Soil Organic Carbon Management Research Articles

Agro-technologies for greenhouse gases mitigation in flooded rice fields for promoting climate smart agriculture

We investigated methane (CH4) and nitrous oxide (N2O), two important greenhouse gases (GHGs) emissions using the closed chamber method from a flooded rice field in Brahmaputra valley of Assam, northeast part of India. We tried to understand the factors responsible for the emission and identify appropriate agro-technologies for their mitigation. Various factors like water level, drainage management, soil organic carbon management, crop management, fertilizer amendment, cultivar type etc. affect the GHG production and emission from the flooded rice soil. In this study, six treatments were employed, namely, farmer's practice (FP), recommended fertilizer dosage (RDF), direct seeded rice (DSR), intermittent wetting drying (IWD), use of efficient methanotrophs (MTH), and use of ammonium sulfate as a nitrogen source for real-time nitrogen management using leaf color chart, (AS). GHG flux was measured through the static closed chamber technique. Soil temperature, pH, and redox potential (Eh) and other soil physico-chemical and biological properties that have a potential role in GHG emission were also assessed. The lowest CH4 flux was observed in IWD treatment. The highest CH4 but lowest N2O flux was observed in RDF thus portraying a tradeoff relationship among these two GHGs. The highest N2O flux was observed in AS. Changes in Eh strongly altered CH4 and N2O emissions. The CH4 flux for the growing season varied from 62.5 to 86.3 kg ha−1 with an average of 72.4 kg ha−1. The average N2O flux was 0.89 kg ha−1 with values fluctuating between 0.72 – and 1.08 kg ha−1. The findings of this study could assist in understanding the factors affecting the source, production, and sink of these two important GHGs. IWD, along with judicious N-based fertilizer use, could provide significant respite from GHG emissions in rice-based agriculture. These climate-smart strategies not only reduce emissions but also have the potential to improve yield.

The Impact of Raw and Composted Food Waste Anaerobic Digestates on Soil Organic Carbon Management: A Pot Study

Abstract Purpose Ever increasing food waste production has promoted anaerobic digestion and composting for its proper management, producing a relevant amount of recycled organic waste (OW) for possible agricultural uses. However, little is known regarding soil carbon management using this type of OW. Methods In this study, an anaerobic digestate from the wet digestion of food waste (WETD), and one from the dry-batch process (DRYD), along with their respective composts (WETC and DRYC), were utilized in a pot test over two growing cycles (84 + 84 days), with and without mineral nitrogen (N) fertilization, and were compared with a bio-waste compost (BWC) and a chemical reference (Chem). At the end of the two growth cycles (days 84 and 168), the ryegrass dry biomass (DW) and the N uptake were assessed. Results The pot soil was analyzed for soil organic carbon (SOC) and the potassium permanganate (KMnO4) oxidizable fraction (CL) as well as δ13C and Δ13C. At day 84, the SOC (g kg− 1) was the highest in DRYD and DRYC (8.53) > WETD and WETC (7.71) = BWC (7.86) > Chem (6.68), and performed similarly at day 168. At day 84, the carbon management index (CMI) was > 100% in all the organic treatments in comparison with Chem, except for WETD. At day 168, a + 30% CMI was registered in WETD and WETC> BWC> DRYD and DRYC> Chem. Conclusion This pattern was related to a generally marked δ13C depletion being confirmed by Δ13C, thus indicating the conservation of the carbon form compost, this very likely being related to the preferential lignin accumulation.

Prediction, mapping, and implication for better soil organic carbon management in Ethiopia

AbstractA precise soil organic carbon (SOC) content estimate is crucial soil quality parameter for agricultural produce and ecological safety. Moreover, geospatial modeling of SOC is critical when there are limited laboratory equipment and chemical reagents for soil analysis. This study used geostatistics—ordinary kriging (OK) and inverse distance weighting (IDW)—to map SOC in Libokemkem area, Northwest Ethiopia, for improved SOC management. About 107 soil samples were obtained from the plow layer at a 20‐cm depth and SOC was determined. Statistical Package for Social Sciences version 24.0 was used to generate descriptive statistics, and geostatistical analysis was also performed on the data using ArcGIS platform. The coefficient of determination (R2) and root mean square error (RMSE) derived from the validation of the predicted maps were used to assess the models. The results revealed homogeneity (coefficient of variation < 10%), low (0.12%–1.74%), and optimal (1.74%–4.06%) mean levels of SOC in study area. The OK showed an R2 of 0.74 and an RMSE of 13%, and the IDW revealed an R2 of 0.69 and an RMSE of 14%. The semivariogram results indicate a moderate dependence for SOC with stable, circular, spherical, exponential, and Gaussian models. We conclude that the sustainable monitoring of SOC is significant in enhancing soil quality. However, further study considering all drivers of spatial variability for SOC in the study and other soil sampling approaches improving performance of the prediction models is needed.

