects of individual independent variables and their interaction. Mean separations were conducted using Tukey’s Honestly Significant Difference (HSD) on ranked data. For non-percentage data, normality was checked, and data were analyzed using analysis of variance (ANOVA) to determine the effects of tillage, cover crops, and their interactions on weed suppression, organic matter content, chickpea growth, and yield. Post hoc analyses were performed using Tukey’s HSD test to separate means at a significance level of p < 0.05. Correlation and regression analyses were used in SigmaPlot (version 16; Graffiti LLC, Palo Alto, CA, USA) to explore the relationships between cover crop biomass and weed suppression, as well as between organic matter content and chickpea yield.

3. Results

Cover Crop Establishment Dynamics: Across all sampling locations, winter rye (WR) and hairy vetch (HV) exhibited slow initial growth followed by a sharply pronounced increase in coverage (mean percentage ± standard error SE) during the final two months prior to termination. In the fall 2022 planting (Figure 2A), the percentage WR coverage increased from 7.8 ± 0.42 at 4 months after planting (MAP) in March to 37.3 ± 1.70 at 5 MAP in April. A similar pattern was observed in the fall 2023 planting (Figure 2B), though the growth trajectory accelerated earlier, with WR and HV coverage increasing markedly during the last three months before termination. The percentage coverage of the legume HV in the 2023 planting, for instance, reached 34.7 ± 7.82 in February (4 MAP), 67.2 ± 6.01 in March (5 MAP), and 73.7 ± 4.83 in April (6 MAP). The WR–HV ratio was 0.6 in the 2022 fall planting compared to only 0.23 in the fall 2023 planting, indicating a more dominant legume component in the latter planting.

Weed (WD) coverage increased steadily during the first 3–4 MAP in both planting years, particularly in areas with persistent infestations of grasses, nutsedge, and broadleaf weeds, as noted in the eastern corner plot section of the study area (Figure 2A,B). The earlier fall planting in 2023 resulted in faster crop establishment, reduced weed pressure, and quicker soil coverage than the fall planting in 2022. As expected, the extent of the bare ground (BG) observed declined steadily with the increased coverage of WR, HV, and WD, implicating progressive canopy closure and improved soil protection over time. Notably, earlier planting in October 2023 facilitated faster crop establishment and accelerated ground coverage compared to the delayed plating in November 2022.

Regression Modeling of Cover Crop and Weed Dynamics: Regression analysis was used to model temporal changes in the ground coverage of WR, HV, WD, and BG over the course of the two fall planting in 2022 and 2023. The best fit models were selected based on the coefficient of determination (R²), significance (p-values), and biological relevance (Figure 3,

Table 1. Summary of the parameters observed and the outcomes of fits for the winter rye and hairy vetch mix cover crop monthly growth dynamics using different regression models.

Parameter Observed	Regression Model	R2 Value	p-Value	Model Parameters (Mean ± SE)
				a	b	X0
Fall 2022 Cover Crop Planting
Winter rye (WR)	Exponent 2-parameter	0.994	0.0002	0.0288 ± 0.0200	1.43 ± 0.14
Hairy vetch (HV)	Exponent 2-parameter	0.999	<0.0001	0.0384 ± 0.0085	1.46 ± 0.04
Bare ground (BG)	Sigmoidal 3-parameter	0.996	0.0005	91.9 ± 5.18	−0.23 ± 0.20	4.16 ± 0.17
Weeds (WD)	Gaussian 3-parameter	0.983	0.0017	16.92 ± 1.21	0.83 ± 0.07	3.75 ± 0.07
Fall 2023 Cover Crop Planting
Winter rye (WR)	Sigmoidal 3-parameter	0.972	0.0047	25.4 ± 4.33	1.15 ± 0.34	3.63 ± 0.55
Hairy vetch (HV)	Sigmoidal 3-parameter	0.990	0.0010	78.1 ± 6.02	0.62 ± 0.13	4.11 ± 0.17
Ground (BG)	Sigmoidal 3-parameter	0.993	0.0005	92.2 ± 4.23	−0.63 ± 0.09	3.68 ± 0.12
Weeds (WD)	Gaussian 3-parameter	0.981	0.0027	14.0 ± 0.89	1.01 ± 0.07	3.77 ± 0.07

).

