Expansion of the Nutrient Stewardship Framework: Implications of the Right Soil on Synthetic and Biological Technologies in Western Canadian Spring Wheat Production
Bibliographic record
Abstract
To ensure the continued success of the agricultural system in meeting global food demand, evaluation of novel strategies or technologies needs to be frequently pursued. This process has been exemplified in Canadian spring wheat (Triticum aestivum) by the 4R framework for nutrient stewardship. This framework embodies management practices to improve sustainability without compromising productivity, specifically around nitrogen (N) fertilizer use. Consequently, the advent of novel technologies and shift towards a soil health paradigm illuminates the challenge of capturing these new practices under the current framework. As such, there is a compelling case for the expansion of this framework to include a 5th R, the right soil. The proposed expansion will allow for new management practices beyond the traditional 4R’s of right source, right rate, right time and right place to permeate into the agricultural system under and established policy. Innovation among technologies that enshrine the essence of nutrient stewardship, such as enhanced efficiency fertilizers (EEF), and those that fall outside the traditional framework, such as microbial biotechnologies, needs to have their viability tested within the Canadian agricultural system. The objective of this body of work was to further our understanding of how new technologies, such as novel nitrogen stabilizers or arbuscular mycorrhizal fungi (AMF) inoculation, can influence N dynamics in Canadian spring wheat. Initially, the focus of this research was placed on gathering insights towards how currently available but not widely used EEF’s (i.e., alternative N source fertilizer formulations and various N stabilizers) could impact the crop productivity of Canadian wheat. This comparison among different N source fertilizers revealed that banded ammonium-nitrate based formulations can offer minimal crop productivity benefits over banded urea. Moreover, incorporation of banded nitrification or urease inhibitors into fertilizer regimes (i.e., EEF’s) provided little agronomic differentiation in spring wheat. This was likely due to the common practice of banding fertilizer limiting N loss potential under environmental conditions experienced in the Canadian Prairies. These observations highlight the need for new technologies or practices to be incorporated into the current paradigm in order to help drive future crop productivity advancements. New technologies that focus on soil health or functionality may have substantial impacts on already productive ecosystems. This can be emulated by the inoculation of symbiotic microbes involved in soil nutrient cycling (i.e., AMF) and has been proposed to be an untapped potential resource. Inoculation of the AMF species Rhizophagus irregularis in spring wheat has historically been met with mixed outcomes but has been shown to substantially influence N cycling and loss mechanisms. Testing of AMF inoculants in Canadian spring wheat has been limited, meanwhile there has been no assessments on the viability of using EFF’s in conjunction with novel biotechnologies. This research evaluated the individual practice of applying nitrification inhibitors or inoculating with Rhizophagus irregularis in a Canadian prairie spring red (CPSR) wheat genotype. This led to reduced N2O emission potential under each individual technology but increased N2O emissions when applied together in a greenhouse setting. Additionally, the field-based efficacy of commercial Rhizophagus irregularis inoculation was tested on numerous Canadian western red spring (CWRS) wheat varieties and a single CPSR variety. The field-based testing showed consistent crop productivity improvements occurring in only the CPSR wheat variety under specific site conditions. This disparity among wheat class was likely due to the physiological differences in varieties, while site specific conditions were influenced by N fertilization practices. These findings highlight the potential of novel biotechnologies to improve wheat productivity, while illuminating the need to appropriately capture their potential within a policy framework. This lends credence to the expansion of the 4R nutrient stewardship framework to include a 5th R, the right soil.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".