Tons Of Cane Research Articles

Socioeconomic evaluation in the intensification of a panela agroindustry

Evaluating the panela agroindustry as an alternative for profitability, viability and decision-making is a vital exercise from the point of view of sustained socioeconomic growth. For the socioeconomic evaluation of the agroindustry intensification, the Peters- Timmerhaus Excel spreadsheet was used. The evaluation included technical production diagnosis, mass balance and production costs of different panela factories depending on the grinding capacity: 10 to 30 tc/d (tons of cane per day) with four production alternatives and two drying variants. Results show that in an intensified panela agroindustry, the economic viability to recover the capital is reached in 2.5 years, with alternatives 1,2 and 4, the last one includes the production of three products (honey, panela and natural sugar) for 20 tc/d and 1.5 years for 30 tc/d using naturally dried bagasse as fuel. A comprehensive understanding of the production area and the interrelationships with other areas of the panela agroindustry allowed to refine possibleproduction lines and socialize the information for rapid decision-making with immediate application projection and revival of the sector, considering that the application of technologies and the intensification of processes improves quality, increases profitability, generates well-being and ensures food security.

Sparse testing designs for optimizing predictive ability in sugarcane populations.

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6×186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA ( ) and for TCH ( ) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12×6) being the most convenient cost-benefit combination.

Use of mill by-products in the fallow in sugarcane production in Australia

Mill by-products such as mud, ash and mud/ash mixtures are known to increase cane and sugar yields. Traditionally, Australian growers broadcast these by-products in the fallow at rates greater than 150 t/ha. However, growers are now banding by-products at less than 100 t/ha, with few guidelines on how to maximise their returns from this practice. Eight commercial size replicated, randomised strip trials were established in bare fallows between Ingham and Proserpine to investigate the impact of by-products banded between 35 and 100 t/ha and broadcast between 140 and 200 t/ha on cane yield, CCS and grower net revenue compared to standard grower fallow practices. The trials were harvested each year over the crop cycle using a commercial harvester. Tonnes of cane harvested and mill CCS provided by the receiving sugar mill were used to calculate tonnes of cane per hectare and grower net revenue per hectare using the Australian cane payment method for each plot in a trial. The application of mud, mud/ash and ash increased cane yield with ash ≥ mud/ash >> mud. The greater the quantity of by-product applied, the greater the cane yield. Conversely, CCS decreased linearly with the quantity of mud, mud/ash, or ash applied. CCS was lowest in mud << mud/ash ≤ ash. Cumulative grower net revenue at the end of the crop cycle was greatest in ash > mud/ash >> mud and was greatest at application rates of 35–50 t/ha > 70–100 t/ha > 140–200 t/ha. Broadcasting mud at 200 t/ha resulted in the growers not recovering the cost of application. Banding by-products containing mud/ash and ash at 100 t/ha or less resulted in the grower recovering the cost of the product by the second or third ratoon. By-products applied at rates as low as 50 t/ha reduced CCS, and further research is needed to improve guidelines on nutrient and water management so that growers using these by-products can maximize their profitability.

Use of continuous genotypes for genomic prediction in sugarcane.

Genomic selection in sugarcane faces challenges due to limited genomic tools and high genomic complexity, particularly because of its high and variable ploidy. The classification of genotypes for single nucleotide polymorphisms (SNPs) becomes difficult due to the wide range of possible allele dosages. Previous genomic studies in sugarcane used pseudo-diploid genotyping, grouping all heterozygotes into a single class. In this study, we investigate the use of continuous genotypes as a proxy for allele-dosage in genomic prediction models. The hypothesis is that continuous genotypes could better reflect allele dosage at SNPs linked to mutations affecting target traits, resulting in phenotypic variation. The dataset included genotypes of 1318 clones at 58K SNP markers, with about 26K markers filtered using standard quality controls. Predictions for tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fiber content (Fiber) were made using parametric, non-parametric, and Bayesian methods. Continuous genotypes increased accuracy by 5%-7% for CCS and Fiber. The pseudo-diploid parametrization performed better for TCH. Reproducing kernel Hilbert spaces model with Gaussian kernel and AK4 (arc-cosine kernel with hidden layer 4) kernel outperformed other methods for TCH and CCS, suggesting that non-additive effects might influence these traits. The prevalence of low-dosage markers in the study may have limited the benefits of approximating allele-dosage information with continuous genotypes in genomic prediction models. Continuous genotypes simplify genomic prediction in polyploid crops, allowing additional markers to be used without adhering to pseudo-diploid inheritance. The approach can particularly benefit high ploidy species or emerging crops with unknown ploidy.

Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies.

Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses (1,225) with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP (incorporating additive, non-additive, and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding.

Performance Analysis of Sugar Cogeneration Plant By Method DEA

Abstract: This study presents a feasibility of export electricity generation for a 7000 tons of Cane per day Crushing capacity factory which currently generates electricity for own consumption alone. The competitive sugar industry with high costs of production makes it necessary for diversification by sugar industry to increase its competitiveness. This will improve the sustainability of the cane sugar industry and the electricity grid by supply of green electricity. The factory and its cogeneration plant performances were examined for possible improvement and diversification into electricity export to the grid. Energy is one of the largest driver in our whole world growth and economy. It is one of the major source to increase the living standards of people and Bagasse is highly used fuel in Sugar mill and can be used as an alternative to Coal based generation. In this study, we have taken the Data from the mill that is power generation record from the Months Oct 22, Nov 22, Dec 22, Jan 23, Feb 23, Mar 23 when mill was working.

Population Dynamics and Estimation of Damage of the Spittlebug Aeneolamia varia on Sugarcane in Colombia by Using remote Sensing and Machine Learning Tools

The spittlebug (Aeneolamia varia) is one of the most important sugarcane pests in Colombia, where a recent increase in population and distribution specially in southwestern Colombia have led to the need for new technologies for integrated pest management. The objectives of this study were to determine the spatial distribution of this pest in commercial sugarcane fields and to validate machine learning (ML) tools for indirect injury detection and impact on yield (damage) using satellite images. This study was carried out in fields grown with the CC 01-1940 variety in El Cerrito, Valle del Cauca, Colombia, where systematic sampling of the populations (number of adults and nymphs per stem) was carried out. The spatial aggregation and distribution were determined using Moran’s index and point patterns, sequence observations, and analysis with distance indicators (Sadie). The indirect injury detection and quantification of the impact on production were carried out with a ML approach using satellite image products with 10 m spatial and five days temporal resolutions, obtained from a Sentinel-2 sensor using Google Earth Engine. The results indicated that spittlebug populations had an aggregate spatial behavior and high spatial dependence. In addition, the ML algorithms predicted spittlebug injury, and the effect on production was estimated at 26.4 tons of cane per hectare, which represented a 17% reduction in the expected yield. The use of spatial analysis and remote sensing tools are an alternative for indirect detection of injury and for understanding population dynamics of the pest in sugarcane, so they can become instrumental for decision-making on an integrated pest management program.

Association technological and agronomic traits sugarcane for the quantitative and qualitative production of brown sugar

Despite the growing demand for the consumption of brown sugar, there is little study on sugarcane cultivars and harvesting times more propitious for the production of brown sugar. In this sense, the objective of this study was to identify linear relationships between agronomic and technological variables of different genotypes and harvest times, to identify direct and indirect effects on the quantitative and qualitative production of brown sugar. For this, an experiment was carried out in a split-plot scheme (genotypes × growing seasons), in a randomized block design with four replications. The variables analyzed were stem height, the number of internodes, apparent sucrose in sugarcane, apparent sucrose from broth, soluble solids content of cane, fiber, purity, total recoverable sugars, average stem mass, the volume of broth extracted, content of soluble solids in the broth, frown sugar apparent sucrose, instrumental color, mass of brown sugar per ton of cane and the amount of broth per ton of cane. The statistical analyzes performed were path analysis and canonical correlation. The amount of juice per ton of cane and ºBrix of the juice can be selected for greater brown sugar production, both had a greater direct effect. With different responses in the three harvests, the canonical correlation showed interdependence between groups of variables, inferring those agronomic variables can be used in the selection of better-quality brown sugar cultivars. If the objective is greater sugar production, season 1 proved to be more promising in relation to the others.