As noted above, cover crop growth patterns differ between the two seasons owing primarily to the planting dates and weather. During the fall 2022 planting, both WR and HV followed an exponential growth trend, which is well described by the two-parameter exponential models, with R² = 0.994 and 0.999, respectively. These models suggest that the crops were still in the rapid expansion phase at termination (5 MAP), most likely due to the delayed planting date (Figure 3a). In contrast, the fall 2023 planting, which began a month earlier, resulted in cover crop growth that more closely fit to the three-parameter sigmoidal models (R² = 0.972 for WR and R² = 0.990 for HV) (Table 1). This shift indicates earlier crop maturity and canopy stabilization, as is evident in HV’s higher late-season coverage (e.g., March and April 2024), as shown on Figure 3b and Table 1.

In both years, BG coverage showed a consistent and rapid decline after planting, attributed to the progressive canopy closure by cover crops and weed growth (Figure 3a,b). This decline was best explained by a three-parameter sigmoidal model with a high accuracy (R² = 0.996 for 2022; R² = 0.993 for 2023) and a negative growth parameter (b = −0.23 for 2022; b = −0.63 for 2023), capturing the progressive decrement of the exposed soil surface as the vegetative growth cover increased (Table 1).

Weed dynamics followed a bell-shaped trajectory typical of early growth and establishment followed by subsequent canopy closure, which was accurately modeled using Gaussian three-parameter curves (R² = 0.983 for 2022; R² = 0.981 for 2023). Weed coverage was, at peak, earlier in February during the fall 2023 planting season (Figure 3b) than in the March peak during the 2022 fall planting (Figure 3a) due to the faster crop establishment and canopy dominance by cover crops when they are planted earlier in the fall. This temporal shift underscores the enhanced weed suppression potential of early planting.

With all components taken together, these models highlight the importance of the planting date in shaping the cover crop growth dynamics and weed suppression efficacy before termination. The transition from exponential to sigmoidal growth patterns between the 2022 and 2023 fall planting seasons and the earlier weed cover in the fall 2023 planting suggest that earlier planting supports more synchronized and complete cover crop development, resulting in improved agro-ecological performance.

Biomass Accumulation and Weed Suppression under Three Cover Crop Canopy Densities: Over 90–95% of the 0.71-hectar study area at Randolph farm achieved a dense cover crop establishment, defined as 96–100% area coverage by either monoculture winter rye (WR) in 2021 or a WR–HV mix in 2022 and 2023. Quadrant-based assessment at the time of termination revealed that dense cover crop zones, as expected, exhibited significantly (p < 0.05) lower weed coverage compared to poorly established (15–35%) and bare (0%) zones (Figure 4A,B). This was consistent across both termination periods in April, demonstrating the critical role of dense canopy in effective weed suppression.

Fresh biomass followed a similar pattern. In the 2022 fall planting, which was terminated in April 2023, total biomass production in densely cover crop grown areas reached 31.41 t/ha for WR and 24.40 t/ha for HV (Figure 4C). These values were significantly higher (p < 0.05) than in poorly covered zones, where biomass reached only 7.36 t/ha for WR and 4.46 t/ha for HV (Figure 4C). The corresponding dry matter accumulation (t/ha) was also highest in dense stands, reaching 15.02 t/ha compared to 5.6 t/ha in poorly dense plots, representing a 168% increase. These results and the differences therein highlight the critical need for a good density of cover crops as it impacts the biomass and dry matter potential of WR–HV mixtures under field conditions.

The biomass from the fall 2023 planting (terminated in April 2024) was higher than that from the fall planting in 2022. For instance, dense plots terminated in April 2024 yielded 1.7 times fresher biomass than those terminated in April 2023, largely driven by a 2.4-fold increase in HV, while WR biomass remained relatively stable.

