Laurent Thuriès

Kinetics of added organic matter decomposition in a Mediterranean sandy soil

Abstract Carbon mineralization kinetics of 17 organic materials were studied in a Mediterranean sandy soil. These added organic matters (AOM) used in the organic fertilizer industry differed in their origin and composition: plant residues from the agri-food industry, animal wastes, manures (plant and animal origin), composts at different composting times and organic fertilizers. The mixtures AOM-soils were incubated under aerobic conditions at 28°C during 6 months. Soil moisture was maintained at 75% water holding capacity and respired-CO2 was regularly trapped into alkali media in closed chambers, then checked by HCl titration. Analyses of CO2 were performed in triplicate at 17 sampling occasions. The mineralized AOM fraction (MAOMF) varied according to the AOM origin: from 12–33% of added C for composts, to 65–90% for animal-originated AOM, with many intermediate patterns for plant-originated AOM. Seven decomposition models from the literature were fitted to actual MAOMF: (a) three consecutive models with two 1st-order-kinetic compartments and three parameters (m1, humification; m2, exchange; m3, decomposition), (b) three parallel models (m4, with two compartments and three parameters; m8, a 1st-order plus 0-order model with three parameters; m5, a three-compartment model with four parameters), and (c) m7, a model with one 2nd-order-kinetic compartment and two parameters. Additionally, m6, a simplified version of m5 was proposed. Models m2 and m7 did not match with actual data or gave a poor fit. By the correlation parameters, the most simple model m4 was chosen instead of the consecutive models m1 and m3. Residual sums of squares were always greater—but not significantly—in m8 than in m4, which confirmed the superiority of the models with two 1st-order compartments against 1st-order plus 0-order models for incubation times higher than 100 days. Model m5 (most of its parameters being not correlated) gave the best predictions of our data. The proposed m6 version gave predictions with similar precision as m4 and appeared powerful with only two parameters (very labile and stable fractions of the AOM). A compromise between the precision of the predictions and the simplicity of the formulae allowed the recommendation of the well-known m4 model, and above all the simpler m6 model.

Prediction by near infrared spectroscopy of the composition of plant raw materials from the organic fertiliser industry and of crop residues from tropical agrosystems

The dynamics of carbon (C) and nitrogen (N) of plant residues and organic fertilisers are of great interest for agricultural and global warming studies. The proportion of the fractions obtained from biochemical analyses (fibres by sequential Van Soest analysis) can be used for predicting both C and N transformation of organic materials in soils. Considering the expensive and time-consuming Van Soest method, the principal aim of this study was to elaborate near infrared (NIR) calibrations for fibres, in order to use them for consecutive studies (for example, our works on transformation of added organics or TAO model). A wide set of organic fertilisers and their raw materials was sampled, including plant materials originating from temperate (especially Mediterranean) and tropical regions. The particular objective of this work was to build NIR calibrations for fibre fractions, along with C and N content, in plant materials used in the organic fertiliser industry and green house gases mitigating strategies. The second particular objective was to test for two levels of validation of the equations previously elaborated: (1) validation with a set of randomly chosen samples that was not considered during the calibration step, (2) extrapolation of the predictive capacity of the equations when applying them to outliers that were previously discarded. The fibres were the best predicted parameters, as R² = 0.95, 0.91, 0.97, 0.97 for neutral detergent soluble, hemicelluloses, cellulose and lignin, respectively, whereas the characteristics of total organic matter had R² varying from 0.87 (N Kjeldahl) to 0.94 (C Dumas). The accuracy of the calibrations developed for fibres was confirmed by the first level of validation, since the standard errors of prediction were close to the corresponding standard errors of cross-validation and the standard errors of calibration. Nevertheless, the calibrations developed for ash and C Dumas were not so good. Surprisingly, at the second level of validation, some outliers were not so badly predicted. This can illustrate the robustness of the calibrations for cellulose, lignin and, to a lesser extent, N Dumas which are key parameters for our modelling works on C and N transformation of added organics in soils.

Evaluation of three incubation designs for mineralization kinetics of organic materials in soil

Abstract Carbon (C) mineralization was assessed during incubations of a Mediterranean sandy soil amended with various organic by‐products covering a wide range of C and nitrogen (N) contents. The laboratory incubation systems consist in measuring continuously the soil respiration (as CO2‐C) in closed chambers, or less current, in pre‐storing soil containers in semi‐open chambers until transferred and measured for CO2‐C evolved in closed ‘measuring‐jars’. The latest were improved, the new designs permitting to test a much greater number of by‐products with a minimum handling. No significant differences were found between the results obtained by the different incubation systems. The storage systems using pre‐storage of soils gave reproducible cumulative CO2‐C curves. Results obtained with the pre‐storage systems could be compared confidently to C mineralization data from studies using permanent closed chambers. One of them was specially reliable and can thus be recommended for long‐term incubation experiments.

