Abstract
The Litterbag-NIRS method demonstrates and studies the microbial biodiversity of soil in an indirect way, observing the quality decay of ground hay, under real field conditions. Three experiments pertaining to a complex microbial consortium inoculated into six horticultural species used litterbags buried for 60 days. The litter was examined, by an SCiO™ smart-NIR spectrometer, to extract information on the type of transformation that had taken place. Chemometric analyses of single spectra were conducted to compare any variability in three experiments. The partial least squares method was used and cross-validated to associate the observed equivalent yield indexes (YI) to the NIR spectra averaged over each productive plot, in each trial, as well as in the pooled dataset. The cross-validated R2 values of the three experiments ranged around 0.66, and the inaccuracy of the estimates fluctuated at around ± 5%. The pooled calibration (R2 = 0.55) showed the presence of outlier treatments, and a marked spectral correlation (R2 = 0.77) with the 1031 and 986 nm wavelengths. In parallel, a complex of 22 NIRS-predicted variables related to chemical decay of the hay-litter, soil characteristics, and soil microbiology was obtained and partially associated to the YI and to microbial inoculation effects. The Hay-Litterbag-NIRS method can be considered useful to indirectly demonstrate that microbial fertility is an integral part of soil fertility, as evidenced by the significant correlations and predictions of the crop yields, and by the unraveling of the tangle of plant-soil relationships.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
16 November 2021
A Correction to this paper has been published: https://doi.org/10.1007/s42729-021-00689-5
References
Addinsoft (2021) XLSTAT statistical and data analysis solution. New York, USA. https://www.xlstat.com
Aguilar-Paredes A, Valdés G, Nuti M (2020) Ecosystem functions of microbial consortia in sustainable agriculture. Agronomy 10:1902. https://doi.org/10.3390/agronomy10121902
Baldi E, Toselli M, Masoero G, Nuti M (2020) Organic and symbiotic fertilization of tomato plants monitored by Litterbag-NIRS and Foliar-NIRS rapid spectroscopic methods. JAR 3:9–26. https://doi.org/10.14302/issn.2639-3166.jar-20-3363
Baldi E, Gioacchini P, Montecchio D, Mocali S, Antonielli L, Masoero G, Toselli M (2021) Effect of biofertilizers application on soil biodiversity and litter degradation in a commercial apricot orchard. Agronomy 11:1116. https://doi.org/10.3390/agronomy11061116
Balestrini R, Lumini E, Boriello R, Bianciotto V (2015) Plant-soil biota interactions. In: Soil Microbiology Ecology Biochemistry. Paul, Eldor A. fourth edition. USA: Elsevier (Academic Press) 311–338
Berg G, Smalla K (2009) Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol Ecol 68:1–13. https://doi.org/10.1111/j.1574-6941.2009.00654.x
Blagodatskaya EV, Blagodatsky SA, Anderson TH, Kuzyakov Y (2009) Contrasting effects of glucose, living roots and maize straw on microbial growth kinetics and substrate availability in soil. EJSS 60:186–197. https://doi.org/10.1111/j.1365-2389.2008.01103.x
Bonanomi G, Cesarano G, Lombardi N, Motti R, Scala F, Mazzoleni S, Incerti G (2017) Litter chemistry explains contrasting feeding preferences of bacteria, fungi, and higher plants. Scientific Reports 7:1–3. https://www.nature.com/articles/s41598-017-09145-w
Cardini A, Pellegrino E, Del Dottore E, Gamper HA, Mazzolai B et al (2020) HyLength: a semi-automated digital image analysis tool for measuring the length of roots and fungal hyphae of dense mycelia. Mycorrhiza 16:1–4. https://doi.org/10.1038/s41598-017-09145-w
Caser M, Victorin ÍM, Demasi S, Berruti A, Donno D, Lumini E, Bianciotto V, Scariot V (2019) Saffron cultivation in marginal Alpine environments: how AMF inoculation modulates yield and bioactive compounds. Agronomy 9:12. https://doi.org/10.3390/agronomy9010012
Cugnetto A, Lajolo L, Vitaloni G, Sarasso G, Borgogno Mondino EC, Nuti M, Giovannetti G, Masoero G (2021) Vineyard clusters monitored by means of Litterbag-NIRS and Foliar-NIRS spectroscopic methods. JAR 3:39–56. https://openaccesspub.org/jar/article/1544
Daniel R (2005) The metagenomics of soil. Nat Rev Microbiol 3:470–478. https://doi.org/10.1038/nrmicro1160
de Vries FT, Thébault E, Liiri M, Birkhofer K et al (2013) Soil food webs explain ecosystem services. PNAS 110:14296–14301. https://doi.org/10.1073/pnas.1305198110
Didion M, Repo A, Liski J, Forsius M, Bierbaumer M, Djukic I (2016) Towards harmonizing leaf litter decomposition studies using standard tea bags—a field study and model application. Forests 7:167. https://doi.org/10.3390/f7080167
Doran J, Kettler T, Tsivou M (1997) Field and laboratory Solvita soil test evaluation. Manuscript University of Nebraska USDA-ARS, Lincoln. https://solvita.com/wp-content/uploads/2019/11/Field-and-Laboratory-Solvita-Soil-Test-Evalution_Doran_1997_USDA_ARS_Lincoln-NE.pdf. Accessed 2021-04-01
Ehret DL, Hill BD, Helmer T, Edwards DR (2011) Neural network modeling of greenhouse tomato yield, growth and water use from automated crop monitoring data. Comp El Agr 79:82–89. https://doi.org/10.1016/j.compag.2011.07.013
FAO (2006) Plant nutrition for food security - a guide for integrated nutrient management. In: FAO: Fertilizer and plant nutrition bulletin 16, Rome, Italy.