Weed Suppression Outcomes under Tilled vs. No-Till Systems: The cover crop termination method had a strong and consistent impact on weed suppression across all three years (p < 0.001). Among the methods tested, green manuring plus with a pre-emergence herbicide (GMH) delivered the lowest relative area under the weed growth curve (rAUWGC), making it the most effective strategy for weed control (

Table 2. Relative area under the weed growth curve (rAUWGC) for chickpea varieties under five cover crop termination methods across three fall planting seasons (2021–2023). Cover crops consisted of winter rye (WR) alone in fall 2021, and a winter rye–hairy vetch (HV) mixture in fall 2022 and fall 2023. Termination methods included crimped (CR), mow-mulched (MM), burnt with (bio)herbicide (BOH/BH), green manured (GM), and green manured plus pre-emergence herbicide (GMH). rAUWGC values represent the cumulative weed pressure per plot. With no significance in the interaction terms, mean separation according to Tukey’s test was only shown for the main plot effect, i.e., the cover crop termination method.

Cover Crop Termination	rAUWGC Fall 2021	rAUWGC Fall 2022	rAUWGC Fall 2023
Crimped (CR)	49.3 a	62.6 ab	15.6 bc
Mow-mulched (MM)	47.7 a	63.3 a	27.3 a
Burnt with (Bio) herbicide (BH/BOH)	41.7 a	64.1 a	21.6 ab
Green manured (GM)	29.4 b	54.2 b	25.9 ab
GM plus pre-emergence herbicide (GMH)	23.8 b	25.0 c	9.4 c
Cover Crop Termination	p < 0.001	p < 0.001	p < 0.001
Variety	p = 0.0119	p = 0.9596	p = 0.4119
Cover Crop Termination × Variety	p = 0.9072	p = 0.1109	p = 0.9199

). While no-till strategies like crimping (CR), mow-mulching (MM), and herbicide burndown (BH/BOH) showed moderate weed suppression, they were consistently less effective than tilled systems, particularly GMH. The added pre-emergence herbicide in GMH greatly enhanced weed control beyond what green manuring alone achieved.

The superior performance of GMH held true across three fall seasons (2021–2023), suggesting that its efficacy is robust to seasonal variability (Table 2). Notably, rAUWGC values under GMH dropped as low as 9.4 in fall 2023, significantly outperforming other treatments like MM (27.3) and BH (21.6). Weed pressure during early assessments (14–21 DAP) was already significantly lower in GM and GMH plots, indicating that these strategies can provide early-season weed suppression, a critical factor in minimizing competition with young chickpea seedlings (Figure 5). Chickpea variety had a statistically significant effect only in fall 2021 (p = 0.0119), suggesting that the observed weed suppression differences are primarily driven by the termination strategy rather than the crop genotype. In that particular year, weed infestation was significantly higher on the desi type, Myles (41.8 a), than the kabuli types, Orion (39.0 ab) or Frontier (32.5 b). The lack of significant interaction between the termination strategy and chickpea variety reinforces that the termination method alone is the dominant factor influencing weed dynamics, regardless of the chickpea type (desi vs. kabuli).

Cumulative Weed Infestation Patterns Over Time: To assess the overall weed pressure dynamics, percentage weed infestation data from all three years were pooled and fitted into a logistic growth model (Figure 5). The model explained the relationship between parameters effectively with the following p-values and R² values for each termination method: BOH (p = 0.0897, R² = 0.9920); CRM (p = 0.0179, R² = 0.997); MM (p = 0.9985, R² = 0.0390); GM (p = 0.0121, R² = 0.9999); and GMH (p = 0.0054, R² = 0.9999). Parameter estimates for maximum weed coverage in no-till systems were higher, ranging from 90 to 95%, compared to the two tilled systems, namely, GM (87%) and GMH (57%). Correspondingly, the time required to reach 50% weed coverage (half-time) varied among the treatments, with GMH taking the longest, at 53 days, reflecting its prolonged suppression effect. In comparison, the half-times for MM, CRM, BOH, and GM were 35, 38, 40, and 46 days, respectively.

