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Legume inclusion into cropping systems has been proposed to maintain high crop yields while offering multiple environmental benefits. However, the effect of legumes as pre-crop on subsequent wheat yield and soil has not been well explored. Thus, a 7-year field experiment was used to determine the interactive effects of mineral fertilization and legumes (peanut, mung bean, soybean, adzuki bean) inclusion on wheat productivity and soil quality. Our results showed that legume inclusion led to a higher wheat yield advantage (52% on average) than maize–wheat rotation under no fertilization but the advantage decreased to 26% with fertilization. All legume–wheat rotation systems supported stable wheat production, where a stronger effect was observed after peanut than after maize. Meanwhile, the wheat yield under legume–wheat systems was more resistant (i.e., less variability in the yield after ceasing fertilization) and more resilient (i.e., recovering more quickly after fertilizer re-application) relative to maize-wheat. Furthermore, soil ecosystem multifunctionality increased by 0.8 times in the topsoil while maintaining soil organic carbon stocks, even with low C and N inputs under legume–wheat. Interestingly, we also observed a positive correlation between wheat yield and soil ecosystem multifunctionality. In conclusion, legume inclusion as a sustainable practice can optimize crop yields by enhancing soil multifunctionality while maintaining soil organic carbon stocks, particularly for integration into low-yielding agroecosystems. [ABSTRACT FROM AUTHOR]

Purpose: The main goal of this study was to determine if ancient wheat varieties could store more carbon than modern ones in the presence or absence of inputs, due to a likely bigger and deeper root system and a slower mineralization rate. Methods: We conducted a field experiment with four modern and four ancient varieties (released before 1960 and often grown without inputs), with and without chemical inputs (nitrogen, herbicide and fungicide taken as a single factor). Root morphology was assessed by image analysis, potential catabolic activities of fructose, alanine, citric acid by MicroResp™ and overall CO2 emissions by incubating soil and roots from each modality for 60 days. Results: The breeding type did not affect root traits, substrates respiration nor CO2 emissions in our environmental conditions. The application of inputs did not affect root traits but influenced the respiration of specific substrates and CO2 emissions. The most noticeable response was due to the "breeding type x inputs" interaction: inputs increased CO2 emissions from soil and root tissues of ancient varieties by 19%, whereas no effect was observed for modern varieties. Conclusion: Taken together, our results did not support the hypothesis that ancient varieties could be more performant than modern ones in storing carbon in our experimental conditions. Increased CO2 emissions by ancient varieties in the presence of inputs showed that ancient and modern varieties differed in their phenotypic plasticity. [ABSTRACT FROM AUTHOR]

Post-harvest crop residues are the main carbon source for arable lands. The aim of this research was to determine the amount and portion in the phytomass of post-harvest crop residues of major crops and carbon, which is input to soil with them. For this purpose, samples of winter and spring wheat, barley, oats, and soybean were taken in five farms of Kursk, Ryazan, and Samara oblasts and in the Chuvash Republic in 2019–2021. The ratio between plant fractions—roots, stubble, stems, leaves, chaff, and grain—was calculated. Post-harvest residues (roots and stubble) of cereal and leguminous crops account for 1/4–1/5 of the dry phytomass: the stubble portion is 1/6–1/10 of the mass and the share of roots does not exceed 10%. The portion of productive part (grains and chaff) is 1/2–1/3, and that of by-products (stems and leaves) is 1/3 for cereals and 1/2 for soybean. The amount of carbon input to the plow layer with root residues decreases in the sequence: winter wheat (569 kg C/ha) > spring wheat (508 kg C/ha) > barley (503 kg C/ha) > oats (488 kg C/ha) > soybean (204 kg C/ha). The stubble of field crops is an additional carbon source for the soil, the amount of which is directly proportional to the plant height: 419 kg C/ha for spring wheat, 405 kg C/ha for winter wheat, 281 kg C/ha for oats, 232 kg C/ha for soybean, and 135 kg C/ha for barley. Regression equations of the dependence of the amount of stubble and root residues on grain yield were calculated for winter wheat. [ABSTRACT FROM AUTHOR]

In the context of climate change, soil is a major pool of stable carbon on earth, yet knowledge on soil carbon turnover is limited. The difference in 13C/12C content observed between C3 and C4 plant crops has been widely used to distinguish the sources of soil organic carbon under continuous monoculture, but not in C3–C4 rotations. We studied the stability of the δ13CSOC content and the effect of no tillage, conventional tillage, and rotary tillage in 4 soils sampled at 0–5, 5–10, and 10–20 cm depth, in 2018–2019 in a field with long-term history of wheat (C3) and maize (C4) rotations. We also analyzed the results from the literature. The results show that the δ13CSOC is not statistically affected by the sampling date, thus allowing to use this method to distinguish C3 and C4 plant contributions in rotations. Moreover, no-tillage favored the preservation of wheat carbon, and this preservation was accentuated at 10–20 cm depth. δ13CSOC is also affected by fertilization and irrigation. The literature confirmed that wheat-derived carbon is better preserved that maize-derived carbon, on the average. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing technology can be used to monitor the soil nutrient changes in a rapid, real-time, and non-destructive manner, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were studied. The effects of different water treatments on soil organic carbon (SOC) content, and the relationship between SOC content and spectral reflectance (350–2500 nm) were studied. 17 kinds of preprocessing algorithm were performed on the original spectral (R), and the five allocation ratios of calibration to verification sets were set. Finally, the model was constructed by partial least squares regression (PLSR). The results showed that the effects of water treatment on SOC content were different in different growth stages of winter wheat. Results of correlation analysis showed that the differential transformation can refine the spectral characteristics, and improve the correlation between SOC content and spectral reflectance. Results of model construction showed that the models constructed by second-order differential transformation were not good. But the ratio of standard deviation to the standard prediction error (RPD) values of the models were constructed by simple mathematical transformation (T0–T5) and first-order differential transformation (T6–T11) can reach more than 1.4. The simple mathematical transformation (T0–T2, T4–T5) and the first-order differential transformation (T6–T10) resulted in the highest RPD in mode 5 and mode 2, respectively. Among all the models, the model of T7 in mode 2 reach the highest accuracy with a RPD value of 1.9861. Therefore, it is necessary to consider the data preprocessing algorithm and allocation ratio in the process of constructing the hyperspectral monitoring model of SOC. [ABSTRACT FROM AUTHOR]

