Geographical Variation in Cover Crop Management and Outcomes in Continuous Corn Farming System in Nebraska

Abstract

Cover crops (CCs) are widely recognized for their numerous benefits, including enhancing soil health, mitigating erosion, and promoting nutrient cycling, among many others. However, their outcomes vary significantly depending on site-specific biophysical conditions and agronomic management practices. This study investigates the geographic variations in cover crop outcomes across Nebraska, focusing on three critical management factors: seeding rate, termination timing, and termination-to-corn planting intervals. Using Decision Support System for Agrotechnology Transfer (DSSAT) simulations, we evaluated the effects of these practices on cover crop biomass, growth stages, and subsequent corn yield across seven sites. The results revealed that corn yield remained resilient across all sites, with no statistically significant differences (p > 0.05) across termination timings, seeding rates, or termination-to-planting intervals. A CC seeding rate analysis showed that biomass tended to increase with higher seeding densities, particularly from 200 to 250 plants m⁻², but the gains diminished beyond that, and few pairwise comparisons reached statistical significance. Termination timing had a significant effect on biomass and growth stages, with delayed termination resulting in greater biomass accumulation and advanced phenological development (e.g., Zadoks > 45), which may complicate termination efficacy. Increasing termination-to-planting intervals led to reduced biomass due to shorter growing periods, though these reductions were not associated with significant corn yield penalties. This study highlights the importance of tailoring CC management strategies to local environmental conditions and agronomic objectives. By addressing these site-specific factors, the findings offer actionable insights for farmers and land managers to optimize both ecological benefits and productivity in Nebraska’s no-till systems.

1. Introduction

Cover crops can provide numerous ecosystem benefits when grown in the fallow intervals between cash crops. These benefits include improving soil structure and controlling erosion [1,2], enhancing soil fertility through the uptake of residual nitrate-nitrogen (${\mathrm{NO}}_3^{-}\mathrm{N}$) during the fall–spring fallow period [3,4,5,6], improving soil water dynamics [7,8,9], and supplying forage for livestock [10,11]. Additionally, cover crops improve weed control [12,13], can increase or maintain crop yields [14], and enhance habitat for wildlife and biological diversity [14]. They also offer climate change mitigation by reducing emissions of greenhouse gases [15] and increasing soil organic carbon [16,17]. Cover cropping has been reported to build systemic buffering capacity and increase yield resistance to extreme and variable climate conditions, such as drought, higher temperatures, and more variable precipitation [18,19]. Among the various species, cereal rye (Secale cereale L.), which is the focus of this study, is particularly prominent in Nebraska for its resilience to harsh winter conditions and its superior growth and nitrogen uptake capabilities, making it a preferred choice for these benefits [20,21]. As a result, cover crops are increasingly being used in Nebraska and the rest of the US. In Nebraska alone, cover crop land area more than doubled (109%) between 2012 and 2017, with a 24% increase from 2017 to 2022 [22,23].

Despite the increasing trend in their use, cover crops are only utilized on about 5.1% of cropland in Nebraska [23], highlighting the gap between potential benefits and their actual adoption [24]. Concerns about potential yield losses from rye on maize and increased management costs have significantly deterred the adoption of cover crops by producers [24,25]. However, the assumption that cover crops consistently lead to yield losses is not universally supported. Research presents mixed outcomes: while some studies report reductions in corn yield following a rye cover crop (e.g., [19,26]), others (e.g., [3,27]) have found neutral or even positive impacts on maize yields. In addition to the impact on corn yield, several studies have demonstrated that the ecosystem benefits of cereal rye cover crops, such as reducing ${\mathrm{NO}}_3^{-}\mathrm{N}$ leaching, erosion control, and increasing soil organic carbon, also vary widely across years, locations, and management practices (e.g., [28,29,30]). This variability indicates that rye’s effects on the maize system and its benefits depend on specific combinations of management choices and environmental conditions [24,31].

Additionally, optimizing economic returns while simultaneously managing multiple ecosystem services presents significant challenges. These challenges arise from the dynamic interactions among these services and the trade-offs and synergies involved, complicating decision making [32,33,34]. To address these complexities and promote broader adoption of cover crops, it is crucial to quantify the actual risks and trade-offs associated with their use. Establishing clear guidelines and management practices can help mitigate these challenges and ensure that cover crops contribute positively to sustainable agricultural practices [25,35].

Thus, the overall goal of this study is to demonstrate how the benefits rendered by cereal rye cover crops and their potential impact on corn productivity vary across different sites in Nebraska, using a simulation modeling approach. This study will also demonstrate how management practices optimized for maximum cover crop benefits and minimum trade-offs differ from site to site. For this, we have selected seven sites in Nebraska (

Table 1. Location of weather stations used for simulation and long-term seasonal precipitation totals (mm) and seasonal average temperatures (°C) computed from 30 years of data (1991–2020). “Climate Div.” refers to climate division.

Weather Station	Climate Div.	Latitude	Longitude	Elevation (m)	Precipitation (mm)				Mean Temperature (°C)
					Spring	Summer	Fall	Winter	Spring	Summer	Fall	Winter
Falls City	9	40.06	−95.60	305	259	350	199	71	11.7	24.1	12.4	−1.6
Memphis	6	41.10	−96.43	349	204	282	143	15	10.3	23.2	11.1	−3.5
Concord	3	42.38	−96.98	440	189	275	125	6	8.6	21.8	9.7	−5.3
Holdrege	8	40.44	−99.37	707	185	268	99	12	9.8	22.6	10.8	−2.5
North Platte	7	41.13	−100.77	844	138	218	78	2	9.3	22.2	10.2	−2.0
Valentine	2	42.88	−100.55	893	178	224	94	34	8.3	22.2	9.5	−3.7
Alliance	1	42.10	−102.87	1217	126	151	64	27	7.8	21.3	9.1	−2.9

and

Table 2. Details of soil profile data used for simulation. * SOC indicates soil organic carbon content in the top 15 cm of the soil profile. Topsoil refers to the surface horizon (0–30 cm), and subsoil summarizes dominant texture(s) between 30 and 100 cm based on field classification. Texture class abbreviations: L = loam, SIL = silty loam, SIC = silty clay, SICL = silty clay loam, SCL = sandy clay loam, FSL = fine sandy loam, VFSL = very fine sandy loam, LFS = loamy fine sand, FS = fine sand.

