Sugarcane Area Research Articles

Effect of Conservation Management on Oxisol in a Sugarcane Area Under a Pre-Sprouted Seedling System

Effect of Conservation Management on Oxisol in a Sugarcane Area Under a Pre-Sprouted Seedling System

Mapping sugarcane globally at 10 m resolution using Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2

Abstract. Sugarcane is an important source of food, biofuel, and farmer income in many countries. At the same time, sugarcane is implicated in many social and environmental challenges, including water scarcity and nutrient pollution. Currently, few of the top sugar-producing countries generate reliable maps of where sugarcane is cultivated. To fill this gap, we introduce a dataset of detailed sugarcane maps for the top 13 producing countries in the world, comprising nearly 90 % of global production. Maps were generated for the 2019–2022 period by combining data from Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2). GEDI data were used to provide training data on where tall and short crops were growing each month, while S2 features were used to map tall crops for all cropland pixels each month. Sugarcane was then identified by leveraging the fact that, among all non-tree species grown in cropland areas, sugarcane is typically tall for the largest fraction of time. Comparisons with field data, pre-existing maps, and official government statistics all indicated high precision and high recall of our maps. Agreement with field data at the pixel level exceeded 80 % in most countries, and subnational sugarcane areas from our maps were consistent with government statistics. Exceptions appeared mainly due to problems in underlying cropland masks or due to under-reporting of sugarcane area by governments. The final maps should be useful in studying the various impacts of sugarcane cultivation and producing maps of related outcomes such as sugarcane yields. The dataset is available on Zenodo at https://doi.org/10.5281/zenodo.10871164 (Di Tommaso et al., 2024a).

Phenological Monitoring of Irrigated Sugarcane Using Google Earth Engine, Time Series, and TIMESAT in the Brazilian Semi-arid

Monitoring sugarcane phenology is essential since the globalized market requires reliable information on the quantity of raw materials for the industrial production of sugar and alcohol. In this context, the general objective of this study was to evaluate the phenological seasonality of the sugarcane varieties SP 79-1011 and VAP 90-212 observed from the NDVI time series over 19 years (2001–2020) from global databases. In addition, this research had the following specific objectives: (i) to estimate phenological parameters (Start of Season (SOS), End of Season (EOS), Length of Season (LOS), and Peak of Season (POS)) using TIMESAT software in version 3.3 applied to the NDVI time series over 19 years; (ii) to characterize the land use and land cover obtained from the MapBiomas project; (iii) to analyze rainfall variability; and (iv) to validate the sugarcane harvest date (SP 79-1011). This study was carried out in sugarcane growing areas in Juazeiro, Bahia, Brazil. The results showed that the NDVI time series did not follow the rainfall in the region. The sugarcane areas advanced over the savanna formation (Caatinga), reducing them to remnants along the irrigation channels. The comparison of the observed harvest dates of the SP 79-1011 variety to the values estimated with the TIMESAT software showed an excellent fit of 0.99. The mean absolute error in estimating the sugarcane harvest date was approximately ten days, with a performance index of 0.99 and a correlation coefficient of 0.99, significant at a 5% confidence level. The TIMESAT software was able to estimate the phenological parameters of sugarcane using MODIS sensor images processed on the Google Earth Engine platform during the evaluated period (2001 to 2020).

Assessment of Soil Physico-chemical Properties in Sugarcane Cultivation Areas of Navsari District, Gujarat, India