Finkel OM, Castrillo G, Paredes SH, González IS, Dangl JL (2017) Understanding and exploiting plant beneficial microbes. Current Op Plant Biology 38:155–163. https://doi.org/10.1016/j.pbi.2017.04.018
Fontaine S, Mariotti A, Abbadie L (2003) The priming effect of organic matter: a question of microbial competition? Soil Biol Biochem 35:837–843. https://doi.org/10.1016/S0038-0717(03)00123-8
Gillon D, Joffre R, Ibrahima A (1999) Can litter decomposability be predicted by near infrared reflectance spectroscopy? Ecol 80:175–186. https://doi.org/10.1890/0012-9658(1999)080[0175:CLDBPB]2.0.CO;2
Gioacchini P, Montecchio D, Ferrari E, Ciavatta C, Masia A, George E, Tonon G (2015) Litter quality changes during decomposition investigated by thermal analysis. iForest Biogeosci Forest 8:827–837. https://doi.org/10.3832/ifor1297-007
Giovannetti M, Mosse B (1980) An evaluation of techniques for measuring vesicular arbuscular mycorrhizal infection in roots. New Phytologist, 489-500. https://doi.org/10.1111/j.1469-8137.1980.tb04556.x
Glick BR, Gamalero E (2021) Recent developments in the study of plant microbiomes. Microorganisms 9:533. https://doi.org/10.3390/microorganisms9071533
Goldring D, Sharon D (2016) Low-cost spectrometry system for end-user food analysis. United States Patent US009377396 B2.
Hansen ML, He Z, Wibowo M, Jelsbak L (2021) A whole-cell biosensor for detection of 2, 4-diacetylphloroglucinol (DAPG)-producing bacteria from grassland soil. App Env Microb 87:1–10. https://doi.org/10.1128/AEM.01400-20
Hart MM, Antunes PM, Chaudhary VB, Abbott LK (2018) Fungal inoculants in the field: Is the reward greater than the risk? Funct Ecol 32:126–135. https://doi.org/10.1111/1365-2435.12976
Hartmann A, Rothballer M, Schmid M (2008) Lorenz Hiltner, a pioneer in rhizosphere microbial ecology and soil bacteriology research. Plant Soil 312:7–14. https://doi.org/10.1007/s11104-007-9514-z
Hijri M (2016) Analysis of a large dataset of mycorrhiza inoculation field trials on potato shows highly significant increases in yield. Mycorrhiza 26:209–214. https://doi.org/10.1007/s00572-015-0661-4
Keuskamp JA, Dingemans BJ, Lehtinen T, Sarneel JM, Hefting MM (2013) Tea Bag Index: a novel approach to collect uniform decomposition data across ecosystems. Meth Ecol Evol 4:1070–1075. https://doi.org/10.1111/2041-210X.12097
Klironomos JN (2003) Variation in plant response to native and exotic arbuscular mycorrhizal fungi. Ecol 84:2292–2301. https://doi.org/10.1890/02-0413
Kraus D (2014) Consolidated data analysis and presentation using an open-source add-in for the Microsoft Excel® spreadsheet software. Medical Writing 23:25–28. https://doi.org/10.1179/2047480613Z.000000000181
Lin D, Wei R, Xu L (2019) An integrated yield prediction model for greenhouse tomato. Agronomy 9:873. https://doi.org/10.3390/agronomy9120873
Masoero G, Delmastro M, Cugnetto A, Giovannetti G, Nuti M (2018) NIRS footprint of bio-fertilizers from hay litter-bags. JAR 1:22–33. https://doi.org/10.14302/issn.2639-3166.jar-18-2084
McLellan TM, Aber JD, Martin ME, Melillo JM, Nadelhoffer KJ (1991) Determination of nitrogen, lignin, and cellulose content of decomposing leaf material by near infrared reflectance spectroscopy. Can J Forest Res 21:1684–1688. https://doi.org/10.1139/x91-232
Mindt E, Wang M, Schäfer M, Halitschke R, Baldwin IT (2019) Quantification of blumenol derivatives as leaf biomarkers for plant-AMF association. Bio-protocol 9:e3301. https://doi.org/10.21769/BioProtoc.3301
Oggiano P, Rastorguev N, Taran A, Torri L, Piochi M, Masoero G, Migliorini P (2021) Impact of a mycorrhizal inoculum on tomato and pepper organic production: a transdisciplinary approach in Northern Italian context. Eprints,
Parisi V, Menta C, Gardi C, Jacomini C, Mozzanica E (2005) Microarthropod communities as a tool to assess soil quality and biodiversity a new approach in Italy. Agr Ecosyst Environ 105:323–33. https://doi.org/10.1016/2Fj.agee.2004.02.002
