Cover crop inter-seeding in organic corn production to reduce resource inputs and soil disturbance and enhance pest control and farm profitability

Objective-1:

Data from previous years showed the importance of conveying the perceived concrete/tangible benefits to soil health, showing the relationship between soil health and carbon sequestration, and equipping growers with the knowledge and/or resources to receive information on the type of cover crops to use and when/how to plant and harvest them. These are key messages to convey to help with the adoption of cover crops. In year 3, we designed a survey embedded in an online experiment (N=443) with farmers in the S-SARE region, we found support for a concreteness spillover effect, where grounding the relatively abstract benefit of carbon sequestration in the concrete benefit of soil health, when shared by a fellow farmer affected attitudes, perceived concreteness of carbon sequestration, perceived trustworthiness, and subsequent intentions to adopt cover crops. Findings also offer implications for enhancing the explanatory power of the theory of planned behavior (TPB).

Qualtrics was used to recruit a sample of S-SARE region farmers. Among the 443 farmers in the S-SARE region, we had participants from Alabama (n = 15), Arkansas (n = 16), Florida (n = 75), Georgia (n = 42), Kentucky (n = 26), Louisiana (n = 15), Mississippi (n = 11), North Carolina (n = 36), Oklahoma (n = 28), South Carolina (n = 19), Tennessee (n = 30), Texas (n = 102), and Virginia (n = 28). We used a 2 (farmer vs. university extension agent) x 2 (soil health benefits vs. soil health and carbon sequestration benefits) between-subjects design and a control condition. Participants were randomly assigned to read a message from Bill, who was either: a South Carolina farmer reaping soil health benefits as a result of cover crop adoption (condition 1, n = 92), a South Carolina (SC) university extension agent talking about soil health benefits as a result of cover crop adoption (condition 2, n = 79), a SC farmer reaping soil health and carbon sequestration benefits as a result of cover crop adoption (condition 3, n = 91), a SC university extension agent talking about soil health and carbon sequestration benefits as a result of cover crop adoption (condition 4, n = 88), or a SC resident reaping the benefits of outdoor cooking (condition 5, n = 93). The stimuli message was developed using the story of one of our cooperating farmers who adopted cover crops.

There were no significant difference among conditions with regards to age (F(4, 438)=1.586, p=0.177), political ideology (F(4,433)=1.147, p=0.334), political party [republicans (c2(4) = 3.077, p=0.545), democrats (c2(4)=2.356, p=0.671), independents (c2(4)=5.307, p=0.257)], race [White (c2(4)=2.456, p=0.653), Black (c2(4)=2.215, p=0.696)], and education (F(4, 438)=2.367, p=0.052). There were however significant difference across conditions with regards to gender (male; c2(4)=11.857, p=0.018), Hispanic ethnicity (c2(4)=9.803, p=0.044), and the state of Virginia (c2(4)=13.192, p=0.0104); these variables were controlled for in all analyses. In other words, participants who were male, Hispanic, or from Virginia were unevenly distributed across conditions (which can sometimes happen with randomization), so we controlled for these imbalances in all analyses.

Before revealing the stimuli, participating farmers were asked questions to assess their quality of life: (i) overall, how satisfied are you with your life as a farmer (1 = “extremely dissatisfied” to 5 = “extremely satisfied;” M=4.39, SD = 0.86), (ii) how would you rate your physical health over the past month (1 = “poor” to 5 = “excellent;” M = 3.79, SD = 0.88), (iii) how would you describe your financial situation as a farmer (1 = “very difficult” to 5 = “very comfortable;” M = 3.56, SD = 1.03), (iv) how would you rate your ability to balance farming responsibilities with personal or family life (1 = “poor” to 5 = “excellent;” M = 3.60, SD = 0.99), (v) how satisfied are you with the environmental conditions (e.g., soil quality, weather patterns, etc.) affecting your farm? (1 = “extremely dissatisfied” to 5 = “extremely satisfied;” M = 3.75, SD = 1.02), and (vi) how supported do you feel by your community as a farmer? (1 = “not supported at all” to 5 = “extremely supported;” M = 3.85, SD = 0.93). These questions were combined (Cronbach’s alpha = 0.84) to create a quality of life variable (M = 3.82, SD = 0.71) and was found to not have any significant differences across conditions (F(4, 438) = 0.832, p=0.505).