Relapse of Cover Crops in No-Till Systems: The regrowth (“relapse”) of cover crops following termination was evident across all no-till systems, and this posed an additional challenge to this study, though its magnitude varied according to the termination method, cover crop species, and time after planting (Figure 6). Winter rye (WR) consistently exhibited higher relapse than hairy vetch (HV), irrespective of the termination method, at a particular time of the assessment. Among the no-till systems, the bioherbicide burn termination resulted in the highest WR relapse, followed by mow-mulching. Crimping (CRM) showed the lowest WR relapse levels, suggesting relatively more effective suppression in the early stages. In contrast, HV relapse was lower, overall, across all systems, with mow-mulching (MM) significantly showing the highest regrowth at both 14 DAP and 58 DAP, while CRM and BOH had comparably lower levels. The observed increase in relapse from 14 to 58 DAP in all treatments indicates partial regrowth over time. These patterns highlight species-specific and method-dependent variability in termination efficacy and underscore the need for integrated strategies, especially for systems relying on mechanical termination alone.

Dry Matter (DM) and Yield of Chickpeas as Affected by Cover Crop Terminations: The termination method significantly (p < 0.001) influenced weed infestation (%) at the time of harvest, DM, and yield (

Table 3. Statistical analysis of the weed infestation (%) at the time of harvest, dry mater (kg/ha), and yield (kg/ha) of four chickpea varieties grown at Randolph farm following three winter rye and hairy vetch cover crop mix terminations.

Cover Crop Termination	Variety	Weed Infestation (%)	Dry Matter (kg/ha)	Yield (kg/ha)
Crimped (CR)	Myles	61.25b	935.6e	111.9d
	Kala Chana		2086.5c	332.9d
	Leader		1940.1c	226.7d
	Orion		1552.7d	424.8c
Green manured (GM)	Myles	80.00a	2146.8c	742.4b
	Kala Chana		2198.4c	785.3b
	Leader		2801.1b	534.3c
	Orion		2735.1b	924.4a
GM plus pre-emergence herbicide (GMH)	Myles	36.00c	2367.8b	944.9a
	Kala Chana		1664.6d	601.7c
	Leader		5889.2a	1394.8a
	Orion		6055.7a	1073.4a
Cover Crop Termination		p < 0.001	p < 0.001	p < 0.001
Variety		p = 0.179	p < 0.001	p < 0.149
Cover Crop Termination × Variety		p = 0.637	p < 0.001	p < 0.019

). The GMH (green manure + herbicide) plots consistently and significantly (p < 0.001) recorded the lowest weed infestation (an average of 36%) and the highest DM and yield, highlighting its better weed suppression and the improved productivity of chickpeas. In contrast, GM alone (without herbicide) significantly showed the highest weed infestation (80%) at the time of harvest, followed by 61.25% of CRM. Crimp mulched (CRM) termination had the lowest DM and yield, reinforcing its limited effectiveness in crop productivity under the conditions of the current study. Variety effects were not significant for yield (p = 0.149) but significantly influenced dry matter production (p < 0.001). A significant interaction between termination method and variety for yield (p < 0.019) suggests that certain chickpea varieties like “Leader” and “Orion” performed better under GMH (Table 3).

Regression analysis consistently revealed a negative correlation between the productivity of chickpeas at harvest (yield kg/ha) and weed infestation (%), irrespective of the cover crop terminations, in this study (Figure 7). The regression analysis of yield from GMH (R² = 0.489, p = 0.0114) and CRM (R² = 0.430, p = 0.0206) was well explained with linear regression, with p-values under p < 0.05 and negative slopes.

From March 2020 to March 2024, several nutrients showed numerical increases, indicating a buildup over the study period (

Table 4. Effects of sampling time (T), soil depth (D), and interaction (T × D) on nutrient (macro and micro) concentrations and chemical properties.