Towards site-specific management of soil organic carbon: Comparing support vector machine and ordinary kriging approaches based on pedo-geomorphometric factors

Site-specific management of soil organic carbon (SOC) is crucial for cleaner, sustainable agricultural production and climate change mitigation. The Neyshabur plain of northeastern Iran is plagued with spatially variable critically low SOC. Thus, in this study management zones (MZs) were developed for the site-specific management of SOC. Soil samples (0–0.3 m) and digital elevation model-derived geomorphometric variables were collected at 288 locations. Soil pH, clay content, available phosphorus, available potassium, and the vertical distance to channel network significantly (p < 0.05) correlated with SOC. Subsequently, support vector machine (SVM) and ordinary kriging (OK) approaches were used to map the spatial variability and develop MZs based on SOC and its above-listed pedo-geomorphometric covariates. Generally, the coefficient of determination (R2) and root mean square error (RMSE) of OK and SVM models used to interpolate pedo-geomorphometric covariates, were similar. However, the Fuzzy Performance Index (FPI) and Normalized Classification Entropy (NCE) indices suggest that the MZs based on the SVM model (FPI = 0.091, NCE = 0.113) are more accurate compared to those determined using the OK (FPI = 0.135, NCE = 0.164) method. Although the results demonstrate the potential of both OK and SVM approaches for site-specific management of SOC in the Neyshabur plain, the average difference of 0.8 g kg−1 SOC in the SVM approach compared to OK (p < 0.05) was found to be significant, especially along transitional boundaries between MZs. Thus, the SVM-based MZs developed in this study can be used for more accurate site-specific application of organic amendments, mineral fertilizers, and management practices that improve carbon sequestration and sorption of chemical contaminants in the Neyshabur plain of Iran.

Evaluation of Soil Organic Carbon Stability in Different Land Uses in Lithuania

The effective management of soil organic carbon (SOC) is highlighted as one of the strategies and cost-effective options for mitigating climate change, while soil nitrogen (N) often is specified as an essential element for plant growth. This study was conducted to evaluate basic soil physical, chemical, and microbial indicators in three major soil types dominated in Lithuania—Arenosols, Retisols, and Cambisols—under forest land, perennial grassland, and arable land. Furthermore, soil microbial biomass carbon (SMBC) and nitrogen (SMBN), their ratio, and soil microbial respiration (microbial CO2) next to SOC and total N were hypothesized to be important measures for assessing SOC stability under different land uses. Therefore, selected soil indicators were evaluated in the surface 0–10 and 10–20 cm mineral soil layers. The study results showed higher concentrations of SOC, N, SMBC, and SMBN, and soil microbial CO2 in forest land and perennial grasslands than in arable land. The higher SMBC/SOC and SNBN/TN ratios indicated a higher ability to accumulate SOC and N in forest land and grasslands. Higher SOC immobilization in forest land and higher N immobilization in arable land were both specified by the obtained SMBC:SMBN ratio. This study identified forest land followed by grassland as the best land management practice that addresses soil C sequestration through higher C immobilisation. Assessing soil in forest land as a control land use next to the agricultural land could be a reasonable soil management practice to evaluate C sequestration in the region. Additionally, it was assumed that evaluation of the SMBC and SMBN concentrations together with soil physical and chemical indicators allow for a more effective assessment of SOC stability. Taken together, these findings support recommendation to develop grassland (and especially forest land systems) through afforestation or within agroforestry system, without reducing the importance of the agricultural sector.

