Comparing cover crop research in farmer-led and researcher-led experiments in the Western Corn Belt

Koehler‐Cole, Katja; Basche, Andrea; Thompson, Laura J.; Rees, Jennifer

doi:10.3389/fsufs.2023.1064251

Cited by 2 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This disparity highlights the perceived value and credibility that farmers place on on-farm research compared to other types of research. It also underscores the importance of conducting research directly on farms to generate recommendations that farmers find more relevant and applicable to their specific contexts and environments (Koehler-Cole et al, 2023;Snapp et al, 2023). On-farm research, hence, has the potential to become a leading catalyst for helping farmers to adopt sustainable practices.…”

Section: Discussionmentioning

confidence: 99%

Perspective of US farmers on collaborative on‐farm agronomic research

Pires,

Krupek,

Carmona

et al. 2024

Agronomy Journal

Self Cite

View full text Add to dashboard Cite

On‐farm research has emerged in recent years as a unique approach to involve farmers and other agricultural stakeholders as active participants in knowledge development and as an effective method of technology and innovation transfer across farms. This study assessed the perspective and knowledge of US farmers regarding on‐farm research via the implementation of a 24‐question survey distributed across most of the US Midwest and South‐Central regions. We found that farmers generally are willing to engage with universities to conduct on‐farm research and were 40% more likely to adopt practices supported by on‐farm research findings than research not conducted on‐farm. Notably, a shift toward conservation practices was made, with cover crops and no‐till at the forefront. Insights of this nature have implications for fostering collaborations, addressing constraints, and maximizing the impact of on‐farm research, offering guidance for sustainable agriculture progress in the United States and beyond. Results from this research survey could be used to initiate much‐needed policies to promote on‐farm research. Further, information on the benefits and drawbacks of on‐farm research could be used in the development of studies that benefit both farmers and researchers.

show abstract

Section: Discussionmentioning

confidence: 99%

Perspective of US farmers on collaborative on‐farm agronomic research

Pires,

Krupek,

Carmona

et al. 2024

Agronomy Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Planting ruzigrass cover crop was found to reduce the soil phosphorus availability ( Almeida et al., 2019 ). Continuing to advance management practices as “systems” that optimize benefits from cover crops while minimizing cash crop yield penalty is a critical element of their success and extended use ( Basche and Roesch-McNally, 2017 ; Koehler-Cole et al., 2023 ; Popovici et al., 2023 ).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, such monitoring could empower farmers with digital tools to support their decision-making for important applications, such as establishing data-driven methodologies to determine optimal termination dates of cover crops, and understanding nutrient release and recycling to cash crops. The biomass accumulation process, however, is complex due to the genotype by environment (GxE) interactions created by differing geographical locations, fluctuating climatic conditions year after year, and variability in farmers’ practices ( Koehler-Cole et al., 2023 ). As such, intensive research is ongoing to determine the optimal types and cultivars of cover crops, the best termination stage, and the suitable termination method for local farmers ( Adetunji et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models

Bai,

Koehler-Cole,

Scoby

et al. 2024

Front. Plant Sci.

Self Cite

View full text Add to dashboard Cite

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.

show abstract

Comparing cover crop research in farmer-led and researcher-led experiments in the Western Corn Belt

Cited by 2 publications

References 40 publications

Perspective of US farmers on collaborative on‐farm agronomic research

Perspective of US farmers on collaborative on‐farm agronomic research

Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models

Contact Info

Product

Resources

About