Limited information is available on the C stabilization mechanism of tropical soils under different management practices including long-term organic manuring, mineral fertilization alone, or in combination with lime. Hence, to understand the effect of continuous application (for 60 years) of organic manure, fertilizer, and lime alone or in combination on an acidic Alfisol, stabilization of soil organic carbon (SOC) was evaluated under maize (Zea mays L.) wheat (Triticum aestivum L.) cropping. There were eight treatments that included farmyard manure (FYM) and nitrogen (N) applied in terms of FYM, additional dose of phosphorus (P) and potassium (K) applied in terms of inorganic fertilizer (FYM + P'K'), FYM + P'K' with liming (FYM + P'K' + L) and NPK alone. These treatments were laid in a randomized block design with three replications. Results indicated that FYM + P'K' plots had maximum amount of SOC inside large macroaggregates. The value was 33 and 92% greater than only minerally fertilized (NPK) and unfertilized control plots, respectively, whereas microaggregate-associated C was highest in plots with FYM + P'K' and lime (FYM + P'K' + L), which was 48 and 183% more than unfertilized control and NPK plots, respectively. Inside soil microaggregates, plots under FYM + P'K' had highest labile C, while NPK + L plots had highest recalcitrant C. Plots with organic amendments contained higher glomalin in large macroaggregates. Plots treated with FYM + P'K' had maximum intra-aggregate particulate organic matter within microaggregates inside macroaggregates (iPOM_mM), which was 28 and 74% higher than NPK and unfertilized control plots, respectively. Total C stock inside the protected microaggregates within macroaggregates was maximum for FYM + P'K' plots. It had 38, 67, and 171% higher C stock than NPK, FYM, and unfertilized control plots, respectively. Interestingly, despite estimated C input in FYM-treated plots was much higher than NPK plots, FYM-treated plots had less C stabilization within microaggregates and within microaggregates inside macroaggregates. Microaggregates within macroaggregates accounted for ~54% of the recalcitrant C content. Thus, macroaggregates stabilization through occlusion of microaggregates was accountable for sequestration of SOC and only FYM application did not promote that mechanism compared to NPK. Carbon stabilization within macroaggregates under FYM plots was mainly governed by amorphous iron oxide. [ABSTRACT FROM AUTHOR]

Purpose: Currently, there is no consensus on how the impacts of land use on the soil organic carbon (SOC) stocks would be best quantified within life cycle assessments (LCA) of agricultural products. The impacts of different decisions were tested within a model-based assessment of soil carbon changes on the life cycle global warming impact for spring wheat produced in two example regions in Finland (Southwest Finland and Northern Savonia) on mineral fields. Methods: Global warming impact for spring wheat was assessed, including CO2 emissions due to the SOC change. The SOC change assessment was made with the soil carbon model Yasso07. The effects of assumptions on land use history were tested, i.e. the initialisation of the model and time horizon of the analysis (20 or 100 years) on the SOC change estimates. Other greenhouse gas emissions contributing to the global warming impact of spring wheat production were assessed using general LCA methodology taking into account the greenhouse gas emissions caused by the production of input materials and fuels, as well as direct and indirect N2O emissions from the soil due to fertilising and the decomposition of crop residues and organic matter, nitrogen leaching and volatilisation and lime application. Results and discussion: The selection of the model initialisation method and timeframe remarkably affected the SOC change estimates. The global warming impact of wheat production, without accounting for SOC changes, was 0.68 and 0.89 kg CO2-eq/kg yield in Southwest Finland and Northern Savonia, respectively. The impact of SOC stock changes on the total global warming impact varied from –4 to 5% in Southwest Finland and from 5 to 21% in Northern Savonia, depending on the assumptions used to initialise the model or the timeframe applied in the analysis. Adding a cover crop as a means to increase the SOC stock removed between –67 and –26% of the total global warming impact in both regions. Conclusions: It is essential that all the decisions made in the analysis are transparently reported and communicated. The choice of assumptions regarding the reference state, model initialisation and time horizon of the assessment period should be made based on the scope and goal definition of the LCA study. [ABSTRACT FROM AUTHOR]

Introduction: 14th Line, "The variation between C3 (~ 27%) and C4 (~ 13%)"; Page no: 2, 1st line "ranging from - 23 - - 40% ... from - 9 to - 19%". 2nd page under title: Materials and methods (Soil sample and analysis): 15th line " SP 13 sp C3 and SP 13 sp C4 are - 27% and - 13%". [Extracted from the article]