To assess the participating farmers’ willingness to adopt practices based on social information factors, they were asked: (i) how often do you collaborate with other farmers when implementing new technologies? (1= “never” to 5 = “always;” M = 3.56, SD = 1.03), (ii) how often do you collaborate with agricultural professionals when implementing new technologies? (1= “never” to 5 = “always;” M = 3.43, SD = 1.14), (iii) how important is external information (e.g., advice from consultants, agricultural extension services) when deciding to adopt new farming practices? (1 = “not at all important” to 5 = “extremely important;” M = 3.87, SD = 0.96), (iv) how much do the actions or opinions of other farmers influence your decision to adopt new practices? (1 = “none at all” to 5 = “a great deal;” M = 3.48, SD = 1.08), and (v) how often do you seek information or training before adopting new agricultural practices? (1 = “never” to 5 = “always;” M = 3.79, SD = 1.04). These questions were combined (Cronbach’s alpha = 0.84) to create a variable of social information adoption orientation (M = 3.63, SD = 0.82) and was found to not have any significant differences across conditions (F(4, 438) = 2.28, p=0.059).

The participating farmers reported relying on university extension agents (n = 137) and other farmers (n = 280) when considering the adoption of new agricultural practices. Both preference for agents (c2(4)=3.642, p=0.457) and other farmers (c2(4)=3.116, p=0.539) were also found to not have any significant differences across conditions.

Following reading the stimuli message, participating farmers were asked to rate the concreteness of benefits using three items. Concreteness of soil health benefits (M = 4.34, SD = 0.73) was constructed using the following three items (Cronbach’s alpha = 0.80): (i) to you, how clear or vague are the soil health benefits (1 = “very vague” to 5 = “very clear;” M = 4.44, SD = 0.85), (ii) how easy is it for you to measure soil health benefits (1 = “very difficult” to 5 = “very easy;” M = 4.33, SD = 0.85), and (iii) how visible are soil health benefits (1 = “not at all visible” to 5 = “very visible;” M = 4.26, SD = 0.89). Similarly, concreteness of carbon sequestration benefits (M = 4.06, SD = 0.88) was constructed using the following three items (Cronbach’s alpha = 0.82): (i) to you, how clear or vague are the carbon sequestration benefits (1 = “very vague” to 5 = “very clear;” M = 4.22, SD = 0.94), (ii) how easy is it for you to measure carbon sequestration benefits (1 = “very difficult” to 5 = “very easy;” M = 4.06, SD = 0.99), and (iii) how visible are carbon sequestration benefits (1 = “not at all visible” to 5 = “very visible;” M = 3.92, SD = 1.15).

Participating farmers were also asked on a 6-point scale (1 = “I don’t know” to 6 = “extremely knowledgeable”; M = 4.98, SD = 1.22). They were also asked on a 5-point scale (1 = “extremely unlikely” to 5 = “extremely likely”) to report to what extent they believed the source was trustworthy (M = 4.26, SD = 0.78) and presented information in an unbiased manner (M = 4.16, SD = 0.97). Perceived attitude of support was measured by asking participating farmers: how much do you support the use of cover crops (1 = “none at all” to 5 = “a great deal;” M = 4.05, SD = 0.91). Perceived subjective norms (M = 4.07, SD = 0.89) was assessed using two items on a 5-point scale (1 = “strongly disagree” to 5 = “strongly agree”; r = 0.713, p < 0.001): (i) “I see farmers, who are like me, using cover crops” (M = 4.01, SD = 0.98), and (ii) “Farmers who are like me, consider it valuable to grow cover crops” (M = 4.12, SD = 0.94). Perceived behavior control was measured on a 5-point scale (1 = “strongly disagree” to 5 = “strongly agree”): “I feel confident in being able to use cover crops” (M = 4.26, SD = 0.88). Finally, to assess intention to adopt, participating farmers were asked on a 5-point scale (1 = “extremely unlikely” to 5 = “extremely likely”): how likely are you to use cover crops next season (M = 4.19, SD = 0.82).