The use of UAS-based high throughput phenotyping (HTP) to assess sugarcane yield

A sensor mounted on an unmanned aerial system (UAS) may enable breeding programs’ selection efficiency. The objectives of this study were to investigate the use of UAS with red, green, and blue (RGB) camera to assess sugarcane yield and to investigate the direct and indirect influences of canopy features on cane yield of sugarcane. A trial was conducted from 2019 to 2021 at the Texas A&M AgriLife Research and Extension Center in Weslaco, Texas, and arranged in a complete randomized block design, consisting of 7 genotypes with 4 replications. Seven UAS image acquisitions were performed at the plant cane stage using an RGB sensor. At the first ratoon stage, five flights were conducted using RGB and multispectral sensors. Ground measurements were obtained, including pol, tons of sugar per hectare (TSH), and tons of cane per hectare (TCH). The results showed that the correlation of canopy features with pol at the elongation phase was poor, compared to those acquired at the maturity phase. Likewise, the most significant correlations between canopy features with TSH and TCH were found in the late elongation to maturity phase. In addition, a good agreement of the validation dataset between the observed and predicted pol (R2 0.58, RMSE 0.77%), TSH (R2 0.68, RMSE 2.24 Mg ha−1), and TCH (R2 0.66, RMSE 13.64 Mg ha−1) was observed. The canopy height model (CHM) and Normalized Difference Vegetation Index (NDVI) had the highest direct effect on TCH. Therefore, assessing sugarcane yield using UAS-based imagery looks promising for High Throughput Phenotyping (HTP) in sugarcane breeding programs.

Sugarcane streak mosaic virus (ScSMV) Resistance Evaluation of Sugarcane Varieties

Sugarcane streak mosaic virus (ScSMV) is the most important viral disease of sugarcane in Indonesia with distribution in almost all commercial sugarcane plantations. The disease causes significant yield losses of both cane tonnage and sugar yield. The use of resistant varieties is the best approach for controlling viral diseases. This study aims to investigate resistance response of several introduced varieties against ScSMV in a glasshouse condition and the impact of the viral infection on chlorophyll and proline content in sugarcane leaves. Sugarcane plants were inoculated using ScSMV inoculum one month after planting using an abrasive pad rubbing method. Disease incidence and severity was observed at week 4-12 after inoculation and variety resistance levels were classified based on disease incidence. Confirmation of the virus was done by RT-PCR. Spectrophotometer was used to measure chlorophyll content at dual wavelengths of 645 and 663 nm, and proline content at wavelengths of 520 nm. The results showed that most of the tested varieties were susceptible to ScSMV. There are six highly resistant varieties, namely SRA 1, SRA 2, N 10-4, N 10-7, N10-9, and N 10-13, but these varieties still require to be tested on a field scale. ScSMV infection generally decrease chlorophyll and proline content. However, the physiological effect of ScSMV infection on chlorophyll and proline content needs further investigation.

Performance analysis and evaluation of ethanol potential of Nzoia Sugar Company Ltd.

The Sugar industry faces several sustainability challenges due to dumping of excess global production which cause stiff competition for high cost sugar producers like Kenya. Through diversification into cogeneration and ethanol production, sugar companies in high cost producing countries can remain competitive and survive in the market. In this study, the technical and economic feasibility of ethanol production is done for the case of Nzoia Sugar Company in Kenya whose milling capacity is 3000 tons of cane per day (TCD). Molasses is taken through a series of stages starting with pretreatment, followed by fermentation and distillation to produce ethanol. The ethanol formed is then transferred to the distillation columns for fractionation to remove water and hence have ethanol rich product. Through further distillation and molecular sieve dehydration the purity and quality of the final ethanol product is enhanced. The main equipment for the process includes the fermenter, boilers, condensers, and distillation columns, design to suit the product characteristics and design capacity of the plant. The analysis showed that ethanol production by a 3000 TCD sugar factory is a profitable venture. The study showed that investment is both technically and economically feasible and will improve the financial performance and sustainability of the sugar factory in a competitive market environment. Therefore, diversification into ethanol production is an important strategy in sustaining the sugar industry in a competitive global market.

