Viabilidade técnica de fontes alternativas de adubação para o alho (Allium sativum L.) vernalizado no planalto Catarinense

Piva, Jonatas Thiago; Besen, Marcos Renan; Ribeiro, Ricardo Henrique; Bastos, Alessandra Carvalho Maciel; Ronsani, Sabrina Carvalho; Piva, Claudia Aparecida Guginski

doi:10.5965/223811711632017239

Cited by 4 publications

(3 citation statements)

References 2 publications

(2 reference statements)

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“…Bulbs showing secondary growth, or damage, were considered nonmarketable. Maximum yield ranged from 7.8 and 13.3 Mg ha -1 over the years; it was comparable to yield of 9.2–13.4 Mg ha -1 –which was recorded by [ 52 ] in the same region.…”

Section: Methodssupporting

confidence: 76%

“…The chosen response curve, and range and number of doses, may also have impact on the optimum nutrient dosage [ 24 , 25 , 61 ]. Results presented by [ 52 ] showed total and marketable garlic yield non-linear response pattern under greenhouse conditions—it reached 320 kg N ha -1 at yield level of 9 Mg ha -1 . Maximum marketable yield of 12.9–15.8 Mg ha -1 , at 200 kg N ha -1 , was recorded under field conditions in Mid-Eastern Santa Catarina State, where soil organic matter ranges from 3.9% to 4.9%, [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

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Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models

Hahn

Parent

Paviani³

et al. 2022

PLoS ONE

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Brazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers’ observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential to reduce production costs and environmental footprint without agronomic loss. Garlic root colonization by arbuscular mycorrhizal fungi likely contributed to P and K uptake. Well-documented data sets and machine learning models could support technology transfer, reduce costs with fertilizers and yield gaps, and sustain the Brazilian garlic production.

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Section: Methodssupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models

Hahn

Parent

Paviani³

et al. 2022

PLoS ONE

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“…Unlike mineral fertilizers, alternative fertilizers release nutrients throughout the crop cycle, as they are dependent on mineralization (Gliessman, 2000). Additionally, alternative fertilizers have low acquisition and production costs, which can increase the profitability of the producer (Piva et al, 2017). Among these products, ash is becoming popular, as it contains calcium and magnesium (Ribeiro, Amendola, Andrade, & Miranda, 2015) and is able to make other nutrients available to plants.…”

Section: Introductionmentioning

confidence: 99%

Growth and physiology of two sunflower cultivars fertilized with sugarcane bagasse ash

et al. 2022

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One way to reduce mineral fertilizers is to use alternative fertilizers instead, such as the byproducts from the food industry. In the present study we evaluated the effects of sugarcane bagasse ash on the physiology, growth, and development of sunflowers (Helianthus annuus L.). We conducted an experiment in a greenhouse using a completely randomized design with two sunflower cultivars (Multissol and Catissol), five sugarcane bagasse ash doses (0.0, 3.125, 4.687, 6.25, and 7.812 mg ha-1), and 5 replicates. At 85 days after planting we determined the plant height; leaf number; stem diameter; internal and external diameter of the flower chapter; leaf area index; shoot dry weight; net assimilation rate; dry mass production rate; relative and absolute growth rate; extravasation of electrolytes; relative water content; photosynthetic pigments (chlorophyll a, b, carotenoids, and the chlorophyll a/b ratio); and soluble carbohydrates, proteins, and proline. The height, number of leaves, and shoot dry mass increased due to the availability of nutrients contained in the ash. The incorporation of ash into the soil increased the photosynthetic activity (chlorophyll a and b) of both sunflower cultivars. The diameter of the stem, leaf area index, and relative growth rate of both sunflower cultivars increased with increasing ash dose. Therefore, the ash can be used as an alternative fertilizer, complementing or replacing mineral fertilizers.

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Potential Waste Application of Several Industries Segments in Brazilian Agriculture: Effects on Physical and Chemical Soil Properties

Besen

Neto

Cassim

et al. 2021

Communications in Soil Science and Plant Analysis

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Viabilidade técnica de fontes alternativas de adubação para o alho (Allium sativum L.) vernalizado no planalto Catarinense

Abstract: Viabilidade técnica de fontes alternativas de adubação para o alho (Allium sativum L.) vernalizado no planalto Catarinense

Cited by 4 publications

References 2 publications

Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models

Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models

Growth and physiology of two sunflower cultivars fertilized with sugarcane bagasse ash

Potential Waste Application of Several Industries Segments in Brazilian Agriculture: Effects on Physical and Chemical Soil Properties

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