We used a structural equation model. The four conditions were treated as dummy variables with the control condition as our reference. We account for gender (male), Hispanic ethnicity, and those coming from Virginia, because as mentioned earlier there were significant differences for these variables across conditions. We included them in the analyses to account for the uneven distribution of these variables across conditions. The data revealed a moderate fit producing a CFI of 0.931, TLI of 0.850, RMSEA of 0.079, and SRMR of 0.033. See Figure 1. Results indicate that the farmer message about soil health benefits alone (b = 0.271, p = 0.013) and soil health and carbon sequestration benefits (b = 0.282, p = 0.011) were positively related to support for adopting cover crops. None of the conditions were related to perceived behavior control or on the perceived concreteness of soil health benefits. The farmer message about soil health and carbon sequestration benefits (b = 0.236, p = 0.044) was positively related to concreteness of carbon sequestration. In other words, when a farmer describes soil health and carbon sequestration, it made the benefits from carbon sequestration more concrete i.e., more visible, measurable, and clear. This condition of farmer message about soil health and carbon sequestration benefits (b = 0.229, p = 0.018) was positively related to perceiving the source as trustworthy. All conditions – farmer with soil health benefits (b = 1.125, p < 0.001), agent with soil health benefits (b = 0.949, p < 0.001), farmer with soil health and carbon sequestration benefits (b = 1.156, p < 0.001), and agent with soil health and carbon sequestration benefits (b = 1.129, p < 0.001) – were positively related to perceiving the source as knowledgeable. None of the conditions affected perceived bias. Attitude (b = 0.341, p < 0.001), subjective norms (b = 0.183, p < 0.001), perceived behavior control (b = 0.193, p < 0.001), concreteness of carbon sequestration (b = 0.226, p = 0.017), and perceived trustworthiness of the source (b = 0.141, p = 0.010) were positively related to intention to adopt cover crops.

Overall, the results indicate that while farmers and agents can come across as knowledgeable, when discussing the benefits of cover crops, farmers sharing this information (more so than agents) can be perceived to be trustworthy sources, be more effective in improving their attitude towards adopting cover crops, and can make the benefits of carbon sequestration seem more concrete. Together these can positively impact farmers’ intentions to adopt cover crops.

Figure 1. The structural equation model

Figure 1. The structural equation model testing the impact of attitudes, norms, behavioral control, concreteness, and source characteristics on intention to adopt cover crops 

Objective-2

Weather conditions across site-years

Rainfall patterns and total amount varied significantly across the site-years (Figure 2). Based on historical weather records and what is normal for the region, 2002 was a dry year in Florence, whereas 2024 was normal. At Clemson, 2023 was a wet year and 2024 was a dry year.

Figure 2. Weather

Figure 2. Cumulative precipitation (a) and daily average temperatures (b) during the corn/cover crop growing seasons at Florence and Clemson, SC in comparison with the historic weather data for the same site (cumulative precipitation normal for a period of 30 years in the first panel and daily average temperature normal for the same 30-year period in the second panel). 

Cover crops performance differences across years and locations

Cover crop biomass varied significantly across years, locations, and cover crop species (Fig. 3). In Florence in 2022, buckwheat produced 169.43 kg/ha, which was approximately 3.4 times higher than mix (50.43 kg/ha) and almost double pigeonpea (86.10 kg/ha), while white clover produced no biomass. Similarly, in Clemson in 2023, buckwheat yielded 273.70 kg/ha, about 1.7 times higher than mix (160.90 kg/ha), 2.5 times higher than pigeonpea (111.30 kg/ha), and 18.7 times higher than white clover (14.60 kg/ha).