Sentinel 2 satellite data from the year 2021 were acquired from the Copernicus site to identify the sugarcane producing area in the Navsari district. Hybrid classification approach i.e., supervised and unsupervised with ground truth data were applied using ERDAS IMAGINE software. After image classification, 2.5 km x 2.5 km grid was prepared in Q-GIS software which along with classified sugarcane area were overlapped for site identification. Then, random soil surface and sub-surface samples were collected with reference from grid of intensive sugarcane growing area. The particle density (2.10 to 2.76 g cm-3 with the mean value of 2.58 g cm-3) and bulk density (1.10 to 1.68 g cm-3 with the mean value of 1.33 g cm-3) of surface soil were found to be lower than sub-surface soil (2.18 to 2.79 g cm-3 and 1.15 to 1.68 g cm-3 with the mean value of 2.62 g cm-3 and 1.42 g cm-3 respectively) while porosity (31.60 to 59.26% with the mean value of 48.39%) and maximum water holding capacity (22.77 to 51.92% with the mean value of 39.60%) of surface soil were found to be higher than sub-surface soil (29.54 to 57.71% and 20.98 to 48.91% with the mean value of 45.60% and 36.81% respectively). The pH of soil surface showed range from 6.08 to 8.37 while pH of soil sub-surface noted range of 6.00 to 8.50. The electrical conductivity of surface and sub-surface soil showed the range of 0.011 to 1.580 dS m-1 and 0.010 to 1.586 dS m-1. The soil organic carbon of surface soil ranged from 0.06 to 0.89% while that of sub-surface soil varied from 0.02 to 0.85%.

Assessment of Soil Nutrient Dynamics in Sugarcane Cultivation Areas of Navsari District: Implications for Sustainable Agriculture

Sentinel 2 satellite data from the year 2021 were acquired from the Copernicus site to identify the sugarcane producing area in the Navsari district. Hybrid classification approach i.e., supervised and unsupervised with ground truth data were applied using ERDAS IMAGINE software. After image classification, 2.5 km x 2.5 km grid was prepared in Q-GIS software which along with classified sugarcane area were overlapped for site identification. Then, random soil surface and sub-surface samples were collected with reference from grid of intensive sugarcane growing area. The available nitrogen, phosphorus, potassium, DTPA-Fe, DTPA-Mn, DTPA-Zn and DTPA-Cu of surface soil ranged from 132.19 to 428.65 kg ha-1, 18.48 to 107.32 kg ha-1, 202.34 to 359.39 kg ha-1, 0.97 to 29.90 ppm, 1.10 to 29.63 ppm, 0.20 to 6.89 ppm and 1.02 to 11.69 ppm while sub-surface soil varied from 101.91 to 388.62 kg ha-1, 10.16 to 99.57 kg ha-1, 138.66 to 323.35 kg ha-1, 0.35 to 24.97 ppm, 0.20 to 29.08 ppm, 0.05 to 3.64 ppm and 0.21 to 8.07 ppm respectively. The soils of sugarcane growing area of Navsari district showed low OC, available N and S while other nutrients were normal in range for sugarcane cultivation.

Area extraction and growth monitoring of sugarcane from multi-source remote sensing images under a polarimetric SAR data compensation based on buildings

ABSTRACT Aiming at the issue of low accuracy in crop mapping and growth monitoring caused by imprecise calibration of radar time-series data, this paper proposed a Synthetic Aperture Radar (SAR) spatio-temporal error compensation method. By constructing a compensation reference image using the buildings with stable backscattering coefficients in the study area, the error correction of time-series SAR data was realized based on the compensation model. The compensated SAR data were used to establish the Sugarcane Growth Index (SGI) according to the growth curve of sugarcane. The sugarcane area was extracted after combining the compensated SGI and optical image, and the accuracy of F1 on sugarcane distribution extraction was 92.12%. There was an improvement of 4.11% compared with no compensation method. On this basis, the newly planted and ratoon sugarcane were further classified by the expectation maximization algorithm based on the compensated SAR image at the seedling stage. The newly planted sugarcane and the ratoon sugarcane could be accurately distinguished with area extraction accuracy of 82.06% and 80.59%, respectively. Moreover, the compensated SAR data of time series during the tillering to maturity stages were used to estimate the sugarcane height, yielding an R 2 value of 0.717 using a quadratic polynomial model. The Spatio-temporal error compensation method for distributed targets proposed in this study can reduce the loss of SAR data across various times and regions, and achieve accurate results of sugarcane distribution extraction and plant height estimation.