Farmer preference for marginal land use and their impact on soil quality

Changes in forest land use into dry lands have become a concern of the global community because of environmental damage. The research objectives were to determine land use priority for agricultural development and its impact on soil quality. The research was conducted in Baras District, Pasangkayu Regency, West Sulawesi. The socio-economic survey was conducted using a focus group discussion technique. Stratified soil surveys were carried out on priority land uses. Analysis of the physicochemical properties of soil was carried out at the Soil Science Laboratory, Faculty of Agriculture, University of Tadulako. Support for the development of farmer institutions, ease of product marketing, financial benefits for farming, and the availability of quality seeds are the dominant factors influencing people’s preference for the priority of developing oil palm farming. Respectively, Soil quality indexes in oil palm blocks aged 3 years, 6 years, and 24 years were 0.5584 (moderate), 0.3072 (slightly poor), and 0.4362 (moderate). Soil quality decreased early in plant growth up to 6 years of age, but improved with effective soil organic carbon management up to 24 years of age.

Carbon Storage in Cropland Soils: Insights from Iowa, United States

The restoration of soil organic matter (SOM, as measured by soil organic carbon (SOC)) within the world’s agricultural soils is imperative to sustaining crop production and restoring other ecosystem services. We compiled long-term studies on the effect of management practices on SOC from Iowa, USA—an agricultural region with relatively high-quality soil data—to highlight constraints on detecting changes in SOC and inform research needed to improve SOC measurement and management. We found that strip-tillage and no-tillage increased SOC by 0.25–0.43 Mg C ha−1 yr−1 compared to losses of 0.24 to 0.46 Mg C ha−1 yr−1 with more intensive tillage methods. The conversion of cropland to perennial grassland increased SOC by 0.21–0.74 Mg C ha−1 yr−1. However, diversifying crop rotations with extended rotations, and supplementing synthetic fertilizer with animal manure, had highly variable and inconsistent effects on SOC. The improved prediction of changes in SOC requires: the use of methods that can identify and disentangle multiple sources of variability; looking beyond total SOC and toward systematic collection of data on more responsive and functionally relevant fractions; whole-profile SOC monitoring; monitoring SOC in long-term studies on the effect of multiple conservation practices used in combination; and deeper collaboration between field soil scientists and modelers.

Predicting spatiotemporal soil organic carbon responses to management using EPIC-IIASA meta-models

The management of Soil Organic Carbon (SOC) is a critical component of both nature-based solutions for climate change mitigation and global food security. Agriculture has contributed substantially to a reduction in global SOC through cultivation, thus there has been renewed focus on management practices which minimize SOC losses and increase SOC gain as pathways towards maintaining healthy soils and reducing net greenhouse gas emissions. Mechanistic models are frequently used to aid in identifying these pathways due to their scalability and cost-effectiveness. Yet, they are often computationally costly and rely on input data that are often only available at coarse spatial resolutions. Herein, we build statistical meta-models of a multifactorial crop model in order to both (a) obtain a simplified model response and (b) explore the biophysical determinants of SOC responses to management and the geospatial heterogeneity of SOC dynamics across Europe. Using 5600 unique simulations of crop growth from the gridded Environmental Policy Integrated Climate-based Gridded Agricultural Model (EPIC-IIASA GAM) covering 86,000 simulation units across Europe, we build multiple polynomial regression ensemble meta-models for unique combinations of climate and soil across Europe in order to predict SOC responses to varying management intensities. We find that our biophysically-explicit meta models are highly accurate (R2 = 0.97) representations of the full mechanistic model and can be used in lieu of the full EPIC-IIASA GAM model for the estimation of SOC responses to cropland management. Model stratification by means of climate and soil clustering improved the performance of the meta-models compared to the full EU-scale model. In regional and local validations of the meta-model predictions, we find that the meta-models largely capture broad SOC dynamics such as the linear nature of SOC responses to residue application, yet they often underestimate the magnitude of SOC responses to management. Furthermore, we find notable differences between the results from the biophysically-specific models throughout Europe, which point to spatially-distinct SOC responses to management choices such as nitrogen fertilizer application rates and residue retention that illustrate the potential for these models to be used for future management applications. While more accurate input data, calibration, and validation will be needed to accurately predict SOC change, we demonstrate the use of our meta-models for biophysical cluster and field study scale analyses of broad SOC dynamics with basically zero fine-tuning of the models needed. This work provides a framework for simplifying large-scale agricultural models and identifies the opportunities for using these meta-models for assessing SOC responses to management at a variety of scales.

