Soil Organic Carbon In Farmland Research Articles

Coupled Vis-NIR spectroscopy with chemometrics strategy for soil organic carbon prediction in the Agro-pastoral Transitional zone of northwest China

Rapidly and accurately grasp the change of soil organic carbon content in farmland, which is of great significance in guiding the timely and effective mastery of farmland soil fertility and improvement of soil physical properties. In this study, an ASD FieldSpec 4 spectrometer was used to collect spectral reflectance data on 128 agricultural soil samples taken from Jingbian County, Yulin City, Shaanxi Province, China. Firstly, descriptive statistics of the SOC in the study area were performed, and secondly, after 10 spectral transformations were performed, the correlation analysis and the Boruta algorithm were used to extract the characteristic wavebands of soil organic carbon, respectively, in order to reduce the redundancy of the data. Finally, by comparing the accuracies of different strategies, we constructed a spectral prediction model of soil organic carbon in farmland of the Northwest Agricultural and Animal Husbandry Intertwined Zone that integrates the optimal preprocessing, feature selection strategy and modelling method. The results indicate that: 1) The mean SOC content of the farmland in the study area was low and at the nutrient deficient level, with the standard errors and coefficients of variation for the modelling and validation sets were 1.596 g kg−1, 1.457 g kg−1, 54 % and 52 %, respectively; 2) The shape and trend of spectral special curves with different SOC contents show consistency, and the SOC content is negatively correlated with spectral reflectance; 3) CA selects more feature bands, but the feature bands are more homogeneous, while the Boruta algorithm can effectively remove irrelevant variables and improve the SOC feature selection effect; 4) The SOC prediction model based on Boruta-FD-RF can be better for soil organic carbon estimation, with R2 of 0.899 and 0.748 for the training set and validation set, respectively, RMSE of 1.432 g kg−1 and 1.967 g kg−1, and RPD of 2.557 and 1.647, respectively. The results show that the SOC model established by integrating optimal spectral pre-processing, feature selection strategy and chemometrics strategy has obvious improvement in prediction accuracy and stability, and this study provides an important reference for the fast and accurate estimation of SOC content in farmland of Agro-pastoral Transitional zone in northwest China.

Comparing Laboratory and Satellite Hyperspectral Predictions of Soil Organic Carbon in Farmland

Mapping soil organic carbon (SOC) accurately is essential for sustainable soil resource management. Hyperspectral data, a vital tool for SOC mapping, is obtained through both laboratory and satellite-based sources. While laboratory data is limited to sample point monitoring, satellite hyperspectral imagery covers entire regions, albeit susceptible to external environmental interference. This study, conducted in the Yuncheng Basin of the Yellow River Basin, compared the predictive accuracy of laboratory hyperspectral data (ASD FieldSpec4) and GF-5 satellite hyperspectral imagery for SOC mapping. Leveraging fractional order derivatives (FODs), various denoising methods, feature band selection, and the Random Forest model, the research revealed that laboratory hyperspectral data outperform satellite data in predicting SOC. FOD processing enhanced spectral information, and discrete wavelet transform (DWT) proved effective for GF-5 satellite imagery denoising. Stability competitive adaptive re-weighted sampling (sCARS) emerged as the optimal feature band selection algorithm. The 0.6FOD-sCARS RF model was identified as the optimal laboratory hyperspectral prediction model for SOC, while the 0.8FOD-DWT-sCARS RF model was deemed optimal for satellite hyperspectral prediction. This research, offering insights into farmland soil quality monitoring and strategies for sustainable soil use, holds significance for enhancing agricultural production efficiency.

Unlike chemical fertilizer reduction, organic fertilizer substitution increases soil organic carbon stock and soil fertility in wheat fields.

Improvements in farmland soil organic carbon (SOC) stock enhance crop yield and soil fertility while mitigating climate change. Rational fertilization in agricultural production is crucial for safeguarding SOC stock. In this study, field experiments were conducted with different ratios of chemical fertilizer reduction and organic fertilizer substitution for three consecutive years (2018-2020) to explore their effects and interlinkages on SOC fractions, soil properties and SOC stock. The results showed that organic fertilizer substitution increased SOC and its fractions content, SOC stock (by 3.98-12.98% and 7.15-18.13%) and soil fertility index (by 11.76-49.26% and 33.33-91.47%) compared to conventional fertilization in 2019 and 2020, while chemical fertilizer reduction had the opposite effect. Moreover, soil properties (except total nitrogen to total phosphorus ratio, N/P) and SOC fractions significantly affected SOC stock, with SOC fractions contributing more than soil properties. The high sensitivity of microbial biomass carbon (MBC) and dissolved organic carbon (DOC) can indicate changes in soil carbon pool. Structural equation modeling (SEM) revealed that organic fertilizer substitution increased SOC content and stock by increasing SOC fractions [recalcitrant organic carbon (ROC) and labile organic carbon (LOC) fractions] content and soil fertility. Our study revealed the corresponding mechanisms of the two fertilization modes affecting SOC stock changes. The use of organic fertilizer substitution is recommended to increase SOC stocks and soil fertility in wheat fields. © 2023 Society of Chemical Industry.

