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Record W7038421366

Improving Chickpea, Durum Wheat, and Mustard Yield, Crop Health, and Soil Fertility with Potassium Chloride, Phosphorus, and Copper Fertilizer

2024· article· en· W7038421366 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2024
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsnot available
Fundersnot available
KeywordsPotashStrawCropFertilizerNutrientSoil fertilityPotassiumSoil water
DOInot available

Abstract

fetched live from OpenAlex

Potash (KCl; 0-0-60) fertilizer is the most economical and widely used source for K and Cl for crop nutrition. However, the link between potash fertilization and crop yield and disease incidence has not been investigated with chickpea, mustard, and durum wheat crops on the prairies. To address this gap, field experiments near Central Butte, SK were completed in 2022 and 2023 along with controlled environment studies using three soils taken from across the Brown soil zone of Saskatchewan where the crops are commonly grown. For the field study, small plot RCBD experiments were set up in a field with treatments of starter potash banded at 40 kg KCl/ha and a control with no KCl at two slope sites: a dry knoll and a moist depression. In both years of the field study, growing season conditions were drier and hotter than normal and there were no significant responses in crop yield or large reductions in disease resulting from the additional starter potash application. This outcome aligned with the soil test values of high exchangeable potassium and K supply rate found in the soil and the limited moisture available during both growing seasons. In the depressional site, greater crop yield and nutrient removal was observed compared to the upslope knoll site due to the higher inherent soil fertility and additional moisture available. Crop and straw tissue analysis revealed no significant increases in K or Cl concentration from the KCl application. Most of the K and Cl uptake in the above ground biomass was in the straw portion rather than the grain, with about 30% of the above ground K and Cl contained and removed in the grain of chickpea and 10% in the grain of durum and mustard. The controlled environment studies examined the effect of starter KCl, monoammonium phosphate (MAP), and copper sulfate (CuSO4) alone and in combination on early crop growth and root and shoot disease incidence of the chickpea, mustard, and durum wheat crops. Even though there were high extractable concentrations and supply rates of K, P, and Cu levels considered above sufficiency in all three soils used, significant early season growth responses to fertilization were sometimes observed, varying by soil and crop type. Increases in early season biomass were found in the mustard grown in the sandy Chaplin association soil as well as the durum wheat grown in the loamy Sutherland association soil, and chickpeas grown in the Swinton association. In some cases where benefits were observed from fertilization, greatest increases were observed from the additive effect of fertilizers. Generally, the plant tissue P, Cu, and Cl concentrations responded more to fertilization than K, possibly due to inherently large K supplies in the soils. Chickpea biomass in the growth chamber was influenced by the presence of an emerging concern, referred to as the chickpea health issue, and there was no observed treatment effect on the severity of the chickpea health issue or other diseases present. Overall, yield and disease incidence responses of chickpea, mustard, and durum wheat to fertilization with KCl alone appear to be muted under dry conditions often found in the field in southern SK soils, but may be attenuated when phosphorus and copper fertilizers are applied under optimal conditions for growth.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.190
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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