Regional spatial variability of soil organic carbon in 0–5 m depth and its dominant factors

Soil organic carbon (SOC) stored in deeper soils is becoming increasingly significant in carbon management and carbon cycle especially in the face of climate and land use alterations. However, spatial variability of deep SOC at a regional scale and its related dominant factors are rarely investigated. To ascertain the vertical distribution and regional spatial variations in the SOC content of deep soil, 1648 soil samples were collected from 67 sites across the Chinese Loess Plateau (CLP) at a depth of 500 cm. Results showed that the mean SOC content of three land use types were recorded as follows: cropland (2.93 ± 0.29 g kg−1) > forest (2.56 ± 0.24 g kg−1) > grassland (2.39 ± 0.29 g kg−1), and the vertical distribution of soil profile from 0 to 500 cm was similar. Furthermore, a significant alteration in SOC content was observed in the soil layer of 0–180 cm among the three land use types (p < 0.05). Spatial distribution maps of SOC content indicated that there was a central region of the northern CLP with low SOC content, which corresponds to the eolian sand area in Ordos. Both the spatial variations and influencing factors of SOC content were found to be depth-dependent, but soil texture and temperature were identified as the dominant factors affecting the 0–500 cm soil profile of the CLP. Clarifying this information is helpful for understanding content level and variation of deep SOC, which can provide a reference for carbon sequestration in deep soil and developing sustainable SOC management for the CLP and other related ecological regions.

Soil organic carbon sequestration in agricultural long-term field experiments as derived from particulate and mineral-associated organic matter

Soil organic matter (SOM) is indispensable for soil health and, in the context of climate change, is considered a significant CO2 sink. Improving agricultural management to increase long-term soil organic carbon (SOC) stocks for mitigating climate change requires tools that estimate short and long-cycling SOM pools. In this study, we analyzed changes in fast-cycling particulate organic matter (POM) and slow-cycling mineral-associated organic matter (MAOM) induced by common management practices, i.e., fertilization and crop rotation in topsoils from 25 Central European long-term field experiments. When relating MAOM-C contents to recent MAOM-C saturation levels, estimated sequestration potentials were only met in coarse-textured soils under appropriate agricultural management or fine-textured soils under extreme organic fertilization. Soil texture, organic fertilization, and below-ground OC inputs through root exudates and root biomass were decisive for estimating MAOM-C, allowing for calibration of a mixed-effects model (Nakagawa’s: marginal R2m = 0.6, conditional R2c = 0.89). While the models containing soil texture and organic fertilization parameters can be validated and generalized (R2 = 0.43), the below-ground OC input predictor substantially decreases the generalizability of the validated models (R2 = 0.14). According to quantile regression models, we estimate the average difference in MAOM-C concentration between well-managed and control site (without organic fertilization) topsoils to 4.1 mg g−1 soil. In dependence on the soil bulk density, this amounts to 1.38 – 1.84 t ha−1 MAOM-C stocks or 5.06 – 10.1 t ha−1 CO2-equivalents. POM-C was difficult to predict (R2 = 0.28), presumably due to strong POM dynamics. The POM-C / MAOM-C ratio can inform on the effects of agricultural practices in before/after management change comparisons. Under increasing SOC concentration, an increasing POM-C / MAOM-C ratio indicates that the effects of organic fertilization do not transfer to real effects on long-term SOC sequestration. Because MAOM-C depends on soil texture, this ratio is also a covariate of soil texture, limiting it for comparisons between sites with different textures. However, our data indicate that agricultural long-term field experiment soils constantly approximate MAOM-C saturation when the POM-C/MAOM-C ratio is >0.35. This ratio might be used as a management goal to prevent organic over-fertilization and N loss, especially on coarse-textured soils. Thereby, the POM-C / MAOM-C ratio can help to optimize SOC management and sequestration on agricultural soils and support climate change mitigation strategies in Central Europe.

