Sugarcane Crop Research Articles

Environmental impact analysis of crop residue burning in Madhya Pradesh: A multivariate comparison across key crops.

This study quantified the environmental impacts of residue burning of major produced and burned crops in Madhya Pradesh, central India. The environmental impacts were quantified using Life Cycle Assessment (LCA) coupled with Monte Carlo simulation of 1000 iterations. Crop wise marginal impacts of the crops have been quantified using Multivariate regression model. The results showed that sugarcane and rice have the highest emissions in key impact categories, such as particulate matter formation (PMF) and global warming potential (GWP), whereas wheat and maize exhibit comparatively lower impacts. The combustion of residues significantly increases marine eutrophication (MEUT), agricultural land use (ALU), terrestrial acidification (TEAF) and GWP. Each kilogram of burned residue results in an increase of 21% in MEUT, 0.05% in ALU, 0.046% in TEAF and 0.028% in GWP, intensifying climate change. The results underscore the immediate necessity for specialized residue management strategies for sugarcane and rice crops. It is advisable to utilize sustainable alternatives such as composting or biochar production to mitigate emissions and enhance soil health, thereby addressing environmental and human health issues.

Biotechnological techniques for sugarcane crop improvement, applications and challenges

Biotechnological techniques for sugarcane crop improvement, applications and challenges

The Role of Sugarcane Trash in Soil Fertility and Plant Growth: A Review

Sugarcane needs a hot and humid climate and can be grown on a variety of soils that can retain moisture. Owing to its long life cycle it heavily depletes soil nutrients during its growth period. Sugarcane, being a heavy feeder requires nearly 208 kg N, 53 kg P2O5, and 280 kg K2O from the soil to produce 100 t ha-1 sugarcane. Sugarcane, being a C4 plant, produces a large quantity of biomass. Approximately 10-15 t of trash, constituting 10-12% weight of cane harvested, is produced by the sugarcane crop. Generally, cane trash contains 68% organic matter, 0.42% N, 0.15% P, 0.57% K, 0.48% Ca, and 0.12% Mg, besides 25.7, 20.45, 236.4, and 16.8 ppm Zn, Fe, Mn and Ca, respectively. Trash conserves soil moisture, C, and N in the soil and provides energy to increase the yield. The trash application conserves the nutrients in the soil, also aids their availability to the plant, and reduces the fertilizer requirements through recycling nutrients in the soil from residue. Organic mulches also create a better physical, chemical, and biological environment of soils and in turn, improve crop productivity. Burning of trash deteriorate the fertility as well as soil health. It also reduces the soil microbial activity and destabilize of soil aggregates. Significant N losses from residues can occur during burning by high temperature volatilization. In the burnt system, >70% of the organic matter and nutrients in the trash are lost to the atmosphere.

Assessment of land suitability and crop water requirements for irrigated sugarcane in the Kuraz Irrigation Scheme, Lower Omo Basin, Ethiopia

ABSTRACT Assessing land suitability is crucial for sustainable agricultural production. This study focuses on evaluating the suitability of land for irrigated sugarcane cultivation and determining crop water requirements within the Kuraz Irrigation Scheme in Ethiopia. A combination of geographic information system (GIS) and analytical hierarchy process (AHP) tools was used to classify land suitability and support decision-making by considering factors such as soil pH, texture, temperature, rainfall, land use-land cover, and slope. The AquaCrop model was utilized to simulate current and future scenarios for biomass, canopy cover, yield, maximum evapotranspiration, and water productivity of sugarcane crops. The model was calibrated for irrigated sugarcane during the 2015–2016 crop season, revealing that the majority of the study area is highly suitable for sugarcane cultivation. Specifically, 28% of the area was classified as highly suitable, 38% as moderately suitable, 21% as marginally suitable, and 13% as unsuitable. These findings highlight the potential for expanding sugarcane cultivation to boost yields and profitability. However, the study also underscores the need to consider potential environmental impacts, such as water usage and soil degradation from agricultural expansion. Additionally, social factors, including labor practices and the possible displacement of local communities, must be taken into account when expanding sugarcane cultivation.