Site	Soil Series	Pedon ID	Latitude	Longitude	SOC * (%)	Soil Texture
						Topsoil	Subsoil
Falls City	Wabash	93NE147011	40.13	−95.67	0.71	SIL	SIC
Memphis	Yutan	89NE155106	41.12	−96.56	1.28	SICL	SICL
Concord	Crofton	S1959NE051003	42.39	−96.96	1.12	SIL	SIL
Holdrege	Holdrege	82NE137001	40.46	−99.41	2.20	SIL	SIL
North Platte	Anselmo	S1969NE111001	41.02	−100.74	1.04	VFSL	VFSL
Valentine	Hennings	84NE031016	42.82	−100.86	0.77	FSL	SCL–L–FSL
Alliance	Alliance	79NE013039	42.17	−103.01	1.06	L	SICL–L–VFSL

), each located in a different climate division, with contrasting soil properties and climate conditions. Employing the Decision Support System for Agrotechnology Transfer (DSSAT [36,37]), the study assesses key management practices, including seeding rate, termination timing, and termination–corn planting intervals, in terms of their impact on ecosystem benefits such as biomass, soil moisture, herbicide efficacy, and subsequent corn productivity.

To guide the analysis, we test several hypotheses: (1) increasing the seeding rate will lead to higher biomass accumulation; (2) delayed cover crop termination will increase biomass and advance growth stages, but may negatively affect corn yield and termination efficacy; and (3) longer intervals between cover crop termination and corn planting will reduce biomass accumulation due to shorter growth periods, but will not significantly impact the subsequent corn yield. This analysis is essential for advancing our understanding of the abiotic mechanisms by which cereal rye impacts corn production and its overall environmental performance. By providing empirical baselines, the study aims to facilitate the quantification of trade-offs and risks associated with cover crop management, thereby contributing to more informed agricultural decision–making and enhanced system sustainability.

2. Materials and Methods

2.1. Crop Models and Cultivars

The CERES-Maize [38] and CERES-Wheat [39] crop models in DSSAT version 4.8 were used to simulate the growth of corn and cereal rye, respectively. These dynamic simulation models operate on a daily time-step to predict crop growth in response to weather, soil, and management strategies. In this study, we adopted the cultivar-specific genetic coefficients for the 111-day-maturity corn hybrid (P1197) and ‘Elbon’ cereal rye variety that were calibrated and validated in [40] using the Genotype Coefficient Calculator (GENCALC, [41]) program in DSSAT.

Table 3. Calibration (cal) and validation (val) statistics for selected physiological traits. Root mean squared error (RMSE) in kg ${\mathrm{ha}}^{-1}$; relative RMSE (RRMSE) in %; $R^2$ is dimensionless.

Trait	$RMS{E}_{cal}$	$RMS{E}_{val}$	$RRMS{E}_{cal}$	$RRMS{E}_{val}$	$R_{cal}^2$	$R_{val}^2$
Corn (P1197 hybrid)
Grain yield (kg ${\mathrm{ha}}^{-1}$)	680	1496	5.9	11.0	0.70	0.65
Unit kernel weight (g)	0.026	0.021	9.3	7.4	—	—
Kernel number (${\mathrm{ear}}^{-1}$)	29	15	6.0	2.6	—	—
Emergence date (days)	1.25	1.30	20	20	—	—
Cereal Rye (Elbon variety)
Biomass (kg ${\mathrm{ha}}^{-1}$)	428	231	23.0	24.0	0.97	0.98
Biomass N content (kg ${\mathrm{ha}}^{-1}$)	9.0	6.1	25	27	0.89	0.90

summarizes the key goodness-of-fit metrics (RMSE, RRMSE, and R²) for grain yield, kernel weight, kernel number, and emergence date (P1197) and for biomass and biomass N content (Elbon rye). The final values of all cultivar-specific genetic coefficients, after calibration, used in our simulations are reproduced in Appendix A (

Table A1. DSSAT v4.8 genetic coefficients for P1197 corn hybrid and Elbon cereal rye variety. Parameters, definitions, parameter ranges, and values (parameter values after calibration) (from [40]).

Parameter	Definition	Unit	Range	Value
P1197 Corn hybrid
P1	Thermal time from emergence to end of juvenile stage	°C	100–400	255
P2	Delay in development per h above 25 °C	Days	0–4	0.042
P5	Thermal time from silking to physiological maturity	°C	600–900	775
G2	Maximum kernels per plant	# plant−1	500–1000	807
G3	Kernel fill rate under optimal conditions	mg d−1	5–12	8.51
PHINT	Thermal time between successive leaf tip appearance	°C	40–55	49.79
Elbon Cereal Rye Variety
P1V	Optimum vernalizing temperature	Days	0–60	50
P1D	Photoperiod response (reduction in rate)	%	0–200	20
P5	Grain filling (excluding lag) phase duration	°C	100–999	450
G1	Kernel number per unit canopy weight at anthesis	# g−1	10–50	20
G2	Standard kernel size under optimum conditions	mg	10–80	60
G3	Standard, non-stressed mature tiller wt (dry)	g	0.5–8	4.0
PHINT	Interval between successive leaf tip appearances	°C	30–150	70

). For the full calibration/validation methodology and definitions of each coefficient, see [40].

2.2. Crop Model Input Data

The crop model simulations were carried out under an irrigated continuous corn cropping system with (CC) and without (NCC) cereal rye cover crops at seven contrasting sites located in seven different climate divisions in Nebraska (Figure 1, Table 1). The simulations cover the period 1991–2020, and historical daily weather data for each site were acquired from the Nebraska State Climate Office’s Nebraska Mesonet (https://mesonet.unl.edu/; accessed on 20 November 2024). The weather data include daily records of total solar radiation incident on the top of the crop canopy, maximum and minimum air temperature, precipitation, wind speed, and relative humidity. Soil profile data for the seven locations were collected from the National Cooperative Soil Survey Soil Characterization database (https://ncsslabdatamart.sc.egov.usda.gov/; accessed on 10 December 2024; Table 2). The soil data collected from the sites reveal significant variations in physical and chemical properties, highlighting the diversity of the soil environments studied.

2.3. Cover Crop Management

Cover crop termination dates: Seven different termination dates, with 5-day intervals starting on 20 April and ending on 20 May, were compared. Corn planting dates were moved accordingly to maintain a 10-day interval between termination and respective corn planting dates. Termination dates were compared and evaluated based on corn yield, cover crop biomass, growth stage, and potential implications on herbicide efficacy averaged over a 30-year simulation period (1991–2020).

