33 research outputs found
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Impact of Irrigation Strategies on Tomato Root Distribution and Rhizosphere Processes in an Organic System.
Root exploitation of soil heterogeneity and microbially mediated rhizosphere nutrient transformations play critical roles in plant resource uptake. However, how these processes change under water-saving irrigation technologies remains unclear, especially for organic systems where crops rely on soil ecological processes for plant nutrition and productivity. We conducted a field experiment and examined how water-saving subsurface drip irrigation (SDI) and concentrated organic fertilizer application altered root traits and rhizosphere processes compared to traditional furrow irrigation (FI) in an organic tomato system. We measured root distribution and morphology, the activities of C-, N-, and P-cycling enzymes in the rhizosphere, the abundance of rhizosphere microbial N-cycling genes, and root mycorrhizal colonization rate under two irrigation strategies. Tomato plants produced shorter and finer root systems with higher densities of roots around the drip line, lower activities of soil C-degrading enzymes, and shifts in the abundance of microbial N-cycling genes and mycorrhizal colonization rates in the rhizosphere of SDI plants compared to FI. SDI led to 66.4% higher irrigation water productivity than FI, but it also led to excessive vegetative growth and 28.3% lower tomato yield than FI. Our results suggest that roots and root-microbe interactions have a high potential for coordinated adaptation to water and nutrient spatial patterns to facilitate resource uptake under SDI. However, mismatches between plant needs and resource availability remain, highlighting the importance of assessing temporal dynamics of root-soil-microbe interactions to maximize their resource-mining potential for innovative irrigation systems
An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter
Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments' dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments' characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSC-KSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e.g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spectral libraries and support the recent global efforts to make soil spectroscopy universally accessible with centralized or shared operating procedures
Erratum to: Crowther et al. reply (Nature, (2018), 554, 7693, (E7-E8), 10.1038/nature25746)
© 2018, Macmillan Publishers Ltd., part of Springer Nature. In this Brief Communications Arising Reply, the affiliation for author P. H. Templer was incorrectly listed as ‘Department of Ecology & Evolutionary Biology, University of California Irvine, Irvine, California 92697, USA’ instead of ‘Department of Biology, Boston University, Boston, Massachusetts 02215, USA’. This has been corrected online
Crowther et al. reply
Resposta a l'article següent: Geste, N. van et al. "Predicting soil carbon loss with warming" in Nature (Feb. 2018), vol. 554, E4-E5. DOI 10.1038/nature2574
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Author Correction: Crowther et al. reply.
In this Brief Communications Arising Reply, the affiliation for author P. H. Templer was incorrectly listed as 'Department of Ecology & Evolutionary Biology, University of California Irvine, Irvine, California 92697, USA' instead of 'Department of Biology, Boston University, Boston, Massachusetts 02215, USA'. This has been corrected online
Recommended from our members
Author Correction: Crowther et al. reply.
In this Brief Communications Arising Reply, the affiliation for author P. H. Templer was incorrectly listed as 'Department of Ecology & Evolutionary Biology, University of California Irvine, Irvine, California 92697, USA' instead of 'Department of Biology, Boston University, Boston, Massachusetts 02215, USA'. This has been corrected online
Crowther et al. reply
Resposta a l'article següent: Geste, N. van et al. "Predicting soil carbon loss with warming" in Nature (Feb. 2018), vol. 554, E4-E5. DOI 10.1038/nature2574