Growth Dynamics of Sugarcane in North Karnataka, India

Sugarcane (Saccharum officinarum) holds immense significance in India's agricultural, economic and industrial spheres. In Karnataka, sugarcane was cultivated on an area of 6.37 lakh hectares with annual production of 61.15 million tonnes (2021-22). Bagalkote district ranked as the state's second-largest sugarcane producer, accounting for 16.82 per cent of the total cultivated area and contributed for 15.06 per cent in the total production of the state. The present study was conducted to know trends in sugarcane cultivation in Karnataka and Bagalkote district. The secondary data on area, production and productivity of sugarcane for a period of 20 years (2001-2021) were collected from the publications of Department of Economics and Statistics (DES) and were analysed using Compound Annual Growth Rate (CAGR) and Cuddy Della Valle Index. The results of the analysis revealed a significant compound growth rate of 3.50 per cent annually for sugarcane production in Karnataka, driven by augmented area and productivity and area under sugarcane expanded by 2.92 per cent per annum. In Bagalkote district, sugarcane production increased by 4.21 per cent annually, attributed solely to the increased cultivated area, even though productivity exhibited a slight decline. The Cuddy-Della Valle Index revealed higher instability in production and area compared to productivity in both Karnataka and Bagalkote district. The study highlights the importance of farmers adopting new high-yielding sugarcane varieties that demonstrate resilience to adverse weather conditions, pests, and diseases as a means to sustain and boost productivity for higher sugarcane production.

Development of ARIMA Model for Forecasting Sugarcane Production in Assam

Background: Assam is a state of northeast India which lives on agriculture. Sugarcane is one of the important cash crops grown in the state. The cultivated area of sugarcane is 29,768 hectare in the state during 2021-22 with a total production of 11,60,025 tones. Sugarcane cultivation is not evenly distributed in all the districts of Assam. The major sugarcane producing districts in state are Karbi Anglong, Nagaon, Sonitpur, Dima Hasao and Golaghat. Methods: This paper is an attempt to develop an appropriate time series model by using Box-Jenkins methodology to forecast the production of sugarcane in the state for few coming years. The study is based on secondary data collected from various publications of Directorate of Economics and Statistics, Govt. of Assam. Year wise time series data on production of sugarcane for a period of 60 years from 1961-62 to 2021-22 have been analyzed for this study. Result: The results of this study establish ARIMA(1,2,1) as an suitable model for forecasting sugarcane production in the state. The proposed forecasting model indicates an increasing trend of sugarcane production in the coming years in Assam.

Research on Sugarcane Recognition based on Joint Spatial and Spectral Information from Satellite-Based Hyperspectral Imagery

Abstract. The total amount of sugarcane planting in China ranks third in the world, and Guangxi is the largest sugarcane planting base in China. In order to help the Guangxi government and industry better understand the annual planting distribution and scale of sugarcane in the whole region, it is convenient to optimize the planting structure and improve the efficiency of resource utilization, so as to ensure the stable development of Guangxi sugar industry. Based on the satellite-based hyperspectral image data of ' Zhuhai No.1 ', this paper carries out sugarcane recognition and area extraction, which fills the application gap of satellite-based hyperspectral image data in extracting sugarcane area in China. A globally optimized extended random walk model was used to identify and extract sugarcane by combining the spatial-spectral information of hyperspectral imagery. Firstly, the Initial probability estimation is performed using the LOR classifier, and then use the data after MNF dimensionality reduction to construct a weighted graph, and finally the probability optimization and recognition are carried out based on the extended random walk model. The results show that the spatial-spectral joint method based on extended random walk can effectively identify sugarcane, and the sugarcane recognition accuracy of 2021 year image can reach 91.4%, the misrecognition rate is 2.8%; the sugarcane recognition accuracy of 2020 year image can reach 90.5 %, the misrecognition rate is 3.1%.