Cover crop termination–corn planting interval: The impact of the length of the interval between CC termination and corn planting was assessed for the 11 May corn planting. This fixed date was chosen to quantify solely the effects of different termination intervals, eliminating variations that could arise from changing the corn planting date. The May 11 corn planting date was selected based on preliminary simulations that showed no statistically significant corn yield reductions compared to earlier dates. Moreover, this date allows sufficient biomass accumulation from cover crops without compromising corn yield and growing season length. The intervals considered were 1, 5, 10, 15, and 20 days before 11 May, corresponding to termination dates of 20 April, 25 April, 30 April, 5 May, and 10 May, respectively, and applied uniformly across all simulation years (1991–2020). In addition to the corn planting date, other agronomic managements, such as planting date and CC seeding rate (i.e., rate of 300 plants m⁻²), were kept uniform across the five intervals compared.

Seeding rate: The impact of the CC seeding rate was assessed by simulating seven different rates, from 200 to 500 plants m⁻² with 50 plants m⁻² increments. These simulations used a uniform termination date (i.e., 30 April), interval (i.e., 10 days), corn planting date (i.e., 11 May), and other management practices, applied uniformly across all simulation years (1991–2020).

Other cover crop agronomic management: The cover crop was drilled immediately after corn harvest using a “no-till drill” at a depth of 3 cm for all sites and scenarios simulated. A uniform planting date of 22 October was applied across all sites and years, based on preliminary assessments of feasible planting windows. In addition, a uniform seeding rate of 300 plants m⁻² was used across all sites and maintained throughout the simulation period (1991–2020), except in the CC seeding rate scenario experiments. The simulation setup assumed a continuous corn rotation under no-till management, with no cover crop use in the initial year (1991), ensuring consistent baseline conditions across sites.

2.4. Corn Agronomic Management

A corn seeding rate of 9 plants m⁻² was used for Falls City, Memphis, and Holdrege, and 8 plants m⁻² was applied for Concord, North Platte, Valentine, and Alliance [42]. This variation in seeding rates was based on regional differences in seasonal rainfall. Sites with higher rainfall received higher seeding rates to optimize growth with available moisture, whereas sites with lower rainfall had reduced rates to minimize competition for limited water resources, ensuring optimal growth and resource efficiency. For all simulations, corn was planted using a “no-till planter” at 5 cm depth and 76 cm row spacing. Corn was fertilized using equally split applications of anhydrous ammonia at planting and 30 days after planting (i.e., an approximate time when corn is at the V8 stage) at a depth of 10 cm. Based on preliminary simulations, a total fertilizer amount of 140 kg N ha⁻¹ was applied at North Platte, Alliance, and Valentine; 180 kg N ha⁻¹ at Concord, Holdrege, and Memphis; and 200 kg N ha⁻¹ at Falls City. Irrigation was applied using a lateral-move sprinkling system at the seven sites: Fall City (170 mm), Memphis (180 mm), Concord (190 mm), Holdrege (210 mm), Valentine (300 mm), North Platte (220 mm), and Alliance (300 mm). These irrigation volumes were based on county-average corn irrigation requirements [43,44], historical irrigation application data [45], and preliminary simulations. The irrigation schedule used an automated growth stage-based algorithm within DSSAT [46], triggering irrigation at the start of three critical growth stages: 25% of the total irrigation volume at floral initiation (prior to VT), 30% at silking (R1), and 45% at the blister stage (R2). A fixed minimum interval of five days was applied between irrigation events across all stages to represent practical irrigation scheduling constraints.

2.5. Statistical Analysis

To determine whether differences in corn yield, cover crop biomass, and other parameters across the various treatment factors were statistically significant, a one-way analysis of variance (ANOVA) was initially conducted. ANOVA is suitable for comparing means across multiple groups and identifying whether at least one group mean significantly differs from the others. Where significant main effects were observed, Tukey’s honestly significant difference (HSD) test was used to conduct pairwise comparisons between group means while controlling for Type I error.

To evaluate whether treatment effects—specifically, the seven termination dates, five cover crop termination–corn planting intervals, and seven cover crop seeding rates—varied across sites, a two-way ANOVA was also performed with the site and the treatment factor as fixed effects, including their interaction. When the interaction was not statistically significant, we presented site-specific one-way ANOVA and Tukey HSD results to capture local patterns. This approach allowed us to explore both overall trends and site-specific responses, depending on the consistency of treatment effects across locations.

To assess the impact of cover crop adoption on subsequent corn yield across all sites, a three-way ANOVA was conducted using cover cropping (CC vs. NCC), termination timing (early vs. late), and site as fixed factors. This model included all two-way and three-way interactions to test for main and interactive effects of treatments across multiple environments. To complement mean-based comparisons, we also assessed yield stability by evaluating interannual variability in simulated corn yield. Specifically, we used the coefficient of variation (CV), yield range, and Levene’s test for equality of variances to compare CC and NCC treatments at each site and termination timing. Levene’s test was used to statistically evaluate whether yield variances differed significantly between treatments, with non-significant results (p > 0.05) indicating comparable levels of interannual variability.

All statistical analyses were performed in Python 3.12 using the statsmodels package for the ANOVA and Tukey HSD tests and scipy for the paired t-tests. A significance level of $\alpha =0.05$ was used throughout.

3. Results and Discussion

3.1. Impact of Cover Crop Adoption on Corn Yield

The three-way ANOVA revealed that site was the only statistically significant factor affecting corn yield (p < 0.001), while neither cover crop adoption (CC vs. NCC) nor termination timing (early vs. late) showed significant main or interaction effects. This result indicates that differences in yield due to cover cropping practices were not statistically significant when averaged across sites and years, reaffirming the general neutrality of cover crop effects on corn yield at the broader scale.

At the individual-site level, yield comparisons between CC and NCC systems (Figure 2) showed very small differences, generally less than 2% across all locations. Specifically, sites like Memphis and North Platte exhibited small yield decreases (approximately −1.22% and −1.16% for early and late terminations, respectively), while Alliance and Valentine showed slight increases (up to +1.2% and +0.9%, respectively, during early terminations). However, consistent with the pooled ANOVA, these variations were not statistically significant and should be interpreted as descriptive patterns rather than inferential conclusions.

These findings align with prior research [47,48], which has shown that cover crops—particularly fall-seeded grasses like cereal rye—typically have neutral to minimal effects on subsequent corn yields. A meta-analysis of 65 studies across North America similarly concluded that cereal rye cover crops do not significantly reduce corn yields, supporting their use in corn-based systems without yield penalties [49].

