Dataset Information

Statistical process control applied to mechanized peanut sowing as a function of soil texture.

ABSTRACT: The successful establishment of agricultural crops depends on sowing quality, machinery performance, soil type and conditions, among other factors. This study evaluates the operational quality of mechanized peanut sowing in three soil types (sand, silt, and clay) with variable moisture contents. The experiment was conducted in three locations in the state of São Paulo, Brazil. The track-sampling scheme was used for 80 sampling locations of each soil type. Descriptive statistics and statistical process control (SPC) were used to evaluate the quality indicators of mechanized peanut sowing. The variables had normal distributions and were stable from the viewpoint of SPC. The best performance for peanut sowing density, normal spacing, and the initial seedling growing stand was found for clayey soil followed by sandy soil and then silty soil. Sandy or clayey soils displayed similar results regarding sowing depth, which was deeper than in the silty soil. Overall, the texture and the moisture of clayey soil provided the best operational performance for mechanized peanut sowing.

SUBMITTER: Zerbato C

PROVIDER: S-EPMC5524413 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Statistical process control applied to mechanized peanut sowing as a function of soil texture.

Zerbato Cristiano C Furlani Carlos Eduardo Angeli CEA Ormond Antonio Tassio Santana ATS Gírio Lucas Augusto da Silva LADS Carneiro Franciele Morlin FM da Silva Rouverson Pereira RP

PloS one 20170724 7

The successful establishment of agricultural crops depends on sowing quality, machinery performance, soil type and conditions, among other factors. This study evaluates the operational quality of mechanized peanut sowing in three soil types (sand, silt, and clay) with variable moisture contents. The experiment was conducted in three locations in the state of São Paulo, Brazil. The track-sampling scheme was used for 80 sampling locations of each soil type. Descriptive statistics and statistical p ...[more]

PMID: 28742095

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Project description:Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.

Project description:AimThe aim of this study was to understand the effects of revegetation on the diversity of bacteria and fungi in soil by sowing a single species and exploring the underlying mechanism.LocationBeijing, China.TaxonPlants and Microbes.MethodsIn a short-term ecological restoration experiment, one natural recovery treatment and three seed sowing treatments were chosen to assess their effects on the alteration of fungal and bacterial diversity. Plant species richness, abundance, and height were investigated. The diversity of fungi and bacteria was analyzed by high-throughput sequencing technologies. Linear mixed-effects model analysis was used to examine the effects of different restoration methods on biodiversity and ecosystem functions. Pearson's correlation analysis, analysis of covariance, and structural equation modeling (SEM) were used to examine the relationship between biodiversity and environmental factors.ResultsSpecies richness and the Shannon-Wiener Index (H') of plants in the sown treatments were lower than in the natural recovery treatment, especially with sowing of Medicago sativa L. Similarly, the sum of the observed species and H' of fungi and bacteria significantly decreased in the sown treatments. Moreover, plant density, community coverage, and soil moisture increased markedly, while soil bulk density decreased in the sown treatments. Importantly, SEM showed that sown treatments reduced the diversity of plants through increasing plant density, while it decreased the diversity of fungi and bacteria through decreasing the plant diversity and increasing soil moisture.Main conclusionsOur findings confirm that ecological restoration by sowing could improve soil conditions, but may be unfavorable to the amelioration of soil microbial diversity in the short-term. Restoration practitioners should consider long-term studies on the dynamics of biodiversity in the above- and belowground after revegetation by native species to achieve goals related to biodiversity conservation.

Dataset Information

Statistical process control applied to mechanized peanut sowing as a function of soil texture.

Publications

Statistical process control applied to mechanized peanut sowing as a function of soil texture.

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