Analyzed Nutrient *	Time	Depth	T × D	Time			Depth
	(T)	(D)		March 20	March 24		Upper	Middle	Lower
OM (%)	- x	*** y	-	1.26	1.32		1.70a z	1.20b	0.97b
ENR	-	-	-	60.8	71.3		65.8	67.8	64.5
P-ppm	-	***	-	114.7	125.3		154.0a	129.7ab	76.3b
K-ppm	***	-	-	123.0a	87.6b		108.8	97.7	109.3
Mg-ppm	-	*	-	84.9	80.7		98.7a	71.2b	78.5
Ca-ppm	-	***	-	509.9	450.9		620.5a	429.7b	391.0b
Na-ppm	***	***	-	9.0b	15.7a		15.0a	10.9b	10.7b
Zn-ppm	-	***	-	2.50	2.62		3.55a	2.60ab	1.53b
Mn-ppm	***	***	-	30.6b	43.1a		47.5a	35.5b	27.5b
Fe-ppm	-	***	-	133.2	152.7		172.0a	151.7a	105.2b
B-ppm	***	***	-	0.16b	0.27a		0.30a	0.18b	0.15b
Parameters with significant variability in interaction of time × depth (T × D)
	Time (T)	Depth (D)	T × D	March 20 Upper	March 20 Middle	March 20 Lower	March 24 Upper	March 24 Middle	March 24 Lower
pH	***	***	***	6.5a	6.0b	5.7b	6.8a	6.9a	6.6a
Acidity	***	***	***	0.40b	0.57b	0.97a	0.17c	0.07c	0.17c
S-ppm	-	-	***	8.7b	8.0b	15.0a	9.0b	9.0b	8.0b

^x “-” indicates non-significant results. ^y Asterisks indicate the level of statistical significance: * p < 0.05, ** p < 0.01, *** p < 0.001. ^z Means followed by different letters within the same row across the three soil depths or sampling time are significantly different (p < 0.05) based on Tukey’s test. OM = organic matter; ENR = estimated nitrogen release; P = phosphorus; K = potassium; Mg = magnesium; Ca = calcium; Na = sodium; Zn = zinc; Mn = manganese; Fe = iron; B = boron; pH = a measure of soil acidity or alkalinity; S = sulfur.

). For instance. OM increased from 1.26 to 1.32, and ENR from 60.8 to 71.3, suggesting enhanced organic input and mineralization. P, Zn, and Fe also showed slight gains. Notably, Na, Mn, and B increased significantly over time. In contrast, K declined significantly from 123.0 to 87.6 over the four-year period, suggesting a potential leaching or crop uptake outpacing replenishment. Soil depth had a significant effect on several soil nutrient and chemical property parameters, independent of time or interaction. Organic matter, P, Ca, Mn, Fe, and B concentrations were consistently highest in the upper 10.16 cm of the soil (top) and declined in the middle and lower layers (Table 4), partly due to the effects of organic inputs by the cover crop and surface-driven nutrient cycling. In contrast, K did not vary with depth, indicating more uniform distribution. For some nutrients, like Na, Zn and Mg, depth had a significant effect but without interaction, possibly suggesting a stable vertical stratification regardless of sampling year.

For variables where time × depth interaction was significant, i.e., pH, acidity, and sulfur (S-ppm), distinct patterns were noticed. In March 2020, lower depths had more acidic conditions (pH 5.7–6.0), while the upper layer was moderately acidic (pH 6.5). By March 2024, surface soil pH had increased to 6.8–6.9, and acidity in all layers dropped sharply, particularly at depth, where it decreased from 0.97 to 0.17. This may partly imply a buffering effect possibly driven by microbial activity or reduced leaching. Sulfur also showed a depth-dependent interaction: the middle layer had consistently lower levels, while the lower layer peaked at 15.0 ppm in March 2020, but the difference diminished by 2024. Time × depth interactions also added an important layer of complexity for some chemical properties, like pH, sulfur content (S-ppm), and acidity. Whereas pH and S-ppm increased across all depths when March 2020 is compared to March 2024 (except for S-ppm, in one case, at the lower depth), acidity consistently decreased in the time period for all the depths across the sampling profile (Table 4).