Application of organic and chemical fertilizers promoted the accumulation of soil organic carbon in farmland on the Loess Plateau

Application of organic and chemical fertilizers promoted the accumulation of soil organic carbon in farmland on the Loess Plateau

Promotion of the Accumulation of Soil Organic Carbon in Farmland on the Loess Plateau by Combined Application of Organic and Chemical Fertilisers

Promotion of the Accumulation of Soil Organic Carbon in Farmland on the Loess Plateau by Combined Application of Organic and Chemical Fertilisers

Organic matter estimation of surface soil using successive projection algorithm

AbstractSoil organic matter (SOM) affects circulation and stability of the soil C pool, and plays a key role in global C change and local ecological balance. Timely and accurate acquisition of large‐scale farmland soil organic carbon (SOC) information is of great significance to the study of SOC reserves and distribution, farmland soil ecological security, and scientific crop management. In this study, we have evaluated the potentiality of visible‐near‐infrared spectroscopy to along with machine learning algorithms to predict SOM content. At present, soil characterization is mainly focused on hyperspectral data measured by remote‐sensing spectral index and spectrometer. This article aims to establish a statistical machine‐learning model to study the efficiency of spectral data to comprehensively evaluate SOM. The results show that after transforming the different forms of the original spectrum and combining with successive projection algorithm (SPA), the characteristic wavelength of organic matter at 350–2,500 nm can be effectively extracted. A combination of spectroscopy and chemometrics techniques can be used as a practical, rapid, low‐cost, and quantitative method to evaluate SOM. Based on SPA, partial least squares regression, support vector machine, random forest, a SOM prediction model with optimal characteristic wavelength is constructed. The best spectral processing method is continual removal combined with SPA, the validation coefficient of determination, root mean square error prediction, and ratio of performance deviation values are 0.82, 1.12, and 2.87, respectively.

Effects of Two Types of Straw Biochar on the Mineralization of Soil Organic Carbon in Farmland

To investigate the effects of biochar on soil carbon composition and transformation, the effects of 1%, 2%, and 5% mass ratios of banana and cassava straw biochar on carbon dioxide release, total organic carbon (TOC), soluble organic carbon (SOC), and enzyme activity in soil were studied in incubation experiments at a constant temperature in the laboratory. The results showed that the cumulative CO2 emissions from cassava straw were 15.82 (1% addition ratio) and 28.14 μg·kg−1 (2%), which were lower than those from banana straw, i.e., 46.77 (1%) and 59.26 μg·kg−1 (2%). After culture, the total organic carbon contents of cassava straw were 8.55 (5%), 5.27 (2%), and 3.98 μg·kg−1 (1%), which were higher than those of banana straw, i.e., 6.31 (5%), 4.23 (2%), and 3.16 μg·kg−1 (1%). The organic carbon mineralization rate in each treatment showed a trend of increasing first, then decreasing, and finally stabilizing. There was a very significant positive correlation between catalase and urease activity in soil with cassava straw biochar and between catalase activity and SOC mineralization with banana straw biochar. It plays an important role in the transformation and decomposition of organic carbon. These results show that the application of biomass carbon can significantly improve the organic carbon content and enzyme activity of farmland soil, increase the cumulative mineralization amount and mineralization rate of SOC, and thus increase the carbon sequestration capacity of soil.

Impact of cultivation on soil organic carbon and carbon sequestration potential in semiarid regions of China

AbstractDynamic changes in soil organic carbon (SOC) have become a popular topic in global research on organic carbon as part of the increasing attention being paid to food security and reducing greenhouse gas emissions. In this paper, the semiarid regions of China were selected as a research focus, and SOC data from 1980 to 2015 were analysed using IBM SPSS Statistics 20.0 software. SOC in farmland varied according to cultivated land type, mulching material type and planting method in the studied regions. The SOC content is 10.3–10.8 g kg−1 in supplementally irrigated land and flat dry land, 7.1–8.7 g kg−1 in terraced dry land and river beach land, and 6.2–6.4 g kg−1 in sloping dry land. The SOC content increased to 16.1–17.4 g kg−1 when crop stalks were used as mulch. The increase was only 11.5–13.5 g kg−1 in soils mulched with film or sandstone. The SOC value in wheat, maize and potatoes sown on single or double ridges was 2.4%–3.2%, 35.7%–36.4% and 4.4%–4.8%, respectively, which are higher than the values for wheat, maize and potatoes sown using the flat planting method. The SOC sequestering potential also varied according to the previously noted factors and was improved from 224.1% to 383.8% depending on cultivated land type, from 96.5% to 182.3% depending on mulching material type and from 96.1% to 191.3% depending on planting method. The SOC sequestering potential can be improved by 453.2%–757.4% with the integration of the optimal cultivated land type, mulching material type and planting method. Thus, there is substantial soil carbon sequestration potential in China's semiarid regions.