Performance analysis and electricity potential for Nzoia sugar factory

Kenyan sugar factories are facing challenges in the market from low cost sugar producers as a result of high cost of production. As a result, a number of factories have closed down while operating factories are struggling with cash flow problems. Diversification into both ethanol and export cogeneration is a promising solution to the challenges. In this study, the feasibility of export electricity generation for a Nzoia sugar factory in Western Kenya is undertaken. The 3000 tons of cane per day crushing capacity (TCD) factory has effective generation capacity of 2.8 MW for own internal consumption against installed capacity of 7 MW. The study showed that the factory can develop a continuous power plant of 15 megawatts (MW) but modernization of the factory is necessary to reduce steam consumption mainly by replacing steam turbine drives with electric drives, increase generation efficiency by use of a condensing extraction turbine. The three boilers which are over 30 years old should be replaced with more efficient modern boilers of steam pressure between 45–60 bars. A well negotiated power purchase agreement that captures the challenges facing the factory should then be signed with a utility company to avoid the challenges in implementation due to external factors.

Growth and Yield Potential of New Sugarcane Varieties during Plant and First Ratoon Crops

Newly released sugarcane varieties need to be adapted to various environments. This research was aimed at examining the growth and yield potential of newly released varieties of sugarcane in the first year as plant cane (PC) and the second year as first ratoon cane (RC1) on dry land. The research was carried out at Wedarijaksa station, Trangkil Sugar Mill area, Pati, Central Java in 2019–2021. Four sugarcane varieties were grown using a double rows system, AAS Agribun, ASA Agribun, AMS Agribun, and CMG Agribun and one commercial variety, PSJK 922. Measurements of crop growth were made periodically: yield components at harvest in PC-RC1, and physiological characteristics 5 months after planting. The results indicate that mean tonnes of cane and sugar per hectare between PC and RC1 decreased by 22.7% and 21.0%, respectively, for AAS Agribun, ASA Agribun, and CMG Agribun due to decreased stem weights. AMS Agribun showed the smallest decrease in tonnes of cane (4%) and increase in tonnes of sugar per hectare (2%) from PC to RC1. The highest number of tonnes of sugar in PC was achieved by ASA Agribun (12.8 t ha−1), slightly above PSJK 922 (12.69 t ha−1). The decline in tonnes of cane and sugar needs to be reduced by the continuously improving cultivation techniques. The mean photosynthetic water use efficiency of tested new varieties was 7.46 µmol CO2 mol H2O−1. These research findings provide information on crop performance and can be used as a basis for selecting varieties to be developed in the region. Further studies will be required to test these new sugarcane varieties in a wide range of agroecological zones in Indonesia.

The First Telomere-to-Telomere Chromosome-Level Genome Assembly of Stagonospora tainanensis Causing Sugarcane Leaf Blight.

The sexual morph Leptosphaeria taiwanensis Yen and Chi and its asexual morph Stagonospora tainanensis W. H. Hsieh is an important necrotrophic fungal phytopathogen, which causes sugarcane leaf blight, resulting in loss of cane tonnage and sucrose in susceptible sugarcane varieties. Decoding the genome and understanding of the basis of virulence is vitally important for devising effective disease control strategies. Here, we present a 38.25-Mb high-quality genome assembly of S. tainanensis strain StFZ01, denovo assembled with 10.19 Gb Nanopore sequencing long reads (~267×) and 3.82 Gb Illumina short reads (~100×). The genome assembly consists of 12 contigs with N50 of 2.86 Mb of which 5 belong to the telomere to telomere (T2T) chromosome. It contains 13.20% repeat sequences, 12,543 proteins, and 12,206 protein-coding genes with the BUSCO completeness 99.18% at fungi (n = 758) and 99.87% at ascomycota (n = 1706), indicating the high accuracy and completeness of our gene annotations. The virulence analysis in silico revealed the presence of 2379 PHIs, 599 CAZys, 248 membrane transport proteins, 191 cytochrome P450 enzymes, 609 putative secreted proteins, and 333 effectors in the StFZ01 genome. The genomic resources presented here will not only be helpful for development of specific molecular marker and diagnosis technique, population genetics, molecular taxonomy, and disease managements, it can also provide a significant precise genomic reference for investigating the ascomycetous genome, the necrotrophic lifestyle, and pathogenicity in the future.