Overall, the results of the soil analysis highlighted that depth remained a strong determinant of physical soil properties. Both soil bulk density and dry weight increased consistently with depth, with the lower 20.32–30.48 cm layer showing the highest values (Figure 8). Although soils sampled from areas with dense cover crops tended to show slightly lower bulk density in the upper layers compared to bare or poorly covered plots, these differences were not statistically significant (p = 0.167), nor was the interaction between cover crop density and depth (p = 0.238); hence, this information is not presented in Figure 8.

4. Discussion

Soil health is fundamental for sustainable agriculture, and cover crops (CCs) are a game-changer in this realm, especially in light of global climate change, including in the southeastern US, where humid and erratic rain during the peak growing seasons are challenging legume production [22,26,27]. Early planting of the cover crop mixes significantly enhanced the growth and establishment of winter rye (WR) and hairy vetch (HV) in the fall 13 October 2023 planting compared to the fall 15 November 2022 and 15 December 2021 plantings. In addition, the October 2023 planting coincided with warmer average temperatures (15.4 °C) and higher precipitation (28.2 mm) compared to October 2022 (13.2 °C, 14.0 mm), creating more favorable conditions for cover crop establishment. The improved early-season moisture and warmth may also have played a role in promoting better germination and root development, which, in turn, set the stage for the enhanced organic matter accumulation observed in the 2023–2024 season. In contrast, the drier and cooler conditions during October 2022 may have limited biomass potential, emphasizing the role of timely fall weather in maximizing cover crop benefits. The combination of the time and prevalent weather conditions has led to quicker canopy closure and better weed suppression during the fall–winter fallow period, underscoring the importance of aligning planting schedules with optimal growing conditions. Research indicates that early planting allows the WR–HV cover crop mix to establish itself more robustly and increase biomass accumulation before winter dormancy, leading to more effective competition with weeds for resources and creating a physical barrier against weeds, further enhancing weed suppression [25,28,29].

Regression analysis of the cover crop growth dynamics (monthly until termination) in this study demonstrated that WR and HV growth followed a logistic growth pattern for the early fall 2023 planting, indicating near-maximal biomass accumulation. In contrast, late-planted cover crops exhibited exponential growth, reflecting insufficient time to reach their growth plateau. This finding suggests that early planting allows CCs to fully utilize the growing season, transitioning through the lag, exponential, and plateau phases and thereby achieving greater biomass accumulation and functional benefits at the time of termination. Of importance are the findings in our study that corroborated those of previous studies [4,30] underscoring the critical role of cover crop (CC) planting for maximizing biomass production, as well as those Ramirez-Garcia et al. [31], who used a similar Gompertz model to describe CC growth dynamics. In the present study, a three-parameter Gaussian model was also found to fit well in representing the decline in weed coverage as WR and HV growth progresses.

Dense cover crop stands effectively suppress weeds by limiting light availability [32,33,34], which agrees with the findings in the current study. This study showed that dense WR–HV cover crop mixtures, which resulted in 97.8% and 98.9% cover crop coverages from the fall 2022 and 2023 plantings, respectively, produced significantly higher fresh biomass and dry matter compared to the poor or no cover crop coverage areas. The 0.71 ha study area was overwhelmingly covered by a dense (96.5%) WR–HV mix, along with some low spots and field peripheries. Fresh biomass in dense plots was 1.7–2.4 times higher than in poorly covered plots, while dry matter accumulation demonstrated a similar proportional increase. This also corroborates findings by Clark et al. [28], who observed that dense cover crops maximize biomass and nutrients.