Buckwheat produced the highest biomass at 1282.50 kg/ha at Florence in 2024, which was 1.5 times higher than mix (854.30 kg/ha), 2.6 times higher than pigeonpea (499.50 kg/ha), and significantly higher than white clover, which again had no measurable biomass. At Clemson in 2024, mix produced the highest biomass at 490.90 kg/ha, which was 1.7 times higher than buckwheat (290.70 kg/ha) and 2.4 times higher than pigeonpea (200.80 kg/ha), while white clover again showed no measurable biomass.

These results demonstrate that buckwheat generally produced the highest biomass across most site-year combinations, with mix outperformed buckwheat in Clemson 2024. The variability observed likely reflects differences in environmental and management conditions, highlighting the adaptability of buckwheat and mix.

Figure 3. Cover crop biomass

Figure 3. Cover crop biomass (kg/ha) for four cover crop treatments (buckwheat, mix, pigeonpea, and white clover in four site-years.

Buckwheat and pigeonpea showed effective weed suppression

Although post-hoc analysis revealed no statistically significant differences between treatments and controls, the numerical differences highlight the potential of specific cover crops and seeding rates to suppress weeds without labor-intensive practices (Figure 4). At Florence in 2022, under conventional tillage, buckwheat at high seeding rate was associated with a weed biomass of only 2054 kg/ha, which was 35.8% lower than the average of the controls. While treatments like mix-high and pigeonpea-high exhibited less suppression, they were still within the same statistical grouping as controls. Under reduced tillage, buckwheat-low and pigeonpea-low achieved weed biomass reductions of approximately 17% compared to controls. At Florence in 2024, multiple treatments showed reductions in weed biomass compared to the controls. For example, pigeonpea-low reduced weed biomass by 34.8%, while buckwheat-low achieved a reduction of 21.5%, while white clover-high was associated with the highest weed biomass. Under reduced tillage, pigeonpea-low and buckwheat-low again performed well, with reductions of 45% and 31%, respectively, compared to controls. At Clemson in 2023, where weed pressure was minimal, most treatments performed comparably to the weeded controls (0 kg/ha) under conventional and reduced tillage conditions. At Clemson in 2024, treatments like mix-low reduced weed biomass by 16%, compared to controls under conventional tillage. Most treatments were comparable to weeded controls in reduced tillage as well. Our findings highlight that treatments such as buckwheat-high, pigeonpea-low, and mix-low consistently reduced weed biomass compared to the manually weeded controls, demonstrating their effectiveness as weed management approaches.

Figure 4. Weed biomass

Figure 4. The effect of interseeded cover crops on weed biomass under conventional (A) and reduced (B) tillage systems.

Corn biomass increased when interseeded with buckwheat and pigeonpea

Corn biomass is a critical indicator of productivity. It reflects the effectiveness of agronomic practices and management strategies in optimizing plant growth. At Florence in 2022, under conventional tillage, buckwheat-low achieved a 147% increase in corn biomass compared to the controls (Figure 5). Though not statistically significant, pigeonpea-high and mix-low, increased corn biomass by 99% and 54%, respectively. Under reduced tillage, all treatments were statistically similar with both controls in terms of corn biomass. At Florence in 2024, buckwheat-low led to 54% more corn biomass compared to control 2 under conventional tillage. Under reduced tillage, pigeonpea-standard led to 62% more corn biomass than the controls. At Clemson in 2023, under both tillage conditions, corn biomass was statistically similar across all treatments and controls. Under reduced tillage, buckwheat-low produced the highest biomass, which was 20% higher than the average of the controls. At Clemson in 2024, under conventional till, buckwheat at low seeding rate increased corn biomass by 78%, while pigeonpea at standard seeding rate increased corn biomass by 127% under reduced till. These findings underscore the potential of specific cover crop treatments, particularly buckwheat and pigeonpea (at recommended or lower seeding rates), to significantly enhance corn biomass production.