Although yield effects were minimal, site-specific interactions between biomass and soil water highlight varied outcomes. In dry regions like Valentine, additional biomass from cover crops appeared to improve soil structure and infiltration, leading to a 4% increase in extractable soil water at termination compared to NCC. ). This likely mitigated moisture competition. In contrast, at wetter sites like Falls City, greater biomass led to up to 13.5% less extractable water under CC. These cases illustrate how biomass-driven water dynamics can shape local outcomes, even when yield impacts remain small.

Yield stability, assessed using descriptive and statistical measures over 30 years of simulated corn yield data, showed little difference between CC and NCC systems (

Table 4. Comparison of yield stability in terms of coefficient of variation (CV), range (maximum–minimum), and Levene’s test for equality of variances in simulated corn yield for no-cover-crop (NCC) and cover crop (CC) systems under early (i.e., 20 April) and late (10 May) termination dates. Levene’s test reports the F-statistic and p-value (F/p), where a non-significant p-value indicates no evidence of unequal variance between treatments.

Site	Termination Timing	CVNCC	CVCC	RangeNCC (kg ${\mathbf{ha}}^{-1}$)	RangeCC (kg ${\mathbf{ha}}^{-1}$)	Levene’s Test (F/p)
Falls City	Early	0.153	0.149	5992	5417	0.002/0.961
Falls City	Late	0.174	0.175	8135	7803	0.028/0.868
Memphis	Early	0.155	0.166	6093	6232	0.114/0.736
Memphis	Late	0.139	0.144	5088	5414	0.002/0.967
Concord	Early	0.149	0.150	5929	6089	0.004/0.953
Concord	Late	0.162	0.146	6244	5010	0.196/0.660
Holdrege	Early	0.154	0.159	7941	7944	0.031/0.862
Holdrege	Late	0.133	0.128	6750	6877	0.129/0.720
Valentine	Early	0.256	0.263	9261	9355	0.051/0.823
Valentine	Late	0.277	0.274	8692	7668	0.032/0.858
Alliance	Early	0.443	0.423	15,368	13,461	0.018/0.895
Alliance	Late	0.417	0.420	11,338	11,021	0.000/0.993
North Platte	Early	0.263	0.273	8908	8960	0.070/0.792
North Platte	Late	0.228	0.230	8298	8163	0.068/0.795

). The Levene’s test results were consistently non-significant across sites and termination timings ($p>0.05$), indicating no statistical evidence of unequal variances between treatments. Coefficients of variation (CVs) also remained comparable between CC and NCC at all sites. Differences in yield ranges were mixed, with no consistent advantage across treatments. However, in a few cases—such as at Concord, Valentine, and Alliance—the range under CC was substantially narrower, suggesting that cover crops may help buffer extreme outcomes in certain environments.

3.2. Cover Crop Termination Date

3.2.1. Corn Yield Trends

Analysis of corn yield did not show statistically significant differences between termination dates across all sites. For Falls City and Valentine, although ANOVA indicated significant differences (p ≈ 0), Tukey’s test revealed no statistically significant pairwise differences among termination dates. The majority of successive termination date comparisons (71.4%) showed minimal yield changes (i.e., between −1% and +1%), highlighting the minimal impact of termination timing on yield (Figure 3). The most notable yield reduction was 5% at Alliance when the termination date was postponed from 15 May to 20 May, whereas the greatest increase occurred at Holdrege, with yields rising when the termination was delayed from 5 May to 10 May. Even when considering the extremes of the earliest (20 April) and latest (20 May) termination dates, yield differences across sites were modest, ranging from a decrease of 9% to an increase of 2%, further underscoring the generally minimal impact of termination timing on yield.

Although differences in long-term mean yields between termination dates were relatively minimal, the variation in minimum yields (that is, the lowest yield across 30 simulation years for each termination date) revealed a different dimension of risk (

Table A7. Summary statistics of simulated corn yield (kg[Dry]/ha) and cereal rye biomass (kg[Dry]/ha) across seven sites in Nebraska for seven termination dates (20 April to 20 May). Values represent the mean, median, minimum, and maximum over 30 simulation years for yield and 29 years for biomass.

Site	Termination Date	Yield (kg[Dry]/ha)				Biomass (kg[Dry]/ha)
		Mean	Median	Min	Max	Mean	Median	Min	Max
Falls City	20-April	10,645	10,550	7677	13,094	1163	823	89	4362
	25-April	10,616	10,644	7068	13,098	1421	1122	147	5099
	30-April	10,607	11,058	7019	13,419	1665	1454	184	5364
	5-May	10,413	10,916	6521	13,251	1931	1826	119	5919
	10-May	10,346	10,852	5580	13,383	2169	2139	234	6095
	15-May	10,620	11,030	4533	13,555	2322	2216	352	6036
	20-May	10,883	10,924	4597	13,320	2512	2245	585	5671
Memphis	20-April	9787	10,244	6000	12,232	211	156	0	1075
	25-April	9688	9988	5590	12,083	297	232	0	1589
	30-April	9621	10,146	5673	11,882	382	284	3	2203
	5-May	9606	10,124	5302	11,836	501	366	0	2492
	10-May	9711	10,324	6267	11,681	584	413	0	2510
	15-May	9709	10,048	6482	11,293	690	551	0	2695
	20-May	9716	9852	7397	11,910	836	842	0	2762
Concord	20-April	10,386	10,666	6657	12,746	144	61	0	525
	25-April	10,321	10,574	6791	12,685	186	110	0	656
	30-April	10,248	10,542	6876	12,610	255	150	0	1232
	5-May	10,226	10,391	7181	12,806	319	209	0	1678
	10-May	10,134	10,408	7715	12,725	379	201	0	1947
	15-May	10,121	10,530	5829	12,601	469	260	0	1866
	20-May	10,002	10,446	5826	12,357	528	295	0	1534
Holdrege	20-April	11,700	12,368	6059	14,003	304	154	0	1399
	25-April	11,690	12,282	6470	14,063	399	258	0	1633
	30-April	11,537	12,017	6304	13,879	555	428	0	2115
	5-May	11,146	12,130	6726	14,019	689	481	0	2415
	10-May	11,720	12,121	7160	14,037	896	719	0	3043
	15-May	11,774	11,936	7650	14,019	1144	801	0	3314
	20-May	11,847	12,645	6863	14,994	1384	1067	0	3772
North Platte	20-April	9676	10,083	4731	13,691	118	77	0	493
	25-April	9570	10,174	4710	14,189	149	93	0	704
	30-April	9482	9822	4189	13,913	201	130	0	1062
	5-May	9559	10,170	4708	13,803	254	142	0	1253
	10-May	9715	10,409	5267	13,430	360	313	0	1490
	15-May	9611	10,290	5567	12,999	536	495	0	1806
	20-May	9401	10,052	5843	12,616	701	568	0	2267
Valentine	20-April	8797	8646	3599	12,954	154	143	0	436
	25-April	8525	8338	3730	12,313	200	195	0	524
	30-April	8489	8318	3569	11,810	271	293	0	626
	5-May	8333	8756	4528	11,521	370	390	0	799
	10-May	8352	8868	4702	12,370	551	482	0	1923
	15-May	8325	8951	4365	11,920	705	683	0	2366
	20-May	8080	8508	3693	12,869	956	912	0	2852
Alliance	20-April	7946	7812	2234	15,695	100	43	0	667
	25-April	7917	7748	2259	14,792	139	53	0	899
	30-April	7895	7680	2270	15,979	186	145	0	1300
	5-May	7832	7754	2128	13,763	280	196	0	1618
	10-May	7813	7698	2817	13,838	411	356	0	1785
	15-May	7582	7504	1509	14,691	532	448	0	2144
	20-May	7199	6718	1064	13,822	718	583	0	2403