Sugarcane yield prediction and genotype selection using unmanned aerial vehicle‐based hyperspectral imaging and machine learning

AbstractSugarcane (Saccharum spp. interspecific hybrids), a high biomass perennial crop, in which manual data collection for early yield prediction, through its growth cycle (∼12 mo long), is labor intensive and time consuming. Alternately, aerial imagery can be explored to predict yield‐related components and high‐throughput phenotyping for genetic selection. In this study, aerial imagery and ground data were collected in Stage IV (final stage of genotype selection) of the Florida sugarcane cultivar development program to evaluate the use of unmanned aerial vehicles in yield prediction (tons of cane per hectare [TCH], sucrose concentration, and tons of sugar per hectare [TSH]) in multiple new genotypes (13 in plant cane crop, nine in first ratoon crop). Aerial imagery data were collected using hyperspectral sensor, and yield data were collected through manual sampling of sugarcane stalks at harvest. The gradient‐boosting regression tree model was selected based on low mean absolute percentage error on multiple dates (April, July, and September) to determine the best timing of yield predictions. Results showed that yield was predicted with greater accuracy in July in plant crop and April in the first ratoon crop. Also, sucrose percentage was predicted with greater accuracy (94% in plant crop and 93% in first ratoon crop) than TCH and TSH. Although only two out of the top five genotypes were common in both selection methods (measured vs. predicted yields) in Stage IV, high accuracy in TCH and sucrose percentage shows that aerial imagery may be useful in making genotype selection in early stages when actual yield estimation is not feasible.

Yields of Promising Sugarcane Clones under Three Different Planting Arrangements

A field experiment was conducted at Kediri, East Java, to examine the influence of row spacing on the yield of five promising clones from August 2018-June 2020. The investigation was of a Split-Plot design with three replications. The main plot consisted of three planting arrangements: SR, single row spacing at 110 cm, DR1, double rows spacing 50+135 cm, and DR2, double rows spacing 50+170 cm. Sub-plots consisted of five promising clones and one check variety: MLG 5, MLG 9, MLG 14, MLG 52, MLG 55, and PS 881. The results suggest no overall significant effect of planting arrangement on sugar yield, though clones may vary. There are significant differences in yield between some clones. Wider rows, DR2, show the highest yield in tonnes of cane per hectare (TCH) in PC (153.94 t/ha) with a slight decrease (14%) in RC-1 due to reduced plant population. Sugar yield in tonnes of sugar per hectare (TSH) in various planting arrangements ranges from 12.26-13.99 t/ha in PC and 10.68-11.93 t/ha in RC-1. MLG 9 can be released as a new variety with high cane and sugar yields.

Energy Analysis in Irrigated Sugarcane Schemes of Awash River Basin, Ethiopia

Sugarcane is one of the important irrigated crops in Ethiopia and its production is highly linked with its energy and water use. In this paper, identifications and quantifications of input and output, direct and indirect energy sources, and energy use of farm operations were carried out on 11 irrigation schemes of Awash River Basin. In order to grow 91.8 to 167.6 tons of cane, 47.9 to 143.4 GJ/ha of total energy was used. Average total input energies of gravity, pump surface and sprinkler scheme categories to grow 109.8, 112.7 and 136.3 ton/ha were 53.6, 68.9 and 129.2 GJ/ha, respectively. Around 90% and 74% total energies of gravity surface and sprinkler schemes were consumed as direct and indirect energies, respectively. Irrigation found to be the most energy consuming operation constituting more than 50% input energy of all scheme categories. Energy efficiency of gravity schemes was 152% and 300% higher than pump driven surface and sprinkler schemes. Energy sequestrated in cane straws burned during harvesting found to be higher than fertilizer and pumping energy demands. Use of cane straws as manure and energy sources have the potential to substitute demands which in turn needs further investigations and analysis.