Classification of sugarcane areas in Landsat images using machine learning algorithms

Monitoring sugarcane areas through remote sensing is essential for planning and management of the national sugarcane industry. The use of machine learning algorithms has provided many benefits to remote sensing. This article aims to compare the prediction quality of three important machine learning methods in identifying sugarcane areas using Landsat images: Logistic Regression (LR), Decision Tree (DT) and RandomForest (RF). LR was applied in three versions: LR without penalization, LR with Ridge penalization (LR-R) and LR with Lasso penalization (LR-L). Data obtained in this study refer to a region of approximately 306,000 ha located in the state of São Paulo, Brazil, which was segmented into approximately 46,000 polygons (observations). Six spectral bands and vegetation indices observed along 17 months resulted in 102 covariates, which were reduced via Principal Component Analysis (PCA). In total, 19 Principal Components were chosen to account for 94.61% of the cumulative explained variance ratio and were used in machine learning methods to classify each polygon as sugarcane or other land covers. The method with the highest accuracy considering testing sample of 20% of data was RF (78.51%), followed by DT (72.30%), LR-L (69.64%), LR-R (69.64%), and LR (69.52%).

Spatiotemporal Trends of the Carbon Footprint of Sugar Production in China

Sugar is the main nutrient source of human beings and the basic raw material of the food industry. It is of great practical significance for China to find out the current situation and future trend of Greenhouse Gas (GHG) emissions from sugar production, and explore measures for reducing GHG emissions from sugar planting and processing. The IPCC GHG emissions accounting method is applied to analyze the carbon footprint of sugar production based on planting management and industrial development data, in which the GHG emissions per unit of sugar product are calculated and analyzed. According to Carbon Peak and Carbon Neutrality targets, the total GHG emissions of China's sugar industry in 2030 and 2060 are predicted and discussed. The results show that: (1) The average GHG emissions per unit yield of sugar show a downward trend from 2004 to 2021. The total GHG emissions of sugar production in China have reached 1319.9 × 104 t CO2eq in 2021, which has not yet reached its peak due to the growth of consumption demand. (2) The average GHG emissions per unit of Beet Sugar (1.477 tCO2eq/t) is higher than that of Cane Sugar (0.825 tCO2eq/t). It is more meaningful to enlarge the planting area of sugarcane than that of sugarbeet in the future. (3) Overall, in 2021 the GHG emissions of sugar production reached 0.91 tCO2eq/t in China, which would decline 15 %–87 % until 2030 and 2060. There is great potential for sugar production to reduce GHG emissions in the future.

Analysis of Sugar Production Based on Productivity and Recovery Rate

The present investigation emphasizes growth and instability of sugarcane in Maharashtra. Sugarcane is the important cash crop of the Maharashtra. An effort has been made in this study to examine the trends in area, production, productivity of sugarcane and to determine the factors which are contributing toward productivity of sugarcane in major sugar producing states of Uttar Pradesh and Maharashtra. It has been observed that area expansion has significantly contributed towards increased production of sugarcane but productivity has remained stagnant. it was found to be higher for Maharashtra than Uttar Pradesh. The data obtained from secondary sources were analysed to examine the factors which effect on the sugarcane acreage in Maharashtra state and Uttar Pradesh for analyse by using linear multiple regression based on Neronian Partial Adjustment Model. The present project attempts to study the trend and growth rates of Sugarcane production Maharashtra. The time series data on area, production and productivity of Sugarcane pertaining to the period 1950-51 to2022-23 were used for the study. useful information for further planning and corrective measures for future development of Maharashtra State. The area, production and productivity of sugarcane crop have been estimated in Maharashtra district. In this study we fit the linear regression model on the data and find the coefficient of model and fit the predictive model. Also, we check the multicollinearity in the data by VIF and find the ridge regression for the given data.