). At most sites, minimum yield declined substantially and progressively with delayed termination, indicating an increased risk of very low yields in unfavorable years when cover crop termination was postponed. For example, at Concord, the minimum yield differed by up to 1889 kg ha⁻¹ across termination dates, compared to just 384 kg ha⁻¹ for mean yields. However, this trend was not uniform: at sites like Holdrege and North Platte, minimum yields were generally higher with later termination dates, suggesting a potential buffering effect under unfavorable seasonal conditions.

Geographic variations in yield responses to cover crop termination timing across the sites do not show a clear pattern. A slightly discernible trend becomes apparent when contrasting drier sites with wetter ones: in drier locations such as Alliance and Valentine, later termination dates tend to slightly increase yield reductions, whereas in the southernmost, wetter sites like Holdrege and Falls City, later terminations correspond with minor improvements in yields. In the drier sites, the potential risk arises from increased biomass depleting more soil moisture. Conversely, in the southern sites, despite higher water consumption by the significant biomass, the ample rainfall and the biomass’s moisture conservation capabilities help offset any adverse effects on yield.

The consistent lack of statistically significant impacts on corn yield, even at sites with apparent variability, underscores the potential for flexible management strategies. Such flexibility allows producers to not only mitigate potential yield penalties but also to capitalize on the agronomic benefits of cover crops, adapting practices to local environmental conditions to optimize both crop productivity and soil health.

3.2.2. Corn Yield Trends

An analysis of corn yield across all sites did not show statistically significant differences between termination dates. A two-way ANOVA revealed strong site effects but no significant main effect of termination timing and no site × termination date interaction (Appendix A 

Table A4. Two-way ANOVA results assessing the effects of site and termination date on corn yield. No post hoc tests were conducted, as termination date and its interaction with site were not statistically significant.

Source	Sum Sq	F-Value	p-Value
Site	$2.16\times {10}^9$	74.04	<0.001
Termination Date	$1.06\times {10}^7$	0.37	0.901
Site × Termination Date	$3.22\times {10}^7$	0.18	1.000
Residual	$6.91\times {10}^9$	–	–

). This indicates that yield differences due to termination timing were minimal and did not vary significantly by site. Across all successive termination date comparisons, 71.4% showed yield changes between –1% and +1%, highlighting the modest influence of termination date on yield (Figure 3). The largest stepwise yield decline was 5%, observed at Alliance when termination was delayed from 15 May to 20 May. Even when comparing the earliest (20 April) and latest (20 May) termination dates, yield differences across sites remained small, ranging from a decrease of 9% to an increase of 2%.

While the statistical analysis did not identify significant yield differences among termination dates, examining the minimum simulated yields (i.e., the lowest yield across 30 years for each termination date) reveals important descriptive patterns (Table A7). In most sites, minimum yields declined substantially and progressively with delayed termination, signaling increased yield risk in unfavorable years when cover crop termination was postponed. For example, at Concord, minimum yields varied by up to 1889 kg ha⁻¹ across termination dates, compared to just 384 kg ha⁻¹ for mean yields. However, this trend was not consistent across all sites: at Holdrege and North Platte, later termination dates were associated with higher minimum yields, suggesting a potential buffering effect of increased cover crop biomass under challenging seasonal conditions.

Although the statistical tests did not indicate significant yield differences among termination dates, descriptive trends suggested subtle geographic variation. In drier regions such as Alliance and Valentine, later termination dates appeared to slightly increase yield reductions, potentially due to greater moisture depletion from increased cover crop biomass. Conversely, wetter southern sites like Holdrege and Falls City showed modest improvements in yields with later terminations, possibly due to higher rainfall buffering water use. These trends, while not statistically significant, may reflect environmental differences across sites. It is also worth noting that corn in this study was irrigated, which likely minimized the effects of moisture competition and dampened any yield response to termination timing that might be more pronounced under rainfed conditions.

Overall, the consistent lack of statistically significant impacts on corn yield—even at sites with apparent variability—supports the feasibility of flexible cover crop management strategies. This flexibility enables producers to tailor termination timing based on environmental conditions, optimizing both yield and the soil health benefits of cover crops.

3.2.3. Cover Crop Biomass and Growth-Stage Trends

The variability in climatic and soil conditions across Nebraska’s diverse climate divisions significantly influenced biomass accumulation and growth-stage progression of the cover crops (Figure 4, Table A7). Southeastern sites, such as Falls City, benefited from higher spring precipitation (259 mm) and warmer temperatures (11.7 °C), consistently exhibiting greater biomass production across termination dates, as noted by [5]. Conversely, northwestern sites, such as Alliance, with their drier (126 mm) and colder (7.8 °C) conditions, recorded lower biomass accumulation, consistent with observations [50] on the amplified impact of environmental stresses on cover crop performance. These patterns align with broader east–west gradients in precipitation and temperature across Nebraska, where wetter eastern regions facilitate sustained vegetative growth compared to the drier west.

Biomass consistently increased with delayed termination across all sites, and this trend was statistically significant when pooled across sites (p < 0.001;

Table A5. Two-way ANOVA and Tukey HSD test results for termination date effects on cover crop biomass.

