An analysis of the response of sugar beet and potatoes to fertilizer nitrogen and soil mineral nitrogen.

Neeteson, J.J.; Zwetsloot, H.J.C.

doi:10.18174/njas.v37i2.16644

Cited by 17 publications

(15 citation statements)

References 6 publications

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“…2). These percentages are similar to those observed by Neeteson & Zwetsloot (1989), who concluded that, during spring and early summer, soil within the 60-100 cm horizon contributed little mineral N to the crop. However, Brown & Dunham (1986) showed that the soil is exploited by the fibrous root system of sugarbeet to a depth of 60 cm within c. 70 days of sowing, and deeper within the following few weeks.…”

Section: Relationships Between N Mtn N Mineralized and N Fertilizersupporting

confidence: 90%

“…In calculating these corrected and adjusted yields of clean beet (CACB) the clean beet was valued at the 1992 'C contracted tonnage' price of £14.48/tonne (Allison & Hetschkun 1995), and the N fertilizer at £0.29/kg. The CACB yields (y) were related to rates of applied N (.v) using the exponential plus linear model as used by George (1984) and Neeteson & Zwetsloot (1989):…”

Section: A T E R I a L S A N D M E T H O D Smentioning

confidence: 99%

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An analysis of the agronomic, economic and environmental effects of applying N fertilizer to sugarbeet (Beta vulgaris)

Allison¹,

Armstrong²,

Jaggard³

et al. 1996

J. Agric. Sci.

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The effects of different rates of N fertilizer (0-180 kg N/ha) were tested on the growth, yield and processing quality of sugarbeet in 34 field experiments in England between 1986 and 1988. The experiments were performed using soil types, locations and management systems that were representative of the commercial beet crop in the UK. The responses obtained showed that current recommendations for N fertilizer use are broadly correct, but large differences occurred on some soil types, in some years, between the recommended amounts and the experimentally determined optima for yield. The divergence was largest when organic manures had been applied in the autumn before the beet crop. Calculations using a simple nitrate-leaching model showed that much of the N in the manures was likely to be leached, the extent of leaching being much less if the manure application was delayed until spring. In these circumstances, spring measurement of inorganic mineral N in the soil could improve fertilizer recommendations. In situations where higher than optimum rates of fertilizer N were used, the extra N had little effect on yield. Increasing the rate from 0 to 180 kg N/ha increased the amount of nitrate left in the soil at harvest by only 8 kg N/ha. The amount of inorganic N released into the soil from crop residues at harvest increased by 50 kg N/ha with N application rate, and the fate of this N has not been established.

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Section: Relationships Between N Mtn N Mineralized and N Fertilizersupporting

confidence: 90%

Section: A T E R I a L S A N D M E T H O D Smentioning

confidence: 99%

An analysis of the agronomic, economic and environmental effects of applying N fertilizer to sugarbeet (Beta vulgaris)

Allison¹,

Armstrong²,

Jaggard³

et al. 1996

J. Agric. Sci.

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show abstract

“…N in seed meal likely led to more vegetative growth and less tuber development. Adequate management of N is critical for optimal potato yield (Sincik et al 2008), whereas excessive available N results in reduced yields (Lauer 1986;Neeteson and Zwetsloot 1989).…”

Section: Discussionmentioning

confidence: 99%

Potato Early Dying and Yield Responses to Compost, Green Manures, Seed Meal and Chemical Treatments

Molina

Tenuta

Hadrami³

et al. 2014

Am. J. Potato Res.

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Verticillium dahliae Kleb. is a soilborne fungal pathogen of many crops. In potato, it is the major causal agent of Early Dying. In Manitoba, potato fields planted with cv. Russet Burbank are infested with highly pathogenic V. dahliae isolates, which can produce up to 90 % disease severity. The objective of the study was to evaluate selected compost, green manure, and seed-meal treatments, in comparison with the soil fumigant Vapam, for their ability to reduce propagule density of V. dahliae in soil and decrease disease, and to enhance potato yield. Select green manure crops (oriental and white mustard, Canada milk vetch, sorghum-sudangrass, rye, alfalfa, oat/pea mixture), organic amendments (composted cattle manure and mustard seed-meal), and Vapam, and crop sequences that contribute to the suppression of Verticillium, or the improvement of

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“…Several variables can impact fertilization at optimum tuber yield: soil type and quality [8,9], organic fertilizers [10,11], preceding crops [12][13][14][15][16], weather conditions [17], irrigation [18], timing, location and chemical form of the fertilizer applied [19], pests and diseases [20] and genetic factors such as cultivar longevity and growth rate [21,22]. Air temperature, photoperiod, day length, intercepted radiation, water abundance, precipitations, root development and crop management were reported to be the driving variables for potato growth and development [8,9,[23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada

2020

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Statistical modeling is commonly used to relate the performance of potato (Solanum tuberosum L.) to fertilizer requirements. Prescribing optimal nutrient doses is challenging because of the involvement of many variables including weather, soils, land management, genotypes, and severity of pests and diseases. Where sufficient data are available, machine learning algorithms can be used to predict crop performance. The objective of this study was to determine an optimal model predicting nitrogen, phosphorus and potassium requirements for high tuber yield and quality (size and specific gravity) as impacted by weather, soils and land management variables. We exploited a data set of 273 field experiments conducted from 1979 to 2017 in Quebec (Canada). We developed, evaluated and compared predictions from a hierarchical Mitscherlich model, k-nearest neighbors, random forest, neural networks and Gaussian processes. Machine learning models returned R 2 values of 0.49-0.59 for tuber marketable yield prediction, which were higher than the Mitscherlich model R 2 (0.37). The models were more likely to predict medium-size tubers (R 2 = 0.60-0.69) and tuber specific gravity (R 2 = 0.58-0.67) than large-size tubers (R 2 = 0.55-0.64) and marketable yield. Response surfaces from the Mitscherlich model, neural networks and Gaussian processes returned smooth responses that agreed more with actual evidence than discontinuous curves derived from k-nearest neighbors and random forest models. When conditioned to obtain optimal dosages from dose-response surfaces given constant weather, soil and land management conditions, some disagreements occurred between models. Due to their built-in ability to develop recommendations within a probabilistic riskassessment framework, Gaussian processes stood out as the most promising algorithm to support decisions that minimize economic or agronomic risks.

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An analysis of the response of sugar beet and potatoes to fertilizer nitrogen and soil mineral nitrogen.

Cited by 17 publications

References 6 publications

An analysis of the agronomic, economic and environmental effects of applying N fertilizer to sugarbeet (Beta vulgaris)

An analysis of the agronomic, economic and environmental effects of applying N fertilizer to sugarbeet (Beta vulgaris)

Potato Early Dying and Yield Responses to Compost, Green Manures, Seed Meal and Chemical Treatments

Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada

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