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Record W7067646601

Nitrogen on Water Impacts on Nitrogen and Water Use Efficiency in Soil Zones With Different Yield Potentials

2006· article· en· W7067646601 on OpenAlexaboutno aff

Bibliographic record

VenueOpen PRAIRIE (South Dakota State University) · 2006
Typearticle
Languageen
FieldChemistry
TopicChemistry and Stereochemistry Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIrrigationNitrogenMineralization (soil science)Water contentMoistureWater-use efficiencyNitrogen cycleWater use
DOInot available

Abstract

fetched live from OpenAlex

A goal in production agriculture is to maximize yields without degrading resources. Increasing demand of food combined with decreasing land reserves necessitates that land become more productive. The tools available to increase productivity include improved plant genetics, pest control, nutrients, and water use efficiency. The objectives of this study were: 1) to determine the interaction between the water and N cycle that impacts corn production; 2) to understand the reaction of corn plants under water and N stresses; 3) to evaluate the site specific application of seasonal N recommendation models using model systems. Field experiments were conducted in 2002, 2003, and 2004. Treatments were natural rainfall, natural rainfall + irrigation, and four N rates (0, 56, 112, 168 kg N ha-1). Plant samples were analyzed for 13C discrimination(􀂩). 15N, and total N using isotope ratio mass spectrometer. An interaction between soil moisture regimes and N rates was not observed in this study. Increasing N rates increased corn grain yields and applying supplemental irrigation increased corn grain yield and soil N mineralization. Yields varied from year to year that depended on soil moisture regimes and climate conditions. Environmental conditions varied from year to year so that moisture regimes and N mineralization varied. This indicated that N utilization was influenced by both N rates and moisture regimes. Grain yields were increased by N rate and water. Plant N uptake was higher at the 112 kg N ha·' and 168 kg N ha·1 than O and 56 kg N ha·1. NUE was decreased as N rate increased. Plant N uptake at 112 kg N ha·' and 168 kg N ha· 1 were similar. These results indicated that 112 kg N ha·1 fertilizer application might be appropriate because yield were similar at 112 and 168 kg N ha·1. Grain N uptake was higher at the 112 kg N ha·1 and 168 kg N ha·1 than O kg N ha·1 and 56 kg N ha·1. Grain N uptake from soil using 8 15N approach was impacted by N rate and water treatment. Grain N uptakes from soil using o15N approach were 80, 99, 96, and 70 kg ha·1 at 0, 56, 112, 168 kg N ha·1, respectively. These results indicated that 112 kg N ha·1 of fertilizer application might be appropriate. Also, yield was higher at 112 kg N ha·1 than at 56 kg N ha·1. Also, using less fertilizer may reduce environmental pollution by nitrate in the water and may maximize profits for producers. Field where moisture regimes were not similar may have different N mineralization. Differential soil N mineralization at different moisture regimes may reduce the need for uniform N applications. Increased N fertilizer increased the N percentage(%) in grain and biomass. The N percentage(%) in the leaves, stem, and whole plant generally decreased as the season progressed. The N percentage(%) was higher in the leaves and most recently expanded leaf( MREL) than tin the stem as the plant growth progressed in 2003 and 2004. Leaves contained more N rich proteins, chlorophyll that stem tissues containing more structural material, such as cellulose and lignin that are low in N. Increased N fertilizer decreased plant 615N values in the grain. Urea had lower c15N value (-1.45 %0) than 615N value (4.67 %0) of soil. Lower o15N values in fertilized plants were attributed to fertilizer N used by plant and N derived from soil having a higher o 15N values than 615N values derived form fertilizer. Plant parts (leaves, stem, most recently expanded leaf (MREL), and whole plant) had different o15N values. These results were attributed to relocation of N. Irrigation increased o15N values. These results suggested that the o15N values were dependent on N sources and temporal changes of environment. The 13C discrimination (!:!,.) values in the leaves were higher in fertilized than unfertilized plants in 2004. The I:!,. values in the stem were lower than those in the leaf tissue. These results can be attributed to differences in the cellular components that comprise these plant parts. Temporal changes were attributed to relocation of C and. Leaves had much lower C/N ratio than stem tissue so that leaves will decompose faster than stover. The C/N ratio decreased with increasing N. Plant parts (leaves, stem, most recently expanded leaf, and whole plant) have different chemical compounds and different I:!,. values. Both carbon loss and carbon gain from the soil, SOCretained, SOCiast, and PCRinco'lJC'rated were influenced by plant I:!,. values. These results suggested that C mineralization may not be minimal at the initiation of C mineralized of added C. The amount of ti in the leaves and stem may need to be taken into account when calculating C mineralization using ti approach. The site-specific application of N recommendation models were evaluated using two model systems. The first system represented areas where yields were limited by water availability. This treatment was designed to represent shoulder and summit areas. The second model system was areas where water stress had a minimal impact on yields. This system represented footslope areas. Yields were higher under low water stress than high water stress. Numerous field studies have had similar results. Economic optimum N rates decreased with increasing fertilizer cost and increased with corn selling values. The economic optimum N rate was lower in the low water stress than the high water stress model system. These results were attributed water stimulating N mineralization. Yields generally increased with increasing fertilizer plus inorganic values. The slopes of the two soil moisture regime were similar. The y-intercept of regression equations were increased by reducing the water stress. These results suggest that in this high organic matter silty clay loam soil, soil water has a large impact on fertilizer response. Different results might be expected if the experiment would have conducted on a low organic matter soil. Soil moisture regime did not influence the slope of the relationship between yields and fertilizer plus inorganic N. Increasing available water increased the y intercept. These results suggest that in production fields with soils having variable yield potentials, the application of regimes based on N models may produce inaccurate results. The calculated RMSE were influenced by fertilizer cost, com values, and recommendation model. The error of South Dakota N recommendation increased with increasing fertilizer to com value ratio, while the error associated with the Iowa and Minnesota recommendations decreased with increasing ratio. A fixed N model generally had lower RMSE values than other state's N recommendation models. The low RMSE values for the fixed model were that it was empirically defined from this data set. The success of fixed model was attributed to the effect of water on N mineralization. Findings for this study suggest that landscape position will impact N recommendations. In high organic matter soils, mineralization may be increased in footslope areas, while in turn may reduce the N requirement. The effect of landscape position may become more pronounced if landscape position impacts soil organic C contents. Findings from this study suggest that a high N rate applied uniformly across landscape positions might be the most economical. Pennock et al. (2001) reported that in a glacial till soil located in Saskatchwan Canada, there was little economic rational for using variable rate fertilizer. Different results were reported using sandy soil in Colorado (Khosla et al., 2004). They reported that variable rate management had a higher profit than uniform N application. Differences between the studies may be attributed to the ability of water to increase N mineralization. The sandy soil in Colorado most likely had a lower mineralization potential than the glacial till soil in Canada. This study showed that yield was influenced by nitrogen and water. These results were against N recommendation based on yield goal approach because yield goal model do not consider water as a limiting factor. Based on soil N mineralization and water regimes, temporal and spatial changes of yield liming factors at different water regimes needed to be considered when N rates were applied. Future research needs to be conducted to determine long-term impact on soil N mineralization and the need for variable N application at different water regimes.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.200
Teacher spread0.183 · 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 teacher head, not a consensus.

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
Published2006
Admission routes1
Has abstractyes

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