Our findings indicate that the tillage that converts the CCs to green manuring (GM) followed by pre-emergence herbicide (GMH) provided the most effective weed suppression, resulting in lower weed infestations in chickpea plots across all monitored time points. GM only showed the next level of suppression during the early stages of growth. Conversely, the no-till treatments, especially those involving burning CCs using organic (BOH) or synthetic (BH) herbicides, mow-mulching (MM), and crimping (CR), exhibited higher weed pressure, significantly hampering chickpea growth. This observation aligns with that of Mirsky et al. [32], who also reported that no-till systems often require additional management practices, such as optimized cover crop termination methods, to achieve weed suppression comparable to tillage-based systems. Additionally, the increased weed infestation in no-till treatments underscores the challenges associated with residue management in achieving adequate weed suppression [33].

The sigmoidal curve observed for weed infestation progress under GMH treatments reflects strong initial weed suppression in chickpea plots, followed by a slower rate of weed emergence as the canopy closes. This aligns with the findings of Teasdale and Mohler [33], who modeled weed suppression as a function of cover crop canopy density. In contrast, the more gradual increase in weed cover under the no-till systems aligns with the findings of other studies [34,35], which noted that residue management in no-till systems often creates heterogeneity in weed emergence patterns due to uneven coverage or decomposition. While the results of this study indicate the clear benefits of tillage in reducing weed pressure, they diverge from some findings in semi-arid or low-rainfall regions, where no-till systems have been shown to better preserve soil moisture and reduce weed germination [36]. This discrepancy likely reflects the differences in climate and crop residue characteristics, underscoring the need for site-specific evaluation when selecting tillage strategies for chickpea production.

Weed suppression studies using five termination strategies on till and no-till systems have been progressing well over the last three years, but some hurdles remain consistent. The planting date that was improved in fall 2023 has shown many promising outcomes, including harvesting from three termination plans. For one, only cover crops alone were not sufficient to significantly suppress weeds in the chickpea system. This has to do with the intrinsic and anatomical architecture of the plant, which does not quickly form a canopy to produce a shading effect, compared with other crops that quickly grow tall and broad-leaved.

Weed suppression studies using five termination strategies in both till and no-till systems were conducted over a three-year period and showed consistent progress, despite a few recurring challenges. The improved planting date implemented in fall 2023 led to several promising outcomes, including successful chickpea harvests from three of the termination strategies (CR, GM, and GMH). One key finding was that CCs alone were insufficient to significantly suppress weeds in the chickpea system at Randolph Farm. This is likely due to the crop’s intrinsic characteristics and anatomical architecture, which limits its ability to form a dense canopy quickly, unlike other crops like maize or soybean, which grow taller and develop broad leaves capable of shading out weeds. These results align with those of Clark [29], who demonstrated that effective weed suppression by cover crops is contingent on their ability to form a dense, rapid canopy. Similarly, Teasdale and Mohler [34] noted that crops with characteristics that reduce light availability significantly suppress weed emergence. This underscores the need for integrating additional weed management strategies to address the inherent limitations of chickpea in suppressing weeds.

Manual weeding using a self-propelled tiller or a hoe proved unfeasible in the no-till system due to the thick organic stubble left by cover crop residues in the crimp mulched, mow-mulched, and (bio)herbicide-treated terminations. The heavy residue layer interfered with the operation of manual tools, highlighting a common challenge associated with residue management in no-till systems. Similar difficulties were noted by Mirsky et al. [32], who observed that cover crop residues in no-till systems can create a physical barrier that complicates mechanical or manual weed control efforts. Conversely, Ashford and Reeves [37] found that while thick mulch layers can obstruct weeding, they are highly effective in suppressing weed emergence and moderating soil temperature, often keeping it cooler during the early growth stages, which can reduce weed germination and, in turn, the need for manual weeding altogether. This underscores a critical trade-off in no-till systems: while heavy stubble enhances weed suppression and improves soil health, it complicates practical field operations, necessitating innovative management strategies.