RGB Composition Obtained by a UAV in the Monitoring of Sugarcane Row Gaps Using the Biophysical Index

Sugarcane crops have a long cycle with successive harvests before re-planting, and row gaps are one of the main problems associated with the yield. The objective of this study was to establish an alternative methodology for measuring the planting and regrowth of sugarcane rows using UAV (Unmanned Aerial Vehicle) images and to compare it with manual measurements. This study was conducted in a 1 ha experimental area under mechanized harvesting. The reference methodology consists of measuring the continuous distances without regrowth between two plants along a planting row, considering distances greater than 0.50 m as gaps and the following gaps classes: >0.5–1.0 m, >1.0–1.5 m, >1.5–2.0 m, >2.0–3.5 m, and >3.5 m. Images were collected from a UAV equipped with a 12-megapixel RGB camera. The number of regrowth gaps measured through imaging for the class of gaps with a length between 0.5 and 1.0 m was eight times higher than field measurement. In the class of gaps with a length between 1.0 and 1.5 m, the result is the opposite, as the field measurement was approximately three times higher than the UAV measurement, with a significant difference in both classes. In the other length classes analyzed, the number of gaps did not show significant differences. Our results suggest that regrowth gaps can be quickly estimated with the proposed methodology for gaps greater than 1.5 m. For gaps smaller than <1 m, the methodology using a UAV is not accurate.

The Potential of Arboreal Tiger Beetle (<em>Derocrania scitiscabra</em> Walker) as a Natural Enemy for the Control of Fall Armyworm (<em>Spodoptera frugiperda</em> JE Smith)

Purpose: The fall armyworm, Spodoptera frugiperda, is a serious pest of maize, sugarcane, and other crops in Sri Lanka. Natural predators and parasitoids are considered the best methods to control this insect pest. The present study investigates the possibility of using an endemic tiger beetle, Derocrania scitiscabra, as a natural predator for the larval stages of S. frugiperda.Research Method: The feeding preferences of D. scitiscabra to live prey versus dead prey, live prey types including different instar stages of S. frugiperda were investigated in the laboratory using choice tests. Fresh minced meat was used as dead prey, while red ants, earthworms and the six larval instar stages of S. frugiperda were used as live prey.Findings: Red ants were the most preferred prey type of D. scitiscabra, and dead prey, earthworms and mature S. frugiperda larvae were not consumed. Early larval instar stages of S. frugiperda were selected as prey, and the beetle showed a high feeding preference for the second larval instar stage. This feeding preference was observed irrespective of whether red ants were present or absent in the same environment. D. scitiscabra may have selected the second larval instars of S. frugiperda due to their small size, high prey density, mobility, and being devoid of injury.Research Limitations: The tests were conducted under laboratory conditions in insectary facilities. However, field investigations are essential to understand the ecological dynamics that affect insect behavior and survival.Originality/value: An endemic beetle is introduced to control S. frugiperda infestations in their early stages of development. The finding may provide an environmentally safe and economically beneficial method to control S. frugiperda

Fusion of Remotely Sensed Data with Monitoring Well Measurements for Groundwater Level Management

In the realm of hydrological engineering, integrating extensive geospatial raster data from remote sensing (Big Data) with sparse field measurements offers a promising approach to improve prediction accuracy in groundwater studies. In this study, we integrated multisource data by applying the LMC to model the spatial relationships of variables and then utilized block support regularization with collocated block cokriging (CBCK) to enhance our predictions. A critical engineering challenge addressed in this study is support homogenization, where we adjusted punctual variances to block variances and ensure consistency in spatial predictions. Our case study focused on mapping groundwater table depth to improve water management and planning in a mixed land use area in Southeast Brazil that is occupied by sugarcane crops, silviculture (Eucalyptus), regenerating fields, and natural vegetation. We utilized the 90 m resolution TanDEM-X digital surface model and STEEP (Seasonal Tropical Ecosystem Energy Partitioning) data with a 500 m resolution to support the spatial interpolation of groundwater table depth measurements collected from 56 locations during the hydrological year 2015–16. Ordinary block kriging (OBK) and CBCK methods were employed. The CBCK method provided more reliable and accurate spatial predictions of groundwater depth levels (RMSE = 0.49 m), outperforming the OBK method (RMSE = 2.89 m). An OBK-based map concentrated deeper measurements near their wells and gave shallow depths for most of the points during estimation. The CBCK-based map shows more deeper predicted points due to its relationship with the covariates. Using covariates improved the groundwater table depth mapping by detecting the interconnection of varied land uses, supporting the water management for agronomic planning connected with ecosystem sustainability.