INTEGRATED ENERGY BALANCE OF THE PRODUCTION OF QUALITY “CACHAÇA” AND BIOFUEL

The production of small-scale farm fuel alcohol is a viable economic activity, but in Brazil, there are few studies on the energy balance of its production. In the present work, the energy balance of the integrated production of quality “cachaça” and fuel alcohol at the farm level was carried out, as well as the determination of technical coefficients in a microdistillery in Minas Gerais (Zona da Mata), Brazil, for the production of sugar cane, cachaça and farm fuel alcohol. The productive capacity of this microdistillery in the analyzed period was 5,271.63 liters of cachaça and 658.95 liters of farm alcohol per day. Energy expenditure in the production of sugar cane (agricultural phase) was 245.14 MJ per ton of cane (MJ TC-1), with 80% of the total of this energy going to the production of cachaça and 20% for the production of sugarcane fuel alcohol. The energy expenditure for the production of cachaça and fuel alcohol from sugar cane was 30.44 MJ TC-1 (industrial phase), of which 80% went to obtain cachaça and 20% for the farm fuel alcohol. The energy output / input ratio was 4.61 (fuel alcohol + excess bagasse). Much of the energy demand is due to the use of nitrogen fertilizers and diesel oil. The farm alcohol produced had a renewability index of 0.27, showing it to be a renewable fuel.

Novel Tools for Adjusting Spatial Variability in the Early Sugarcane Breeding Stage.

The detection of spatial variability in field trials has great potential for accelerating plant breeding progress due to the possibility of better controlling non-genetic variation. Therefore, we aimed to evaluate a digital soil mapping approach and a high-density soil sampling procedure for identifying and adjusting spatial dependence in the early sugarcane breeding stage. Two experiments were conducted in regions with different soil classifications. High-density sampling of soil physical and chemical properties was performed in a regular grid to investigate the structure of spatial variability. Soil apparent electrical conductivity (ECa) was measured in both experimental areas with an EM38-MK2® sensor. In addition, principal component analysis (PCA) was employed to reduce the dimensionality of the physical and chemical soil data sets. After conducting the PCA and obtaining different thematic maps, we determined each experimental plot’s exact position within the field. Tons of cane per hectare (TCH) data for each experiment were obtained and analyzed using mixed linear models. When environmental covariates were considered, a previous forward model selection step was applied to incorporate the variables. The PCA based on high-density soil sampling data captured part of the total variability in the data for Experimental Area 1 and was suggested to be an efficient index to be incorporated as a covariate in the statistical model, reducing the experimental error (residual variation coefficient, CVe). When incorporated into the different statistical models, the ECa information increased the selection accuracy of the experimental genotypes. Therefore, we demonstrate that the genetic parameter increased when both approaches (spatial analysis and environmental covariates) were employed.

Use of different doses of dinamic: a research study

Introduction: In the sugarcane production scenario, Brazil stands out as the world's largest producer. In this context, straw is the most recent co-product of the crop, and its accumulation in the field was triggered by the change in the harvesting method. In raw cane areas, drastic reductions in the incidence of grass weeds and high infestations with Ipomoea spp and Euphorbia heterophylla are observed. In this regard, Among the herbicides registered for the cultivation of sugarcane, with potential for use in raw cane, amicarbazone is registered in Brazil under the trade name of Dinamic. Objective: To evaluate the effect of different doses of Dinamic on sugarcane production. Materials and Methods: Area Information, Biometric Methodology, Monthly Rainfall in the Experiment Period, Monitoring Image, Soil Classification. Variables were presented in percentage, mean and standard deviation format. Depending on the Gaussian distribution (Normality test), comparisons of variables were performed using the One-Way ANOVA Test (Tukey) between the variables in the present study, considering p<0.05 with statistical significance. Results and conclusion: The treatments showed no statistically significant difference (p<0.05; Tukey 5%) in tonne of cane per hectare and internodes in both farms. Still, the diameter of farm 63728 did not show a statistical difference, however, in farm 63010 the diameter and tillers/m showed a statistically significant difference (p<0.05), with the best dose being 0.8 (2x) and 1.2/ 1.6 respectively. For height, the treatments showed a statistically significant difference (p<0.05; Tukey 5%), with Dinamic at 1.4 kg/ha being the best dose for this variable on farm 63728, and the best dose for farm 63010 was 2.0 kg/ha, and in both farms, the doses were higher than Dinamic.