Transformation and distribution of straw-derived carbon in soil and the effects on soil organic carbon pool: A review

Farmland soil organic carbon (SOC) pool is a crucial component of global carbon cycle. Due to the widely-implemented straw returning, crop straws have become the primary exogenous carbon source for agricultural soils. The conversion and distribution of straw-derived carbon in soil directly affect the composition and contents of SOC, with further influence on soil nutrient cycling. Based on recent studies, this review investigated the factors impacting the transformation and distribution of straw-carbon; introduced the microbial composition that contributes to the assimilation of carbon from straw; and summarized the effects of straw-carbon on the composition, content, and turnover of SOC. Additionally, we proposed the future research regarding the effects of abiotic factors on the bio-transformation of straw-carbon; the interaction between biotic and abiotic factors during the straw carbon transformation processes; the coupling of carbon and nitrogen from straws into the soil carbon and nitrogen cycles; and the effective control over the transformation of straw-carbon that enters the active or stable soil organic carbon pool. The purpose was to reveal variation characteristics of SOC during straw returning, and provide theoretical basis and technical support for the efficient fertilization and carbon sequestration of straw returning.

Factors acquisition and content estimation of farmland soil organic carbon based upon Internet of Things

Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon (SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things (IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.

Changes in soil carbon and nitrogen under long-term cotton plantations in China

SUMMARYCotton is the dominant crop in the northern Xinjiang oasis of China; it accounts for 0·78 of the total planting area and represents a major contribution to economic development. The objective of the present study is to determine how cotton plantation age affected chemical and microbiological properties of the soil. The time substitution method was used on plantation farmlands, reclaimed from uncultivated land 0, 5, 10, 15 and 20 years ago. A total of 250 soil samples, at depths of 0–200, 200–400, 400–600, 600–800 and 800–1000 mm, were collected from cotton fields in 10 farms of each age category. There were significant differences in soil organic carbon (SOC), total soil nitrogen (TSN), soil available nitrogen (SAN), soil microbial biomass carbon (SMBC) and soil microbial biomass nitrogen (SMBN). There were also differences in the activities of cellulase, invertase and urease between soil layers and plantation ages, and these were most evident in the 200–400 mm layer. The cumulative rates of SOC and SMBC in the 0–1000 mm soil layer at the 5-, 10-, 15- and 20-year sites were 0·89, 0·99, 1·01 and 0·92 mg/kg/yr and 16, 16, 16 and 15 mg/kg/yr, respectively, compared to that at the control site (0 year). The cumulative amounts of SOC and SMBC increased gradually and then decreased, reaching a maximum at plantation ages of 13·1 years and 11·1 years, respectively. This suggests that incorporation of post-harvest cotton residues could be used as an effective measure to improve SOC in farmland of Xinjiang Oasis, and may be recommended for adoption in cotton growing in semi-arid oasis agriculture.

Soil organic carbon stocks in China and changes from 1980s to 2000s

AbstractThe estimation of the size and changes of soil organic carbon (SOC) stocks is of great importance for decision makers to adopt proper measures to protect soils and to develop strategies for mitigation of greenhouse gases. In this paper, soil data from the Second State Soil Survey of China (SSSSC) conducted in the early 1980s and data published in the last 5 years were used to estimate the size of SOC stocks over the whole profile and their changes in China in last 20 years. Soils were identified as paddy, upland, forest, grassland or waste‐land soils and an improved soil bulk density estimation method was used to estimate missing bulk density data. In the early 1980s, total SOC stocks were estimated at 89.61 Pg (1 Pg=103 Tg=1015 g) in China's 870.94 Mha terrestrial areas covered by 2473 soil series. In the paddy, upland, forest and grassland soils the respective total SOC stocks were 2.91 Pg on 29.87 Mha, 10.07 Pg on 125.89 Mha, 34.23 Pg on 249.32 Mha and 37.71 Pg on 278.51 Mha, respectively. The SOC density of the surface layer ranged from 3.5 Mg ha−1 in Gray Desery grassland soils to 252.6 Mg ha−1 in Mountain Meadow forest soils. The average area‐weighted total SOC density in paddy soils (97.6 Mg ha−1) was higher than that in upland soils (80 Mg ha−1). Soils under forest (137.3 Mg ha−1) had a similar average area‐weighted total SOC density as those under grassland (135.4 Mg ha−1). The annual estimated SOC accumulation rates in farmland and forest soils in the last 20 years were 23.61 and 11.72 Tg, respectively, leading to increases of 0.472 and 0.234 Pg SOC in farmland and forest areas, respectively. In contrast, SOC under grassland declined by 3.56 Pg due to the grassland degradation over this period. The resulting estimated net SOC loss in China's soils over the last 20 years was 2.86 Pg. The documented SOC accumulation in farmland and forest soils could thus not compensate for the loss of SOC in grassland soils in the last 20 years. There were, however, large regional differences: Soils in China's South and Eastern parts acted mainly as C sinks, increasing their average topsoil SOC by 132 and 145 Tg, respectively. In contrast, in the Northwest, Northeast, Inner Mongolia and Tibet significant losses of 1.38, 0.21, 0.49 and 1.01 Pg of SOC, respectively, were estimated over the last 20 years. These results highlight the importance to take measures to protect grassland and to improve management practices to increase C sequestration in farmland and forest soils.