Deep tillage enhanced soil organic carbon sequestration in China: A meta-analysis

The increasing atmospheric carbon dioxide (CO2) causes serious environmental problems, soil organic carbon (SOC) sequestration is suggested as an efficient strategy to offset global CO2 emissions. Deep tillage (DT) can change soil properties and the input and loss of OC, and then influence SOC sequestration across the whole soil profile. However, the effect of DT on SOC sequestration relative to conventional tillage (CT) under different environmental factors and management practices is unclear in China. In this study, we evaluated the response of SOC stock to DT based on collected field data (447 observations) with a meta-analysis. DT significantly increased SOC stock by 7.36% over CT, with a greater increase for subsoiling (8.76%) than for deep ploughing (DP) (5.85%). Subsoiling enhanced SOC across the 0–40 cm soil layer and the greatest increase occurred at 0–10 cm layer (11.41%); DP enhanced SOC stock at 10–40 cm layer, but had no effect on SOC of 0–10 cm layer. DP and subsoiling did not affect SOC storage below 40 cm depth. According to subgroup analysis, the high rainfall, fine soil texture, residue retained, double cropping, and high rate of N fertilization greatly promoted SOC stock compared with the low rainfall, sandy texture, residue removal, monoculture, and low N fertilizer input under DT. DT significantly enhanced SOC storage under low initial SOC (<15 g kg−1), but did not affect SOC when the initial SOC exceeded 15 g kg−1 compared with CT. However, the experiment duration, initial SOC and bulk density (BD) did not significantly affect the response of SOC to DT. Overall, this study has supplied a reference for SOC management in mitigating climate change, and the specific environmental factors and management practice should be considered when utilizing DT to enhance SOC sequestration in different regions of China.

Evaluation of projected soil organic carbon stocks under future climate and land cover changes in South Africa using a deep learning approach

Environmental degradation and carbon emissions have become a major global concern. This has forced policymakers to consider strategic and long-term contingencies to increase carbon sequestration capacity and mitigate the effects of climate change. Soil organic carbon (SOC) provides a reliable long-lasting mechanism to ameliorate climate change and regulate carbon fluxes. However, unanticipated rates of climate change coupled with the dynamic nature of land-use transformation threatens current mitigation approaches and can jeopardise carbon stock assimilation. To effectively manage and protect SOC stocks, large-scale projections that accurately model both current and future SOC pools are necessary. Hence, this study modelled the effects of simulated climate and land-cover change on SOC inventories across South Africa up to the year 2050. A digital soil mapping strategy in concert with a deep neural network (DNN) was used to model current SOC stocks distribution. Subsequently, WorldClim general circulation models and a space-for-time substitution (SFTS) method were used to derive future SOC stocks under four shared socio-economic emission pathways. Results show a relatively high accuracy with RMSE of 7.44 t/h for current stocks, while future stocks ranged from 11.37 to 13.56 t/h. Depending on emission rates, results showed a reduction in SOC inventories, with overall SOC stocks declining from 5.64 Pg to between 4.97 and 5.38 Pg by 2050. Meanwhile, forests, which account for approximately 1.2 Pg of total SOC in South Africa, were found to have lost more than 1% of their total coverage by 2050. These findings provide a glimpse into the state of South Africa's current and future SOC stock inventories and the influence of climate and land-use change. These findings are valuable to among others policymakers, land use managers and climate change experts in assessing the long-term feasibility of South Africa's existing SOC management protocols and land-use planning agenda. However, to adequately protect future SOC stocks, current land-use planning frameworks need to be re-adjusted to prioritize pressing environmental concerns.

Mapping soil organic carbon distribution across South Africa's major biomes using remote sensing-topo-climatic covariates and Concrete Autoencoder-Deep neural networks

The management of soil organic carbon (SOC) stocks remains at the forefront of greenhouse gas mitigation. However, unprecedented anthropogenic disturbances emanating from continued land-use change have significantly altered SOC distribution across global biomes leading to considerable carbon losses. Consequently, understanding the spatial distribution of SOC across different biomes, particularly at larger scales, is critical for climate change policy formulation and planning. Advancements in remote sensing, availability of big data, and deep learning architecture offer great potential in large-scale SOC mapping. In this regard, this study mapped SOC distribution across South Africa's major biomes using remotely sensed-topo-climatic data and Concrete Autoencoder-Deep neural networks (CAE-DNN). From the different deep neural frameworks tested, the CAE-DNN model (developed from 26 selected covariates) achieved the best accuracy with an RMSE value of 7.91 t/ha (about 20 % of the mean). Results further showed that SOC stock correlated with general biome coverage, as the Grassland and Savanna biomes contributed the most (32.38 % and 31.28 %) to the overall SOC pool in South Africa. However, despite their smaller footprint, Forests (44.12 t/h) and the Indian Ocean Coastal Belt (43.05 t/h) biomes demonstrated the highest SOC sequestration capacity. The restoration of degraded biomes is advocated for, in order to boost SOC storage; but a balance between carbon sequestration capacity, biodiversity health, and the adequate provision of ecosystem services must be maintained. To this end, these findings provide a guideline to facilitate sustainable SOC stock management within South Africa's major biomes and indeed other regions of the world.