Chemical characterization of soil organic matter in differents management practices in the Cerrado-Pantanal ecotone

Different land uses and occupations have a direct impact on edaphic attributes, which can be identified by soil quality indicators, with organic matter being an excellent edaphic quality indicator. The objective of the work was to chemically characterize soil organic matter (SOM) in different management systems in the municipality of Aquidauana, Mato Grosso do Sul, Brazil. Soil samples were collected in five areas with different management practices, in addition to an area of native Cerrado forest (NF), and the managed areas were: exposed soil (ES), conventional tillage system (CTS), no-tillage (NTS), permanent pasture (PP) and sugarcane (SC). In addition to the determination of bulk density (Bd), total organic carbon (TOC) contents and stocks were quantified, as well as carbon (C) contents and stocks of the humified fractions: humic acid (AH), fulvic acid (AF) and humin (HUM), with subsequent calculation of HA/FA and alkaline extract/humin (AE/HUM) and TOC stratification index (SI). There were no significant changes for Ds after six years of implementation of management systems. TOC levels and stocks were higher in PP and NTS areas. The HUM fraction was predominant, with higher contents and stocks of all fractions for the PP and NTS areas, demonstrating the greater intensity of the humification process in these management systems, with greater stability of C in the soil after six years of experiment. The PCA indicated a difference between the areas that present conservation practices in the ES and CTS areas. The NF, NTS, PP and SC areas contribute to SOM stabilization.

Tithonia diversifolia: beneficial companion plant to control Yellow Aphids on sugarcane fields in Tanzania

To find sustainable ways to protect its sugarcane areas from the key pest Sipha flava (Hemiptera: Aphididae) in north Tanzania, the local company TPC Limited funded a 6 months project in coordination with the French Agricultural Research Centre for International Development (CIRAD) to assess different agroecological practices, such as the use of companion plants around sugarcane fields. In this study, the influence of the flowering species Tithonia diversifolia located at the field edges as a potential banker plant for conservation biological control of the yellow sugarcane aphid was evaluated. Predatory species were identified by direct capture and/or regular observations and the intensity of aphid damage was evaluated through number of colonies. Twenty-four aphid predator species were identified being hosted by T. diversifolia, and the vicinity of this plant reduced by 86% the number of S. flava colonies in the sugarcane edges. These species belonged mostly to family Coccinellidae (ladybugs), Syrphidae (hoverflies), Chrysopidae (green lacewings), Dolichopodidae (long legged flies). This is the first paper evaluating T. diversifolia’s potential as a banker plant in sugarcane crops for pest control.

Biochar Mitigates the Negative Effects of Microplastics on Sugarcane Growth by Altering Soil Nutrients and Microbial Community Structure and Function.

Microplastic pollution in sugarcane areas of China is severe, and reducing the ecological risks is critical. Biochar has been widely used in soil remediation. This study aims to explore the effects and mechanisms of microplastics combined with or without biochar on sugarcane biomass, soil biochemical properties in red soil through a potted experiment. The results show that, compared with control (CK), treatments with microplastics alone reduced the dry biomass of sugarcane, soil pH, and nitrogen (N) and phosphorus (P) contents by an average of 8.8%, 2.1%, 1.1%, and 2.0%, respectively. Interestingly, microplastics combined with biochar could alleviate the negative effects of microplastic accumulation on sugarcane growth and soil quality. There were significant differences in the bacterial community alpha diversity indices and compositions among different treatments. Compared with CK, treatments with microplastics alone obviously decreased the observed operational taxonomic units (OTUs) and the Chao1 and Shannon indices of soil total bacteria (16S rRNA gene-based bacteria) while increasing them in phoD-harboring bacteria. Microplastics combined with biochar treatments significantly increased the abundance of Subgroup_10 for the 16S rRNA gene and treatments with microplastics alone significantly increased the relative abundance of Streptomyces for the phoD gene compared to CK. Moreover, compared with microplastics alone, the treatments with microplastics combined with biochar increased the relative abundance of Subgroup_10, Bacillus, Pseudomonas in soil total bacteria, and Amycolatopsis and Bradyrhizobium in phoD-harboring bacteria, most of which can inhibit harmful bacteria and promote plant growth. Additionally, different treatments also changed the abundance of potential microbial functional genes. Compared to CK, other treatments increased the abundance of aerobic ammonia oxidation and denitrification but decreased the abundance of nitrate respiration and nitrogen respiration; meanwhile, these four functional genes involved in N cycling processes were obviously higher in treatments with microplastics combined with biochar than in treatments with microplastics alone. In conclusion, microplastics combined with biochar could alleviate the negative effects of microplastic accumulation on sugarcane biomass by altering soil nutrients and microbial community structure and function.