Espectroscopia VIS-NIR-SWIR na cultura da cana-de-açúcar (Saccharum officinarum L.) para fins fitossanitários

ABSTRACT: One of the causes of productivity loss in sugarcane cultivation has been associated with the species Diatraea saccharalis, also known as the sugarcane borer. Therefore, this study evaluated the feasibility of using hyperspectral sensors to obtain the leaf spectral response of sugarcane in different periods of infestation at leaf and canopy levels to diagnose D. saccharalis damage in advance. The study included three varieties of sugarcane: CTC9003BT, CTC4, and RB966928. The insecticide Altacor® was used to control pestsin half of the plots. Data collection occurred at the following stages: sprouting, tillering, stalke longation, and early maturation. Data regarding relative water content (RWC), leaf spectral signature in the laboratory and canopy, vegetation indices (NDVI and MCARI), productivity, purity, and total recoverable sugar (TRS) were collected to evaluate the borer effects on the sugarcane crop. The highest RWC was observed for CTC9003BT (60.15%) without insecticide and 59.0% with insecticide. The visible (400-680 nm) and near-infrared (750-1300 nm) spectral bands identified spectral variations in plants with and without sugarcane borer. The percentage of sugarcane borer showed high and negative correlations between productivity, NDVI, and TRS, with coefficients of -0.68, -0.76, and -0.76, respectively. The NDVI and MCARI indices effectively detect plants under stress, but their variation is influenced by multiple factors, making it difficult to associate them with a single problem (sugarcane borer).

Physio- Biochemical and Molecular Changes Associated with Waterlogging Stress in Sugarcane: A Review

The term waterlogging refers to a condition of short/ long term water stagnation due to reduced capacity of the drainage system. In India, physical degradation of soil due to waterlogging has been estimated to be 8.5 million ha. It usually occurs due to rise in water table in almost all the canal irrigated areas with poor drainage system. Effects of waterlogging are augmented by secondary salinization and it aggravated the incidence of certain pests and diseases. In sugarcane, waterlogging is one of the major abiotic stresses affecting cane and sugar productivity. In many parts of the India, like eastern UP, northern Bihar and deltaic region of Tamil Nadu, sugarcane suffers from waterlogging during elongation phase because of heavy monsoon rains and poor drainage facilities. Higher water table during grand growth phase adversely affects cane weight and shoot population which occurs due to shift in respiratory metabolism from aerobic to anaerobic condition. Waterlogging not only reduced root growth, leaf emergence rate but also caused destruction of root function, hormone balance and nutrient availability. Sugarcane crop affected with waterlogging stress showed aerial rooting, cane lodging and decreased cane and sugar yield. Under water-logging condition, some morphological, anatomical, physiological and biochemical changes take place in plant for the sake of adaptation/survival. Present paper is an overview of physiological, morphological, anatomical, biochemical and molecular changes associated with waterlogging tolerance in sugarcane.

Registry of mermithid (Mermithidae) parasites of spittlebug nymphs (Hemiptera: Cercopidae) in sugarcane crops in Veracruz, Mexico