Figure5. Corn biomass

Figure 5. The effect of interseeded cover crops on corn biomass under conventional (A) and reduced (B) tillage systems.

Buckwheat boosts corn yields

Corn grain yield was generally unaffected by most interseeded cover crops under both conventional and reduced tillage treatments across all locations (Figure 6). However, some specific cover crop treatments significantly enhanced corn yield compared to those under the control plots. Across all treatments and locations, buckwheat at the standard seeding rate consistently exhibited a yield advantage, improving corn yield >30-40%. This consistent performance underscores the potential of buckwheat at the standard seeding rate as a reliable option for enhancing corn yield under varying management practices and environmental conditions.

Figure 6. Corn grain yield

Figure 6. The effect of interseeded cover crops on corn grain yield under conventional (A) and reduced (B) tillage systems.

Interseeded cover crops did not reduce soil moisture

Across all locations and years, volumetric water content (VWC) was not reduced by interseeded cover crops at any seeding rate under either conventional or reduced tillage treatments when compared to both control plots (Figure 7). This indicates that interseeding cover crops do not adversely impact soil moisture availability, a critical factor for crop productivity.

Figure 7. Soil volumetric water content

Figure 7. The effect of interseeded cover crops on soil volumetric water content under conventional (A) and reduced (B) tillage systems.

Bulk density, penetration resistance, and root traits

Interseeded cover crops had no effects on bulk density and penetration resistance. Corn root traits are currently under analysis.

 Cover crop interseeding enhanced soil function

 The soil health benefits of cover crops were assessed under conventional and reduced tillage systems at Clemson in 2023 and 2024. The impact of cover crops on β-glucosidase (BG) and leucine aminopeptidase (LAP) activity was evaluated. The enzyme BG and LAP are critical for C and N cycling. Interseeded cover crops did not increase BG activity (Figure 8). LAP activity increased when buckwheat and pigeonpea were interseeded at standard seeding rate and the mix at high seeding rate. Interseeded cover crops did not alter soil active C content in the 1st year, however, in the 2nd year, higher soil active C content was observed when buckwheat and the mix were interseeded at high seeding rate and pigeonpea at all seeding rates (Figure 9). This indicates improved soil biological activity and carbon availability to soil microorganisms.

Figure 8. Soil enzyme activity

Figure 8. The effect of interseeded cover crops on soil enzyme activity. Data were pooled across conventional tillage and reduced tillage.

Figure 9. Active soil carbon content

Figure 9. The effect of interseeded cover crops on active soil carbon content. Data were pooled across conventional tillage and reduced tillage.

Conclusions

At Clemson and Florence, buckwheat was the best performing interseeded cover crop in terms of biomass production. Interseeded cover crops did not reduce corn biomass, grain yield, and soil water content in the organic corn production system under conventional or reduced tillage conditions. Interseeding appeared to have a positive impact on corn grain yield; e.g., corn yield and biomass were increased when interseeded with buckwheat- at standard seeding rate, it increased corn grain yield up to 43 %, and at low seeding rate, it increased corn biomass up to 52%. Our results did not support the possibility of interseeded cover crops reducing the available soil water to the cash crops. Buckwheat and pigeon pea appeared to have a weed-suppression effect as well. Interseeded cover crops also enhanced soil function. The results from our study indicate a positive impact of cover crop interseeding on corn performance, soil moisture retention, soil health, and weed suppression.

Objective-3

In 2023, we found fewer herbivore pests, and only half as much corn earworm damage to corn ears in inter-seeded plots that had living ground cover, relative to control plots (Figure 10).  Beneficial predatory insects tended to be more abundant in inter-seeded plots but were not significantly different from controls.

Figure 10. Herbivore and predatory insects

Figure 10. Herbivore and predatory insect responses to inter-seeded cover crop treatments (a), and cover crop effects on herbivore damage to organic corn cobs in Clemson, SC, 2023.