Land Use and Land Cover Trends and Their Impact on Streamflow and Sediment Yield in a Humid Basin of Brazil’s Atlantic Forest Biome

Understanding the trends in land use and land cover (LULC) is crucial for modeling streamflow and sediment yield, particularly in hydrological basins. This study examined the impact of LULC on the dynamics of streamflow and sediment yield within a humid tropical basin of the Atlantic Forest biome in Brazil, focusing on the period from 2000 to 2016. Changes in LULC were analyzed using annual MapBiomas data products for the same period. The Soil and Water Assessment Tool (SWAT) model was deployed to simulate streamflow and sediment yield based on LULC changes. To investigate temporal trends in LULC, a suite of non-parametric statistical tests, including the Mann–Kendall, Pettitt, and Sen’s slope estimator tests, was employed. Ecological diversity indices such as Shannon–Weaver, Simpson, and Pielou were applied to assess forest fragmentation, along with the Forest Fragmentation Index. The results revealed a growing trend in urban and sugarcane areas, coupled with a decline in dense vegetation, mangroves, and other forms of dense vegetation. With regard to the correlation between land uses and hydrological variables, the findings indicate minor variations in hydrological balance, attributable to the not-so-significant changes among the studied land-use scenarios, except for sediment yield estimates, which showed more considerable alterations. Notably, the estimates for 2000 and 2013–2016 were the most divergent. In a broader scientific context, this research conclusively establishes that the incorporation of dynamic LULC data into the SWAT model augments the precision and robustness of simulations pertaining to agricultural watersheds, thereby enabling a more comprehensive hydrological characterization of the study area.

Classification of Sugarcane Area Using Landsat 8 and Random Forest based on Phenology Knowledge

Indonesia is one of the largest countries globally with an area for planting sugarcane. The current problem is that determining the planting area of sugarcane is still done conventionally; this is very limited and wastes time. Thus, knowing the sugarcane planting area becomes essential for policymaking through Remote Sensing technology. However, the challenge of Remote Sensing is the limited data due to weather and the spectral variability of other plants. So, it is necessary to classify based on phenological knowledge. The study aims to classify sugarcane areas based on phenological knowledge using Remote Sensing and Machine Learning. This application finished on the cloud platform Google Earth Engine (GEE) through Landsat 8 satellite imagery data. The knowledge of sugarcane phenology was built based on the Normalized Difference Vegetation Index (NDVI) spectral value and built with the harmonic model. In addition, classification is accomplished by object-oriented (OO) methods for segmentation classification. Object-oriented is solved by the Simple Non-Iterative Clustering (SNIC) algorithm for spatial cluster identification, the Gray-Level Co-occurrence Matrix (GLCM) for texture extraction, and the Random Forest algorithm for Land Use-Land Cover (LULC) classification. The results of the accuracy analysis using the confusion matrix and the classification of sugar cane areas based on phenological knowledge obtained the best results with an overall accuracy of 95.9% with a Kappa coefficient of 0.92. It can be concluded that a classification approach with knowledge of plant phenology can help better classify the availability of land for plantations in the future.