SObjective: To make known the presence of parasite mermithids of the spittlebug nymph in different sugarcanelocalities of the state of Veracruz, Mexico.Design/methodology/approach: The nematodes were collected in sugarcane soils after observation of theemergence of the pest from the spittlebug nymphs (in situ), while some others were obtained after collectingspittlebug nymphs in the field and the pest’s emergence in the laboratory. The death of the nematodes wasprovoked with hot water (sterile distilled, 60 °C), and they were measured and stored in Eppendorf tubes of 1.5mL in formaldehyde 4% and kept in refrigeration at 4 °C.Results: This report is presented as evidence of pest mermithids that emerge from the spittlebug nymph hostin sugarcane crops in eight localities of the state of Veracruz. Although their identification at the species levelcould not be done, the length of each mermithid found was recorded, as well as its relationship with the host.Limitations on study/implications: When the presence of mermithids was verified and their location wasidentified in diverse localities, the opportunity of continuing with the collection of specimens was justified andtheir identification could be formally performed, as well as the implementation of biological control studieswith the use of these native nematodes; also, the exploration of their roles in the control of nymphs from thesugarcane crop.Findings/conclusions: The presence of mermithid parasites of spittlebug nymphs in the sugarcane crop ineight localities in the mountainous region of Veracruz was verified in situ. Their emergence was classifiedinitially on nymphs of the spittlebug (Hemiptera: Cercopidae) that belong to the genera Aeneolamia and Prosapia

Assessment of leaf nitrogen diagnostics on sugarcane crop using spectroradiometry

Assessment of leaf nitrogen diagnostics on sugarcane crop using spectroradiometry

E2E model from crop suggestion to harvesting time prediction in sugarcane plant utilizing machine learning and deep learning

AbstractThe automated system for enhancing plant growth presents an innovative approach to optimize quality of sugarcane cultivation for four main sugarcane growing zones. It includes issues like recommendation of crops based on soil nutrients, diagnosis of disease in the leaf and stem images of sugarcane, weed detection and harvesting time prediction. The research work proposed in the article presents an innovative two-stage approach for object detection and classification in agricultural imagery. Initially, YOLOv8 (You Only Look Once) is employed to accurately detect objects within images, delineating them with precise boundary boxes. Subsequently, the focus of hybrid model integrating Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, known as Contextual Long Short-Term Memory (CLSTM), is employed. This dual-stage methodology harnesses the speed and accuracy of YOLOv8 for robust object localization, while the CLSTM model ensures nuanced classification, contributing to comprehensive and accurate approach for object detection and crop-weed differentiation in agricultural scenarios. The proposed approach is compared with the four DL algorithms for identifying weeds in sugarcane crops and subsequently assessed their accuracy and F1 score performance. At a learning rate of 0.002, the findings of CLSTM showcase superior precision at 98.5%, recall at 97.8%, F1 score at 98.1%, and an overall accuracy of 97.7%. The subsequent task is harvesting time prediction, which entails identifying the best time to harvest sugarcane based on the planting period, weather predictions, and sugarcane brix value. The implementation of this automated system not only enhances the productivity of sugarcane cultivation but also serves as a model for sustainable and resource-efficient agriculture.

Enhancing sugarcane leaf disease classification through a novel hybrid shifted-vision transformer approach: technical insights and methodological advancements.

In the agricultural sector, sugarcane farming is one of the most organized forms of cultivation. India is the second-largest producer of sugarcane in the world. However, sugarcane crops are highly affected by diseases, which significantly affect crop production. Despite development in deep learning techniques, disease detection remains a challenging and time-consuming task. This paper presents a novel Hybrid Shifted-Vision Transformer approach for the automated classification of sugarcane leaf diseases. The model integrates the Vision Transformer architecture with Hybrid Shifted Windows to effectively capture both local and global features, which is crucial for accurately identifying disease patterns at different spatial scales. To improve feature representation and model performance, self-supervised learning is employed using data augmentation techniques like random rotation, flipping, and occlusion, combined with a jigsaw puzzle task that helps the model learn spatial relationships in images. The method addresses class imbalances in the dataset through stratified sampling, ensuring balanced training and testing sets. The approach is fine-tuned on sugarcane leaf disease datasets using categorical cross-entropy loss, minimizing dissimilarity between predicted probabilities and real labels. Experimental results demonstrate that the Hybrid Shifted-Vision Transformer outperforms traditional models, achieving higher accuracy in disease detection of 98.5%, making it crucial for reliable disease diagnosis and decision-making in agriculture. This architecture enables efficient, large-scale automated sugarcane disease monitoring.

