Mid-Season Recovery from Nitrogen Stress in Winter Wheat

Morris, K. B.; Martin, K. L.; Freeman, K. W.; Teal, R. K.; Girma, Kefyalew; Arnall, Daryl B.; Hodgen, P. J.; Mosali, Jagadeesh; Raun, W. R.; Solie, John B.

doi:10.1080/01904160600567066

Cited by 25 publications

(20 citation statements)

References 24 publications

(4 reference statements)

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“…The model-based fertilizer prescription lead to N use efficiency increase and enabled the design of N prescriptions adapted to plants demand Long et al (2000) Montana (USA) Spring wheat Five management zones were obtained from the N-recommended range (mapped values of the N-removed+the N-deficit), by specifying cut-off values. Uniform and variable-rate N treatments were randomly assigned in a randomized block design Proteins were significantly enhanced by spatially variable N application and variability in protein levels was reduced within the whole field Morris et al (2006) Oklahoma (USA) Winter wheat A randomized complete block design was carried out with 15 treatments and 4 replications. Top-dress N rate was determined utilizing the algorithm of Raun et al (2002) and measuring spectral reflectance by means of a hand-held sensor…”

Section: Soft Red Winter Wheatmentioning

confidence: 99%

“…In particular, this management strategy could raise profitability of crop production thanks to the increased efficiency of N recovery by the crop. Morris et al (2006) determined in-season top-dress N rate by means of an active hand-held sensor and an algorithm developed at Oklahoma State University. The results proved that top-dress N rate generally could obtain maximum wheat yields, compared to other treatments that received both pre-plant and top-dress N rates, even when early-season N stress was present.…”

Section: Impact On Wheat Yield and Nitrogen Use Efficiencymentioning

confidence: 99%

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Precision nitrogen management of wheat. A review

Diacono

Rubino

Montemurro³

2012

Agron. Sustain. Dev.

323

170

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Conventional farming has led to extensive use of chemicals and, in turn, to negative environmental impacts such as soil erosion, groundwater pollution and atmosphere contamination. Farming systems should be more sustainable to reach economical and social profitability as well as environmental preservation. A possible solution is to adopt precision agriculture, a win-win option for sustaining food production without degrading the environment. Precision technologies are used for gathering information about spatial and temporal differences within the field in order to match inputs to site-specific field conditions. Here we review reports on the precision N management of wheat crop. The aims are to perform an investigation both on approaches and results of site-specific N management of wheat and to analyse performance and sustainability of this agricultural practice. In this context, we analysed literature of the last 10-15 years. The major conclusions are: (a) before making N management decisions, both the measurement and understanding of soil spatial variability and the wheat N status are needed. Complementary use of different sensors has improved soil properties assessment at relatively low cost; (b) results show the usefulness of airborne images, remote and proximal sensing for predicting crop N status by responsive in-season management approaches; (c) red edge and near-infrared bands can penetrate into higher vegetation fraction of the canopy. These narrowbands better estimated grain yield, crop N and water status, with R 2 higher than 0.70. In addition, different hyperspectral vegetation indices accounted for a high variability of 40-75 % of wheat N status; (d) various diagnostic tools and procedures have been developed in order to help wheat farmers for planning variable N rates. In-season adjustments in N fertilizer management can account for the specific climatic conditions and yield potential since less than 30 % of spatial variance could show temporal stability; (e) field studies in which sensor-based N management systems were compared with common farmer practices showed high increases in the N use efficiency of up to 368 %. These systems saved N fertilizers, from 10 % to about 80 % less N, and reduced residual N in the soil by 30-50 %, without either reducing yields or influencing grain quality; (f) precision N management based on real-time sensing and fertilization had the highest profitability of about $5-60 ha −1 compared to undifferentiated applications.

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Section: Soft Red Winter Wheatmentioning

confidence: 99%

Section: Impact On Wheat Yield and Nitrogen Use Efficiencymentioning

confidence: 99%

Precision nitrogen management of wheat. A review

Diacono

Rubino

Montemurro³

2012

Agron. Sustain. Dev.