Crop Classification and Crop Acreage Estimation Using Geospatial Technology in the Upper Gangetic Plains of Uttarakhand, India

Timely and accurate crop mapping plays an important role in food security, economic and environmental policies. Crop maps are also utilized for agro-environmental assessments and crop water usage monitoring. As a result, accurate and timely crop classification is essential for agricultural management and monitoring. Because it provides periodic large-scale observations of ground objects, satellite remote sensing has been regarded as an advanced tool to characterize crop types and their distributions on a regional scale. High-resolution, multispectral images of October 13, 2021, December 7, 2021 and March 6, 2022 of sentinel-2 satellite released by the European Space Agency (ESA) have been used for classification. Ground truth points have been collected manually with the android app ‘Mapmarker’ and Google Earth. Further, pre-processing of satellite imageries such as resampling, mosaicking and sub-setting have been done with the Sentinel Application Platform (SNAP) software. Crop classification and acreage estimation was conducted using Maximum Likelihood approach. It is the first time an attempt was made to estimate cropping intensity using geospatial technology in the upper Gangetic plains of Uttarakhand state. Rice and sugarcane areas of 108,884 ha and 11,479 ha, respectively, were estimated from the October 13, 2021 image. Pea crop area was estimated as 6,227 ha from December 7, 2021 image. Using March 6, 2022 image, wheat and mustard crop areas were estimated as 105,334 ha and 2,018 ha, respectively.

Chemical Composition of Organic Carbon in Tropical Soils Under Agricultural Cultivation and Peat-Swamp Forest

ABSTRACT Soil organic carbon (SOC) is important for agricultural production and from an environmental perspective. This research determined the chemical composition of SOC and studied factors related to its distribution for various crops and peat-swamp forests in the tropical region. In total, 21 topsoil samples were collected from areas of paddy rice, sugarcane, cassava, pineapple, oil palm, para rubber, and peat-swamp forest in Thailand. Four groups of SOC chemical composition were investigated (carbonyl C, aromatic C, O-alkyl C, and alkyl C) using solid-state 13C nuclear magnetic resonance spectroscopy (NMR). The dominant peak of the NMR spectra indicated that SOC originated from a variety of lignin compound units. The results revealed the dominant SOC chemical component was O-alkyl C (36.57–50.90%), followed by alkyl C (22.32–32.60%), aromatic C (16.65–23.41%), and carbonyl C (7.99–10.95%), respectively. Most of the SOC chemical components were derived from plant debris, particularly biodegraded lignin compounds. There were slightly differences between the distributions of the SOC functional groups in each vegetation type, perhaps partially as a result of the specific management activities for each crop, including tillage. Furthermore, alkyl C was related to some soil properties (silt and clay fractions), while O-alkyl C was related to available P in the soil. The hydrophobicity index, aromaticity index, and degree of humification were in the ranges 0.69–1.12, 0.17–0.23, and 0.46–0.92, respectively. These findings provided SOC chemical composition data of several economic crops to further support soil management for crop production and carbon stock preservation in tropical soils.

Load-Bearing Capacity of an Oxisol under Burned and Mechanized Harvest Sugarcane Crops

The change in land use and the expansion of mechanized sugarcane production systems have led to an increase in soil compaction levels. Preconsolidation pressure may be used as a useful measure for soil mechanical state, management, and planning of mechanization systems. This study aimed to assess the soil compressive behavior, soil physical properties, and spatial variability of preconsolidation pressure of an Oxisol in sugarcane fields under burned harvest and mechanized harvest and the effects of land use change. The physical soil attributes (granulometry, soil water content, bulk density, total porosity, and macro and microporosity) and preconsolidation pressure were evaluated at 0.00–0.10-m, 0.10–0.20-m, and 0.20–0.30-m layers. The soil load-bearing capacity models were constructed from σp values for soil water contents. We mapped the assessed soil attributes from crossing points in a sampling mesh with regular 10 m intervals in each area and evaluated them via geostatistics. Land-use change towards sugarcane production systems promoted soil compaction. The mechanized harvesting system increased the soil load-bearing capacity in the water range corresponding to the friability region in subsurface layers. The preconsolidation pressure and soil water content exhibited spatial dependence in the sugarcane areas, regardless of the management system employed in the harvesting operations.