Exploring the relationships between biomass production, nutrient acquisition, and phenotypic traits: testing oat genotypes as a cover crop

Ma, Bao–Luo; Haan, Brad de; Xue, Allen; Chen, Yuanhong; Silva, Nayana D. G. de; Byker, Holly P.; Mountain, Nathan; Yan, Weikai

doi:10.1080/01904167.2022.2068436

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…However, of all crops produced in short-growing season regions, canola offers the highest cash value per hectare, contributes significantly to farm profitability, and serves as a renewable feedstock for biofuel production (Blackshaw et al 2011). It is harvested earlier than maize and soybean and may provide an opportunity for cover crops in regions with a shorter growing season, such as eastern Canada (Ma et al 2022). Therefore, it is critical to develop beneficial management practices, to maximize crop productivity while minimizing GHG emissions and C footprint from canola crop production.…”

Section: Introductionmentioning

confidence: 99%

Canola productivity and carbon footprint under different cropping systems in eastern Canada

Liang

Herath

et al. 2023

Nutr Cycl Agroecosyst

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Diversified crop rotation with an appropriate sequence may be a promising strategy for increasing crop productivity while reducing greenhouse gas emissions (GHGs) and lowering carbon (C) footprint for more sustainable agricultural systems. The objectives of this study were to (i) assess the agronomic performance and C footprint of canola (Brassica napus L.) production in different cropping systems, and (ii) better understand how canola could be adapted to existing cropping systems in eastern Canada. A four-year canola-based phase rotation study, including maize (Zea mays L.), wheat (Triticum aestivum L.), and soybean (Glycine max L.), started in 2011 and continued for two cycles in Ottawa, ON; Montreal, QC; and Canning, NS. It was found that, compared to continuous monoculture (canola, maize or wheat), diversified cropping systems increased crop yields by an average of 32% and reduced the C footprint of all rotations by 33%, except under severe heat and drought conditions. The effect of rotation on yield and C footprint of canola production varied significantly among site-years. At Ottawa, the canola following soybean (SC) had 12% higher canola yield than monoculture canola (CC), 5 and 8% higher canola yield than canola following wheat (WC) or maize (MC). At Montreal, canola yield ranked as MC > SC > WC > CC. At Canning, the highest canola yield was in WC (21%) and SC (13%). Overall, most SC rotations had the lowest C footprint, and CC cropping had the highest C footprint, with only a few exceptions. Regardless of the cropping system, canola required more N input and was high in oil and protein in the harvested product, and produced the highest C footprint, while soybean had the lowest C footprint at all three sites. Our findings indicate that a diversified cropping system with canola production following soybean significantly improved canola yield while lowering the C footprint. However, profitable and sustainable canola production in eastern Canada is threatened by climate change-induced drought and heat stress.

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Section: Introductionmentioning

confidence: 99%

Canola productivity and carbon footprint under different cropping systems in eastern Canada

Liang

Herath

et al. 2023

Nutr Cycl Agroecosyst

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Optimizing crop nitrogen use efficiency: Integrating root performance and machine learning into nutrient management

Wen,

2024

Advances in Agronomy

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Prediction of Cereal Rye Cover Crop Biomass and Nutrient Accumulation Using Multi-Temporal Unmanned Aerial Vehicle Based Visible-Spectrum Vegetation Indices

et al. 2023

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In general, remote sensing studies assessing cover crop growth are species nonspecific, use imagery from satellites or modified unmanned aerial vehicles (UAVs), and rely on multispectral vegetation indexes (VIs). However, using RGB imagery and visible-spectrum VIs from commercial off-the-shelf (COTS) UAVs to assess species specific cover crop growth is limited in the current scientific literature. Thus, this study evaluated RGB imagery and visible-spectrum VIs from COTS UAVs for suitability to estimate concentration (%) and content (kg ha−1) based cereal rye (CR) biomass, carbon (C), nitrogen (N), phosphorus (P), potassium (K), and sulfur (S). UAV surveys were conducted at two fields in Indiana and evaluated five visible-spectrum VIs—Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), Red Green Blue Vegetation Index (RGBVI), and Excess of Greenness (ExG). This study utilized simple linear regression (VI only) and stepwise multiple regression (VI with weather and geographic data) to produce individual models for estimating CR biomass, C, N, P, K, and S concentration and content. The goodness-of-fit statistics were generated using repeated K-fold cross-validation to compare individual model performance. In general, the models developed using simple linear regression were inferior to those developed using the multiple stepwise regression method. Furthermore, for models developed using the multiple stepwise regression method all five VIs performed similarly when estimating concentration-based CR variables; however, when estimating content-based CR variables the models developed with GLI, MGRVI, and RGBVI performed similarly explaining 74–81% of the variation in CR data, and outperformed VARI and ExG. However, on an individual field basis, MGRVI consistently outperformed GLI and RGBVI for all CR characteristics. This study demonstrates the potential to utilize COTS UAVs for estimating in-field CR characteristics; however, the models generated in this study need further development to expand geographic scope and incorporate additional abiotic factors.

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Exploring the relationships between biomass production, nutrient acquisition, and phenotypic traits: testing oat genotypes as a cover crop

Cited by 3 publications

References 31 publications

Canola productivity and carbon footprint under different cropping systems in eastern Canada

Canola productivity and carbon footprint under different cropping systems in eastern Canada

Optimizing crop nitrogen use efficiency: Integrating root performance and machine learning into nutrient management

Prediction of Cereal Rye Cover Crop Biomass and Nutrient Accumulation Using Multi-Temporal Unmanned Aerial Vehicle Based Visible-Spectrum Vegetation Indices

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