Factor		Sum Sq	df	F-Value	p-Value
Site		$3.852\times {10}^8$	6	168.49	<0.001
Termination Date		$9.656\times {10}^7$	6	42.33	<0.001
Site × Termination Date		$1.709\times {10}^7$	36	1.25	$0.152$
Residual		$5.228\times {10}^8$	1372	—	—
Group 1	Group 2	Mean Diff	p-Adj	Lower	Upper
5-May	10-May	143.759	0.555	−93.293	380.810
5-May	15-May	293.483	0.005	56.431	530.534
5-May	20-April	−307.084	0.003	−544.135	−70.032
5-May	20-May	470.123	0.000	233.072	707.175
5-May	25-April	−221.724	0.084	−458.776	15.328
5-May	30-April	−118.498	0.759	−355.549	118.554
10-May	15-May	149.724	0.504	−87.328	386.776
10-May	20-April	−450.842	0.000	−687.894	−213.791
10-May	20-May	326.365	0.001	89.313	563.416
10-May	25-April	−365.483	0.000	−602.534	−128.431
10-May	30-April	−262.256	0.019	−499.308	−25.205
15-May	20-April	−600.567	0.000	−837.618	−363.515
15-May	20-May	176.640	0.296	−60.411	413.692
15-May	25-April	−515.207	0.000	−752.259	−278.155
15-May	30-April	−411.980	0.000	−649.032	−174.929
20-April	20-May	777.207	0.000	540.155	1014.259
20-April	25-April	85.360	0.939	−151.692	322.411
20-April	30-April	188.586	0.222	−48.465	425.638
20-May	25-April	−691.847	0.000	−928.899	−454.796
20-May	30-April	−588.621	0.000	−825.672	−351.569
25-April	30-April	103.227	0.859	−133.825	340.278

). The lack of a significant interaction between site and termination date (p = 0.152) suggests that the influence of termination timing on biomass accumulation was broadly consistent across Nebraska’s diverse climate divisions. However, Tukey’s post hoc test revealed that biomass increases were not always statistically distinct between closely spaced termination dates. In fact, six of the eight non-significant pairwise comparisons occurred between successive termination dates (e.g., 25 April vs. 30 April, 10 May vs. 15 May), indicating that short delays of five days or less often did not result in meaningful biomass gains. This finding highlights that significant biomass increases generally require more than brief delays in termination.

Despite the consistent direction of response, the magnitude of biomass increase varied regionally. Southeastern sites like Falls City exhibited gradual biomass accumulation across termination dates, while drier and cooler western sites, such as Alliance and North Platte, showed sharper gains—likely due to delayed vegetative development under limiting environmental conditions. Intermediate sites like Holdrege demonstrated steady biomass increases, consistent with their moderate spring temperature and precipitation. In Valentine, gains were more concentrated around early May, suggesting that short windows of favorable conditions can substantially shape cover crop growth dynamics even in more constrained climates.

Growth-stage progression followed predictable trajectories across sites, advancing to more mature stages with delayed termination (Figure 4). Eastern sites, such as Falls City and Memphis, frequently reached advanced growth stages by late termination dates, as reflected in their Zadoks scale scores (e.g., 81—dough development stage at Falls City; and 68—end of flowering stage at Memphis by 20 May). These findings align with [5], highlighting how warmer and wetter conditions accelerate phenological development in eastern Nebraska. Conversely, cooler and drier sites like Alliance and Valentine exhibited slower growth-stage progression. For example, at Alliance, cover crop remained at Zadoks 49 by 20 May, reflecting constraints imposed by its low average spring temperature (7.8 °C) and limited precipitation during critical months. Intermediate sites like Holdrege and North Platte showed transitional trends, with delayed termination pushing growth closer to or beyond flowering stages (e.g., Zadoks 66 for Holdrege and 65 for North Platte at 20 May). These patterns underscore regional variations in growth-stage progression and the practical challenges of managing cover crops at advanced developmental stages, where termination timing becomes critical for effective management. This overall pattern was supported by the statistical analysis, which showed significant main effects of both site and termination date on growth-stage progression (p < 0.001 for both;

Table A6. Two-way ANOVA and Tukey HSD test results for Zadoks scale by termination date (pooled across sites).

Effect		F-Value		p-Value
Site		101.939		<0.001
Termination Date		109.014		<0.001
Site × Termination Date		0.466		0.997
Group 1	Group 2	Mean Diff	p-Adj	Lower	Upper
10-May	15-May	6.2616	0.014	0.7605	11.7627
10-May	20-April	−18.4842	0	−23.9853	−12.9831
10-May	20-May	13.9591	0	8.4580	19.4602
10-May	25-April	−14.2236	0	−19.7247	−8.7225
10-May	30-April	−10.2852	0	−15.7863	−4.7841
10-May	5-May	−6.2296	0.0148	−11.7307	−0.7285
15-May	20-April	−24.7458	0	−30.2469	−19.2447
15-May	20-May	7.6975	0.0008	2.1964	13.1986
15-May	25-April	−20.4852	0	−25.9863	−14.9841
15-May	30-April	−16.5468	0	−22.0479	−11.0457
15-May	5-May	−12.4911	0	−17.9922	−6.9900
20-April	20-May	32.4433	0	26.9422	37.9445
20-April	25-April	4.2606	0.251	−1.2405	9.7617
20-April	30-April	8.1990	0.0002	2.6979	13.7001
20-April	5-May	12.2547	0	6.7536	17.7558
20-May	25-April	−28.1828	0	−33.6839	−22.6817
20-May	30-April	−24.2443	0	−29.7454	−18.7432
20-May	5-May	−20.1887	0	−25.6898	−14.6876
25-April	30-April	3.9384	0.3449	−1.5627	9.4395
25-April	5-May	7.9941	0.0004	2.4930	13.4952
30-April	5-May	4.0557	0.3088	−1.4454	9.5568

).

In addition to differences in growth stage at termination, sites showed variation in the timing of peak biomass gains. In cereal rye, the highest rate of biomass accumulation typically occurs during the stem elongation phase (Zadoks 30–39), when vegetative growth is most vigorous [51]. Falls City reached this stage early, coinciding with the greatest biomass gains between 20 and 25 April (22% increase). In contrast, at Alliance, stem elongation occurred later (Zadoks 34–37 by 5 May), aligning with its sharpest biomass increase (51%) at that date. These site-specific growth trajectories reflect how environmental conditions influence both phenological development and biomass accumulation dynamics.

3.2.4. Trade-Offs and Management Implications

Herbicide efficacy: While delayed termination maximizes biomass accumulation, it presents challenges in Nebraska’s no-till farming systems that are reliant on herbicides as the primary termination method. Zadoks scale 59, which marks the cereal rye heading stage, was selected as a cutoff because herbicide efficacy significantly declines beyond this stage, complicating termination as plants transition to reproductive growth [51,52,53]. This can lead to poorly controlled cover crops that compete with the subsequent corn crop, reducing yields [54]. Consequently, termination timing becomes crucial to balance the biomass benefits of delayed growth with practical and effective termination strategies.