Objective-4

Cost and revenue results

            Figures 11 and 12 illustrate the revenue and net returns from corn grain yield with interseeded cover crops under conventional and reduced tillage conditions for the years 2022 and 2023 respectively. The green bar in the figures represents net returns, while the orange bar represents the revenue generated. These bar graphs are set to compare the economic outcomes across multiple cover crop treatments with different seeding rates and two control groups with no cover crops. Ctl1 in the figures represents the control treatment 1 with no cover crops but received fertilizers, whereas Ctl2 stands for the control treatment 2 with no cover crops or fertilizers. 

In 2022, all categories under both tillage systems appeared to generate positive revenue. However, net returns vary significantly. Some categories, especially under reduced tillage, show negative net returns, meaning that the costs associated with those particular seeding rates and cover crops might have exceeded the revenue generated from the yield. In 2023, the net returns are much more favorable, with fewer categories showing losses. This could be due to increased yield efficiency, and reduced costs due to no irrigation.

Conventional tillage shows a consistent pattern of revenue exceeding net returns, but in 2023, the gap between revenue and net returns narrows compared to 2022, indicating better profitability.

Figure 11. Revenue and net returns from corn yield in 2022

Figure 11. Revenue and net returns from corn yield with interseeded cover crops under conventional tillage and reduced tillage conditions in 2022.

Figure 12. Revenue and net returns from corn yield in 2023

Figure 12. Revenue and net returns from corn yield with interseeded cover crops under conventional tillage and reduced tillage conditions in 2023.

Partial budget analysis results

Economic advantage of cover crops in comparison with no-cover crop, no-fertilizer control

When cover crop treatments were compared with a control treatment which did not involve fertilizer costs, it was apparent that any economic advantage of cover crop inter-seeding across treatments was primarily driven by seeding rates defined as high, low, and standard (Table 5&6; Figure 13). Increased seeding rates typically result in higher seed costs, which has a substantial effect on the total expenses related to each treatment. In 2022, treatments within the conventional tillage system consistently produced negative net effects across cover crop varieties and seeding rates, implying that the additional costs of cover cropping frequently outweighed the benefits in this setting. For example, higher-seeding rates of pigeon pea had considerably negative results, with net effects of -$672. This significant loss is due to higher input costs ($876) and lower returns ($204), demonstrating that high-input treatments in conventional tillage may lack short-term profitability. Similarly, in 2023, the net effect of pigeon pea at high seeding rate was -1,091, owing to high increased expenditures ($753) and significant reduced returns ($338). This finding emphasizes an economic issue associated with high-input cover crop methods under conventional tillage, as cost increases were not sufficiently offset by economic improvements in either year.

Economic advantage of cover crops in comparison with fertilized no-cover crop control

The results were not different even when cover crop treatments were compared with a control treatment which did involve fertilizer costs. Under conventional tillage, cover crop treatments often had negative net returns in 2022 (Table 7, Figure 13), indicating that the costs of cover cropping frequently outweighed the benefits. For example, the high seeding rate for pigeon pea resulted in a net effect of -$278, driven by large additional costs of $483, and minimal returns of $204. Similarly, when interseeded at high seeding rates, buckwheat, had a net effect of -$286. However, buckwheat at standard and low seeding rate resulted in a positive net effect under conventional and/or reduced tillage conditions in 2022. In 2023 (Table 8), although the overall consequences remained primarily negative, certain treatments resulted in lower losses. For example, buckwheat at high seeding rate resulted in a net effect of $16 under conventional tillage, representing greater returns of $264 that largely offset the additional expenses. In contrast, high pigeon pea seeding rates again resulted in a negative net effect of -$821 under conventional tillage due to increased costs and lower returns. The net negative effect of cover crops was apparent under reduced tillage conditions as well in 2023.     