Land degradation resistance potential of a dry, semiarid region in relation to soil organic carbon stocks, carbon management index, and soil aggregate stability

AbstractDry stable aggregates, soil organic carbon (SOC) stocks, and aggregate‐associated carbon are measured as vital factors for preserving soil quality. Different land use systems strongly impacts the services of the ecosystem to influence soil degradation and loss of carbon. Little is known about the dry aggregates stability and SOC stocks in the dry, semiarid region of Balochistan, Pakistan or how these resist land degradation. This study aimed to assess the impact of several land‐use systems on SOC stocks, the distribution of soil aggregates, and the carbon management index. The treatments consisted of five land‐use systems, namely, grassland, cropland, fallow land, forests, and vegetation, typically prevail in the region. Three layers of soil were used to sample it (0–15, 15–30, and 30–45 cm) and sieved into five aggregate classes (&gt;8, 8–5, 5–2, 2–0.25, and &lt;0.25 mm). The grasslands had significant higher SOC concentrations (12.83 g kg−1), SOC stocks (19.44 mg ha−1), liability index (0.89 g kg−1), nonlabile C (9.56 g kg−1), labile C (0.35 g kg−1), carbon management index (204.6), and easily oxidizable carbon (3.27 g kg−1), when compared with fallow land. Moreover, grassland and forestland soils contained larger macroaggregates (&gt;0.25 mm), although the fallow land‐use system had a higher degree of micro aggregates (&lt;0.25 mm). We found strong relationship of soil SOC with SOC concentration of soil aggregates and other labile fractions of carbon. Therefore, grassland and forestland promotes the aggregate‐associated SOC and carbon management index by enhancing the soil aggregation and is therefore a possible managing option to develop the SOC sequestration potential of eroded dry soils.

Trash management and Trichoderma harzianum influencing photosynthesis, soil carbon sequestration, and growth and yield of sugarcane ratoon in subtropical India

Soil organic carbon management is a key component for crop growth and provides sustainability in the cropping system. In subtropical India, soil organic carbon in agricultural soils continuously decreases, and most soils have very low organic carbon content. Trash management in the sugarcane ratoon crop improves soil quality parameters and supports crop growth favourably. Further inoculation of Trichoderma has also shown a synergistic response with trash application on nutrient availability in the ratoon crop. Improving soil quality also influences physiological activity, net photosynthesis, and crop yield. The present investigation aimed to determine the effect of trash management and Trichoderma application on changes in soil chemical and biological properties besides net photosynthetic rate, dry matter accumulation, and nutrient uptake by sugarcane ratoon crop to assess the sustainability of the system. Thus a field experiment was conducted with four treatments viz., (i) sugarcane trash mulching without Trichoderma harzianum, (ii) trash removal without Trichoderma, (iii) trash mulching + Trichoderma and (iv) trash incorporation + Trichoderma in a randomized block design (RBD) and five replications. Trash incorporation and inoculation of Trichoderma influenced net photosynthetic rate (NPR) positively. The lowest NPR (19.14 µmol/m2/s) was recorded under trash removal without Trichoderma. An increment of 16.15 % in total dry matter accumulation was recorded under trash incorporation + Trichoderma compared to trash removal without Trichoderma. Trash incorporation and Trichoderma accumulated the highest N in leaf, cane, and roots. Thus NPK uptake could be improved by the application of trash and Trichoderma application. The mean available K content in soil was significantly improved by 12.48 % compared to the initial level. An improvement of 33.6 kg K/ha was recorded after the harvest of the ratoon crop. The highest soil organic carbon (15.28 Mg/ha) was recorded with mulching of sugarcane trash under Trichoderma inoculation. The highest bacterial population was recorded under trash incorporation with Trichoderma. Soil microbial biomass carbon (SMBC) could be improved by 1.82 times under trash mulching + Trichoderma as compared to the initial status. Trash incorporation and Trichoderma application recorded the highest mean cane weight (1053 g) in ratoon crop. Trash incorporation+ Trichoderma registered an increase of 16.19 % increase in sugarcane yield as compared to trash removal without Trichoderma. However, sugar yield in a similar treatment was improved by 27.8 %. Thus trash application along with Trichoderma harzianum holds great promise in sustaining soil health and sugarcane and sugar yields in subtropical India.

