Categorical Analysis of Spatial Variability in Economic Yield Response of Corn to Nitrogen Fertilization

Kyveryga, Peter; Blackmer, T. M.; Caragea, Petruţa C.

doi:10.2134/agronj2010.0411

Cited by 11 publications

(7 citation statements)

References 42 publications

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“…It has been reported that rainfall amounts observed in May right before N fertilizer applications and in June just after N applications are the most interesting, because fertilizer nitrate content is subject to mobilization by water (Kyveryga et al, 2011). Another reason for interest is the fact that the few days before and right after the N application, rainfall has a positive effect on N response (Herlihy and O'keeffe, 1987;Tremblay et al, 2012).…”

Section: Economical Optimal Nitrogen Rate Asmentioning

confidence: 99%

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: 99%

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Kablan¹,

Chabot²,

Mailloux³

et al. 2017

Agronomy Journal

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“…However, climatic conditions, fertilization, soil quality, irrigation, soil management and crop rotation systems are key factors of success that vary widely among agroecosystems. Uncertainty in optimum nutrient dosage often leads growers to apply ‘insurance’ fertilization against the risk of yield loss 7 . Excessive fertilization leads not only to economic loss but also to increased incidence of diseases 8 – 11 , product loss during storage, and environmental damage such as nitrate leaching and N 2 O emissions 12 and surface water eutrophication by phosphates 13 .…”

Section: Introductionmentioning

confidence: 99%

Feature-specific nutrient management of onion (Allium cepa) using machine learning and compositional methods

Hahn,

Kurtz,

de Paula

et al. 2024

Sci Rep

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While onion cultivars, irrigation and soil and crop management have been given much attention in Brazil to boost onion yields, nutrient management at field scale is still challenging due to large dosage uncertainty. Our objective was to develop an accurate feature-based fertilization model for onion crops. We assembled climatic, edaphic, and managerial features as well as tissue tests into a database of 1182 observations from multi-environment fertilizer trials conducted during 13 years in southern Brazil. The complexity of onion cropping systems was captured by machine learning (ML) methods. The RReliefF ranking algorithm showed that the split-N dosage and soil tests for micronutrients and S were the most relevant features to predict bulb yield. The decision-tree random forest and extreme gradient boosting models were accurate to predict bulb yield from the relevant predictors (R2 > 90%). As shown by the gain ratio, foliar nutrient standards for nutritionally balanced and high-yielding specimens producing > 50 Mg bulb ha−1 set apart by the ML classification models differed among cultivars. Cultivar × environment interactions support documenting local nutrient diagnosis. The split-N dosage was the most relevant controllable feature to run future universality tests set to assess models’ ability to generalize to growers’ fields.

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“…Large uncertainty in EORN assessment often leads growers to add "insurance extra N" against risk of yield loss [17][18][19]. A great challenge is to predict site-specific EONR accurately [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Simulating Maize Response to Split-Nitrogen Fertilization Using Easy-to-Collect Local Features

Parent,

Deslauriers

2023

Nitrogen

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Maize (Zea mays) is a high-nitrogen (N)-demanding crop potentially contributing to nitrate contamination and emissions of nitrous oxide. The N fertilization is generally split between sowing time and the V6 stage. The right split N rate to apply at V6 and minimize environmental damage is challenging. Our objectives were to (1) predict maize response to added N at V6 using machine learning (ML) models; and (2) cross-check model outcomes by independent on-farm trials. We assembled 461 N trials conducted in Eastern Canada between 1992 and 2022. The dataset to predict grain yield comprised N dosage, weekly precipitations and corn heat units, seeding date, previous crop, tillage practice, soil series, soil texture, organic matter content, and pH. Random forest and XGBoost predicted grain yield accurately at the V6 stage (R2 = 0.78–0.80; RSME and MAE = 1.22–1.29 and 0.96–0.98 Mg ha−1, respectively). Model accuracy up to the V6 stage was comparable to that of the full-season prediction. The response patterns simulated by varying the N doses showed that grain yield started to plateau at 125–150 kg total N ha−1 in eight out of ten on-farm trials conducted independently. There was great potential for economic and environmental gains from ML-assisted N fertilization.

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Categorical Analysis of Spatial Variability in Economic Yield Response of Corn to Nitrogen Fertilization

Cited by 11 publications

References 42 publications

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Feature-specific nutrient management of onion (Allium cepa) using machine learning and compositional methods

Simulating Maize Response to Split-Nitrogen Fertilization Using Easy-to-Collect Local Features

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