EFFECTIVENESS OF TRICHODERMA SPECIES IN THE MANAGEMENT OF SUGARCANE WILT (FUSARIUM SACCHARI)

Sugarcane is a major cash crop in Pakistan. It has a great contribution in world’s economy. Many diseases cause many losses in yield of sugarcane. Fungi are considered as abundant traitorous pathogens that obstruct the viable growth of sugarcane crop. Wilt disease caused by fungal pathogen Fusarium sacchari caused many losses in sugarcane production in Pakistan. Fusarium sacchari is a causative fungus of wilt in sugarcane. Biocontrol agent Trichoderma spp. used in the management of sugarcane wilt. These bio‐agents help in diseases management and play important role in increasing the crop yield. For this reason, these bio‐agents are used for biological maintaenace of crop diseases and research globally. The use of PDA medium in vitro revealed antagonistic activity of various Trichoderma isolates against the sugarcane wilt pathogen Pathogenicity test also executed to check the most aggressiveness strain of Fusarium spp. to cause disease in sugarcane plant. In vivo, Trichoderma multiply on different substrates and corn Cobbs is good byproduct Trichoderma multiply on it and mix in soil of pots. Pot experiment performed with different treatments to control wilt disease by using of antagonist’s to check disease incidence and disease reduction.

The Fungus Phlebiopsis flavidoalba’s Pathogenicity and Virulence Toward the Fluted Scale (Praelongorthezia acapulcoa) Pest of Rice and Sugarcane Crops

Sugarcane is one of the main crops in Morelos State, Mexico. The presence of the insect pest Praelongorthezia acapulcoa (Morrison), commonly known as the fluted scale insect, was observed in sugarcane and rice crops, causing losses of up to 30% of production in both crops. In this work, a fungus isolated from the mycosic cadavers of P. acapulcoa was identified as the basidiomycete Phlebiopsis flavidoalba (Cooke) Hjortstam via morphological and molecular identification using the ITS, tef 1, and 28S regions. Its pathogenicity toward P. acapulcoa was verified in laboratory tests, causing a mortality rate higher than 60%. Its virulence toward P. acapulcoa, estimated as the mean lethal concentration (LC50), was 9.7 × 106 conidia mL−1. This work constitutes the first report about a basidiomycete with direct entomopathogenic activity and biological control of the fluted scale insect, P. acapulcoa.

Assessment of Sugarcane Cultivation Practices in Sitapur District of Uttar Pradesh, India

The success of sugarcane farming hinges on the intricate interplay of various the adoption of advanced technological practices. The study was conducted during the year 2022-2023 in Machhrehta and Gondlamau blocks of Sitapur district of Uttar Pradesh to study the extent of adoption of Sugarcane growers on improved sugarcane cultivation practices. A sample size of 120 respondents was selected based on random sampling procedure. Personal interviews with the help of a structured schedule were taken for data collection. The study revealed that majority of the respondents (51.67%) had medium level of adoption of improved sugarcane cultivation practices followed by 19.17% high and 29.16% low level of adoption of Sugarcane cultivation practices by the respondents. The study analyzed that high adoption found in Field Preparation Sugarcane with 94.78 MPS and respondents had less adoption of seed treatment in Sugarcane crop with 34.89MPS.

Energy Budgeting of Different Cropping Sequences in the Indian Upper Gangetic Plains