Effects of land use type and tank components on soil properties and sustainability of tank cascade system in the Dry Zone of north central Sri Lanka

CONTEXTVillage tank cascade system (VTCS) is a globally important agricultural heritage system in Dry Zone watersheds of Sri Lanka. Lands in these watersheds have been under changing management conditions in the recent past, hence their sustainability has been suspected. OBJECTIVEObjectives of this study were to determine current status of soil properties in different land uses and tank components of a VTCS with respect to their position in the watershed, and to assess sustainability of this agricultural system using soil organic carbon management index (CMI) as a proxy. METHODSStudy was conducted in Mahakanumulla VTCS in North Central Dry Zone of Sri Lanka. Soil samples (n = 120) were collected from lands under forest, scrub/shrub, paddy and upland crops representing all elevations in two linear transects located in higher and lower positions along the main valley. Soils from components of tanks in upper and lower positions were also collected. Soil texture and some chemical properties were measured. The CMI in different land uses was calculated using total organic carbon (TOC) and permanganate oxidizable carbon contents. RESULTS AND CONCLUSIONSEffect of land use type on soil properties did not vary with position in the watershed (p > 0.05). An increase in available phosphorus and sulphur was observed in agricultural lands. Significant decreases in TOC (33%) and total N (43%) contents were observed in paddy lands compared to those of forest lands (p < 0.01). However, CMI indicated that management practices in paddy lands and upland soils are still sustainable but scrub/shrub lands need improvements to arrest their degradation. Electrical conductivity, pH, available P, K and Zn were significantly higher in lower positions while total N was lower. Thaulla (inundation area) and Perahana (filter) tank components had regulated flow of clay particles while Kattakaduwa (interceptor) has significantly high available P and TOC contents due to high wetness. Principal component analysis revealed factors that control soil erosion, soil organic matter content, water movement, cation availability and P dynamics governed the variability of major soil properties. Accumulation of soluble salts in lands of lower positions and increase of available plant nutrients in agricultural lands suggest that management practices so far adopted have slightly degraded soil fertility in this VTCS. SIGNIFICANCEFurther degradation of soils in lower positions and scrub/shrub lands in the watershed will negatively affect the livelihood of villagers and the environment. Hence, appropriate land management policies should be introduced to this agricultural system to ensure their sustainability.

Impact of biochar and manure application on in situ carbon dioxide flux, microbial activity, and carbon budget in degraded cropland soil of southern India

AbstractBiochar application is attracting attention to be an effective soil organic carbon (SOC) management to prevent land degradation, though quantitative information of its effect on carbon dioxide (CO2) flux and associated microbial responses is still scarce, especially in degraded tropical agroecosystems. We conducted a 27‐month field experiment with periodically measuring environmental factors, CO2 efflux rate, microbial biomass C (MBC), and SOC stock, and evaluated the impact of land management (control (C), biochar (B; 8.2 Mg C ha−1), farmyard manure (FYM) (M; 1.1 Mg C ha−1 yr−1), and a mixture of both (BM) on CO2 flux, microbial responses (MBC and qCO2 as microbial activity) and C budget, in tropical alkaline cropland of southern India. Based on the relationship between the CO2 efflux rate and environmental factors, cumulative CO2 flux was estimated at 2.4, 2.7, 4.0, and 3.7 Mg C ha−1 in the C, B, M, and BM treatments, respectively. Biochar application increased soil moisture though did not affect CO2 flux, causing a positive C budget (6.7 Mg C ha−1), because of the limited response of microbes to increased soil moisture due to the small amount of SOC. Biochar and FYM combined application did not increase CO2 flux compared with FYM alone, contributing to the largest SOC increment (8.9 Mg C ha−1) with a positive C budget (9.1 Mg C ha−1), due to little difference of microbial responses between the two treatments. Hence, biochar application combined with FYM could be an effective SOC management in the degraded cropland of southern India.