Parton W, Silver WL, Burke IC, Grassens L, Harmon ME (2007) Global-scale similarities in nitrogen release patterns during long-term decomposition. Sci 315:361–364. https://doi.org/10.1126/science.1134853
Regvar M, Vogel-Mikus K, Severkar T (2003) Effect of AMF inoculum from field isolates on the yield of green pepper, parsley, carrot, and tomato. Folia Geobot 38:223–234. https://doi.org/10.1007/BF02803154
Ryan MH, McCully ME, Huang CX (2003) Location and quantification of phosphorus and other elements in fully hydrated, soil-grown arbuscular mycorrhizas: a cryo-analytical scanning electron microscopy study. New Phytol 160:429–441. https://doi.org/10.1046/j.1469-8137.2003.00884.x
Santoni M (2015) Utilizzo del metodo litter-bag per lo studio del processo di decomposizione di diverse specie da sovescio nel dispositivo sperimentale Montepaldi Long Term Experiment (MOLTE) per il confronto di sistemi colturali biologici e convenzionali. Tesi Mag. Un. Firenze, 2013/2014:1–131. https://drive.google.com/file/d/1QA3CevUlFzN0dfpJAiFR9e8L6G6OlSjg/view?usp=sharing. Accessed 2020-10-12
Schütz L, Gattinger A, Meier M, Müller A, Boller T, Mäder P, Mathimaran N (2017) Improving crop yield and nutrient use efficiency via biofertilization-a global meta-analysis. Front Plant Sci 8:2204. https://doi.org/10.3389/fpls.2017.02204
Tassone S, Masoero G, Peiretti PG (2014) Vibrational spectroscopy to predict in vitro digestibility and the maturity index of different forage crops during the growing cycle and after freeze- or oven-drying treatment. Animal Feed Sci Techn 194:12–25. https://doi.org/10.1016/j.anifeedsci.2014.04.019
Volpato S, Masoero G, Giovannetti G, Nuti M (2020) Arbuscular Mycorrhizal Biofertilizers sources in the Potato (Solanum tuberosum) plant show interactions with cultivars on yield and litterbags spectral features. JAR 2:10–17. https://doi.org/10.14302/issn.2639-3166.jar-20-3185
Volpato S, Masoero G, Mazzinelli G, Balconi C, Locatelli C, Lanzanova S, Ardigò A, Giovannetti G, Nuti M (2019) Spectroscopic and foliar pH model for yield prediction in a symbiotic corn production. JAR 2:1–18. https://doi.org/10.14302/issn.2639-3166.jar-19-3089
Wagner MR, Tang C, Salvato F, Clouse KM, Bartlett A, Vintila S, Phillips L, Sermons S, Hoffmann M, Balint-KurtiKleiner PJM (2021) Microbe-dependent heterosis in maize. PNAS 118(30):e2021965118. https://doi.org/10.1073/pnas.2021965118
Wang M, Schäfer M, Li D, Halitschke R, Dong C, McGale E, Paetz C, Song Y, Li S, Dong J, Heilin S (2018) Blumenols as shoot markers of root symbiosis with arbuscular mycorrhizal fungi. eLife 7:e37093. https://doi.org/10.7554/eLife.37093
Watt M, Kirkegaard JA, Passioura JB (2006) Rhizosphere biology and crop productivity—a review. Soil Res 44:299–317. https://doi.org/10.1071/SR05142
Acknowledgements
Thanks are due to the Fondazione CRT (Torino) and SERMIG (Torino) for the valuable supports with the experiments. Thanks are also due to Marguerite Jones for her clever adaptation of the manuscript to the English language.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The first sentence of the abstract of this article as originally published contained a grammatical error and has been corrected to read: The Litterbag-NIRS method demonstrates and studies the microbial biodiversity of soil in an indirect way, observing the quality decay of ground hay, under real field conditions.
Rights and permissions
About this article
Cite this article
Masoero, G., Oggiano, P., Migliorini, P. et al. Litterbag-NIRS to Forecast Yield: a Horticultural Case with Biofertilizer Effectors. J Soil Sci Plant Nutr 22, 186–200 (2022). https://doi.org/10.1007/s42729-021-00643-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42729-021-00643-5