Background: This study is meant to examine the energy requirement and energy input- output of different cropping sequences .The crucial objective to conduct experiment to understand energy efficiency using inputs and outputs which were disbursed in cropping sequences. Hence, it is need of the hour to identify the most remunerative and cost effective cropping sequence with high energy efficient for UGP of India. Methods: This study was carried out at the research farm of ICAR-Indian Institute of Farming Systems Research, Modipuram, during 2017-2021.The divergent cropping sequences viz. sugarcane-ratoon-wheat (CS1); rice-wheat-dhaincha (CS2); pigeonpea +maize-chickpea-okra (CS3); maize-berseem-black gram (CS4); sorghum-mustard-green gram (CS5) and Napier+cowpea/berseem (CS6) were compared in reference to curtail higher energy inputs through selected alternate cropping sequences. The obtained energy values were calculated by multiplying the amount of inputs and outputs by using energy conversion factors. Result: Maximum inputs energy consumed by sugarcane crop alone (33.14x103 MJ ha-1). Results shown that irrigation, seed, fertilizers and diesel required higher energy for the completion of cultural operations. However, higher inputs energy was used in irrigation followed by seed and fertilizers, respectively. In regard to per cent energy intake through inputs, the highest energy spent was for irrigation (35.30 MJ ha-1) and fertilizer (23.80 MJ ha-1).The wheat equivalent yield was higher in sugarcane-ratoon-wheat (125.58 t ha-1). Maximum output energy was with above system (596.70×103 MJ ha-1). Highest net energy returns was counted with sugarcane-ratoon-wheat (549.37x103 MJ ha-1), energy ratio (12.60) and energy profitability (11.60). Indeed, energy efficiency was highest in same system (1657.50) followed by maize-berseem-black gram (1421.96).

Strategy to improve the micropropagation of sugarcane plants using carbon dioxide injection

Objective: To develop a strategy for the improvement of micropropagation of sugarcane plants in the central region of Veracruz, using carbon dioxide injection. Design/methodology/approach: Intelligent control and micropropagation techniques are used in this development. Results: Improved micropropagation of sugarcane plants using carbon dioxide injection is demonstrated using intelligent control techniques. Findings/conclusions: With this controlled carbon dioxide injection system, research can be carried out to obtain the most approximate values to contribute to better growth and adaptability in sugarcane crops. This can guarantee shorter adaptation times and thus generate greater economic gains for the producers.

Predicting Green Water Footprint of Sugarcane Crop Using Multi-Source Data-Based and Hybrid Machine Learning Algorithms in White Nile State, Sudan

Water scarcity and climate change present substantial obstacles for Sudan, resulting in extensive migration. This study seeks to evaluate the effectiveness of machine learning models in forecasting the green water footprint (GWFP) of sugarcane in the context of climate change. By analyzing various input variables such as climatic conditions, agricultural data, and remote sensing metrics, the research investigates their effects on the sugarcane cultivation period from 2001 to 2020. A total of seven models, including random forest (RF), extreme gradient boosting (XGBoost), and support vector regressor (SVR), in addition to hybrid combinations like RF-XGB, RF-SVR, XGB-SVR, and RF-XGB-SVR, were applied across five scenarios (Sc) which includes different combinations of variables used in the study. The most significant mean bias error (MBE) was recorded in RF with Sc3 (remote sensing parameters), at 5.14 m3 ton−1, followed closely by RF-SVR at 5.05 m3 ton−1, while the minimum MBE was 0.03 m3 ton−1 in RF-SVR with Sc1 (all parameters). SVR exhibited the highest R2 values throughout all scenarios. Notably, the R2 values for dual hybrid models surpassed those of triple hybrid models. The highest Nash–Sutcliffe efficiency (NSE) value of 0.98 was noted in Sc2 (climatic parameters) and XGB-SVR, whereas the lowest NSE of 0.09 was linked to SVR in Sc3. The root mean square error (RMSE) varied across different ML models and scenarios, with Sc3 displaying the weakest performance regarding remote sensing parameters (EVI, NDVI, SAVI, and NDWI). Effective precipitation exerted the most considerable influence on GWFP, contributing 81.67%, followed by relative humidity (RH) at 7.5% and Tmax at 5.24%. The study concludes that individual models were as proficient as, or occasionally surpassed, double and triple hybrid models in predicting GWFP for sugarcane. Moreover, remote sensing indices demonstrated minimal positive influence on GWFP prediction, with Sc3 producing the lowest statistical outcomes across all models. Consequently, the study advocates for the use of hybrid models to mitigate the error term in the prediction of sugarcane GWFP.

Integration of Mycorrhizae, Azotobacter and Pseudomonas spp (PSB) with NPK and their Effects on Sugarcane Crop and Soil Health in Uttar Pradesh, India

Integration of Mycorrhizae, Azotobacter and Pseudomonas spp (PSB) with NPK and their Effects on Sugarcane Crop and Soil Health in Uttar Pradesh, India