Conclusions

The economic analysis of cover crop inter-seeding under conventional and reduced tillage systems for corn production in 2022 and 2023 highlights the financial dynamics involved in adopting these practices. Economic advantage of cover crop inter-seeding across treatments was primarily driven by seeding rates. Increased seeding rates typically result in higher seed costs, which has a substantial effect on the economic viability of cover crops. Treatments with high seeding rates, particularly those utilizing pigeon pea, frequently resulted in significant losses, indicating that such treatments may require more tuning to be economically viable. While interseeded cover crops generally resulted in a net negative effect, the magnitude of economic loss was relatively lower for buckwheat when interseeded at standard or low seeding rates.

It is important to note that these results are sensitive to changes in relative prices for cover crop options. The sensitivity of these results to price changes emphasizes the significance of regularly tracking input prices and updating economic evaluations to reflect current market conditions. Changes in cover crop seed prices may alter the cost-benefit landscape, making some treatments more appealing or even shifting the optimal choice of cover crop and seeding rate. This calls for a sensitivity analysis of the net returns with respect to the changing cover crop prices. Such an approach would provide more substantial economic insights and allow for better adaptable advice for producers dealing with shifting market situations. By simulating a variety of price scenarios, researchers can gain a better understanding of which cover crop and seeding rate combinations are likely to stay economically viable in different price contexts.

Figure 13. Net returns from cover crops

Figure 13. Net returns from cover crops.

Table 5. 

Red stands for reduced tillage; Conv stands for conventional tillage; BW stands for buckwheat; WC stands for white clover; PP stands for pigeon pea; std stands for standard

Table 6. Partial budgeting/ economic analysis of cover crop inter-seeding in corn production under different tillage systems in 2023

Red stands for reduced tillage; Conv stands for conventional tillage; BW stands for buckwheat; WC stands for white clover; PP stands for pigeon pea; std stands for standard

Table 7. Partial budgeting/ economic analysis of cover crop inter-seeding in corn production under different tillage systems in 2022

Red stands for reduced tillage; Conv stands for conventional tillage; BW stands for buckwheat; WC stands for white clover; PP stands for pigeon pea; std stands for standard

Table 8. Partial budgeting/ economic analysis of cover crop inter-seeding in corn production under different tillage systems in 2023 

Red stands for reduced tillage; Conv stands for conventional tillage; BW stands for buckwheat; WC stands for white clover; PP stands for pigeon pea; std stands for standard

Objective-5

The project team made multiple presentations and publications.

Presentations:

