Using residual analysis in electromagnetic induction data interpretation to improve the prediction of soil properties

Lu, Chunfeng; Zhou, Ziwen; Zhu, Qing; Lai, Xiaoming; Liao, Kaihua

doi:10.1016/j.catena.2016.09.018

Cited by 11 publications

(3 citation statements)

References 32 publications

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“…Much of the recent research effort to improve EMI-based mapping or measurement of soil properties including soil moisture content have focused on the use of (i) multicoil and multifrequency devices [125][126][127], (ii) calibration algorithms [128][129][130], or (iii) inversion approaches for 2D or quasi 3D projection of ECa [131][132][133]. ECa is a bulk response to the proximal environment, incorporating both inherent soil properties (soil minerology and texture), and variable properties (temperature, moisture content, salinity, and soil density) [120,124,134,135].…”

Section: Electromagnetic Induction (Emi)mentioning

confidence: 99%

Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture

Hardie

2020

Sensors

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The measurement of soil moisture in agriculture is currently dominated by a small number of sensors, the use of which is greatly limited by their small sampling volume, high cost, need for close soil–sensor contact, and poor performance in saline, vertic and stony soils. This review was undertaken to explore the plethora of novel and emerging soil moisture sensors, and evaluate their potential use in agriculture. The review found that improvements to existing techniques over the last two decades are limited, and largely restricted to frequency domain reflectometry approaches. However, a broad range of new, novel and emerging means of measuring soil moisture were identified including, actively heated fiber optics (AHFO), high capacity tensiometers, paired acoustic / radio / seismic transceiver approaches, microwave-based approaches, radio frequency identification (RFID), hydrogels and seismoelectric approaches. Excitement over this range of potential new technologies is however tempered by the observation that most of these technologies are at early stages of development, and that few of these techniques have been adequately evaluated in situ agricultural soils.

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Section: Electromagnetic Induction (Emi)mentioning

confidence: 99%

Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture

Hardie

2020

Sensors

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“…Some studies have used spatial regression models for data estimation, such as studies on estimated milk production (Ponciano & Scalon, 2010), the analysis of mixed forests in northeastern China (Lou, Zhang, Lei, Li, & Zang, 2016), the estimated impact of urbanization on air quality (Fang, Liu, Li, Sun, & Miao, 2015), the application of a spatial model to predict the number of electric vehicles in the Philadelphia area (Chen, Wang, & Kockelman, 2015), the estimated unemployment rate in Romania (Simionescu, 2015), the estimated malaria incidence in Northern Namibia in 2009 (Alegana et al, 2013), the delimitation of disease risk zones (Charras-Garrido et al, 2013), the relationship between tax evaluation bands and domestic energy consumption in London (Tian, Song, & Li, 2014), the application of a spatial regression model to identify land-cover types in China (Song, Du, Feng, & Guo, 2014), the estimated impact of agriculture on the sale of houses in Pennsylvania (Yoo & Ready, 2016), the estimated frequency of floods in the Northern United States (Ahn & Palmer, 2016), understanding the causes of the reforestation in Vietnam in the 1990s (Meyfroidt & Lambin, 2008), the estimated soil carbon stocks in the city of Chahe, China (Guo et al, 2017), the use of regression models to determine whether residual analysis through electromagnetic induction can be used to survey soil properties (Lu, Zhou, Zhu, Lai, & Liao, 2017), and the use of spatial regression on Cellular Automata to explain and simulate soil alterations (Ku, 2016).…”

Section: Introductionmentioning

confidence: 99%

<b>Regression models for prediction of corn yield in the state of Paraná (Brazil) from 2012 to 2014

2018

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This study aimed to identify areas that showed spatial autocorrelation for corn yield and its predictive variables (i.e., average air temperature, rainfall, solar radiation, soil agricultural potential and altitude) and to determine the most appropriate spatial regression model to explain this culture. The study was conducted using data from the municipalities of the state of Paraná relating to the summer harvests in

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“…Scanlon et al 1999), and mapping soil physical properties (e.g. Lu et al 2017), as well as spatial variability in soil moisture (e.g. Brevik et al 2006;Hossain et al 2010).…”

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confidence: 99%

Field-scale variability in site conditions explain phenotypic plasticity in response to nitrogen source in Pinus radiata D. Don

et al. 2019

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Aims. Productivity of forest ecosystems is constrained by site resource availability and utilisation at an individual tree level. A better understanding of nitrogen (N) nutrition addition to forest ecosystems is critical for maintaining optimal plantation productivity, given the influence of an environment gradient, genetics, and their interactions. Methods. We studied the aboveground growth response in a plantation setting of ten commercial P. radiata genotypes to N-fertilisation using three different N sources, and also assessed the effect of on-site environmental factors on this response. We compared, on equimolar basis, the effect of N-fertilisation with inorganic N (NH4NO3), organic N (L-arginine), and the two N sources combined (L-arginine:NO3-) to that of unfertilised trees on tree height, diameter, descriptors of microsite variability, and climate / seasonal information. After 2.5 years of fertilisation, genotype-specific variation in aboveground growth response to N sources were measured, and these were significantly influenced by field-scale heterogeneity. Results. Across P. radiata genotypes, trees treated with inorganic N forms showed suppressed growth compared to unfertilised trees, while trees fertilised with organic N (either alone or in combination with inorganic N) were not significantly different than the untreated controls. We provide evidence of 2 significant interactions between N source and genotype, N source and cover as well as genotype and microsite variability affecting temporal trends in tree volume. Conclusions. We conclude that an understanding of field-scale variability in soil properties and associated environmental variables is essential for understanding genotype performance as they are crucial determinants of intraspecific variation in response to N-fertilisation. INTRODUCTION Given the longevity of sessile species, phenotypic plasticity provides an opportuntity to sense, change, and adapt their growth and physiological responses to cope with changes in environmental conditions (Bradshaw 1965; Corcuera et al. 2010). An ongoing challenge is to clarify which biological traits are manifested as genotypic variation from evolutionary and breeding history (G), phenotypic response to environmental variability (E) or combinations of both (G × E). Abundant evidence supports the hypotehsis that plant species and population responses differ across different environments (e.g. Schlichting and Levin 1984; Valladares et al. 2000). However, genotype-specific variation can be exhibited when grown in comparable conditions, affecting adaptive capacity and, consequently, tree performance (Souza et al. 2017). The flexibility and adaptability of tree genotype requires special attention, not only to understand the complexity of traits in breeding programmes, but also for the greater ecological impact that the increasing inter-seasonal climate changes may have in long-lived organisms. Pinus radiata D. Don (alt. Monterey pine) is the most common introduced softwood plantation species growing in temperat...

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Using residual analysis in electromagnetic induction data interpretation to improve the prediction of soil properties

Cited by 11 publications

References 32 publications

Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture

Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture

<b>Regression models for prediction of corn yield in the state of Paraná (Brazil) from 2012 to 2014

Field-scale variability in site conditions explain phenotypic plasticity in response to nitrogen source in Pinus radiata D. Don

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