The exceedance probability of Zadoks scale 59, based on 30 years of simulation data, demonstrates varied risk profiles across Nebraska’s sites and termination dates (Figure 5). At Falls City, the rapid increase in exceedance probability suggests significant risk for termination delays beyond 25 April, particularly for farmers with low risk tolerance. Probabilities escalate above 50% by late April, posing challenges for herbicide effectiveness and crop competition. Memphis and Holdrege show similar increases in risk by mid-May, with termination beyond 10 May exceeding probabilities of 24% and 28%, respectively, for these sites. In contrast, drier sites such as Alliance, Concord, and Valentine exhibit a more gradual increase, allowing greater flexibility. Probabilities remain controlled even by 20 May, at 10% for Alliance, 20% for Concord, and 24% for Valentine. For these sites, termination timing can extend further into late May without significantly compromising herbicide efficacy, offering a balance between maximizing biomass and mitigating termination risks. These trends reflect how local climate conditions shape phenological development and associated termination risks.

Economic considerations: Building on the herbicide efficacy concerns discussed earlier, delaying cover crop termination can significantly impact economic factors due to the reduced efficacy of herbicides on more mature plants. As cover crops grow taller and denser, they develop greater resistance to standard herbicidal treatments, often requiring increased doses or more frequent applications to achieve effective control [55,56,57,58]. Sites like Falls City, where cover crops rapidly reach advanced stages, may face particularly steep increases in management costs if termination is delayed beyond late April. Conversely, sites such as Alliance and Concord, where growth stages progress more gradually, might experience a more moderate cost impact. This allows for slightly delayed termination without incurring excessively high economic penalties, offering some flexibility for farmers managing these sites.

Environmental benefits: Increased biomass from sufficiently delayed termination—particularly beyond five days—can enhance ecosystem benefits, including improved weed suppression, moisture retention, and reduced soil erosion. Additionally, this biomass contributes to increased soil organic carbon by returning organic matter to the soil, thereby improving soil health and long-term productivity [52]. However, these benefits must be carefully weighed against potential risks. High levels of biomass can hinder crop establishment by reducing optimal seed placement, providing a suitable habitat for seed and seedling-feeding herbivores, and complicating the application of supplemental fertilizers [51,52]. Additionally, allelopathy may negatively impact the growth of subsequent cash crops like corn, especially in systems with delayed termination. These trade-offs highlight the importance of tailoring termination timing to site-specific conditions and cropping system requirements, balancing environmental benefits with agronomic risks to optimize outcomes for Nebraska’s no-till farming systems.

3.3. Cover Crop Termination–Corn Planting Interval

Corn yield was not significantly affected by variation in the termination–planting interval across sites (ANOVA: p = 0.996;

Table A8. Two-way ANOVA results for corn yield by termination–planting interval.

Effect	Sum Sq	F-Value	p-Value
Site	$1.50\times {10}^9$	54.57	<0.001
Termination–Planting Interval	$7.92\times {10}^5$	0.04	0.996
Site × Interval	$1.56\times {10}^5$	0.001	1.000
Residual	$4.65\times {10}^9$	–	–

). Although Figure 6 shows a slight upward trend in yield with longer intervals at some sites, the differences were minimal. Yield variation between the shortest and longest intervals ranged from 32 kg ha⁻¹ at Valentine to 126 kg ha⁻¹ at Holdrege. Field studies have similarly reported no significant yield impacts from modest changes in the interval. Near Gothenburg, Nebraska, no yield differences were found among 1-, 3-, and 5-week intervals between cereal rye termination and corn planting [59]. Research from Rowley, Iowa, found only a 2.5% yield reduction between 21- and 3-day intervals [60]. These findings suggest that farmers can maintain flexibility in managing termination and planting operations without significant yield penalties, providing valuable leeway during busy spring fieldwork.

3.3.1. Cover Crop Biomass Across Termination–Planting Intervals

Consistent with our hypothesis, increasing termination–planting intervals led to reductions in cover crop biomass across all sites due to shorter growing periods (Figure 7). For every 5-day increase in the interval, biomass was reduced by an average of 20% (122 kg ha⁻¹) across all sites and intervals. The largest reductions typically occurred when the interval increased from 1 to 5 days, while the smallest reductions were noted from 15 to 20 days—around 40% smaller than the reduction observed during the earlier interval increase. This pattern is particularly influenced by the timing of termination relative to seasonal weather changes; as the interval approaches early May—when temperatures and precipitation increase—biomass accumulation accelerates. Consequently, extending the growing window by just a few days during this period (e.g., from a 5-day to a 1-day interval) yields disproportionately larger biomass gains than similar extensions earlier in the cooler April conditions. This pattern is especially relevant given that corn planting was set for 11 May in the simulation, making early May a critical window for biomass accumulation before cover crop termination.

This trend was statistically supported by a two-way ANOVA, which revealed significant effects of both interval and site on biomass accumulation (p < 0.001), but no significant site × interval interaction (p = 0.391;

Table A9. Two-way ANOVA and Tukey HSD test results for biomass by termination–planting interval (pooled across sites).

Effect		F-Value		p-Value
Site		63.99		<0.001
Interval		21.17		<0.001
Site × Interval		1.05		0.391
Group 1	Group 2	Mean Diff	p-Adj	Lower	Upper
10-day	15-day	−111.2	0.502	−301.97	79.58
10-day	1-day	276.1	0.0008	85.28	466.83
10-day	20-day	−211.5	0.021	−402.25	−20.70
10-day	5-day	135.4	0.297	−55.39	326.16
15-day	1-day	387.2	<0.001	196.47	578.02
15-day	20-day	−100.3	0.604	−291.06	90.49
15-day	5-day	246.6	0.0039	55.80	437.35
1-day	20-day	−487.5	<0.001	−678.30	−296.75
1-day	5-day	−140.7	0.259	−331.44	50.10
20-day	5-day	346.9	<0.001	156.08	537.63

). Furthermore, post hoc comparisons showed that biomass differences between intervals spaced 10 days or more were typically significant, while those between adjacent 5-day intervals (e.g., 1 vs. 5, 5 vs. 10) were not. This suggests a nonlinear response to shortening the growing period, where incremental reductions alone may not drastically impact biomass, but cumulative losses over longer intervals become more consequential.