Yield data from this study demonstrated that chickpeas under tilled systems (GMH and GM) produced higher yields compared to the no-tilled crimped (CRM) system. Specifically, GMH exhibited the most effective weed suppression and maintained relatively stable yields across varying weed infestations, with a moderate correlation coefficient (R² = 0.489, p = 0.0114). Conversely, yield from the crimped treatment was significantly lower, and it was less effective in mitigating the negative effects of increasing weed pressure (R² = 0.430, p = 0.0206). These results corroborate the findings of Mirsky et al. [32], who noted that mechanical termination methods, such as crimping, often leave residues that create physical weed barriers but can limit nutrient availability to crops, particularly in legume systems like chickpeas that depend on optimal root–soil interactions. In contrast, the lower yields under the no-till CRM system diverge from studies in semi-arid regions, where no-till systems have often been shown to outperform tillage in legume production by preserving soil moisture and reducing erosion [37].

The steady increase in organic matter in the topsoil (0–10.16 cm) and the modest improvements in nitrogen cycling, as reflected by ENR, validate the long-term benefits of cover crop systems. These findings are consistent with those of others who have highlighted the contributions of CCs to soil organic matter and nutrient availability [14]. However, the limited changes in deeper soil layers indicate the need for CCs with deeper-rooting or additional management practices to enhance vertical nutrient distribution [38]. Decker et al. [39] also reported an increased soil carbon by 19–30% in the top 5 cm of silt loam soil in the southeastern USA, but the CCs in their study did not improve soil health indicators. Phosphorus stability across cycles, coupled with slight declines post-termination, suggests that phosphorus uptake by CCs is an important factor in nutrient cycling. The observed potassium depletion in lower soil layers may indicate nutrient translocation and uptake, as noted by Thorup-Kristensen et al. [40]. This calls for strategic nutrient management to prevent deficiencies in future cropping cycles.

This study underscores the multifaceted agronomic benefits of the WR–HV cover crop system in the short term and its added potentials in the long term, including soil health and weed suppression. However, the variability in performance based on planting timing and termination strategies highlights the need for site-specific considerations. Early planting and effective termination strategies, such as GMH supplemented with weed removals in the initial stages, can maximize the agronomic benefits of WR–HV CCs and increase profitability. Long-term adoption of these CCs can also significantly enhance soil health and nutrient cycling, but careful management is needed in order to address the site-specific challenges of weed pressure in no-till systems.

Integrating cover crops into broader cropping systems will require continuous monitoring and adaptive management strategies to balance ecological and agronomic goals. Future studies should prioritize strategies that ensure the early planting and rapid canopy establishment of selected CCs that suit the climatic conditions of central and southern Virginia [25,41]. Manual or mechanized approaches for creating a desired thick cover crop layer in the no-till mow-mulched and crimp-mulched systems should be explored. This also includes the choice of (bio)herbicide for the cover crop burn treatment. Effective termination is vital and a key for successful weed suppression in chickpea and other crop systems. This prevents the relapse of CCs, a consistent problem in this study, and ensures weed suppression.

5. Conclusions

Early planting of winter rye–hairy vetch (WR–HV) cover crop mixtures significantly improved biomass accumulation and weed suppression, particularly when aligned with favorable fall weather conditions. Dense WR–HV stands, established through October planting, maximize canopy closure, enhance organic matter accumulation, and outperform late-planted or sparse stands in terms of both growth and functional benefits. Till-age-based termination methods, especially green manuring with pre-emergence herbicide (GMH), proved most effective in suppressing weeds and supporting higher chickpea yields compared to no-till systems. Cover crops alone were insufficient to fully suppress weeds in chickpea due to the crop’s slow canopy development, emphasizing the need for integrated weed management practices. Manual weeding in no-till systems was hampered by heavy cover crop residues, highlighting a key operational trade-off between residue-based weed suppression and field accessibility. Soil health improvements were primarily observed in the upper 10.16 cm, with organic matter and nitrogen cycling enhanced over time, although deeper nutrient distribution remained limited. Site-specific factors such as planting time, termination strategy, and crop characteristics must be considered to optimize cover crop benefits, particularly in chickpea systems with limited competitive growth. Long-term adoption of WR–HV cover crops holds promise for sustainable soil health and weed control, but successful implementation hinges on early planting, effective termination, and adaptive field management.