323

170

View full text Add to dashboard Cite

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“…Morris et al (2006) used a GreenSeeker hand‐held sensor and its algorithm to determine topdress N in winter wheat. Nitrogen applied topdress at Feekes 5 resulted in maximum or near‐maximum yields in four of six site‐year combinations when compared with other treatments receiving both preplant and topdress N, even when early‐season N stress was present (0 kg N ha −1 provided before sowing).…”

Section: Relationship Of Diagnosis With In‐season Nitrogen Recommendamentioning

confidence: 99%

Strategies to Make Use of Plant Sensors‐Based Diagnostic Information for Nitrogen Recommendations

2009

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Improvements of nitrogen use effi ciency (NUE) may be achieved through the use of sensing tools for N status determination. Leaf and canopy chlorophyll, as well as leaf polyphenolics concentrations, are characteristics strongly aff ected by N availability that are oft en used as a surrogate to direct plant N status estimation. Approaches with near-term operational sensors, handheld and tractor-mounted, for proximal remote measurements are considered in this review. However, the information provided by these tools is unfortunately biased by factors other than N. To overcome this obstacle, normalization procedures such as the well-fertilized reference plot, the no-N reference plot, and relative yield are oft en used. Methods to establish useful relationships between sensor readings and optimal N rates, such as critical NSI (nitrogen suffi ciency index), INSEY (in-season estimated yield), and the relationship between chlorophyll meter readings, grain yield, and sensor-determined CI (chlorophyll index) are also reviewed. In a few cases, algorithms for translating readings into actual N fertilizer recommendation have been developed, but their value still seems limited to conditions similar to the ones where the research was conducted. Near-term operational sensing can benefi t from improvements in sensor operational characteristics (size and shape of footprint, positioning) or the choice of light wavebands more suitable for specifi c conditions (i.e., genotype, growth stage, or crop density). However, one important limitation to their widespread use is the availability of algorithms that would be reliable in a variety of soil and weather conditions.

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“…This finding is important for assessing the reliability and stability of the chlorophyll measurements when chlorophyll meters are used to help optimize in-season N fertilization under different site conditions. Addition of a fertilizer could supply young plants with adequate amounts of N for growth even in situations where the plants will soon experience shortages of N (Morris et al, 2006). This situation is impossible to describe unless the symptom of an N deficiency is clearly distinguished from the deficiency itself, i.e., amount of N fertilizer to correct deficiency.…”

Section: Divergence Of Chlorophyll Measurementsmentioning

confidence: 99%

Nitrogen deficiency and recovery in sustainable corn production as revealed by leaf chlorophyll measurements

Zhang

Blackmer

et al. 2007

Agron. Sustain. Dev.

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Assessing economic and environmental impacts of nitrogen (N) fertilization in production agriculture is important for preventing unnecessary application of N fertilizer and avoiding losses of this N into water body. Chlorophyll meters are often used to evaluate N management practices and diagnose deficiencies of N in corn (Zea mays L.). Chlorophyll meter readings (CMRs) are usually interpreted relative to reference readings taken from plants having ample N to maximize rates of growth. Although measurements taken with chlorophyll meters provide a scale for estimating the sufficiency of N for corn growth, effects of above-optimal supplies of N on the measurements have not been studied when diagnosing N deficiencies during vegetative growth stages. Here, temporal trends in chlorophyll measurements were monitored in trials where various rates of N were applied soon after planting and (or) after symptoms of N deficiency had developed due to the changes of N status in soil and demand for N during corn growth. Divergence of chlorophyll measurements from the reference readings occurred in situations where plants having too little N were compared with plants having adequate N. In contrast, convergence of chlorophyll measurements with the reference readings after application of fertilizer N during the growing season indicated partial or complete recovery of the plants from the deficiency of N. The recovery can be explained by considering that luxury production of chlorophyll occurred at higher rates of fertilization or by interactions of N with soil water and other nutrients supplied during corn growth. Observations that plants can partially or completely recover from periods with inadequate N for chlorophyll production suggest that the leaf chlorophyll measurements taken early in the season should not be always expected to highly correlate with final yields of grain. Therefore, it is important to recognize the possible recovery of chlorophyll production and to avoid N losses to the environment. corn (Zea mays L.) / nitrogen deficiency / chlorophyll meter / luxury production / temporal trend

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Mid-Season Recovery from Nitrogen Stress in Winter Wheat

Cited by 25 publications

References 24 publications

Precision nitrogen management of wheat. A review

Precision nitrogen management of wheat. A review

Strategies to Make Use of Plant Sensors‐Based Diagnostic Information for Nitrogen Recommendations

Nitrogen deficiency and recovery in sustainable corn production as revealed by leaf chlorophyll measurements

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