When comparing the magnitude of reductions across sites, Falls City exhibited the largest average decrease in biomass (268 kg ha⁻¹), while North Platte and Valentine showed the smallest (68 kg ha⁻¹). These patterns align with the regional precipitation gradient. Wetter sites with higher initial biomass experienced larger absolute reductions but often retained relatively high biomass levels (e.g., Falls City still maintained > 900 kg ha⁻¹ at the 20-day interval). In contrast, at drier sites like Valentine and North Platte, even modest absolute losses translated into proportionally larger impacts on biomass, with potentially greater consequences for ecosystem services like erosion control and soil moisture conservation.

3.3.2. Balancing Yield and Biomass with Termination–Planting Intervals

Our analysis in Section 3.3.1 and Section 3.3.2 indicated that while corn yield remained relatively unaffected across different termination intervals, there was a notable increase in biomass production at shorter intervals. This suggests that managing termination intervals to favor biomass does not necessarily incur yield penalties, thus allowing farmers to enhance ecological benefits of increased biomass, such as erosion control, soil organic matter improvement, moisture conservation, and maintaining productivity under variable climatic conditions without sacrificing crop yield.

To illustrate the practical impacts of these benefits, our detailed analysis of soil moisture levels, derived from an average of 30 years of simulation data for the top 60 cm of soil, underscores the critical influence of biomass (Figure 8). Increased cover from biomass at shorter intervals, particularly at a 1-day interval, has been shown to significantly conserve soil moisture by reducing evaporation rates at all sites. This conservation is particularly significant at relatively drier sites like Alliance and North Platte. For example, soil moisture at Alliance and North Platte for the top 30 cm of soil was found to be approximately 31% and 15% higher at the 1-day interval compared to longer intervals. This is particularly important in managing the variability and challenges posed by increasingly erratic rainfall patterns.

3.4. Cover Crop Seeding Rates

The two-way ANOVA test results indicated a statistically significant overall effect of seeding rate on cover crop biomass (p = 0.0003;

Table A10. Two-way ANOVA and Tukey HSD test results for cover crop biomass by seeding rate (pooled across sites). SR—seeding rate.

Effect		F-Value		p-Value
Site		63.99		<0.001
Interval		21.17		<0.001
Site × Interval		1.05		0.391
Site		92.12		<0.001
Seeding Rate		4.31		<0.001
Site × Seeding Rate		0.19		1.000
Group 1	Group 2	Mean Diff	p-Adj	Lower	Upper
SR200	SR250	65.75	0.967	−142.50	274.00
SR200	SR300	115.36	0.659	−92.89	323.61
SR200	SR350	163.28	0.237	−44.97	371.53
SR200	SR400	192.42	0.092	−15.83	400.67
SR200	SR450	226.24	0.023	17.99	434.49
SR200	SR500	243.72	0.010	35.47	451.97
SR250	SR300	49.62	0.992	−158.63	257.87
SR250	SR350	97.53	0.811	−110.72	305.78
SR250	SR400	126.67	0.551	−81.58	334.92
SR250	SR450	160.49	0.257	−47.76	368.74
SR250	SR500	177.98	0.152	−30.28	386.23
SR300	SR350	47.91	0.994	−160.34	256.16
SR300	SR400	77.05	0.930	−131.20	285.30
SR300	SR450	110.88	0.700	−97.37	319.13
SR300	SR500	128.36	0.535	−79.89	336.61
SR350	SR400	29.14	1.000	−179.11	237.39
SR350	SR450	62.97	0.974	−145.28	271.22
SR350	SR500	80.45	0.915	−127.80	288.70
SR400	SR450	33.82	0.999	−174.43	242.07
SR400	SR500	51.31	0.991	−156.95	259.56
SR450	SR500	17.48	1.000	−190.77	225.73

). However, post hoc Tukey’s tests revealed that only two out of 21 pairwise comparisons reached significance, suggesting that while biomass tended to increase with seeding rate, differences between specific levels were generally small. Despite limited pairwise significance in the Tukey HSD test, the long-term mean biomass data showed a consistent trend of increasing biomass with higher seeding rates across all sites (Figure 9). The largest biomass gains were typically observed between the two lowest seeding rates (200 and 250 plants/m²), with increases reaching up to 22% at Holdrege and 20% at North Platte. In contrast, biomass increases between higher successive seeding rates were generally smaller—often below 10%—and occasionally negative, such as the 5% drop from 450 to 500 at Concord. This trend of diminishing returns suggests that although increasing seeding rates boosts biomass, the marginal gain decreases with each increment. These findings highlight a consistent but plateauing response, supporting the conclusion that moderate seeding rates may offer an efficient balance between biomass benefits and input costs. Furthermore, seeding rate had no significant effect on corn yield across sites, and yield differences between seeding rates were typically within ±1%, reinforcing the limited agronomic impact of increasing seeding density.

This trend of diminishing returns with increasing seeding rate has been similarly reported in field studies. For instance, ref. [61] found that biomass production did not scale proportionally with higher cereal rye seeding rates, emphasizing that additional seed input does not always translate to greater biomass. Other management factors, particularly planting timing, may interact with seeding rate to influence outcomes. As shown in [62], early planting can amplify the benefits of higher seeding rates, while late-season sowing—such as in November—tends to reduce or nullify these gains. Economic studies have further shown that unless seeding rates are aligned with specific goals (e.g., ecosystem services), the added seed cost may not yield proportional returns [63,64,65]. These findings underscore the importance of considering both environmental and economic context to guide efficient and sustainable cover crop management.

4. Conclusions and Recommendations

This study underscores the importance of adapting cover crop management to local climatic and soil conditions to optimize benefits and minimize trade-offs. Key findings point to the critical role of termination timing, termination–planting intervals, and seeding rates in shaping agronomic and ecological outcomes across diverse Nebraska sites.

Cover crop termination timing had the greatest influence on biomass accumulation and associated ecosystem benefits. While delayed termination enhanced biomass, it also increased risks—such as reduced herbicide efficacy—particularly when growth advanced beyond Zadoks 59. The optimal timing varied by site, with wetter, warmer areas requiring earlier termination and drier regions offering more flexibility. Cover crop termination-to-planting intervals had limited impact on corn yield, which remained stable across tested intervals. However, cover crop biomass declined as the interval increased, with sharper losses at wetter or biomass-rich sites. This suggests the potential to adjust intervals flexibly without major yield penalties while still preserving biomass where needed. Cover crop seeding rate variations did not significantly affect biomass or yield across sites. While moderate increases offered minor gains in wetter areas, returns diminished beyond 300 plants/m², making higher rates less cost-effective.

Altogether, these findings highlight the need for site-specific strategies that balance ecological benefits, operational feasibility, and economic considerations to support more resilient and sustainable cropping systems.