Spatial modelling approach and accounting method affects soil carbon estimates and derived farm-scale carbon payments

Improved farm management of soil organic carbon (SOC) is critical if national governments and agricultural businesses are to achieve net-zero targets. There are opportunities for farmers to secure financial benefits from carbon trading, but field measurements to establish SOC baselines for each part of a farm can be prohibitively expensive. Hence there is a potential role for spatial modelling approaches that have the resolution, accuracy, and estimates to uncertainty to estimate the carbon levels currently stored in the soil. This study uses three spatial modelling approaches to estimate SOC stocks, which are compared with measured data to a 10 cm depth and then used to determine carbon payments. The three approaches used either fine- (100 m × 100 m) or field-scale input soil data to produce either fine- or field-scale outputs across nine geographically dispersed farms. Each spatial model accurately predicted SOC stocks (range: 26.7–44.8 t ha−1) for the five case study farms where the measured SOC was lowest (range: 31.6–48.3 t ha−1). However, across the four case study farms with the highest measured SOC (range: 56.5–67.5 t ha−1), both models underestimated the SOC with the coarse input model predicting lower values (range: 39.8–48.2 t ha−1) than those using fine inputs (range: 43.5–59.2 t ha−1). Hence the use of the spatial models to establish a baseline, from which to derive payments for additional carbon sequestration, favoured farms with already high SOC levels, with that benefit greatest with the use of the coarse input data. Developing a national approach for SOC sequestration payments to farmers is possible but the economic impacts on individual businesses will depend on the approach and the accounting method.

Changes in soil organic carbon stocks from reducing irrigation can be offset by applying organic fertilizer in the North China Plain

Elaborate water and fertilization management to increase soil organic carbon (SOC) accumulation is important in maintaining high agricultural productivity and a sound ecological environment. Here, a field experiment was conducted for eight years to investigate the effects of irrigation regimes and fertilizer types on SOC stocks and labile organic carbon concentrations (particulate organic carbon, POC; dissolved organic carbon, DOC; and microbial biomass carbon, MBC). The irrigation regimes were no irrigation (W0); presowing irrigation (W1); presowing and jointing irrigation (W2); and presowing, jointing, and anthesis irrigation (W3). The fertilizer types were no fertilizer (C0), mineral fertilizer only (C1), half mineral plus half cattle manure (C2), and cattle manure only (C3). Thus, sixteen treatments were created and laid out in a split block design with triplicates. Both irrigation and manure application distinctly enhanced SOC accumulation. POC, DOC, and MBC were mainly affected by manure application in surface soil (0–20 cm) and by irrigation in deep soil (40–60 cm). The interaction between irrigation and fertilization on SOC and labile organic carbon concentrations was not significant in the 0–60 cm soil layer. The SOC stocks and relative SOC sequestration rate increased with increasing irrigation and cattle manure inputs. There was no significant difference in SOC stocks between W2C2 (108.15 Mg C ha−1) and W3C1 (109.05 Mg C ha−1), as well as between W1C2 (90.25 Mg C ha−1) and W2C1 (87.07 Mg C ha−1), indicating that in W2 and W1, replacing half of the nitrogen with organic manure can result in equal SOC stocks caused by increasing irrigation under mineral fertilizer alone. This study emphasizes the important role that irrigation plays in SOC management and provides theoretical support to improve the SOC stock by reducing irrigation and applying cattle manure in the North China Plain.

Revealing the scale- and location-specific relationship between soil organic carbon and environmental factors in China's north-south transition zone

The spatial variability of soil organic carbon (SOC) is scale- and location-dependent and controlled by various environmental factors. However, the location- and scale specific factors underpinning SOC variation often demand further investigation. This holds particularly true for topographically complex environments like China’s north-south transitional zone. In this paper, wavelet analysis was used to determine the relationship between SOC and environmental factors in the region. The results showed that SOC exhibits an obvious transition effect from north to south, especially at 140 km north of the transect. Elevation was the strongest single factor to explain SOC variations (percent area of significant coherence (PASC) = 25.20%, average wavelet coherence = 0.53 at all scales). Normalized difference vegetation index (NDVI), elevation, land surface temperature (LST) and topographic moisture index (TWI) were the strongest combination of factors explaining variation in SOC (PASC = 43.80%, average wavelet coherence = 0.94 at all scales). Edaphic factors, vegetative factors, climatic factors, topographic factors and their interactions jointly controlled the variation of SOC, and showed increasing predictive power at increasing spatial scales. Our results reveal the spatial sequence changes of SOC and the scale-location dependencies between SOC and environmental factors in China’s north-south transition zone, which can be used for modeling, mapping and management of SOC at different scales.