Effects of Weather, Nitrogen and Population on Corn Yield Response<sup>1</sup>

Bondavalli, Bruno; Colyer, Dale; Kroth, Earl M.

doi:10.2134/agronj1970.00021962006200050038x

Cited by 21 publications

(16 citation statements)

References 5 publications

(5 reference statements)

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“…Other studies have demonstrated that corn yield response to fertilizer N in North America appears to be affected by the total precipitation during June and July and temperatures during July and August (Bondavalli et al, 1970;Jeutong et al, 2000). In the Montérégie region, Almaraz et al (2008) demonstrated that May precipitation and July temperature were strongly associated with yield variability in corn fields, and these climatic factors explained more than half of this variability.…”

Section: Economical Optimal Nitrogen Rate Asmentioning

confidence: 95%

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Kablan¹,

Chabot²,

Mailloux³

et al. 2017

Agronomy Journal

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Corn (Zea mays L.) yield response to N has been found to vary spatially within a field. The objective of this study was to examine how grain corn yield response to N varies with planting date, soil texture, and spring weather across sites and years in the Montérégie region. Trials were conducted from 2002 to 2004 and 2006 to 2010, at 11 sites with 23 hybrids and four N application rates, for a total of 45 site-years. Each site-year involved five or six N rates ranging from 80-90 to 240 kg N ha -1 . Grain yield response to N rates varied among site-years. Trials were separated into two groups based on optimal and late planting dates. Significant differences in grain yield among the applied N rates were observed in all of the site-years planted at optimal dates (from 8.8-14.7 Mg ha -1 ), and in most of those planted late (8.5-12.8 Mg ha -1 ). Economic optimum nitrogen rates (EONR) ranged less widely for site-years planted on optimal dates (180-237 kg N ha -1 ) than for those planted late (132-237 kg N ha -1 ). The EONR was affected by soil textural classes and rainfall. On coarse-textured soils, more N was needed to optimize grain yield in years with wet growing seasons. These results suggest that the current N recommendations for corn in Quebec should consider the variability in response associated with site-specific effects of planting date, soil texture, and weather.

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Section: Economical Optimal Nitrogen Rate Asmentioning

confidence: 95%

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Kablan¹,

Chabot²,

Mailloux³

et al. 2017

Agronomy Journal

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“…However, most of the high stable genotypes are less predictable across different crop management practices since plant breeders often perform analysis of two-way data (genotype x site or GxE) for several consecutive years to detect stable genotypes without taking crop management practices into account. Previous studies on crop management practices suggest that optimization of management practice alter the external environment that a maize plant live in, which result in scale or rank shift in its performance [100,101]. This relative shift of genotype performance from one environment to another across management practices is known as genotype x environment x management interaction (GxExM) [102,103].…”

Section: Plant Density Effect On Grain Yield Informationmentioning

confidence: 99%

Response of Maize Grown Under High Plant Density; Performance, Issues and Management - A Critical Review

Sher¹,

Khan²,

Cai³

et al. 2017

Adv Crop Sci Tech

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Modern cropping is based on relatively high plant density. The improved grain yield per unit area of modern maize (Zea mays L.) hybrids is due to the increased optimum plant population rather than the improved grain yield per plant. High plant density has been widely used to enhance grain yield in maize. Subsequently we review the effect of planting density on physiology, phenology, morphology, nitrogen use efficiency, water use efficiency grain yield information in maize crop. At higher plant populations reduced grain yield also results from the increased pollento-silking interval and the following barrenness. However, it may lead to higher risk lodging hence causing significant yield loss of the crop. Future insights are morphological and physiological basis controlling barren and stalk lodging resistance. How root traits, and anatomy of sheath and stem of maize plants correspond to high plant population and a further study on the physiological and biological basis of organ development that may govern the mechanisms of high plant density would be essential for future research.

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“…The models should be based on a variable combination of soil, crop and climate. According to the reports of Bondavalli et al (1970) [2] and Nielson et al (1996) [3], the most important weather variables that affect grain yield prediction of maize (Zea mays L.) in the U.S. Corn Belt region were temperature occurring during the months of May and August. However, other studies also Downloaded by [Umeå University Library] at 22: 23 19 November 2014 stressed the importance of weather variables on crop yields (Teigens & Thomas, 1995;Teigens, 1991) [4,5].…”

Section: Introductionmentioning

confidence: 99%

Extended Probabilistic Latent Semantic Analysis Model For Topics In Time-Stamped Images

Liao

Wang

Ding

et al. 2011

Intelligent Automation & Soft Computing

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Artificial neural network is a powerful tool for modelling and prediction in various fields. Tobacco management specialists need simple and accurate techniques to evaluate tobacco growth and cultivation in the growth period. The objective in this study is to develop a back-propagation neural network (NN) model to accurately predict tobacco growth under various climatic and soil conditions in China. The proposed model used the field test data at 11 locations in main tobacco areas in China. As the output of the NN, the dry weight of tobacco plants was significantly influenced by dry weigh preceding stage, time for sampling, average temperature, relative humidity, soil organic matter and available P value, so these 6 environment factors were taken as the NN input. In the study, an optimal NN model was developed with 6 inputs, 1 output and 19 neurons in the hidden layer. With the same test data, the proposed NN model could perform better than the conventional regression model in terms of the relative error ranges and relative coefficient.

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Effects of Weather, Nitrogen and Population on Corn Yield Response¹

Cited by 21 publications

References 5 publications

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Response of Maize Grown Under High Plant Density; Performance, Issues and Management - A Critical Review

Extended Probabilistic Latent Semantic Analysis Model For Topics In Time-Stamped Images

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Effects of Weather, Nitrogen and Population on Corn Yield Response1

Cited by 21 publications

References 5 publications

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Response of Maize Grown Under High Plant Density; Performance, Issues and Management - A Critical Review

Extended Probabilistic Latent Semantic Analysis Model For Topics In Time-Stamped Images

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Product

Resources

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Effects of Weather, Nitrogen and Population on Corn Yield Response¹