Rapid Prediction of the Lignocellulosic Compounds of Sugarcane Biomass by near Infrared Reflectance Spectroscopy: Comparing Classical and Independent Cross-Validation:

Among cultivated tropical Poaceae, sugarcane (Saccharum spp.) has the highest potential for energy production, mainly thanks to its agronomic traits. Modelling is the best way to design new sugarcane cropping systems for multi-use biomass production focusing on an energetic valuation of fibre co- and by-product. On the other hand, sugarcane industries have to rapidly adapt to changes in quality characteristics of biomass. Both require quality assessment using fast, efficient and robust analytical methods to determine biomass characteristics and/or to adjust processes. In this study, near infrared (NIR) reflectance spectroscopy was assessed for the prediction of the lignocellulosic compounds of sugarcane biomass. A total of 228 samples were taken from three genotypes grown at four contrasting locations in Reunion Island (SW Indian Ocean) and harvested at three ages during one plant crop cycle. The field samples were separated into five anatomical parts (millable stalk, top of the stalk, green leaf blade, green leaf sheath and trash), ground using two different methods and then analysed by the sequential van Soest method. Finally, 456 powders were scanned using a NIR XDS monochromator. Modified partial least square (MPLS) regression was applied on spectra scatter-corrected with standard normal variate and detrend followed by second derivative (SNVD-D2). Four calibration models were developed from leave-one-out location calibration data sets. To avoid over-optimistic results, independent validation was carried out at each location. This original validation method demonstrated the actual potential of our NIR model to predict the lignocellulosic compounds of independent sugarcane samples. At the same time, the performance of the NIR model will facilitate the timely supply of reference values for use in ecophysiological growth models.

Near infrared reflectance spectroscopy applied to model the transformation of added organic materials in soil

Raw, mixed and composted organic materials (OM) from agricultural and urban wastes were subjected to biochemical analyses, near infrared (NIR) reflectance spectroscopy and laboratory incubations. Respiration during incubations was accurately predicted using a decomposition model [transformation of added organic materials, (TAO)] of very labile, intermediary resistant, and stable OM fractions. Calibrations using NIR spectra were developed to determine the very labile and stable fractions of OM used to predict three-month OM mineralisation in soil. This study has confirmed that OM decomposition is mainly driven by OM quality on a short-term basis. The wavelengths contributing heavily to the prediction of very labile and stable OM components and molecular functions of these fractions were identified. The resulting TAO–NIR spectroscopy model is an efficient tool to study the degradation of natural molecules and its management for plant growth and sustainability of ecosystems. As a sub-model of a more complex C cycle model, it can instantaneously simulate labile and stable fractions of various organic inputs in soil and, as a non-destructive and easily portable spectroscopic method, could be used to assess C dynamics on a regional scale.

Near infrared reflectance calibration optimisation to predict lignocellulosic compounds in sugarcane samples with coarse particle size

Frequent variations in spectral intensity due to particle size and/or of particle size distribution are observed in plant products processed in powder form and scanned with near infrared reflectance (NIR). In this study, two grinders, with differences in time consumption, practicality and providing homogenates with different particle size range and distribution, were tested to evaluate their effects on NIR spectra. Optimisation of NIR calibration was necessary before predicting lignocellulosic compounds in sugarcane (Saccharum spp.) samples with coarse particle size to supply a pre-existing ecophysiological growth model. Sixty samples from three varieties, grown in four contrasting pedoclimatic areas and from five anatomical parts were scanned and then analysed by biochemical fractionation. Different calibration methods, resulting in a combination of multiple linear regressions (MLR) applied to three calibration sets (fine, coarse and mixed particle sizes) treated with six data pretreatments—first derivative (D), second derivative (D2), multiplicative scatter correction (MSC), standard normal variate and detrend (SNVD), standard normal variate and detrend successively followed by first derivative (SNVD-D) or second derivative (SNVD-D2)—were investigated. The best NIR model statistical values were obtained by calibration developed on a mixed calibration set treated by SNVD-D2. Results confirmed that NIR spectroscopy could be an accurate and efficient method to predict lignocellulosic compounds in different botanical parts of sugarcane samples when used as input to an ecophysiological growth model.

Agricultural Organic Waste Recycling to Reduce Greenhouse Gas Emissions

Organic waste recycling in agriculture can enhance the efficiency of nutrient cycles and directly or indirectly reduce major and increasing sources of greenhouse gas emissions. It can also boost soil fertility and agricultural resilience to climate change. There is considerable potential for improving recycling that has been studied from the farm to the territorial scale. We present research results concerning the improvement and introduction of recycling practices on several scales and concerning associated biophysical processes allowing more reliable assessment of greenhouse gas emission balances. Whether concerning the resilience of agricultural systems or the mitigation of emissions, the agricultural waste recycling potential is highest on the territorial scale, especially when the spatial concentration of various wastes is high, e.g. in periurban areas around fast-growing megacities in developing countries. CIRAD has developed recycling management methods and support tools and is enhancing knowledge on processes that determine the climate footprint of recycling. The aim is to fill the many knowledge gaps regarding greenhouse gas emission factors and determinants of organic matter bioprocessing in tropical conditions.