• Madrid L., Narayanan S., Ye R. 2025. Cover crop interseeding enhances corn yield and soil function in organic systems. CANVAS Annual Meetings, Nov 9-12, Salt Lake City, UT.
• Tallapragada, M., Nian, Y., Lamie, R. D., & Narayanan, S. (2025, August). The communication and the conveyor of concreteness spillover effects: An exploration in the context of cover crop adoption. 108th annual conference of the Association for Education in Journalism and Mass Communication (AEJMC), San Francisco, CA.
• Madrid L.B., Ye R., Narayanan S. 2025. Intrseeded cover crops foster complementarity over competition in organic corn production. CAFLS Graduate Research Symposium, August.
• Madrid L.B., Ye R., Narayanan S. 2025. Cover crop interseeding for enhancing the sustainability of organic corn production. American Society of Agronomy Southern Branch Annual Meeting. Feb 2-4, Irving, TX.
• McCarthy B, Madrid L., Narayanan S. 2025. Effects of Cover Crop Interseeding on Root Morphological Traits of Organic Corn. SARE-YES Poster.
• Blubaugh C. Co-optimizing diversity and prosperity in agroecosystems (Invited seminar). University of Illinois Urbana-Champaign, Natural Resources and Environmental Sciences: (3/2024)
• Blubaugh C. Multi-trophic consequences of biodiversity in agroecosystems (Invited seminar). The Land Institute, Salina, KS (2/2024)
• Blubaugh C. Multi-trophic consequences of biodiversity in agroecosystems (Invited seminar). University of Illinois Urbana-Champaign, Department of Entomology: (1/2024)
• Blubaugh C. Balancing diversity and prosperity in horticulture systems (Invited seminar). University of Illinois Urbana-Champaign, Department of Crop Sciences (3/2024)
• Madrid L., Ye R., and Narayanan S. 2024. Cover crop interseeding for enhancing the sustainability of organic corn production. ASA-CSSA-SSSA Annual Meetings, Nov 10-13, San Antonio, TX.
• Sonti, Rohini. An economic analysis of cover crop inter-seeding in organic corn under conventional and reduced tillage systems in South Carolina. Masters Defense- Thesis Chapter (Nov 2024)
• Madrid L.B., St Aime R., Ye R., Narayanan S. 2024. Cover crop interseeding for enhancing the sustainability of organic corn production. CAFLS Graduate Research Symposium, August 19-20, Pendleton, SC.
• Conti O., Mancini B.S., Madrid L., Narayanan S. 2024. Corn root morphology in a cover crop interseeded system. SARE-YES Poster.
• Mancini B., Madrid L., Narayanan S. Corn root morphology in a cover crop interseeded system. The Summer Program for Research Interns (SPRI) Symposium. July 19, 2024. Clemson, SC.
• Madrid L.B., St Aime R., Ye R., Narayanan S. 2024. Cover crop interseeding in organic corn production system. Cover Crop Field Day, April 18, Campobello, SC.
• Cover crop interseeding in organic production system. Southern Agronomy Society of America Annual Meeting (Feb 3-5, 2024)
• Co-optimizing diversity and prosperity in agroecosystems (Invited department seminar). University of Illinois Urbana-Champaign, Natural Resources and Environmental Sciences: (3/2024)
• Multi-trophic consequences of biodiversity in agroecosystems (Invited seminar). The Land Institute, Salina, KS (2/2024)
• Narayanan, S. Seven years of cover crop research in South Carolina: Lessons learned and future directions. National Cover Crop Summit (Virtual). March 14-16, 2023.
• Madrid LB, St Aime R., Narayanan S. “Cover-up” for environmentally friendly farming. Southern Cover Crop Conference, February 14-15, 2023, Baton Rouge, Louisiana.
• Turmon K, Madrid L, and Narayanan S. 2023. Cover crop interseeding in organic corn production. SARE-YES Poster.
• Participated in 2023 PREC and PDREC field days

Publications:

• Blubaugh, C.K., Huss, C.P., Lindell, H.C, Spann, G., and Basinger, N.T. 2025. Cover crops dismantle keystone ant/aphid mutualisms to enhance insect pest suppression and weed biocontrol. Agricultural and Forest Entomology. (In press)
• Madrid L.B., Sehgal A., Ye R., Siddique K.H.M., Hein K., Topp C., Narayanan S.* 2025. Cover crop root traits and their contribution to soil processes and agroecosystem sustainability. Agriculture, Ecosystems & Environment. Under Review.
• Liu D., Sehgal A., Madrid L.B., Narayanan S. 2025. Building climate-resilient agriculture: From plant physiology to breeding and agronomic innovation. In: Smart crop development – Adapting agriculture to climate change. Rustgi S., Gahlaut V., and Jaiswal V. (Eds.). Springer-Nature. Under Review.
• St Aime R., Bridges W.C., Jr., Narayanan S. 2023. Interseeded cover crops did not reduce the performance of silage corn in the sandy loam soils of South Carolina. Agrosystems Geosciences and Environment. 6:e20364.
• Holmes, K. D., & Blubaugh, C. K. 2023. A Guide to 23 Global Syntheses of Plant Diversity Effects: Unpacking Consensus and Incongruence across Trophic Levels. The Quarterly Review of Biology, 98, 121-148. https://doi.org/10.1086/726687

Co-PI Idassi interacted with underserved farmers in multiple meetings, workshops, and conferences. He conveyed the project results in these venues. Our project was covered by the Clemson News Media, which was reprinted by multiple news magazines/newspapers. This facilitated outreach to public. The project team members were in frequent communication with farmers, educators, and stakeholders which also helped to disseminate the project results and expected outcomes and provide consultations.